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Sustainability Magazine
The inaugural issue will
be published May 2025

The IEEE Energy Sustainability Magazine will be

dedicated to the dissemination of information and
practices on all matters related to environmental
sustainability and climate change, as they relate to
electric power system operation and planning.
 magazine

January 2025 Show Issue

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on the columns &

cover departments
3 Letter
4 Editors’ Voice
10 IEEE PES Grid Edge Technologies
Conference and Expo 2025
Organizing Committee
12 2025 IEEE PES Grid Edge
Technologies Conference &
Exposition Program
INGRAM PUBLISHING

features
40 Distributed Energy Resources and Electric  etecting Anomalies for Fire Prevention in
83 D

contents
Vehicle Adoption Distribution Systems
By Rick Siepka, Julio Romero Agüero, By Jhi-Young Joo, Christabella Annalicia,
Marty O’Baker, Don Hall, and H. Lee Willis Apoorv Pochiraju, Ozgur Alaca, Ali Riza Ekti,
52 Beyond Demand Response Michael Balestrieri, Hamed Valizadeh Haghi,
By Andrei Costache, Bruce Redmond, and Abder Elandaloussi
Jean-Philippe Montandon, and Dean Sharafi 91 I ntegrating Behind-the-Meter
58 Data Fusion and Interoperable Control Grid Edge Technologies Into
Wholesale Electricity Markets
By Gowtham Kandaperumal, Bo Chen,
By Alex Papalexopoulos , Shmuel Oren,
Keith DSouza, Ruoxi Zhu, and Brooks Glisson
and Hung-po Chao
67 Empowering a Proactive Grid With Power
Quality Visibility 101 The Red Sea Microgrid
By Nick Nakamura, Luis Vega, By Hongwu She and Hua Zheng
and Kamron Tangney
76 Impact of Medium- and Heavy-Duty Electric
Vehicles and the Grid
By Richard Fioravanti, Lisha Sun, Robert Mushet,
Anderson Bolles, Alex Moffat, and Matt Belden

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2 ieee power & energy magazine January 2025 Show Issue

letter
Jackie Peer, Vice President, Conferences & Meetings, IEEE Power & Energy Society

Empowering Progress:
Help Shape the Future
of Energy at the 2025
IEEE PES Grid Edge
Technologies Conference
and Exposition
A Welcome Message from Jackie Peer,
Vice President Conferences & Meetings,
IEEE Power & Energy Society

I
IT’S NO SECRET THAT THE ENERGY grid technologies, and the vital role of leaders and startups will work
landscape is rapidly evolving, but this maintaining grid resilience. While these in teams to develop potential
evolution is perhaps most evident at the themes are not foreign to any of us, the funded engagements that solve
grid edge—the critical frontier where discussions about them that happen here for critical grid edge challenges.
technology, sustainable practices, and are different than those at other events, ✔ The Ph.D. Dissertation Chal-
customers converge. emphasizing a holistic approach that not lenge, which will spotlight the
To continue enhancing the productiv- only embraces technological advance- next generation of thought lead-
ity, efficiency, and interoperability of our ments but also considers policy reform, ers in our field.
power systems, we must also converge. economic growth, stakeholder engage- San Diego Gas & Electric (SDG&E)
Which is why I’m thrilled to invite you ment, and community empowerment. is proud to once again support this event
to join me at the IEEE PES Grid Edge Our comprehensive technical pro- as the host utility—and San Diego cer-
Technologies Conference & Exposi- gram will offer over 100 sessions, tu- tainly isn’t a bad place to be in Janu-
tion, 21–23 January 2025 in sunny San torials, poster sessions, and five highly ary. I look forward to welcoming you to
Diego. This event serves as a meeting anticipated Super Sessions, where ex- “America’s Finest City” and am eager
point for policymakers, utilities, munici- perts from across industries will tackle to see how we can collectively drive the
palities, big tech, and beyond, creating issues such as grid modernization, en- future of the grid edge. Thank you for
connections that fuel our ability to drive ergy storage, and the future of electric being part of our powerful community.
progress at the grid edge. utilities in a decarbonized world. See you 21–23 January!
That progress comes from col- We’re also excited to see the latest
laborating to address the challenges innovations across the exhibit floor, Warm regards,
currently facing us, including the defin- and for special features including:
ing challenge of our century: climate ✔ Five technology stages, where
change. The event will focus on our you can explore advancements
role in combatting the climate crisis in electrification, sustainability Jackie Peer
and other issues, with key themes cen- and resiliency, AI, and DERs— Vice President, Conferences &
tered around the integration of renew- and meet the people behind to- Meetings, IEEE Power &
able energy, the expansion of smart day’s most promising early-stage Energy Society
startups.
Digital Object Identifier 10.1109/MPE.2024.3471268
✔ The inaugural Grid Edge In-
p&e
Date of current version: 11 November 2024 novation Challenge, where 180 

January 2025 Show Issue ieee power & energy magazine 3

 editors’ voice
Innocent Kamwa

empowering the
grid edge to propel
decarbonization

T
THE INAUGURAL IEEE PES GRID ✔ Advancements in energy stor- ✔ Innovations in EV infrastructure
Edge Technologies Conference and age technologies, including bat- and their integration into the
Exposition occurred from 10 to 13 teries and other storage systems, grid to support sustainable trans-
April 2023 in San Diego, CA, USA. to support grid stability and portation; and
The IEEE PEM was already a proud efficiency; ✔ Addressing the critical cyber se-
partner of this widely praised event ✔ Integrating distributed energy re- curity and data privacy issues in
through the publication of a dedicated sources (DERs) to create virtual an increasingly digital and inter-
special issue distributed to all confer- power plants that can provide reli- connected grid.
ence attendees (not to be confused able and flexible power solutions; This year again, the magazine is
with the conference proceedings). The ✔ Implementing Internet of Things providing attendees with a special issue
special issue published for 2023 Grid (IoT) technologies to create smarter, that works as a colorful, print vehicle to
Edge Technologies featured seven pa- more efficient urban environments; share common knowledge about some
pers, including three reprints of popu-
lar Grid Edge Technologies articles
published over the previous three
years in the magazine. The selected
papers addressed the empowerment of
the grid edge to think and act through
microgrids, mobile storage, and dis-
tributed energy resources (DER),
among others, to decarbonize the hu-
man world while serving our modern
lifestyles (Fig. 1). The scope of the
grid technology conference suite, set
to occur every odd year, covers various
innovative technologies to enhance the
efficiency, productivity, and interoper-
ability of the electric grid. Some of the
key technologies currently considered
hot topics include:
✔ Decentralizing the energy grid
to increase resilience and reduce
dependence on centralized pow-
er generation;
✔ Utilizing 5G technologies to sup-
port dynamic, near real-time op-
erations and enhance grid asset
management through data inte-
gration and intelligent analytics;

Digital Object Identifier 10.1109/MPE.2024.3481560 figure 1. A vision of the interactions between the grid edge technologies and
Date of current version: 11 November 2024 modern lifestyles (picture inspired by Copilot).

4 ieee power & energy magazine January 2025 Show Issue

2025 Grid Edge Special Issue Guest Editorial
By Marianna Vaiman, Damir Novosel, Hong Chen, and Saman Babaei

Consumers and the utility infrastructure connect at the edge regulatory challenges, there are also many opportunities
of the electric grid. The term “grid edge” is very broad and the associated with these technologies and include monitor-
definition will continue to evolve due to grid transformation. ing, protection, and control; power system planning and
With increased integration of distributed energy resources operation; power quality; coordination of controllable
(DERs) such as solar, battery electrical energy storage, electric devices; and improved data management. There is also a
vehicles (EVs), and Internet of Things (IoT) smart devices, con- need for a new market structure and business model, while
trollability and flexibility at the consumer side significantly in- addressing weather, physical, and cybersecurity concerns.
creases. Power systems, while creating opportunities for vari- These challenges all involve many stakeholders, including
ous users particularly at the distribution level have, as a result, customers, transmission and distribution system opera-
become more dynamic and complex. This complexity arises tors, regulators, developers, manufacturers, and research-
from the need to accommodate the convergence of millions ers. This special issue is for us to dive into the topics and
of diverse intelligent devices connected at the edge, as well as promote grid edge technologies.
to harmonize their operation with the grid. As the industry undergoes a major transformation to
Grid edge technologies are becoming an important meet decarbonization goals, it requires an unprecedented
part of a reliable and efficient power grid operation (e.g., level of innovation in developing and deploying novel grid
participating as virtual power plants to respond to dispatch edge technologies. This should be addressed holistically,
signals). Though there are various technical, economic, and with multiple axes of innovation, including advanced power
 system analytics, the use of artificial intelligence, commu- [A2] describes a DER orchestration pilot project in
nication infrastructure, and workforce development. Western Australia’s Wholesale Electricity Market. The
With this in mind, our focus shifts to addressing interop- pilot shows that through aggregation and orchestration
erability and various interdependencies. For example, we of residential DER assets into a Virtual Power Plant (VPP),
need to consider the interdependencies among generation it is possible to unlock the full benefit of customer DERs
resources that include transmission and distribution systems; through market price integration and active dispatch. Simi-
gas and electrical infrastructure; transportation and electric lar to conventional generators, the VPP provides energy,
utility industries; and power electronics and power systems, capacity, and frequency control to the system; it also re-
among others. As we build the grid of the future with a high sponds to transmission and distribution network needs,
penetration of electronics-based sources, our focus shifts to while providing value to all customers. VPPs achieve bet-
real-time situational awareness, analysis, and control of dis- ter alignment between the energy market and household,
tribution grids using advanced sensors. We see integrated supporting reliable and efficient grid operation.
transmission, distribution, and resource planning becoming [A3] addresses utilities’ need to manage a communica-
very important, especially for high levels of EV deployment tion-enabled, data-intensive, grid-edge controllable grid
and data centers. With the impact of EVs and data centers through comprehensive and transformative technology de-
on the grid becoming more prominent, accurate modeling ployment. The field demonstration of an interoperable grid-
— especially for dynamic studies — will be critical for the edge management architecture is fostering the integration
effective planning and operation of the grid, to maintain its of distributed generation at the distribution level and facili-
reliability and resilience. At the same time, there is a strong tating the aggregation and integration of advanced sensors,
need to improve forecasting methodologies such as weather, distributed and artificial intelligence applications, EVs, elec-
electrification, and load forecasting, to better predict, and trified loads, and other grid-edge devices. This technology
also prevent, major system events such as cascading outages. will improve monitoring and ­detection, thus enabling proac-
The industry is now paying much more attention to probabi- tive maintenance and improving reliability. The capability to
listic scenario analysis, to account for uncertainties. integrate and analyze data is critical for utilities to plan and
This issue focuses on a wide range of novel grid edge visualize the system better, enabling them to serve custom-
technologies, approaches, and frameworks. It presents ers with prompt support.
both innovative pilot projects and lessons learned from [A4] demonstrates how high-fidelity data, provided in
existing successful implementations. an intuitive and timely manner, is critical for understanding
One of the key factors in making the innovative grid grid health. The authors describe case studies that show
edge technologies and frameworks presented in this is- how new grid dynamics present unique challenges for grid
sue a reality is having a champion, or champions, in the operators to solve. The power quality monitors with re-
utilities, who are willing to implement them, as this could mote communications used in these applications provide
be a significant effort. This is why many articles in the is- the system visibility necessary to shift from a costly and
sue feature authors from the utilities who are pioneering resource consuming reactive system, to a system with ac-
innovation and leading industry transformation. It takes a tionable data streams capable of empowering a proactive
village to deploy each innovative technology and solution! grid. Continuous monitoring and compliance reporting at
critical infrastructure locations provide actionable data
In This Issue that help to develop mitigation strategies before unfore-
[A1] addresses EVs within the overall distribution plan- seen issues developed into costly outages.
ning framework for grid modernization, including DERs [A5] develops a rigorous and practical process to iden-
and data centers. This approach focuses on integration tify the location and size of medium and heavy-duty EV
of planning and operations, as well as coordination with charging electrical load and their grid impact. Various sce-
generation resource and transmission activities. The au- narios, including state regulation emission targets and EV
thors identify gaps in the present processes and provide adaption curves, as well use of hydrogen vehicles, were
the overall roadmap to address those gaps with practical applied for the San Diego Gas & Electric (SDG&E) terri-
focus on the Dominion Energy Virginia needs and targets tory. One of the main findings is the necessity of having a
to prepare the company for the future. The latest tech- granular, bottom-up approach, as those EVs will be charg-
nological and process approaches have been addressed, ing at specific locations, thus resulting in significant grid
including the emphasis on forecasts with temporal and impact. Hourly analysis was required to address new peaks
spatial granularity. depending on the EV routes.

6 ieee power & energy magazine January 2025 Show Issue

 [A6] describes testing and deploying new da-
ta-driven technologies that can detect potential
equipment issues on the distribution electric grid
before they ignite a fire. It addresses relevant ef-
forts and approaches to detect arcing in distribu-
tion systems, focusing on detecting arcing signa-
tures in waveform measurements obtained from
system measurement units, such as digital fault re-
corders, protection relays, and power quality me-
ters. Since arcing detection in distribution systems
poses many challenges, the authors discuss some
of these challenges and present promising analyti-
cal techniques to address them, leveraging signal
processing and artificial intelligence specifically in
distribution systems.
[A7] presents a VPP offer methodology via sup-
ply functions determining the optimal VPP energy
offers maximizing DER value from market participa-
tion, participating in energy, capacity, and ancillary
services markets. The paper also presents a process
and cloud-based VPP aggregation platform for inte-
grating heterogeneous Behind the Meter (BTM) DER
smart devices into VPPs for wholesale energy mar-
ket participation. The platform aggregates, moni-
tors, and controls IoT devices and other BTM DERs
that are installed in residential and/or commercial
buildings, thereby transforming them to Grid-inter-
active Efficient Buildings (GEBs).

Acknowledgments
We would like to thank the authors for their out-
standing contributions. We extend a special thank
you to Marianna Vaiman, the Associate Editor for
the issue, and Sharri Shaw, the Assistant Editor of
IEEE Power & Energy Magazine, for their reviews
and valuable feedback, in addition to Maria Pro-
etto from the IEEE Power & Energy Society for fa-
cilitating the submission process. And of course, a
very special thanks to Innocent Kamwa, the Editor-
in-Chief of IEEE Power & Energy Magazine, for the
opportunity and his guidance on this issue.
Marianna Vaiman, V&R Energy, Los Angeles, CA
90049, USA, marvaiman@vrenergy.com
Damir Novosel, Quanta Technology, Raleigh, NC
27607, USA, DNovosel@quanta-technology.com
Hong Chen, PJM Interconnection, Audubon, PA
19403, USA, Hong.Chen@pjm.com
Saman Babaei, Right Analytics, Los Angeles, CA
90017, USA, saman.b@therightanalytics.com
 of the key technologies populating the The team of Guest Editors was highly the Assistant Editor and the IEEE
grid edge. These technologies will un- experienced in the magazine pro- publications team still had a highly
doubtedly be discussed in the confer- cesses and worked largely without demanding finish line. Yes, we always
ence sessions or showcased on the exhi- any close interaction with me. For underestimate the duration of the re-
bition floors. Our goal, of course, is not this, I want to express my heartfelt view process, which various human
to cover the entire spectrum of technol- thanks to them. The authors must also factors can delay despite being pro-
ogies discussed during the conference, be commended because we pushed cessed semi-automatically in Scholar
as the proceedings will do that well. them hard to get the papers through One. We thank them, especially Ma-
The accompanying special issue is just three review stages, which is unusual ria Proetto and Catherine Van Sciver,
a selection of topics from a call to major for invited papers. They constantly for their patience and collaboration in
industry players made by the appointed responded despite working in the in- producing this special issue. We are
Guest Editors, who are all top experts dustry, where writing papers is never already thinking about how to make
in Grid Edge Technologies. Coming a career aspirational goal. We hope the 2026 T&D special issue closing
from utility and industry, where lead- that you, our esteemed reader, will smoother and, hopefully, at least one
ing and executing innovation projects to notice the high quality of each paper month earlier than the 2025 Grid Edge
create business value is the essence of and that they will be rewarded in the special issue (yes, we can dream). All
their role, these experts relied on their long term by a high Xplorer popular- industry authors planning to attend the
deep knowledge concerning ongoing ity score. T&D conference in 2026 can contact
grid-edge transformation projects, their In closing, there is a “last but not me at innocent.kamwa@gel.ulaval.
related challenges, and opportunities least paper” produced by authors from ca to discuss related publication op-
to invite authors worldwide to discuss Region 10 [A8] and included in this portunities. However, remember that
their more recent work that has resulted special issue. This is further proof the magazine publishes only scientific
in implementation, or that served as the of the IEEE PEM’s commitment to papers using a specific template, with
basis for new regulations, standards, or a diverse source of authorships and requirements described on our web
in-house work procedures evolution. the worldwide distribution of the site . The papers in [A1-A8] give you
The result is a set of seven original, in- projects featured in the magazine a glimpse of the format and style of
vited papers presented in the guest edi- over the years, as technology has no papers appropriate for IEEE PEM if
torial (blue box). boundaries. Received as an unsolic- you are interested in submitting a pa-
ited paper and processed through the per for the 2026 T&D special issue, no
Wrap-Up and Prospects corresponding independent review later than 1st March 2025.
Casual readers of the magazine may process, this paper describes a fully
not have noticed that each year, we operational microgrid in the Red Sea For Further Reading
have six issues plus one extra issue Coastal Areas (IEEE Region 8), with I. Kamwa, “Bottom-up support: Grid-
published as part of the Grid Edge a grid-forming battery energy storage edge technology [Editors’ Voice],”
Technology conference during odd system and a solar photovoltaic sys- IEEE Power Energy Mag., vol. 21,
years and the T&D conference during tem, to deliver 100% renewable ener- no. 2, pp. 4–6, Mar./Apr. 2023, doi:
even years. So, last year, we published gy. The microgrid operates in perma- 10.1109/MPE.2023.3247830.
a 2024 T&D special issue, whose nent islanding mode, maintaining grid I. Kamwa and M. Vaiman, “Acceler-
Guest Editor was our reliable asso- stability without linking to the bulk ating the grid of tomorrow: Responding
ciate editor, Dr. Marianna Vaiman. power system. It supports a variety to an industry-wide transformation [Edi-
Publishing a special issue like this is of loads, including commercial and tors’ Voice],” IEEE Power Energy Mag.,
like working overtime to produce two industrial facilities, as well as various vol. 22, no. 2, pp. 4–8, Mar./Apr. 2024,
magazine issues simultaneously. For critical loads. Emphasizing sustain- doi: 10.1109/MPE.2024.3352948.
context, this editorial is being written ability, the microgrid aims to have a
in early October, and the Grid Edge net-positive impact on biodiversity. Appendix: Related Articles
issue will be wrapped up and mailed Addressing technical challenges such [A1] 
R . Si e p k a , J. R . A g ü e r o, M .
to members in mid-November if our as autonomous operation, black start, O’Baker, D. Hall, and H. L. Willis,
plan works well. However, we are large transformer energization, and “Distributed energy resources and
also wrapping up, at the same time, fault ride-through was a key focus electric vehicle adoption: A distri-
the regular 2025 November/Decem- during the system’s design and com- bution planning strategy and road
ber issue. Only my predecessor, Steve missioning phases. map ,” IEEE Power Energy Mag.,
Widergren, can understand and ap- Despite playing it safe by conven- vol. 22, no. 6, pp. 40–51, Nov. 2024,
preciate the implied workload. For- ing the first meeting with Guest Edi- doi: 10.1109/MPE.2024.3463588.
tunately, working as a team is more tors scoping the issue and inviting the [A2] 
A . Costache, B. Redmond, J.-P.
than summing up individual efforts. authors at the end of January 2024, Montandon, and D. Sharafi,

8 ieee power & energy magazine January 2025 Show Issue

 “Beyond demand response: In- advanced power quality moni- pp. 83–90, Nov. 2024, doi: 10.1109/
tegrating aggregated DERs in toring,” IEEE Power Energy MPE.2024.3462306.
Western Australia’s wholesale Mag., vol. 22, no. 6, pp. 67–75, [A7] 
A. Papalexopoulos, S. Oren,
electricity market,” IEEE Power Nov. 2024, doi: 10.1109/MPE. and H.-P. Chao, “Integrating
Energy Mag., vol. 22, no. 6, pp. 2024.3466121. behind-the-meter grid edge
52–57, Nov. 2024, doi: 10.1109/ [A5] 
R. Fioravanti, L. Sun, R. Mush- technologies into wholesale elec-
MPE.2024.3457542. et, A. Bolles, A. Moffat, and M. tricity markets: A novel method-
[A3] 
G. Kandaperumal, B. Chen, K. Belden, “Impact of medium- and ology using virtual power plants,”
DSouza, R. Zhu, and B. Glis- heavy-duty electric vehicles and IEEE Power Energy Mag., vol.
son, “Data fusion and interop- the grid: An address-level/bottom- 22, no. 6, pp. 91–100, Nov. 2024,
erable control: Engineering the up california case study,” IEEE doi: 10.1109/MPE.2024.3473852.
grid of the future,” IEEE Power Power Energy Mag., vol. 22, no. [A8] 
H. She and H. Zheng, “The Red
Energy Mag., vol. 22, no. 6, pp. 6, pp. 76–82, Nov. 2024, doi: Sea microgrid: A 100%-renew-
58–66, Nov. 2024, doi: 10.1109/ 10.1109/MPE.2024.3456042. able grid for the new city,” IEEE
MPE.2024.3470729. [A6] 
J.-Y. Joo et al., “Detecting anom- Power Energy Mag., vol. 22, no.
[A4] 
N. Nakamura, L. Vega, and K. alies for fire prevention in distri- 6, pp. 101–108, Nov. 2024, doi:
Tangney, “Empowering a pro- bution systems: Challenges and 10.1109/MPE.2024.3433318.
active grid with power quality analytical techniques,” IEEE
p&e
visibility: Taking advantage of Power Energy Mag., vol. 22, no. 6, 

NEW!
IEEE PES TRANSACTIONS ON ENERGY MARKETS,
POLICY, AND REGULATIONS

IEEE Transactions on Energy Markets, Policy, and Regulation is a

rigorously peer-reviewed journal disseminating relevant knowledge
and developments in the organization and structure of rapidly
evolving multi-energy supply and demand systems.

LEARN MORE

Digital Object Identifier 10.1109/MPE.2024.3487024

January 2025 Show Issue ieee power & energy magazine 9

 IEEE PES GRID EDGE TECHNOLOGIES
CONFERENCE AND EXPO 2023
2025
ORGANIZING COMMITTEE

Position Name
Chair Wayne Bishop
Co-Chair Babak Enayati
Executive Chair Kevin Geraghty (SDGE)
Immediate Past Chair Ahad Esmaeilian
Technical Program Chair Hong Chen
Technical Program Co-chair Dan Sabin
Technical Committee - Members at Large Yannan Sun
Technical Committee - Members at Large Zongjie Wang
Technical Committee - Members at Large Ebrahim Vaahedi
Coordinator of Super Session Panels Chan Wong
Coordinator Poster Session/Local SD PES Chapter Chair Hassan Ghoudjehbaklou
Stage - Artificial Intelligence (AI) Andrija (Ani) Sadikovic (chair)
Stage - Artificial Intelligence (AI) Abder Elandaloussi (Co-chair)
Stage - Electrification Nikoo Kouchakipour (chair)
Stage - Electrification John Hofman (Co-Chair)
Stage - Distributed Energy Resources Shahab Afshar, Ph.D., CAPM (chair)
Stage - Distributed Energy Resources Honghao Zheng (Co-chair)
Stage - Sustainability and Resiliency Martha Symko-Davies (chair)
Stage - Sustainability and Resiliency Shishir Shekhar (Co-chair)
Stage - Start-ups and Innovation Showcase H.G. Chissell (chair)
Exhibits & Sponsorship Committee Chair Milad Soleimani
Co-Chair Sandy Norris
Exhibits / Sponsorships Committee Iman Kiaei
Exhibits / Sponsorships Committee Ana Ospina
Exhibits / Sponsorships Committee Tamara Bacejac
Local Organizing Committee (LOC) Chair Nick Moran (SDGE)
Co-Chair Alex Moffat (SDGE)
Communications Local Organizing Committee Humberto Gurmilan
Technical Tour Coordinator Lianna Rios
Secretary Mariana Resener
Treasurer Tim Gitau
Assistant Treasurer Praveen Kumar
European Outreach Luka Strezoski
PhD Dissertation Contest Chair John Zhang
PhD Dissertation Committee Co-chair Junbo Zhao
PhD Committee Aaron St. Ledger
PhD Committee Luo Xu
PhD Committee Junbo Zhao
PhD Committee Larissa Affolabi
PhD Committee Saeed Manshadi
PhD Committee Xin Chen
PhD Committee Stelios Dimoulias
PhD Committee Nadine Kabbara
Start-Up Showcase Committee Chair H.G. Chissell

10 ieee power & energy magazine January 2025 Show Issue

 Start-Up Showcase Committee Co-chair Saeed Manshadi
Start-Up Showcase Committee Sheikh Jakir Hossain
Start-Up Showcase Committee Abder Elandaloussi
Marketing and Outreach Larissa Affolabi
Marketing and Outreach Sacha Fontaine
Sponsorships, exhibits, and marketing Kaveh Aflaki
Marketing Alan Ross
Local SD PES Chapter Chair Hassan Ghoudjehbaklou
SD Chapter Fred Raddatz
SD Chapter Saeed Manshadi
PES VP of Conferences and Meetings Jackie Peer
PES Staff Tim Licitra
PES Staff Dan Toland
PES Staff Kathy Heilman
PES Staff Maria Proetto
PES Staff Jenny Brown
Meeting Planner, IEEE CEE Lu Lelong
MDG Agency Tyler Day
MDG Agency Rachel Schrichte
MDG Agency Tyler Watson
CEM llc - Exhibit and Sponsorship Sales Shawn Boon

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January 2025 Show Issue ieee power & energy magazine 11

 2025 IEEE PES GRID EDGE TECHNOLOGIES
CONFERENCE & EXPOSITION PROGRAM

Program details as of August 2024, please check the python-based distribution OPF tool (DistOPF) that develops
conference website and app for any updates. power flow models as a constraint from the standard network
Monday, January 20, 2025 inputs, allowing users to focus on other aspects. Specifically,
this python-based package can quickly create the underly-
Automated DERMS with Dynamic Rate (tutorial) ing mathematical equations of the power flow model for any
Monday, January 20, 2025 1:00 PM-5:00 PM Room 1A distribution system and allows researchers to avoid the need
Session Chair: Ashkan Kian, Quanta Technology to repeatedly model power flow models and instead leverage
existing models that can be readily selected. It also formu-
The Automated Distributed Energy Resource Management lates standard OPF related constraints. Thus, this package
System (DERMS) represents a groundbreaking advancement alleviates challenges to formulate OPF problems to develop
in grid management. By integrating demand response (DR) and provide grid-support functionality and enhance grid
optimization into the DERMS engine’s objective function, resilience for the power distribution system. In short, the Dis-
the Automated DERMS enables efficient management of reg- tOPF package/tool will provide researchers/users a unique
istered behind-the-meter (BTM) assets. These assets are piv- platform (i) to create new OPF algorithms by leveraging for-
otal in enhancing grid reliability, resiliency, and affordability. mulated unbalanced power flow models as the optimization
This tutorial will explore its integration with the CalFUSE constraints, (ii) to benchmark developed OPF algorithms (for
pilot project, focusing on managing registered behind-the- both planning stage solutions and operation stage controls)
meter (BTM) assets for optimal DR programs. This tutorial for the distribution networks using provided models/solu-
aims to equip participants with the knowledge and practical tions, and (iii) to create extensive training data to train the
skills to leverage Automated DERMS effectively. advanced machine learning based OPF algorithms.”
1. Dr. Rabayet Sadnan, Research Scientist, Pacific North-
Presenter(s): west National Laboratory (PNNL)
• Dr. Ashkan R. Kian (Quanta Technology) 2. Dr. Anamika Dubey, Associated Professor, School of
• Mark Martinez (SCE) Electrical Engineering and Computer Science, Washing-
ton State University
Open-Source Tool for Optimal Operation & Control for 3. Nathan Gray, Research Assistant, School of Electrical
Power Distribution Systems (tutorial) Engineering and Computer Science, Washington State
Monday, January 20, 2025 1:00 PM-5:00 PM Room 1B University
Session Chair: Dr. Rabayet Sadnan, Pacific Northwest
National Laboratory (PNNL) Modelling and Control of Customer Systems for Evalu-
ating Distribution Grid Response (tutorial)
The increasing demand for advanced grid support functional- Monday, January 20, 2025 1:00 PM-5:00 PM Room 2
ity from a large number of DERs has sparked significant inter- Sponsored By: IEEE PES Grid Edge Technologies
est in that focuses on optimization methods for large-scale Conference
unbalanced power distribution systems, aimed at enhancing Session Chair: Dr. Trevor Hardy, Pacific Northwest National
operational efficiency and resilience. Both the traditional Laboratory
mathematical optimization methods and machine-learning
(ML) -based approaches are gaining traction to attain opti- The changing landscape with the proliferation of distrib-
mal solutions for the scaled power distribution systems. uted energy resources (DERs) introduces new challenges
However, when developing any optimal power flow (OPF) for utility operations and business as they face a large num-
algorithm for distribution system, it is crucial to integrate ber of DERs with limited to no visibility and have to plan
unbalanced distribution power flow models as constraints. for infrastructure to accommodate their unpredictability.
This poses the most challenging aspects of formulating any The traditional planning process is becoming less linear
OPF problem. Besides, for ML-based approaches, creating and more democratized, as modern distribution systems
extensive training data is time-consuming task that signifi- require more engineering analysis that can capture the com-
cantly delays the process. This tutorial aims at presenting a plex dynamics of DERs to process interconnection requests,

12 ieee power & energy magazine January 2025 Show Issue

forecast loads at higher granularity, analyze non-wires process. Furthermore, sensors and sensor behaviors need to be
alternatives and ensure efficient and reliable operation. emulated with grid simulations to perform modeling studies
• Burhan Hyder - PNNL- burhan.hyder@pnnl.gov
This tutorial aims to provide an overview of state-of-the- • Tse-Chun Chen - PNNL- tse-chun.chen@pnnl.gov
art approaches for modeling customer DERs and incorpo-
rating them with existing distribution models. The tutorial AI-Driven Innovations in Electrification (tutorial)
will start with a discussion on different physics-based DERs Monday, January 20, 2025 1:00 PM-5:00 PM Room 8
models and discuss how the Transactive Energy Simulation Sponsored By: IEEE PES Grid Edge Technologies Conference
Platform (TESP) provides modularized interface towards Session Chair: Dr. Mohsen Aleenejad, MathWorks
minimizing the barriers of modeling and enabling more Session Chair: Chengli He, MathWorks
efficient analysis (even with non-transactive systems). Next,
we will present software-APIs for incorporating different With the increasing adoption of renewables, decentraliza-
DERs (including HVACs, EVs, PVs, and battery systems) tion of energy infrastructure, and the electrification of
with representative regional characteristics. Finally, we transportation, new challenges arise, including the need for
will demonstrate how the TESP platform could be used for enhanced reliability and efficiency.
complex use-cases including growth projections, response
during extreme-events and potential of incentive-based Dr. Mohsen Aleenejad, MathWorks, maleenej@mathworks.com
controls. The use-cases will include a hands-on tutorial on
using the APIs to model DERs with prototypical feeders Digital Twin Development for Grid-Forming Inverters
and evaluate control schemes using GridLAB-D. and Microgrid Applications (tutorial)
• Dr. Trevor Hardy, Pacific Northwest National Laboratory Monday, January 20, 2025 1:00 PM-5:00 PM Room 9
•  Dr. Monish Mukherjee, Pacific Northwest National Lab- Sponsored By: IEEE PES Grid Edge Technologies Conference
oratory Session Chair: Jose Montoya Bedoya, MathWorks
•  Dr. Meghana Ramesh, Pacific Northwest National Labo- Session Chair: Chengli He, MathWorks
ratory
• Jessica Kerby, Pacific Northwest National Laboratory Grid-forming inverters and microgrids are helping to
transform the electrical grid by enabling renewable energy
DER Gateways - Bridging the Gap Between Utility and integration and improving grid stability. Digital twins, or
DER (tutorial) virtual models of these systems’ electrical and software
Monday, January 20, 2025 1:00 PM-5:00 PM Room 7A components, are the key to optimize performance, monitor
Sponsored By: IEEE PES Grid Edge Technologies operations, and mitigate anomalies.
Conference
Session Chair: Ben Ealey, EPRI Jose Montoya Bedoya, Jose Montoya Bedoya

The DER functionalities specified in some grid codes (e.g. Distribution Load Forecasting in An Electrification
IEEE 1547 & Rule 21), and the associated communica- World (tutorial)
tion interfaces are not suitable for direct integration with Monday, January 20, 2025 1:00 PM-5:00 PM Room 10
monitoring and control systems (e.g., DERMS). These func- Sponsored By: IEEE PES Grid Edge Technologies Conference
tionalities were designed only to expose the raw, inherent Session Chair: Farnaz Farzan, Quanta Technology
capabilities of the DER, but (intentionally) omit additional
logic, management features, and security requirements This tutorial covers the principles of load forecasting for
because these were believed to vary by utility and region. distribution planning with a focus on the change in growth
Added business logic, centralized management, and cyber- and characteristics of load introduced by electrification. The
security capabilities are needed by utilities, and DER net- course begins with the planning process and load forecast
work gateways can provide these capabilities. as its first building block. It will cover the key require-
• Ben Ealey, EPRI ments of a robust and defendable forecast via bottom-up and
• Ajit Renjit, EPRI top-down approaches, will review several proven methods,
and will discuss modern techniques for end-use growth to
Synchrophasor Data Analytics software and Synthetic forecast electrification trends. Instructors will discuss the
Sensor Data Generation (tutorial) concepts related to magnitude, temporal (e.g., 8,760-hr fore-
Monday, January 20, 2025 1:00 PM-5:00 PM Room 7B casts), spatial, and weather normalization aspects of fore-
Session Chair: Kaveri Mahapatra, Pacific Northwest cast. The course will study legacy methods the industry used
National Laboratory for forecasting in past when it faced major load. The course
drills down into different components of transportation and
Sensors play a critical role in supporting day-today grid oper- stationary electrification forecast including light, medium
ations and they are essential to operator’s decision making and heavy vehicles, transit, charging networks, and building

January 2025 Show Issue ieee power & energy magazine 13

 electrification. Finally, approaches to address uncertainty in * 25GET0347, Emerging developments in microgrid
forecast and its implications will be discussed. technology.
• Lee Willis (Author of Spatial Electric Load Forecast- M. RENO, Sandia National Laboratories
ing, and Power Distribution Planning Reference Books
* 25GET0348, Microgrids- Industry Perspective
(CRC)). Quanta Technology
E. VAAHEDI, OATI
• Farnaz Farzan, Quanta Technology
• Gerardo Sanchez: Quanta Technology * 25GET0349, Island networked microgrid: pre-engineer-
ing specification and pre-deployment considerations.
Blockchain Technology for Power Systems (tutorial) V. RABL, Quanta Technology
Monday, January 20, 2025 1:00 PM-5:00 PM Room 11A
Sponsored By: IEEE PES Grid Edge Technologies Conference Navigating Electricity Grid Transformation and Energy
Session Chair: Disha L Dinesha, Indian Institute of Science Transition (panel session)
Tuesday, January 21, 2025 1:00 PM-2:00 PM Room 10
Blockchain technology is gaining momentum in revo- Session Chair: Farrokh Rahimi, Oati
lutionizing power systems amidst the global transition
towards renewable energy sources and climate change The growing share of intermittent renewable genera-
mitigation. As power systems evolve from large-scale, tion, distributed energy resources (DERs), electrification
centralized systems to networks of small-sized, distrib- of transportation, coupled with increased frequency and
uted electricity systems, blockchain’s decentralized ledger severity of extreme weather events are causing operational
capabilities offer efficient transaction management for uncertainties that challenge system reliability, market effi-
small-scale power systems. With features like tamper- ciency and operational economics. Many of the current
resistant data, privacy protection, smart contracts, and power systems business practices, methods and tools are
real-time settlement, blockchain facilitates secure and based on requirements, technology, and grid architecture
transparent energy transactions. Integrated with advance- with centralized generation and conforming loads. The
ments in smart grids and microgrids, blockchain enables growing forecast inaccuracies result in energy market inef-
peer-to-peer energy transactions, better grid management, ficiencies, excessive generation and transmission reserves,
and real-time payments. Hence, the application of BCT as well as reliability challenges including grid congestion
in energy industry is being investigated rigorously. This and shortfall of adequate resource flexibility for ramping
tutorial offers a comprehensive introduction to blockchain and energy balancing.
technology, covering its fundamental principles, compo-
nents, operational mechanisms, and potential applica- This panel session will address how these factors are shap-
tions within power systems. Additionally, the tutorial will ing the electricity grid transformation and energy transi-
include a live demonstration on how to use blockchain tion. The panelists include a cross section of experts and
platforms such as Ethereum and Cosmos. stakeholders involved in operation of Energy Markets,
Transmission Operations, Distribution Operations, and
Disha L Dinesha, PhD Research Scholar, Indian Institute Grid-Edge technologies. They will present how they envi-
of Science sion the emerging business modes and operational solutions
to address these challenges.

Tuesday, January 21, 2025 * 25GET0144, Emerging grid operations architecture in sup-
Breakfast and Networking (breakfast) port of greater levels of intermittent renewable generation
Tuesday, January 21, 2025 7:00 AM-8:00 AM Ballroom 6C-F A. IPAKCHI, OATI
* 25GET0145, Addressing energy orchestration, enhanced
Conference Lunch (luncheon) data exchange requirements between distribution and trans-
Tuesday, January 21, 2025 12:00 PM-1:00 PM Ballroom 6C-F mission, and between balancing and reliability coordination
areas
Microgrids for Power System Resilience: Present and H. ALARIAN, CAISO
Future (panel session)
Tuesday, January 21, 2025 1:00 PM-2:00 PM Room 11B * 25GET0146, Methods and processes for enhancing grid
Session Chair: Babak Enayati, Luma Energy resilience (versatile microgrids)
L. RAGSDALE, North Carolina Electric Cooperative
* 25GET0345, Advances in microgrid protection: field
implementation. * 25GET0147, Enhancing methods and tools for granu-
a. ZAMANI, Quanta Technology lar and “bottom-up” net-load forecasting considering the
impact of DERs, electrification of transportation, and
* 25GET0346, Microgrids- utility case study demand-side management programs
J. CASTANEDA, Southern California Edison (SCE) M. SHAHIDEHPOUR, IIT

14 ieee power & energy magazine January 2025 Show Issue

* 25GET0266, Intelligent Transmission and Distribution * 25GET0162, Demand response as a VPP was only the
System to Enable Clean and Affordable Energy Delivery first step
B. ENAYATI, Lumapr M. DUESTERBERG, OhmConnect
* 25GET0148, Application of Dynamic Hosting Capacity * 25GET0163, VPPs enable renewables to offer better ancil-
and Dynamic Operating Limits/Envelopes for enhanced lary services
distribution grid operation P. MOUTIS, City College of New York
F. ALBUYEH, OATI
* 25GET0164, Local DER orchestration: A pilot project
from PG&E
“Virtual Power Plants” the new name for “Distributed
Y. WU, PG&E
Energy Resources” (panel session)
Tuesday, January 21, 2025 1:00 PM-2:00 PM Room 1B * 25GET0165, Utilities’ efforts to coordinate energy
Session Chair: Grant Ruan, MIT resources far & wide
C. SMITH, CAMUS Energy
Driven by the ambitious electrification and renewable energy
* 25GET0166, Non-intrusive enforcement of decentralized
plans, the future electric power grids around the world will
stability protocol for inverter-based resources
undergo a rapid landscape change characterized by the large-
T. HUANG, San Diego State University
scale integration of distributed energy resources (DERs).
There is a revolutionary idea to promote the construction of
Role of 5G/6G Communications in the evolution of DER
virtual power plants (VPPs), which actively aggregate and
and the Distribution System (panel session)
control the heterogeneous DERs on the grid edge. VPPs are
Tuesday, January 21, 2025 1:00 PM-2:00 PM Room 7A
expected to offer a unique bottom-up solution to turn DERs
Session Chair: Bora Akyol, Powin Energy
from a previous “trouble maker” into a truly “valuable asset”.
In this panel, listen to industry experts discuss how ubiquitously
The Department of Energy has actively funded a few prom-
available 5G/6G communications have the potential to improve
inent start-ups and projects of VPPs, starting with demand
the resilience, flexibility and efficiency of the grid edge while
response on peak load mitigation in California five years
making integration of DER more reliable and easier.
ago and expanding to batteries and on-roof photovolta-
ics in New York and Texas last year. VPP is on a growing * 25GET0167, Role of 5G/6G Communications in the evo-
trajectory towards a future that may look radically differ- lution of DER and the Distribution System
ent from the decades-old way we operate today’s electric B. AKYOL, Powin Energy
grid and electricity market. Although the full picture of the
* 25GET0168, Role of 5G/6G Communications in the evo-
challenges and opportunities is not completely clear, the
lution of DER and the Distribution System
energy community is showing interests in VPP’s potential
B. EALEY, EPRI
to improve the cost efficiency, activate previously unfore-
seen flexibility, and deliver high-quality grid services when * 25GET0169, Role of 5G/6G Communications in the evo-
called upon. More and more practices suggest that VPP lution of DER and the Distribution System
has the potential to become the plug-and-play standard S. LAVAL, Eaton
for all distributed generation, battery storage, and demand
* 25GET0170, Role of 5G/6G Communications in the evo-
response resources in the very near future.
lution of DER and the Distribution System
J. OGLE, PNNL
This panel comprises some of the VPP originators from
government, industry, and academia and seeks to inform * 25GET0171, Role of 5G/6G Communications in the evo-
the audience about the current state of VPP R&D and the lution of DER and the Distribution System
future lying ahead. Our panelist will generously share their J. BARTLETT, SEL Inc.
professional opinions and first-hand experience regarding
* 25GET0172, Role of 5G/6G Communications in the evo-
VPP’s decarbonization value, flexibility and grid service
lution of DER and the Distribution System
delivery, reliability and stability issues. We also cover the
S. CHANDLER, Guidehouse Consulting
utilities’ practices of coordination and local orchestration
strategies for DERs. The knowledge sharing will facilitate
Modernization of Power Delivery Systems - Building the
an inspiring technical discussion across the entire commu-
Grid of the Future (panel session)
nity and promote face-to-face communication between dif-
Tuesday, January 21, 2025 1:00 PM-2:00 PM Room 9
ferent stakeholders.
Session Chair: Julio Romero Aguero, Quanta Technology
* 25GET0161, The value of VPPs to implement national
decarbonization goals This panel session will discuss the initiatives currently
J. DOWNING, Department of Energy being pursued by electric utilities to modernize power

January 2025 Show Issue ieee power & energy magazine 15

 delivery systems. The panel will focus on key requirements * 25GET0282, Deployment and Grid Resilience
for designing and building future power delivery systems Partnerships
to enable the adoption of inverter-based DER and electric J. BRIONES, DOE
transportation, while ensuring climate change preparedness
against major events, including: Advanced Technologies for Secure and Efficient Power
Transfer (panel session)
1. Advanced volt-var control of power delivery systems with Tuesday, January 21, 2025 1:00 PM-2:00 PM Room 8
high penetration of inverter-based resources (e.g., utiliza- Session Chair: Saifur Rahman, Virginia Tech
tion of power electronics-based equipment like low-volt-
age Static Var Compensators (SVC) and inverter-based With the advent of grid-connected distributed energy
DER vs. electromechanical devices) sources including solar, wind and storage, their value prop-
2. Advanced monitoring, protection, automation, and con- osition and optimum control challenges bring uncertainty
trol (e.g., utilization of advanced sensors, implementa- to grid operation. Let us take the case of solar photovolta-
tion of advanced distribution automation schemes, moni- ics. The intermittency of solar output decreases its value,
toring and control of utility-scale and behind-the-meter especially if this intermittency occurs during the peak load
DER, etc.) periods. On the other hand, high PV output during low-load
3. Implementation of real-time distribution system op- periods, like mid-morning, causes voltage problems on
erations, Advanced Distribution Management Systems the distribution grid. The other challenge is the so-called
(ADMS), DER Management Systems (DERMS), mi- “duck curve”. In this case the higher PV output in the early
crogrids, and Distributed Energy Storage (DES) afternoon causes the net load on the grid drop to a level
4. Modern planning, modeling, and analysis approach- even below the morning load seen by the utility. The steep
es (e.g., Integrated Distribution Planning, load and drop in the net load in the early afternoon, and the steep
DER forecasting, DER and electrification hosting rise in the net load starting in the late afternoon causes
capacity) operational challenges for the utility. The availability long-
5. Foundational infrastructure alternatives (e.g., weather duration storage can somewhat ameliorate this problem. But
hardening, strategic undergrounding). battery storage is expensive and consumes a lot of space.
The inverter-based resources like solar PV, wind and bat-
* 25GET0267, Modern and Future Distribution System
tery cannot provide system inertia like what is offered by
Operation
large thermal or hydro generators. Technological advances
J. ROMERO AGUERO, Quanta Technology
in grid- forming inverters are being deployed to address this
* 25GET0268, DER Readiness in Future Power Delivery gap. Panelists will discuss the operation of these advanced
Systems - Trends, Challenges and Solutions technologies for secure and efficient power transfers into
S. PANDEY, Commonwealth Edison the modern-day grid.
* 25GET0269, Modernization of Power Delivery Systems - * 25GET0284, Solid State Transformers
LUMA Energy’s Experience J. LAI, Virginia Tech
B. ENAYATI, Lumapr
* 25GET0285, Grid-forming inverters role in improving
* 25GET0270, Modernization of Power Delivery Systems - resilience in grids
PacifiCorp’s Experience B. KROPOSKI, National Renewable Energy Lab
R. NAHIR, PacifiCorp
* 25GET0286, Secure and Efficient Power Transfer
* 25GET0343, Modernization of Power Delivery Systems - L. JONES, Edison Elecrtric Inst.
Southern Company’s Experience
* 25GET0287, Technologies for flexible and resilient power
A. INGRAM, Southern Company
systems of the future
G. SALGE, Hitachi Energy
Fortifying Energy Storage: Cybersecurity, Supply
Chain, and Reliability Management (panel session)
An Update from PES Big Data Analytics Subcommittee -
Tuesday, January 21, 2025 1:00 PM-2:00 PM Room 1A
All are welcome (panel session)
Session Chair: Emma Stewart, Idaho National Laboratory
Tuesday, January 21, 2025 1:00 PM-2:00 PM Room 7B
* 25GET0279, Session Chair: Yannan Sun, Oncor
E. STEWART, Idaho National Laboratory
* 25GET0298, Data Analytics for Energy Storage
* 25GET0280, Threat Landscape for BESS and IBR D. WU, Pacific Northwest National Laboratory
M. CULLER, Idaho National Laboratory
* 25GET0299, Big Data & Analytics for Security and Resil-
* 25GET0281, Cybersecurity and OT in BESS Deployment ience of Power Systems
M. YOUNG, Hunt Energy Network J. YAN, Concordia University

16 ieee power & energy magazine January 2025 Show Issue

* 25GET0300, Big Data Analytics for Synchro-Waveform The grid edge is rapidly becoming a focal point of trans-
Measurements formation within the energy sector, driven by the integra-
J-Y JOO, Lawrence Livermore National Laboratory tion of state-of-the-art smart building technologies, electric
vehicles, and distributed energy resources. This transfor-
* 25GET0301, Physics-informed Machine Learning in
mation is not just redefining our national energy infrastruc-
Smart Grid
ture but also introducing a paradigm shift towards energy
N. YU, University of California, Riverside
efficiency, advanced storage solutions, and innovative dis-
* 25GET0302, Edge Computing Infrastructure for Grid tribution strategies.
Edge Sensing and Control
J. LIAN, Oak Ridge National Laboratory Our goal is to illuminate the wide array of opportunities and
challenges presented by the strategic deployment of grid
IEEE PES Workforce Initiative - How PES can assist edge technologies. We will demonstrate how these technolo-
fresh graduate to speed up the learning curve (panel gies not only empower consumers and utility providers with
session) valuable data for better decision-making but also support
Tuesday, January 21, 2025 1:00 PM-2:00 PM Room 11A the adoption of decentralized and distributed control algo-
Session Chair: Chan Wong, Landis and Gyr rithms. These developments are vital for improving energy
resilience and encouraging the uptake of clean energy.
* 25GET0305,
C. WONG, Landis and Gyr
An essential aspect of our discussion will focus on the
* 25GET0306, How PES can assist fresh graduate to speed integration of big data and computing algorithms within
up the learning curve the grid edge, unlocking advances in data analytics and
M. LAUBY, NERC machine learning. These capabilities are pivotal for pro-
cessing the vast amounts of data generated at the grid edge,
* 25GET0307, How PES can assist fresh graduate to speed
thereby facilitating accurate system predictions, and opti-
up the learning curve
mizing energy distribution in real-time.
K. GARG, SEL Inc
* 25GET0308, How PES can assist fresh graduate to speed Furthermore, we’ll highlight the impact of synchrophasor
up the learning curve technologies in enhancing intelligent transmission asset
J. NYANGON, DOE monitoring and generator model validations. Leveraging
utilities’ experiences with PMU-based applications, we will
Weathering the Firestorm: Grid Edge Resources for an delve into their role in the proof of concept, validation of
Equitable Wildfire Resiliency (panel session) pilot deployments, and how real-time simulation facilities
Tuesday, January 21, 2025 1:00 PM-2:00 PM Room 2 can support their broad adoption.
Session Chair: Ali Arabnya, Quanta Technology
However, the journey towards digital transformation at the
* 25GET0361, Weathering the Firestorm: Grid Edge
grid edge is accompanied by cybersecurity challenges. Our
Resources for an Equitable Wildfire Resiliency
session will address the imperative of protecting our energy
D. HAUGHTON, LUMA Energy
infrastructure against potential threats, ensuring its reli-
* 25GET0362, Weathering the Firestorm: Grid Edge ability and safety.
Resources for an Equitable Wildfire Resiliency
B. PIERRE, Sandia National Labs Bringing together experts from academia, national labo-
ratories, and utilities, the panel will provide a comprehen-
* 25GET0363, Weathering the Firestorm: Grid Edge
sive outlook on the practical implementations of grid edge
Resources for an Equitable Wildfire Resiliency
technologies, addressing cybersecurity challenges, and
A. ARABNYA, Quanta Technology
envisioning the future of our energy systems. Attendees
* 25GET0364, Weathering the Firestorm: Grid Edge will gain a comprehensive understanding of the latest inno-
Resources for an Equitable Wildfire Resiliency vations, including the crucial role of synchrophasor data,
M. SYMKO-DAVIES, The National Renewable equipping them to contribute to a secure, sustainable, and
Energy Laboratory efficient energy future.
* 25GET0136, Interoperable Control Framework for
Empowering the Grid Edge: Innovations, Integration,
Enhanced Coordination and Integration
and Cybersecurity in the New Energy Landscape (panel
B. CHEN, Commonwealth Edison Company
session)
Tuesday, January 21, 2025 2:15 PM-3:15 PM Room 7A * 25GET0137, Advanced Data Processing and Control
Session Chair: Tianqiao Zhao, Brookhaven National Algorithms for Edge Device Deployment
Laboratory T. HONG, University of Georgia

January 2025 Show Issue ieee power & energy magazine 17

 * 25GET0138, Synchrophasor Data for Smart Substation Sharing Utility Experiences with Data Analytics and
Solutions AMI (panel session)
R. POURRAMEZAN , New York Power Authority Tuesday, January 21, 2025 2:15 PM-3:15 PM Room 7B
Session Chair: David Hart, Quanta Technology
* 25GET0139, Ensuring Data Integrity at the Edge in Grid
Applications - Strategies and Technologies
Clean energy mandates and DERs, electrification, and
T. ZHAO, Brookhaven National Laboratory
severe weather are all impacting the distribution grid. AMI
and AMI 2.0 combined with data analytics offer utilities
Eliminating Grid Initiated Wildfires while Protecting
new ways to monitor and control the distribution grid. A
Critical Infrastructure from Wildfire (panel session)
group of utilities will present their experiences and future
Tuesday, January 21, 2025 2:15 PM-3:15 PM Room 2
plans with data analytics and AMI.
Session Chair: Brian Pierre, Sandia National Labs
* 25GET0212, Experiences with Data Analytics and AMI
The U.S. Department of Energy has a significant focus on M. CARPENTER, ONCOR
Wildfire Resilience. This panel aims to discuss R&D efforts to
* 25GET0213, AMI 2.0 and Distributed Intelligence
mitigate the ignition and decrease consequences of major wild-
S. MUNDY, CenterPoint Energy
fires through new tools and improved information pre-wildfire,
better planning for wildfires, early response during wildfires to * 25GET0214, Grid Rebuild and Modernization through AMI
increase safety and minimize damage, and accelerated recov- A. PAASO, LUMA Energy
ery following wildfires to maximize energy availability.
* 25GET0215, AMI 2.0 and Transforming the Grid
M. RENO, Sandia National Laboratories
Better wildfire modeling, monitoring, and planning tools
allows significant reduction in consequences once a wild-
Utility Experience with Microgrids (panel session)
fire ignites, and optimal investments, protection schemes,
Tuesday, January 21, 2025 2:15 PM-3:15 PM Room 9
control methods, and vegetation management can reduce
Session Chair: Bill O’Brien, SDGE
the probabilities of grid ignited wildfires.
Session Chair: Kamal Garg, SEL Inc
This session will discuss the various utilities experience in
Responding early and effectively during wildfires is key
implementing microgrids. The topic will discuss the lessons
to reducing wildfire consequences. A better understanding
learned and design details. The panel session will discuss
of wildfire ignition and lightning ignition probabilities can
the experiences of various utilities including initiation of
improve wildfire response time. Better grid component model-
projects, conceptual design, detail design, communication
ing and monitoring can reduce grid ignited wildfires and bet-
details, field testing and in-service results.
ter uncertainty modeling and visualization can help decision
makers understand when and if Public Safety Power Shutoffs * 25GET0218, SDGE - Microgrid Design - TBD
(PSPS) are needed and when and if evacuations are needed. L. ABCEDE, SDG&E
* 25GET0219, SDGE - Microgrid Protection - TBD
Many utilities are creating or have created PSPS plans to
K. LAWLOR, SDG&E
mitigate wildfire ignition. Significant R&D is focused on
the reduction of the probability of grid-initiated wildfire * 25GET0371, PG&E Microgrid Design
ignition, and the reduction of consequence if wildfires M. JENSEN, PG&E
occur, especially to critical infrastructure. If we can help
reduce wildfire ignition and impact, we can mitigate PSPS. Fleet Electrification (panel session)
Tuesday, January 21, 2025 2:15 PM-3:15 PM Room 8
* 25GET0201, Mitigation, Response, and Recovery from
Session Chair: Alexandria Moffat, SDG&E
Natural Hazards Impacts to Energy Infrastructure
J. DYGERT, U.S. Department of Energy * 25GET0291, Fleet Electrification - Lessons Learned
* 25GET0202, Mitigate Wildfire Electric Grid Ignition and
Inverter Based Resource (IBRs) Interconnection and
Wildfire Consequences to Critical Infrastructure
Penetration Issues (panel session)
B. PIERRE, Sandia National Labs
Tuesday, January 21, 2025 2:15 PM-3:15 PM Room 10
* 25GET0203, Assessing Wildfire Risk and Mitigation Sponsored By: IEEE PES Grid Edge Technologies Conference
Effectiveness to the Electric Grid Session Chair: Jonathan Sykes, Quanta Technology
H. EAGLESTON, Sandia National Labs
* 25GET0357, Impacts of IBR on Protection Systems 1of4
* 25GET0204, Intelligent Control of Distributed Energy M. JENSEN, PG&E
Resources for Wildfire Risk Mitigation
* 25GET0358, Impacts of IBR on Protection Systems 4of4
M. PARVANIA, University of Utah
J. HOLBACH, Quanta Technology

18 ieee power & energy magazine January 2025 Show Issue

* 25GET0359, Impacts of IBR on Protection Systems 2of4 * 25GET0366, Advanced Edge Control for Grid Resiliency
R. BAUER, NERC C. ZHANG, Eaton
* 25GET0360, Impacts of IBR on Protection Systems 3of4 * 25GET0367, Systematic Decoupling Control of Grid-
K. LAWLOR, SDG&E Edge Inverters for Enhanced Grid Services
L. HE, University of Illinois Chicago
Grid Forming Inverters: Current Status and Roadmap
Toward Wide Use by Electric Power Utilities and Inde- Best Practices for Wildfire Mitigation (panel session)
pendent System Operators—Utility, Developer, and Tuesday, January 21, 2025 3:30 PM-4:30 PM Room 2
Regulatory Perspectives (panel session) Session Chair: Mike Beehler, MBA, LLC
Tuesday, January 21, 2025 2:15 PM-3:15 PM Room 11B
Session Chair: Farnoosh Rahmatian, Nugrid Power Fire season is year round. California and the West is faced
Session Chair: David Elizondo, Quanta Technology with a dramatic increase in the number and intensity of wild-
fires. Regulators and other stakeholders demand a safer and
* 25GET0378, Plans and Experiences of National Grid with
more resilient T&D system. Learn the costs and benefits of
Grid Forming Inverters.
new fire mitigation strategies like sensor systems, covered
R. KARANXHA, National Grid
conductors and strategic undergrounding. Discover the chal-
* 25GET0379, ISO New England Experiences with Grid lenges of planning, permitting and executing these initiatives
Forming Inverters from Western utilities directly engaged in major programs.
X. LUO, ISO New England
* 25GET0149, Best Practices for Wildfire Mitigation
* 25GET0380, Plans and Experiences of Vermont Electric J. WOLDEMARIAM, San Diego Gas & Electric Co.
Power Company with Grid Forming Inverters
* 25GET0150, Best Practices for Wildfire Mitigation
D. POULIN, VELCO
S. BORDENKIRCHER, Arizona Public Service
The role of data in the energy transition (panel session) * 25GET0151, Best Practices for Wildfire Mitigation
Tuesday, January 21, 2025 2:15 PM-3:15 PM Room 11A D. TREICHLER, Oncor
Session Chair: Anand Shah, Stantec
* 25GET0152, Best Practices for Wildfire Mitigation
R. KONDZIOLKA, Western EIM
Integrating Hybrid Energy Storage Systems, Hydrogen
Energy, and Hardware-in-the-Loop Simulation (panel
Shedding the Light on Grid Communication Architec-
session)
tures for High DER and Electrification (panel session)
Tuesday, January 21, 2025 2:15 PM-3:15 PM Room 1A
Tuesday, January 21, 2025 3:30 PM-4:30 PM Room 7A
Session Chair: Jianhui Wang, Southern Methodist University
Session Chair: Jacob Reidt, PNNL
Integrating Hybrid Energy Storage Systems, Hydrogen
Transitioning to a decarbonized, highly reliable, and resilient
Energy, and Hardware-in-the-Loop Simulation
energy future within the target timeframes established by
* 25GET0316, Advancing the Grid Edge: Integrating policy makers across the globe will require a dramatic trans-
Hydrogen Energy for a Sustainable Future formation of grid architectures and operation. A key enabler
D. WU, Pacific Northwest National Laboratory to achieving these goals is to harness flexibility that large
scale integration of distributed energy resources (DER) can
* 25GET0317, Use of hardware-in-the-loop to derisk field
provide through coordination across transmission, distribu-
deployment of hydrogen assets
tion, and grid edge systems. This coordination will require
K. PRABAKAR, National Renewable Energy
new communication architectures and security approaches
Laboratory
for a system that will be much more reliant on data and intel-
* 25GET0318, Hybrid HESS and BESS microgrids ligence across the grid and across many new stakeholders.
P. WITTENBERG, Energy Vault Unfortunately, as the grid itself is evolving dramatically, the
communication requirements for these new forms of critical
Control and operation of grid-edge DERs (panel session) communication systems are unclear. Regulators, utilities,
Tuesday, January 21, 2025 2:15 PM-3:15 PM Room 1B technology providers are challenged to develop long-term
Session Chair: Lina He, University of Illinois Chicago strategies for the grid communications and security infra-
structures to ensure they have the right systems in place at
* 25GET0319, TBD
the right time to support the necessary coordination.
T. TBD, TBD
* 25GET0365, TBD This panel will discuss the evolving grid communications
X. WU, AES US Utilities needs, challenges, and uncertainty across transmission,

January 2025 Show Issue ieee power & energy magazine 19

 distribution, and grid edge systems in future high DER and scientists, researchers, educators, students, industrial utility
electrification scenarios. Emerging Cybersecurity scenar- experts, and other stakeholders who are engaged in the PES
ios that focus on grid operations with large scale integration community, research, and education.
of utility and non-utility distributed energy resources and
* 25GET0189, High-resolution risk assessment for power
high electrification of will be explored. Ongoing work by
systems during evolving climate extremes
the U.S. Department of Energy to quantify grid communi-
L. XU, Princeton University
cation architecture requirements and provide a path forward
to address gaps in standards, technologies, or processes will * 25GET0190, How Oncor is impacted by extreme weather
be highlighted. The conversation will be facilitated through events
an engaging roundtable discussion among industry thought Y. SUN, Oncor
leaders including Department of Energy, utility, and
* 25GET0264, Challenges in protection, control and opera-
research perspectives with integrated Q&A opportunities.
tion of logical energy networks
A. NASSIF, Pacific Northwest National Laboratory
Key Takeaways: The audience will gain insights into the evolv-
ing grid communications architectures and cybersecurity * 25GET0191, Adaptive learning-based planning for large-
implications for future high DER and electrification scenarios. scale power grids
X. CHEN, Texas A&M University
* 25GET0173, Shedding the Light on Grid Communication
Architectures for High DER and Electrification
Advancements in Smart Grid Interoperability and Dis-
J. OGLE, PNNL
tributed Intelligence through Edge Computing: Ben-
* 25GET0174, Shedding the Light on Grid Communication efits, Challenges and Practical Use Cases (panel session)
Architectures for High DER and Electrification Tuesday, January 21, 2025 3:30 PM-4:30 PM Room 8
N. HORNER, DOE Session Chair: Rajarshi Roychowdhury, AES US Utilities
* 25GET0175, Shedding the Light on Grid Communication * 25GET0197, Practical Lessons from the Edge - A ‘How-
Architectures for High DER and Electrification to’ Guide on Ramping Up Grid-Edge Capabilities
R. MACWAN, NREL R. SMITH, AES US Utilities
* 25GET0176, Shedding the Light on Grid Communication * 25GET0198, Innovative Solutions for Real-Time Analysis
Architectures for High DER and Electrification and Control of Smart Grids
S. CHAMBERS, Avista Utilities M. VAIMAN, V&R Energy
* 25GET0177, Shedding the Light on Grid Communication * 25GET0199, Proactive / Smart Consumers on the Edge -
Architectures for High DER and Electrification How Utilities Could Respond
M. SUBIETA, Nokia X. WU, AES US Utilities
* 25GET0200, Use of Interoperability and Edge Integration
Enhancing Grid Resilience: Planning and Moderniza-
Software to Optimize Integration of Distributed Energy
tion for Climate Adaptation (panel session)
Resources
Tuesday, January 21, 2025 3:30 PM-4:30 PM Room 11B
W. MALCOLM, Open Energy Solutions
Session Chair: Luo Xu, Princeton University
Energy Storage Networks as Grid Assets: Challenges
Climate change is intensifying extreme weather events such
and Opportunities (panel session)
as tropical cyclones, heatwaves, and wildfires, which pose
Tuesday, January 21, 2025 3:30 PM-4:30 PM Room 1A
significant challenges to the resilience of power systems.
Session Chair: Kaustav Chatterjee, Pacific Northwest
The average annual number of weather-related power out-
National Laboratory
ages increased by roughly 78% during 2011-2021, compared
to 2000-2010. In this panel, current state-of-the-art grid
Converter-interfaced energy storage systems are becoming
planning and modernization strategies toward a resilient
increasingly important as the grid transitions to a future with
power system under climate extremes will be presented. By
high renewable penetration. These resources play a vital role in
bridging climate and energy, discussions will cover a range
balancing supply and demand, providing flexibility, and ensur-
of topics including high-resolution risk assessment, grid
ing the stability and resilience of the electric grid. Traditionally,
hardening strategies, and climate-informative system plan-
the storage resources in the bulk power system have operated
ning considering various extreme weather events in a chang-
as large-scale peripheral devices supporting ancillary services.
ing climate. Towards grid sustainability and resiliency, this
However, with the recent advancements in power electronics
panel gathers diverse perspectives from both industry lead-
and distributed control designs, there is potential for a para-
ers and academic experts, creating a comprehensive benefit
digm shift. To this end, in recent years, grid-forming inverters
for a broad spectrum of participants, including engineers,
have emerged as a promising technology for interfacing energy

20 ieee power & energy magazine January 2025 Show Issue

storage to power networks. With suitable control designs, grid- utility and IPP sectors, innovative technologies will also play
forming storage resources can be made sufficiently fast to remarkable roles in the future energy development market to
respond to grid disturbances at transient time scales. Coordi- comply FERC Order 2023. At this panel, invited speakers
nated networks of storage resources operating at the grid edge from broad sectors of the energy industry will take the stage
can act as an energy buffer between the transmission-distribu- to show the potential of innovative solutions and technolo-
tion systems, enhancing the grid’s reliability multifold. Thus gies, and how equity investors, engineers and regulatory can
motivated, this panel reviews the state of the art in grid-form- bond together to enable the greener energy infrastructure
ing energy storage, highlights recent advancements, identifies with sound financial successes. All are welcomed to join this
potential gaps, and discusses new research directions. The panel town hall meeting type panel session to learn evidence of
comprises experts from academia, national labs, and industry business success with new technologies, snap thought sparks
who share their insights, experiences, and perspectives on the among the speakers, and share your own views at Q&A sec-
future of energy storage. The panel will discuss fast frequency tion with the room. At this panel, we will present the best of
support, frequency regulation, damping enhancement, and dis- today and big potentials of innovations in renewable energy
turbance containment and localization utilizing grid-forming sectors. Together, along with the speakers, we will denote
resources. Novel approaches for distributed and consensus today, and light up tomorrow.
control of coordinated storage networks will also be presented.
* 25GET0265, Role of Technology Innovations in Renew-
The panel will also discuss device and system-level protection
able Energy Development, Asset Management and Repower
systems for grid-forming storage resources. To reduce risks and
J. SPENCER, Exus Renewables NA
accelerate deployment, hardware-in-the-loop (HIL) simulation
and testing of converter-interfaced energy storage systems is * 25GET0260, Critical Transmission Line Technology
critical. The panel, therefore, will also discuss HIL simulation Breakthrough for Grid Reliability and Sustainability
and testing techniques for grid-connected energy storage sys- J. HUANG, TS Conductor
tems. Selected control applications developed and tested using
* 25GET0261, Novel Transformer Technologies for Renew-
HIL techniques will be illustrated as case studies. Real-world
able and Distributed Energy Resource Integration : Oppor-
field experiences of utilities from the deployment and integra-
tunities and Challenges
tion of energy storage will also be presented. The panel will
P. OHODNICKI, CorePower
close with a round of Q&A and interactions.
* 25GET0262, Enabling Advanced Technologies for
* 25GET0179, Coordinated frequency regulation in grid-
Renewable Energy Integration
forming storage network via safety-consensus
X. TAN, Hitachi Energy
S. KUNDU, Pacific Northwest National Laboratory
* 25GET0180, Disturbance containment and oscillation Solving renewables and EV grid integration challenges
damping using grid-forming storage networks through system and operations planning using Digital
 K. CHATTERJEE, Pacific Northwest National Twins (panel session)
Laboratory Tuesday, January 21, 2025 3:30 PM-4:30 PM Room 9
Session Chair: Shishir Shekhar, Landis & Gyr AG
* 25GET0178, Leveraging energy storage analytics and
software tools for grid-edge applications * 25GET0293, Faroe Islands - Towards 100% Renewable
T. NGUYEN, Sandia National Laboratories Energy with Smart EV Charging and VPP
T. NIELSEN, SEV Faroe Islands
* 25GET0181, Device and system level coordinated protec-
tion of grid-forming inverter dominated power systems * 25GET0294, Accelerating Heavy Duty EV Fleet Electri-
A. BANERJEE, Siemens Technology fication in India with Smart Depot Energy Management
S. DHANANKAR, Tata Motors Smart City Mobility
* 25GET0183, TBD (LUMA’s experience with BESS proj-
Solutions Ltd
ects in Puerto Rico)
D. HAUGHTON, LUMA Energy * 25GET0338, Enabling Renewable Integration with AMI
and DERMS
Enabling innovative technologies in the next generation R. HOVSAPIAN, NREL (National Renewable
renewable energy integration (panel session) Energy Lab)
Tuesday, January 21, 2025 3:30 PM-4:30 PM Room 10
Session Chair: Jay Liu, Exus Renewables NA Advanced applications and data fusion – leveraging
high-resolution grid-edge sensor data for actionable
Renewable Energy has become a dominant fuel mix com- intelligence (panel session)
ponent in power grids’ planning pipelines with significant Tuesday, January 21, 2025 3:30 PM-4:30 PM Room 7B
interconnection challenges across the industry. Besides adap- Session Chair: Farnoosh Rahmatian, Nugrid Power
tive engineering methods and business model evaluations at Session Chair: Honghao Zheng

January 2025 Show Issue ieee power & energy magazine 21

 * 25GET0374, Event analysis using combination of tradi- FERC’s Order No. 2222 represents a significant step toward
tional waveform records and MHz wideband grid-edge elec- large-scale DER integration. However, several challenges
tric field sensor measurements to improve grid resiliency persist regarding DERA’s participation in wholesale mar-
S. VEGA, LUMA Energy kets. First, energy harnessed from aggregated DERs must
pass through the distribution grid that is managed by the
* 25GET0375, Data fusion platforms to integrate various
DSO, so a coordination mechanism among DSO, DERAs,
utility data to provide simple actionable intelligence for
and ISO is crucial to ensure the distribution network reliabil-
utility operators and engineers
ity and open access to all DERAs. Second, Customers with
F. AMINIFAR, Quanta Technology
DERs currently operate under incumbent regulated utilities
* 25GET0376, Novel traveling wave-based protection and benefit from net energy metering. DER aggregators
scheme for distribution systems with high penetration face the challenge of devising profitable and competitive
of DER (distributed energy resources) using machine aggregation strategies to attract and retain customers away
learning from their incumbent utility providers. Efficiently aggre-
M. JIMENEZ APARICIO, Sandia National Lab. gating DERs and intelligently managing risks associated
with renewable energy uncertainty are key considerations.
* 25GET0377, Systematic distribution grid sensor deploy-
ment to manage high penetration of DER and wildfire
This panel aims to bring together experts in industry and
risk
academia to discuss the latest progress and challenges of
A. OROZCO, San Diego Gas & Electric
aggregating DERs and participating in the wholesale elec-
tricity market. The main content includes but is not limited to
T&D grid of the future- How resilience is sparking inter-
(i) coordination mechanisms among ISO, DERA, and DSO,
disciplinary interest (panel session)
and other open issues facing industry and academia, and (ii)
Tuesday, January 21, 2025 3:30 PM-4:30 PM Room 11A
algorithms for DERA to participate in the wholesale market.
Session Chair: Elli Ntakou, Eversource Energy
* 25GET0320, Operational Coordination among Grid Edge
* 25GET0313, Eversource’s Resilience Planning
Asset Operators, Aggregators, DSOs, and the RTO/TSO
C. TAJMAJER, Eversource Energy
F. RAHIMI, Oati
* 25GET0314, Resilience Presentation
* 25GET0321, Virtual Power Plant Aggregation and Risk
D. KUSHNER, Luma Energy
Based Participation in Wholesale Energy Markets
* 25GET0315, NREL Resilience Presentation A. PAPALEXOPOULOS, ECCO International, Inc.
B. JEFFERS, National Renewable Energy Laboratory ZOME Energy Networks, Inc.
* 25GET0322, TBD
Distributed Energy Resource Aggregator Participation
P. KLAUER, California ISO
in the Wholesale Market (panel session)
Tuesday, January 21, 2025 3:30 PM-4:30 PM Room 1B * 25GET0323, TBD
Session Chair: Lang Tong, Cornell University C. CHEN, Cornell University
Session Chair: Cong Chen, Cornell University
*Diversity & Inclusion in IEEE PES – Fostering the
Breakthroughs in the advanced information technology
Sense of Belonging (panel session)
encompassing sensing, control, optimization, and AI make
Tuesday, January 21, 2025 4:45 PM-5:45 PM Room 11A
it possible for large-scale aggregation of small-sized but
Session Chair: Jelena Ponocko, SP Energy Networks
ubiquitous distributed energy resources (DERs) anytime,
anywhere over the distribution network operated by a dis-
IEEE Power and Energy Society (PES) shares the mission
tribution system operator (DSO). DER aggregators (DERA)
of IEEE, to foster technological innovation and excellence
can coordinate and operate massive DERs like virtual
to benefit humanity. This mission requires and embraces
power plants or virtual storage, participate in electricity
the talents and perspectives of people with different per-
markets, and conduct emergency load reduction. Recogniz-
sonal, cultural, and disciplinary backgrounds. In an increas-
ing the potential of DERAs, the Federal Energy Regulatory
ingly global world, where different cultures, races, genders
Commission (FERC) issued Order No. 2222. This land-
and identities are ever more closely connected, IEEE PES is
mark ruling aims to remove policy barriers, allowing third-
strongly committed to meeting everyone’s needs equally as
party private companies to aggregate DERs and participate
one of the main pillars of sustainable growth.
directly in wholesale electricity markets operated by Inde-
pendent System Operators (ISOs). Companies like Tesla,
This panel will bring together experts of various disciplinary
Sunrun, General Motors, Ford, and Google are already
backgrounds who are directly involved in promoting diver-
piloting DERA projects, leveraging advanced algorithms,
sity, equity and inclusion (DEI) in education, workplace
hardware, and innovative business models.

22 ieee power & energy magazine January 2025 Show Issue

and global technical institutions such as IEEE. The panel initiatives and reduce wildfire risks in rural and remote
will be interactive, including presentations by panelists but regions.
focusing on moderated discussion that will engage the audi- • 
Resiliency and Recovery: Enhanced resiliency against
ence. The scope of the discussion will be around the current weather-related power outages and faster recovery after
challenges in DEI, both regional and global, unconscious disruptions through modular microgrid systems.
bias, examples of good practice, what we can change here • 
Sustainability and Renewable Integration: Greater inte-
and now and what will take time. gration of renewable energy sources, promoting sustain-
able and environmentally friendly power generation.
* 25GET0140, TBC
• 
Energy Access and Quality: Improved energy access and
D. AL-QADI, AECOM
quality for rural and remote communities, fostering eco-
* 25GET0141, TBC nomic development and enhanced quality of life.
M. BLAIR-LOY, University of California San Diego
BoxPower and PG&E will share multiple case studies,
* 25GET0142, TBC
including both deployed projects and projects under devel-
M. CHAGANTI, CenterPoint Energy
opment, to demonstrate the successful implementation of
* 25GET0143, TBC standalone power systems. They will also discuss how their
J. PONOCKO, SP Energy Networks respective utilities are using the same approach at a port-
folio level for aging infrastructure in need of upgrade or
* 25GET0388, TBC
replacement. This session aims to provide decision-makers
N. UZELAC, G&W Electric
within utilities with actionable strategies and insights on the
‘how’ of successful microgrid deployment, drawing from
Reimagining Resilience: Remote Grids as a Cost-Effective
real-world experiences and best practices.
Solution to Grid Hardening (panel session)
Tuesday, January 21, 2025 4:45 PM-5:45 PM Room 11B * 25GET0153, Reimagining Resilience: Remote Grids as a
Session Chair: Noa Schachtel, BoxPower Cost-Effective Solution to Grid Hardening
A. CAMPUS, BoxPower
In an era where over 60% of the U.S. distribution lines have
* 25GET0154, Reimagining Resilience: Remote Grids as a
exceeded their 50-year life expectancy and the frequency of
Cost-Effective Solution to Grid Hardening
extreme weather and wildfires is escalating, utilities face the
B. BOVARNICK, PG&E
dual challenge of upgrading aging infrastructure and ensur-
ing safety in rural and remote areas. With a large portion of
Emerging Needs for Grid-Forming Inverter and EMT
the 5.5 million miles of distribution lines in the U.S. travers-
Analysis in DER-Rich Power Systems (panel session)
ing forested and mountainous areas, these lines risk starting
Tuesday, January 21, 2025 4:45 PM-5:45 PM Room 9
wildfires or causing outages during severe storms and Public
Session Chair: Wenzong Wang, EPRI
Safety Power Shutoffs (PSPS). The problem of an aging grid
has led to a significant increase in utility distribution spend-
Driven by the clean energy goals in the United States and
ing, soaring by more than 64% in the last two decades, with
around the world, renewable energy (e.g., PV, wind) pen-
the cost of undergrounding lines reaching $2M-$5M per mile.
etration continues to increase in electric power systems.
Conventional synchronous machine-based generations
In this session, BoxPower CEO and co-founder Angelo
are being replaced by inverter-based renewable resources
Campus and Pacific Gas & Electric Remote Grid Product
(IBRs) in both the transmission and distribution grid. This
Manager Ben Bovarnick will discuss several case studies
shift necessitates advanced inverter control technologies to
to show how standalone power systems have helped one
maintain system stability and new simulation capabilities to
of the largest utilities in the country reduce costs, mitigate
evaluate the impact of IBRs.
wildfire risk, and improve energy resiliency. Emphasizing
lessons learned from programmatic, portfolio-scale imple-
On the inverter technology side, most of today’s IBRs are
mentation, they will highlight what has proven effective in
based on grid-following inverters, which are prone to sta-
deploying these systems. Attendees will gain insights into
bility issues and operational challenges in an inverter-dom-
how a holistic approach accelerates deployment.
inated power system. Therefore, they are not sufficient to
support the grid of the future. On the other hand, grid-form-
Key Discussion Points:
ing (GFM) inverters represent an emerging and promising
• 
Programmatic Planning and Implementation: Insights on
technology to enable the future inverter-dominated power
how to strategically plan and implement remote grid so-
system. Even though there have been real-world applica-
lutions, from initial site assessment to deployment.
tions of GFM inverters in transmission and distribution
• 
Cost and Risk Management: How remote grids can pro-
systems, the value of GFM inverters and its performance
vide significant cost savings related to grid hardening
requirements are not fully understood by the industry.

January 2025 Show Issue ieee power & energy magazine 23

 Regarding new simulation capabilities, evaluating the and environmental sustainability. However, this decentral-
impact of large-scale DERs and microgrids and investi- ized and interconnected nature also exposes the energy
gating phenomena such as transient overvoltage, control infrastructure to new and evolving cybersecurity threats. To
instability, harmonics, faults, and unintentional islanding safeguard the integrity, availability, and confidentiality of
necessitates electromagnetic transient (EMT) analysis. these critical assets, there is an exigent need for an advanced
Utilities are increasingly adopting EMT analysis to address cybersecurity platform that combines Artificial Intelligence
these needs. However, EMT studies, especially with DERs, (AI) and Situational Awareness (SA) techniques.
are still relatively new to distribution utilities, presenting
challenges ranging from resource limitations to creating This special session aims to present secure AI approach,
network models and acquiring product-specific DER mod- including federated machine learning algorithms (FML)
els from vendors. Furthermore, there is a lack of widely and other secure machine learning and deep learning, to
agreed-upon guidelines on when specific EMT studies detect anomalies in the grid network, quantify its develop-
should be performed. ment through analytical methods and tools, and explore
market pathways to integrate into the commercialization
This panel discusses the emerging needs to enable DER- and industry adoption. This session will consist of invited
rich power systems from two perspectives: advanced talks, presentations, and panel discussions from national
inverter technology, and new simulation capabilities. It will labs, universities, and industry members to highlight the
discuss the use cases for, and requirements of, GFM invert- ongoing research and development (R&D) efforts and
ers in both transmission and distribution systems, including future direction of works that will enhance the security and
both microgrid and blue-sky scenarios. Additionally, it will resilience of energy systems.
discuss the emerging needs for EMT analysis to study the
grid impacts when interconnecting large-scale DERs and The topic of focus includes:
microgrids. Perspectives and real-world experiences will be 1. Novel Tools and methods for developing anomaly detec-
shared by transmission and distribution system operators, tion systems for DER-based monitoring and control sys-
an engineering firm, and a research institute. tems.
2. Data-driven and system-level resilience metric to assess
* 25GET0155, Transmission Impacts of Integrating High
the grid performance during cyber-threats.
Levels of DER
3. Federated Machine Learning (FML)-based cybersecu-
K. ARAMAKI, HECO
rity solutions for wide-area control system
* 25GET0156, Experience with Grid Forming Inverters in 4. Ongoing industry efforts in developing cybersecurity
Microgrid Applications and the Associated EMT Studies framework and risk-mitigation strategies.
K. CHEN, Duke Energy
This panel plan to discuss the application of secure AI in
* 25GET0157, Experience with Grid-Forming Battery
developing the cybersecurity solution for DERs. It will
Energy Storage System in Grid-Connected Operation
also discuss new tools developed by national labs and uni-
L. YANG, Eversource Energy
versities related to grid cybersecurity. Presentation slides
* 25GET0158, Understanding the dynamic behavior of will highlight the ongoing state-of-the-art research efforts
DERs by performing EMT Studies between national labs, university, and industry collabora-
S. IQBAL, Eversource Energy tors. Also, these slides will be prepared more generically
with simpler terms to capture participants from industry,
* 25GET0159, Navigating DER Integration: Insights from
academic, and business experts. After the presentations,
Hundreds of EMT Studies
participants will be given opportunities to ask questions
J. TURNER, RLC Engineering
to the panel. The panel session chair will coordinate with
* 25GET0160, Role of grid-forming inverters and EMT moderator and presenters to manage time and support ques-
studies in present and future power systems tion and answer (Q&A) session while ensuring that each
N. BILAKANTI, EPRI participant will have an opportunity to ask questions and
connect with presenters for later follow-up.
Secure AI-Integrated Attack Resilient System for Dis-
* 25GET0184, Techniques, Tactics, and Proceedures to Pre-
tributed Energy Resources (panel session)
vent Artificial Intelligence Threats on Distributed Energy
Tuesday, January 21, 2025 4:45 PM-5:45 PM Room 7A
Resources
Session Chair: Vivek Kumar Singh, National Renewable
W. EDWARDS, Schweitzer Engineering Laborato-
Energy Laboratory
ries, Inc.
The rapid proliferation of Distributed Energy Resources * 25GET0185, Application of State Estimation to Identify
(DERs) has revolutionized the energy landscape, providing and Correct Anomalous Measurements
numerous benefits such as improved reliability, efficiency, B. JOHNSON, University of Idaho

24 ieee power & energy magazine January 2025 Show Issue

* 25GET0186, Resilient Control Systems and the Advance- A look-ahead subsurface sensor system that would take
ment of the Distributed Cyber Feedback Loop advantage of unmanned aerial vehicles (UAV) and electro-
C. RIEGER, TRECS Consulting magnetic (EM) resistivity techniques to avoid damaging
existing utilities when undergrounding powerlines. The pro-
* 25GET0187, Federated Learning-based Cybersecurity Sit-
posed system pairs an EM sensor on an underground drill
uational Awareness Tool for Distributed Energy Resources
string and an antenna mounted to a UAV flying overhead
V. KUMAR SINGH, National Renewable Energy
to expand the distance and sensitivity of object identifica-
Laboratory
tion underground. The system would use machine learning
* 25GET0188, Secure Configuration Management and Resil- interpretation and high-resolution imaging capabilities to
ient Response Orchestration for Distributed Energy Resources provide real-time guidance for the drill path.
S. TALUKDER, Eaton
“Error-Free Splicing Machine for Underground Power
Grid Overhaul with Proactive and High-speed Under- Cables”
grounding for Reliability, Resilience, and Security A hands-free power cable splicing machine fits down a util-
(GOPHURRS) - an ARPA-E Program Overview and ity hole and operating in underground vaults to reduce the
Progress (panel session) share of splicing-caused medium-voltage network failures
Tuesday, January 21, 2025 4:45 PM-5:45 PM Room 2 from 60-80% to less than 5% and dramatically improve
Session Chair: Philseok Kim, ARPA-E, US Department of the workforce safety by reducing the time the underground
Energy cable splicing crews spend in underground vaults.

ARPA-E is a transformative, high-risk, high-reward future Attendees will learn new emerging opportunities in this
energy technology funding agency in the U.S. Department space and how an early stage technologies could be rapidly
of Energy. This panel introduces the GOPHURRS program translated to pilot-testing ready prototypes in fast-paced
launched in early 2024 (12 projects, 3 years, $34M) and 3-year research projects launched and managed by ARPA-E.
brings project performers to discuss each project goals, prog-
* 25GET0192, Transformative grid technology R&D proj-
ress, challenges, and lessons learned. GOPHURRS program
ects funded by ARPA-E
seeks to reduce costs, increase speed, and improve the safety
P. KIM, ARPA-E, US Department of Energy
of undergrounding operations and the surrounding commu-
nities by developing technologies focused on automation, * 25GET0193, Peristaltic Conduit with Stiff Structure and
damage prevention, error elimination, and simplifying the Compliant Skin
construction of underground MV power distribution grids in K. DALTORIO, Case Western Reserve University
urban/suburban areas.
* 25GET0194, Borehole Underground Reconnaissance and
Real-time Obstacle Wayfinder (BURROW)
Highlights of topics to be discussed include:
J. POLLOCK, OceanIt Laboratories, Inc.
“Peristaltic Conduit with Stiff Structure and Compliant Skin”
A worm-inspired construction tool that could cheaply and * 25GET0195, Error-Free Splicing Machine for Under-
quickly install underground distribution powerlines in busy ground Power Cables
urban and suburban environments. The proposed robotic S. SIRIPURAPU, Prysmian
tool consists of a sleeve of expanding and contracting mate-
* 25GET0196, Integrated Thermal Spallation Drill for Het-
rials that digs underground like an earthworm while lay-
erogeneous Ground Conditions
ing conduit as it goes and avoids existing infrastructure
R. ZILLANTE, Phoenix Boring
obstacles by making tight turns with 5 feet turning radius
compared to 1,000 feet from conventional tools.
Use of Distribution Battery Energy Storage Systems for
Microgrids and other Grid Edge Services – Real World
“Integrated Thermal Spallation Drill for Heterogeneous
Considerations & Applications (panel session)
Ground Conditions”
Tuesday, January 21, 2025 4:45 PM-5:45 PM Room 1A
A small-diameter ultrafast tunneling construction tool to
Session Chair: Xuan Wu, AES US Utilities
underground power lines in heterogeneous soil conditions.
The tool can operate in both hard rock and soft sediments
Electric utilities are being compelled to upgrade their existing
and enable cost-effective undergrounding by combining
distribution systems due to the rising demand for load by resi-
thermal spallation drilling with conventional horizontal
dential, commercial, and industrial customers on radial distri-
drilling dramatically reducing the time and cost required
bution networks. In addition, as customers and regulators care
for underground installations.
increasingly about the reliability and resilience of the distribu-
“Borehole Underground Reconnaissance and Real-time tion systems, additional investments could be needed to meet
Obstacle Wayfinder (BURROW)” the expectation. However, the cost of implementing these

January 2025 Show Issue ieee power & energy magazine 25

 system upgrades may not be justifiable considering the short Grid Monitoring for Decarbonization: Advancing
duration of peak loads or low frequency of extreme events or Load Modeling for Sustainable Power Grids (panel
outages, resulting in a lower utility of the affected distribu- session)
tion assets. Therefore, non-wire alternatives (i.e., distribution Tuesday, January 21, 2025 4:45 PM-5:45 PM Room 1B
energy storage systems) and microgrids become more and Session Chair: Bhaskar Mitra, PNNL
more attractive. The use of a battery energy storage system
(BESS) directly connected to the distribution system or inside Industry Viewpoints on IEC 61850 (panel session)
a microgrid is an advanced solution to tackle this challenge of Tuesday, January 21, 2025 4:45 PM-5:45 PM Room 8
managing power flows and providing backup power. Session Chair: Rich Hunt, Quanta Technology

For a distribution BESS/microgrid project, optimal planning Managing and Controlling a Unified Grid (panel
(i.e., siting and sizing of the BESS) is the first step. Opti- session)
mization algorithms have been recently popularized for this Tuesday, January 21, 2025 4:45 PM-5:45 PM Room 10
purpose. Steady-state and electromagnetic transient simula- Session Chair: Marianna Vaiman, V&R Energy
tion studies are needed to verify how the BESS/microgrid
protection and control system can respond to various sys- Technology Integration at the Grid Edge: Communica-
tem and BESS failures. Hardware-in-loop testing has been tion, Data and Interoperability (panel session)
widely used to verify the protection and control designs Tuesday, January 21, 2025 4:45 PM-5:45 PM Room 7B
from computer simulations. Communication infrastructure Session Chair: Bo Chen, Commonwealth Edison Company-
and protocol options need to be carefully evaluated and Session Chair:
selected to meet the performance requirements while con-
* 25GET0370, Interoperable Control Framework for Inte-
sidering the cost optimization. Once engineering and design
grating DERs and Grid-Edge Devices
is completed, the BESS will be installed, and future opera-
B. CHEN, Commonwealth Edison Company
tion & maintenance needs to be strategized such as health
monitoring, data collections, maintenance schedules, etc. * 25GET0373, Advanced Analytics for Improving Resil-
ience at the Grid Edge
This panel will gather a few industry experts from utilities, ven- M. VAIMAN, V&R Energy
dors, consultants, and academia to discuss the opportunities,
* 25GET0381, What is Happening at the Grid Edge?
challenges, use cases, and lessons learned related to distribution
H. ZHENG, MISO
BESSs and microgrids from the planning, analysis, engineering
& design, project executions, and operation perspectives. * 25GET0383, An Open-Source Platform to Integrate
Behind-the-meter DERs for Load Flexibility
* 25GET0205, Visions on Non-Wire Alternatives and Grid
J. ZHOU, Slipstream
Edge Intelligence for Distribution Networks
R. SMITH, AES US Utilities * 25GET0384, Unlocking Interoperability in Distribution
Automation through Open Standards
* 25GET0206, Bronzeville Community Microgrid – Design
D. ISHCHENKO, Eaton
& Testing of Microgrid Master Controller
R. KOTHANDARAMAN, ComEd
Tuesday Evening
* 25GET0207, Improving the Acceptance and Integration Opening Reception (reception)
of Distribution Battery Energy Storage Systems Tuesday, January 21, 2025 6:00 PM-8:00 PM
W. MALCOLM, Open Energy Solutions
* 25GET0208, Modular Energy Storage Integrated
Microgrids – Flexibility in Operating Boundaries Wednesday, January 22, 2025
F. KATIRAEI, Innoversa Mobile Solutions Breakfast and Networking (breakfast)
Wednesday, January 22, 2025 7:00 AM-8:00 AM
* 25GET0209, Battery Storage Technology and its Applica-  Ballroom 6C-F
tions in Grid Edge
C. ZHANG, Eaton Grid Edge Technologies PHD Dissertation Challenge
* 25GET0210, Blackstart Distribution Grids using Battery Session I (panel session)
Energy Storage Systems and other DERs Wednesday, January 22, 2025 10:00 AM-12:00 PM
Z. WANG, Iowa State University
Revolutionizing Electrical Distribution Grids with
* 25GET0211, Sizing a Distribution Connected BESS to DERMS (Stage panel)
Improve Circuit Reliability Wednesday, January 22, 2025 11:00 AM-12:00 PM- DER Stage
R. ROYCHOWDHURY, AES US Utilities Session Chair: Luka Strezoski , Dermag Consulting

26 ieee power & energy magazine January 2025 Show Issue

Start Up Lightning Pitch (other) The electrification of transportation is a key catalyst in
Wednesday, January 22, 2025 11:00 AM-11:45 AM achieving decarbonization goals outlined in the Interna-
tional Energy Agency’s Net Zero Emissions by 2050 Sce-
Conference Lunch (luncheon) nario. The power sector plays a critical role in ensuring a
Wednesday, January 22, 2025 11:30 AM-1:00 PM - Hall AB1 stable electricity supply for charging electric vehicles (EVs)
and harnessing the flexibility of EVs through seamless inte-
Advancing Grid Stability and Renewable Integration gration with the power system.
with Grid-Forming Inverter-Based Technologies (Stage
panel) As electric vehicles become part of the transportation eco-
Wednesday, January 22, 2025 11:30 AM-12:30 PM - Sus- system, there’s an evolving standardization of EV and elec-
tainability and Resilience Stage trical vehicle service equipment (EVSE) and implementing
Session Chair: Reza Pourramezan, New York Power Authority new industry protocols to improve interoperability. Keep-
ing up with these advancements and ensuring technology
This panel session explores the transformative potential of compliance with the latest regulations and standards has
Grid-Forming Inverter-Based Resources (GFM IBRs), fea- become increasingly challenging.
turing a lineup of distinguished panelists. The discussion
will highlight the critical role of GFM IBRs in enhanc- The realization of sectoral coupling between the electric energy
ing grid stability and enabling the seamless integration of and transportation sectors becomes apparent when adopting a
renewable energy sources and battery storage. Participants systemic perspective. This approach reveals the indispensable
will learn about the advantages of GFM technology, notably interplay between these sectors, shedding light on previously
its capacity to offer stability services traditionally provided overlooked dynamics. Electric mobility extends beyond indi-
by synchronous generators. Emphasizing the importance vidual sectors, necessitating collaboration among institutions,
of modeling and simulation, the session will explore how stakeholders from mobility, power, building, and real estate
these tools are essential for studying, proving concepts, and sectors. Effective engagement and planning across these sectors
de-risking GFM IBR technologies. Attendees will engage require breaking down silos within ministries and the industry.
with challenges and opportunities GFM technologies pres-
ent, gaining insight into effective strategies for harness- However, global expansion poses challenges for charge
ing their potential. This session promises a rich dialogue point operators and e-mobility service providers, particu-
on leveraging GFM IBRs for a resilient and sustainable larly in navigating diverse protocols, regulations, multiple
energy future combining theoretical insights with potential currencies, and incorporating roaming capabilities into
solutions. This panel promises an engaging exploration of their networks. We need to start with a holistic understand-
Grid-Forming Inverter-Based Resources (GFM IBRs) tech- ing of the sectoral coupling between the sectors, and the
nologies, providing attendees with a deep understanding of functions of the different classes of entities involved. As we
their grid implications and effective integration strategies. transition to electrified transportation, government, utili-
ties, fleet managers, and technical experts have important
* 25GET0220, Overview of Grid-Forming IBR technologies
roles to play to help architect a system that is reliable, resil-
R. POURRAMEZAN , New York Power Authority
ient, and scalable, in order to ensure a smooth transition.
* 25GET0221, Current practices of contingency analysis at
utility control rooms In this panel session, experts from various industries came
H-D CHIANG, Cornell University together to explore the critical aspects of transitioning
towards a low-carbon future. The steps taken by different
* 25GET0222, Stability impact and potential benefit of
agencies while electrifying their fleet, the lessons learnt
GFM-based IBRs with system of synchronous generators
and best practices that can be adopted as we move forward.
Z. ZHANG, Binghamton University - SUNY
The discussion centered on the urgent need to harmonize
* 25GET0223, GFM IBR Modeling techniques, simulation efforts in these key areas to achieve significant reductions
strategies and scenario recommendations in greenhouse gas emissions from the transportation sector.
J. MAHSEREDJIAN, Polytechnique Montreal
* 25GET0251, TBD
B. MYERS, CharIN
Start Up Lightning Pitch (other)
Wednesday, January 22, 2025 12:00 PM-12:45 PM * 25GET0252, TBD
S. PAL, Portland General Electric
Driving Toward Decarbonization: Uniting Industries for
* 25GET0253, TBD
Electric Mobility and Sustainable Energy (Stage panel)
C. IRWIN, Department of Energy
Wednesday, January 22, 2025 12:15 PM-1:15 PM - Electri-
fication Stage * 25GET0254, TBD
Session Chair: Bhaskar Mitra, PNNL B. MITRA, PNNL

January 2025 Show Issue ieee power & energy magazine 27

 Assessing the DER Cybersecurity Standards Landscape Session Chair: Jason Engstrom, ITRON
(Stage panel)
Wednesday, January 22, 2025 1:00 PM-2:00 PM - DER Stage When the voltage on your grid is stable and distributing
Session Chair: Danish Saleem, National Renewable Energy electricity as intended, everything is status quo. But what
Laboratory (NREL) happens when there are disruptions to the power flow?
Detecting anomalies such as voltage sags/swells, spikes and
* 25GET0255, Assessing the DER Cybersecurity Standards
notches to harmonics, electrical noise, switching transients
Landscape
and frequency variations can identify these issues and fur-
M. MORALES, Department of Energy
ther restore steady voltage.
* 25GET0256, Assessing the DER Cybersecurity Stan-
dards Landscape Grid-specific events such as capacitor bank switching,
U. SADOT, Solar Edge feeder contact by objects such as trees or animals, weather
events such as lightning or ice, vehicle collisions and equip-
* 25GET0257, Assessing the DER Cybersecurity Standards
ment failures can cause disruptions to voltage and current.
Landscape
In addition to grid-specific events, there are also customer-
R. GUPTA, Underwriters Laboratory
created events caused by equipment malfunctions, arcing
* 25GET0258, Assessing the DER Cybersecurity Standards and even the use of “energy-efficient” technologies (such as
Landscape variable frequency drives) that can be spread to neighboring
M. CULLER, Idaho National Laboratory communities depending on the size of the customer load.

Grid Edge Technologies PHD Dissertation Challenge Utilities are investigating the capability to ultimately build
Session II (panel session) a distributed intelligence anomaly agent. To detect these
Wednesday, January 22, 2025 1:00 PM-3:00 PM anomalies, a combination of statistical measures are applied
to characterize the shape of the voltage waveform. On every
Enabling Data Centers and AI (Stage panel) 1/2 cycle (120 per second), these measures are compared
Wednesday, January 22, 2025 1:00 PM-2:00 PM - to thresholds that detect any abnormal waveforms. These
Artificial Intelligence Stage thresholds were derived by recording hundreds of millions
Session Chair: Andrija Sadikovic, Quanta Technology of samples over several weeks during “blue-sky” days on an
actual distribution grid as well as comparing them to known
The demand for AI development and usage is surpassing all use cases of tree/animal/vehicle-induced faults recorded by
expectations, driving an unprecedented need for data center the Dept. of Energy and EPRI.
capacity and strategic locations. This panel brings together
* 25GET0232, Investigating the Detection of Waveform
four distinguished representatives from electric utilities and
Anomalies in Order to Maintain Grid Stability
data center developers/owners. They will delve into the chal-
J. ENGSTROM, ITRON
lenges and opportunities associated with interconnecting
data centers to meet the burgeoning AI demand. Join us for
Impact of Medium-Heavy Duty Fleet Electrification on
an insightful discussion on innovative solutions and collab-
Utility Grids: Techniques for Identifying and Estimat-
orative strategies to support the future of AI infrastructure.
ing Loads (Stage panel)
* 25GET0353, Enabling Data Centers and AI Wednesday, January 22, 2025 1:45 PM-3:00 PM -
M. CARPENTER, ONCOR Electrification Stage
Session Chair: Richard Fioravanti, Quanta Technology
* 25GET0354, Enabling Data Centers and AI
M. GARDNER, Dominion
Today, stakeholders are beginning to recognize the unique
* 25GET0355, Enabling Data Centers and AI challenges presented by electrification of medium/heavy-
H. GRENE, Microsoft duty and Light Duty fleets. Utilities and stakeholders are
recognizing that due to (1) the size of the vehicle batter-
* 25GET0356, Enabling Data Centers and AI
ies, (2) the concentration of vehicles making up the fleets,
M. DOOLAN, CBRE
and (3) the clustering of facilities that tend to occur with
commercial fleet operations, the potential grid impact of
Grid Edge Innovation Venture Challenge (other)
the charging loads in specific “hot spots” can be signifi-
Wednesday, January 22, 2025 1:00 PM-3:00 PM Room 1
cant. In some areas around ports and cargo airports, esti-
mates are projecting loads of 250+ MW, putting pressure
Investigating the Detection of Waveform Anomalies in
on grid infrastructure and utility planning. Fleets are com-
Order to Maintain Grid Stability (Stage panel)
mercial operations, meaning there is less flexibility in alter-
Wednesday, January 22, 2025 1:30 PM-2:30 PM - Sustain-
ing charging patterns and charging windows. Finally, due
ability and Resilience Stage

28 ieee power & energy magazine January 2025 Show Issue

to societal impacts of emissions, regulatory initiatives may will also have to be reflected in the load and DER forecast-
also be implemented to accelerate the transition. ing methods, and improved coordination between operation
and planning will be key to the IGP framework.
For Utilities that are trying to plan their system to accom-
modate transportation electrification, this can create quite a This panel will leverage the work of the Energy Systems
challenge. The concentrated vehicle counts and clustering of Integration Group (ESIG) Long-Term Load and DER Fore-
facilities in specific areas means that new, large loads may casting taskforce, which is expected to publish a report in
be linked to only one or two substations, severely stress- the last quarter of 2024, on best industry practices and gaps
ing the infrastructure. Utilities are challenged to understand related to the state-of-the-art in load and DER forecasting,
where these loads will develop, when will the loads occur, including topics like:
and ultimately, how big the loads will be. • Data coordination and sharing: within utility depart-
ments and between utilities, ISOs and other key stake-
In estimating the loads, utilities are further challenged holder (including states, municipalities, communities,
with finding data sources that can accurately estimate the and key customers and and DER market players)
number of vehicles, locations of facilities, or provide the • Large loads demand growth: tracking and forecasting
necessary data to disaggregate potential loads to the feeder • DER and electrification forecasting methods: customer
level. In addition, adoption and propensity models are often adoption
geared towards light-duty vehicle adoption; hence, there • Demand and grid-edge flexibility: DER and load man-
are challenges in understanding the timelines and pace that agement and control
fleet operators will follow in adopting the vehicles. • Policy-driven forecasting: scenarios and uncertainty

This panel will examine steps leading utilities are taking to This panel will also leverage the work of the Grid Flex-
address these challenges and estimate the fleet loads. The ibility Task Force under the IEEE Power & Energy Society
panel will discuss techniques utilized to estimate the loads, (PES) and discuss the importance of flexibility at the grid-
outreach that is occurring to develop a better understand- edge as a key tool to manage the uncertain load growth from
ing of the goals of fleet operators, and how to plan for a new industry loads and electrification of transportation and
future where large load pockets can be created. The panel building sectors. Grid-edge flexibility can be enabled by
will comprise of utilities current facing these challenges proactive grid investments as well as through grid orches-
and facility operators that are expecting into introduce large tration of demand and DERs in order to better prepare and
loads from electrification initiatives. react to new uncertainties in technology performance, con-
sumer adoption, and usage of electricity.
* 25GET0331, Address Level Analysis of MHD Electrifica-
tion Impact on Utility Grids * 25GET0224, Load Forecasting and Grid-Edge Flexibil-
R. FIORAVANTI, Quanta Technology ity: Key Elements of Integrated Grid Planning
S. PANDEY, Commonwealth Edison
Start Up Lightning Pitch (other)
* 25GET0225, Load Forecasting and Grid-Edge Flexibility:
Wednesday, January 22, 2025 2:00 PM-2:45 PM
Key Elements of Integrated Grid Planning
J. BLACK, ISO New England
Load and DER Forecasting and Grid-Edge Flexibility:
Key Elements of Integrated Grid Planning (Stage panel) * 25GET0226, Load Forecasting and Grid-Edge Flexibil-
Wednesday, January 22, 2025 2:15 PM-3:15 PM - DER Stage ity: Key Elements of Integrated Grid Planning
Session Chair: Julieta Giraldez, Electric Power Engineers J. XIE, National Grid
* 25GET0227, Load Forecasting and Grid-Edge Flexibility:
Given the speed with which the grid landscape is chang-
Key Elements of Integrated Grid Planning
ing and the increasing penetration of DERs in the power
A. XIE, Camus Energy
grid, the utility grid planning process is evolving into an
“integrated grid planning process” that analyzes different
Crossing the Chasm – Pathway to High Levels of DER
sources of energy, their benefits to the grid and its partici-
Integration and Utilization (Stage panel)
pants and users, and how the sources reliably serve load.
Wednesday, January 22, 2025 2:45 PM-3:45 PM - Sustain-
This panel, formed by grid planners from utilities and
ability and Resilience Stage
grid operators and vendors, will discuss two key elements
Session Chair: Jim Ogle, PNNL
to successfully implementing integrated grid planning: 1)
Advanced load and DER forecasting as the backbone to pro- * 25GET0326, TBD
actively plan and operate the grid, and 2) Grid flexibility to T. TBD, TBD
deal with uncertainties in investment, operations, and deci-
* 25GET0339, Distribution Grid Transformation
sion making. A future with operational grid-edge flexibility
J. PALADINO, Department of Energy

January 2025 Show Issue ieee power & energy magazine 29

 * 25GET0340, Transmission - Distribution - Edge Opera- * 25GET0039, A Transient Simulation Framework for the
tional Coordination Impact Analysis of Power System Load Altering Attacks
P. DE MARTINI, Newport Consulting G. TIAN, Texas A&M University at Galveston
Q. Z. SUN, University of Central Florida
* 25GET0341, Technology Roadmaps for a Transforming Grid
N. SONG, Seven Lakes High School
J. SMITH, EPRI
* 25GET0040, Consequence Based Framework for
* 25GET0342, Planning for Distribution Transformation
Deployment of Cloud Solutions in the Digital Energy
S. PAL, Portland General Electric
Transition
J. MORGAN, Idaho National Laboratory
Student Poster Competition Session (poster session)
E. STEWART, Idaho National Laboratory
Wednesday, January 22, 2025 3:00 PM-5:00 PM - Hall AB1
R. STOLWORTHY, Idaho National Laboratory
J. BRIONES, Department of Energy
Grid Edge Conference Poster Session (poster session)
J. WHITAKER, Department of Energy
Wednesday, January 22, 2025 3:00 PM-5:00 PM - Hall AB1
* 25GET0042, Smart Charging of Residential Electric
* 25GET0005, OPTIMIZATION OF UTILITY SUBSTA-
Vehicles: Statistical Analysis Using Real-World Datasets
TION WITH IEC 61869 MERGING UNIT TECHNOLOGY
R. DA SILVA, Federal University of Para
M. MADHU, Siemens Smart Infrastructure Inc USA
C. AFFONSO, Federal University of Para
G. DE MATTOS PINTO, Siemens Industry
* 25GET0043, Dynamic Boundary Microgrids Under
* 25GET0014, Preventing Arbitrage to Enable Preferences
Privatization Considerations
in Distributed Peer-to-peer Electricity Trading
M. STARKE, Oak Ridge National Laboratory
A. MUSGRAVE, The University of British Columbia
 A. SUNDARARAJAN, Oak Ridge National
R. KHATAMI, Southern Illinois University
Lab­oratory
C. CHEN, The University of British Columbia
G. LIU, Oak Ridge National Laboratory
* 25GET0019, Transfer Learning Enhanced Deep Rein- M. CHINTHAVALI, Oak Ridge National Laboratory
forcement Learning for Volt-Var Control in Smart Grids
* 25GET0044, Deconfliction of Centralized and Distrib-
K. KUMAR, Iowa state university
uted ADMS and DERMS Applications
G. RAVIKUMAR, Iowa State University
A. ANDERSON, PNNL
* 25GET0022, Federated Controls for Distributed Energy A. FISHER, PNNL
Resource Management Applied to a Feeder with High Solar S. POUDEL, PNNL
Generation and Battery Storage O. VASIOS, PNNL
Y. LIN, National Renewable Energy Laboratory G. BLACK, PNNL
A. REIMAN, PNNL
* 25GET0025, Grid-Forming Inverters: Evaluating Per-
formance and Industry Implications for Grid Stability and * 25GET0050, Grid Services Demonstration Using Service-
Renewable Energy Integration Oriented DERMS
A. ABDELHADI, Wartsila North America M. ADHAM, Portland State University
H. GHAFFARZADEH, Wartsila North America S. KEENE, Portland State University
T. SLAY, Pacific Northwest National Laboratory
* 25GET0028, Probabilistic Framework for Assessing Gen-
J. KOLLN, Pacific Northwest National Laboratory
eration Interconnection Costs in Cluster-Based Queues
R. BASS, Portland State University
H. ATHARI, Simple Thread
A. TENHUNDFELD, Simple Thread * 25GET0052, New Strategy of Performing Dynamic
Resistance Measurement on Resistor and Reactor Load Tap
* 25GET0029, Microgrid Control Latency Prediction with
Changers
PMU Data from Hardware-in-the-loop Experiments
M. FERREIRA, Quanta Technology
Z. MA, Arizona State University
G. MILOJEVIC, DV Power
Q. CUI, Chongqing University
L. MAI, Arizona State University * 25GET0053, Towards a Bass Diffusion Forecasting
Y. WENG, Arizona State University Approach for the UK smart meter roll-out
J. VERMA, University of Birmingham
* 25GET0032, Transfer Learning Trained LSTM Models
D. DONALDSON, University of Birmingham
for Household Load Profile Forecasting
G. WILSON, University of Birmingham
R. KLEIN-SEETHARAMAN, Yale University
X. ZHU, Oregon State University * 25GET0055, Swing Contract-Based Valuation for Distrib-
B. MATHER, National Renewable Energy Laboratory uted Energy Resources in Transactive Energy Systems: A
Reinforcement Learning Approach

30 ieee power & energy magazine January 2025 Show Issue

 S. A. R. NAQVI, Pacific Northwest National S. POUDEL, Pacific Northwest National Laboratory
Laboratory J. OGLE, Pacific Northwest National Laboratory
T. RAMACHANDRAN, Pacific Northwest National A. REIMAN, Pacific Northwest National Laboratory
Laboratory
* 25GET0067, Cyber Security of a Smart Power Distribu-
S. BHATTACHARYA, Pacific Northwest National
tion System; Cyber Subsystem Use Case
Laboratory
V. BOBATO, Texas A&M University
A. SOMANI, Pacific Northwest National Laboratory
K. THONGMAI, Texas A&M University
* 25GET0056, Evaluating Direct and Indirect Influence on A. GOULART, Texas A&M University
EV Charging Stations Across the US K. BUTLER-PURRY, Texas A&M University
G. WEAVER, Idaho National Laboratory
* 25GET0070, Deep Learning-based Autoencoder for Fault
D. EISENBERG, Naval Postgraduate School
Detection and Localization in DER-Microgrids
E. STEWART, Idaho National Laboratory
A. HUSSAIN, Iowa State University
* 25GET0057, Interoperable, Inverter-Based Distributed G. RAVIKUMAR, Iowa State University
Energy Resources Enable 100% Renewable and Resilient
* 25GET0071, Machine Learning-Assisted Distribution
Utility Microgrids
System Network Reconfiguration Problem
L. ABCEDE, San Diego Gas & Electric Company
R. ASIAMAH, Georgia Institute of Technology
K. MCGRATH, San Diego Gas & Electric Company
Y. ZHOU, National Renewable Energy Laboratory
A. PRATT, National Renewable Energy Laboratory
A. ZAMZAM, Ascend Analytics
K. PRABAKAR, National Renewable Energy Laboratory
A. BLANKENSHIP, San Diego Gas & Electric Company * 25GET0073, Day-Ahead Operational Forecast of Aggre-
 A. MORADPOUR, San Diego Gas & Electric gated Solar Generation Assimilating Mesoscale Meteorol-
Company ogy Information
M. ARAGON, San Diego Gas & Electric Company G. TERRÉN-SERRANO, UCSB
J. CARDENAS, San Diego Gas & Electric Company R. DESHMUKH, UCSB
N. SHANMUKH, San Diego Gas & Electric Company M. MARTÍNEZ-RAMÓN, UNM
M. SYMKO-DAVIES, National Renewable Energy
* 25GET0078, Effective Capacity of a Battery Energy Stor-
Laboratory
age System Captive to a Wind Farm
* 25GET0058, Data-Driven Outage Management Scheme V. A. VAISHAMPAYAN, College of Staten Island-
for Enabling Resilience During Extreme Events City University of New York
S. BAJAGAIN, Dominion Energy T. ANTONY, College of Staten Island-City Univer-
S. POUDEL, Pacific Northwest National Laboratory sity of New Yor
M. G. YU, Pacific Northwest National Laboratory  A. YOGARATHNAM, Brookhaven National
 M. MUKHERJEE, Pacific Northwest National Laboratory
Laboratory
* 25GET0080, Centralized Controller for Photovoltaic
* 25GET0059, Estimation of Peak Demand Reduction MPPT, Bidirectional Charging, and Dynamic Load Control
using Smart Thermostats: A Texas Case Study Using Deep Reinforcement Learning
D. KIM, Texas A&M University R. WIENCEK, Tennessee State University
A. KARNGALA, Texas A&M University S. GHOSH, Tennessee State University
L. XIE, Texas A&M University
* 25GET0081, Preserving data privacy and improving scal-
* 25GET0060, Non-linear SoC Balancing Control for Bat- ability with distributed learning in edge devices
tery Energy Storage Systems Improving the Provision of M. BORNSTEIN, University of Maryland
Frequency Regulation N. NAZIR, Pacific Northwest National Laboratory
N. BUCHINSKIY, Tohoku University J. DRGONA, PNNL
S. KUNDU, PNNL
* 25GET0061, Strategies and Solutions for Effective Peer-
V. ADETOLA, PNNL
to-Peer Energy Trading in Modern Grids
 A. A. MAMUN, University of North Carolina at * 25GET0082, Improving Grid Resilience and Reliabil-
Charlotte ity through Dynamic Line Rating during Ice Precipitating
B. CHOWDHURY, University of North Carolina at Conditions
Charlotte S. RAJENDRAN, Brookhaven National Laboratory
 A. YOGARATHNAM, Brookhaven National
* 25GET0062, A Layered Framework to Characterize Dis-
Laboratory
tributed Applications in Power Systems
T. ZHAO, Brookhaven National Laboratory
R. SADNAN, Pacific Northwest National Laboratory
S. ENDO, Brookhaven National Laboratory

January 2025 Show Issue ieee power & energy magazine 31

 V. ARAVINTHAN, Wichita State University * 25GET0096, Power Quality Grid Measurements from
M. YUE, Brookhaven National Laboratory IOT Sensor Network
T. LAUGHNER, Lifescale Analytics
* 25GET0084, Optimal Topology Design in Radially
J. ANDERSON, Whisker Labs
Distributed Electrification Scenarios with Distributed
B. MARSHALL, Whisker Labs
Generation
W. WANG, Advanced Power and Energy Program, * 25GET0097, Microgrid Building Blocks for Dynamic
University of California, Irvine Decoupling and Black Start Applications
R. FLORES, Advanced Power and Energy Program,  S. ACHARYA, Pacific Northwest National
University of California, Irvine Laboratory
J. BROUWER, Advanced Power and Energy Pro- P. MANA, Pacific Northwest National Laboratory
gram, University of California, Irvine H. MAHMOOD, Pacific Northwest National Laboratory
F. TUFFNER, Pacific Northwest National Laboratory
* 25GET0085, Hybrid DLAA-FDI: A Practical New Cyber-
 A. K. BHARATI, Pacific Northwest National
security Threat in the Era of High-power DERs
Laboratory
Z. ZHOU, University of Wyoming
D. DUAN, University of Wyoming * 25GET0098, Field Study on Short-Time Voltage Har-
monic Distortion in Incipient Faults Using Low Voltage
* 25GET0086, Interpretability, Explainability and Trust
Waveform Data
Metrics in Anomaly Detection Method for Power Grid
Y. SEYEDI, Hubbell Inc.
Sensors
L. PIYASINGHE, Hubbell Inc.
D. WANG, Pacific Northwest National Laboratory
R. SMITH, Hubbell Inc.
T-C CHEN, Pacific Northwest National Laboratory
 K. MAHAPATRA, Pacific Northwest National * 25GET0100, Analysis of Effective IBR Actuators for
Laboratory Forced Oscillation Mitigation
N. L. THOTAKURA, University of Tennessee Knoxville
* 25GET0087, Feasibility Study of Full Vehicle Electrifica-
Z. YI, University of Tennessee Knoxville
tion in Dense Urban Regions
Y. LIU, University of Tennessee Knoxville
M. ELSAYED, City University of New York
K. ZAFAR, City University of New York * 25GET0101, Distributed Resilient Control of DC
A. ABDELBARY, City University of New York Microgrids Under Polynomially Unbounded FDI Attacks
A. MOHAMED, City University of New York Y. WANG, University of Connecticut
M. RAJABINEZHAD, University of Connecticut
* 25GET0090, Wrapper-Based Interfaces to Enable Whole-
O. BEG, University of Texas Permian Basin
sale Markets Simulation for Transactive Energy Studies
S. ZUO, University of Connecticut
J. HASTINGS, Washington State University
A. BOSE, Washington State University * 25GET0102, Cyber Security Use Case on a Smart Power
 M. MUKHERJEE, Pacific Northwest National Distribution System; Physical Subsystem
Laboratory K. THONGMAI, Texas A&M University
T. HARDY, Pacific Northwest National Laboratory V. BOBATO, Texas A&M University
K. BUTLER-PURRY, Texas A&M University
* 25GET0091, Digital Filter Design for Point-on-Wave Sen-
A. GOULART, Texas A&M University
sors: Highlighting Frequency Response Limitations and
Enhancing Grid Simulation Accuracy * 25GET0111, Placement of Utility Scale Battery Storage
A. WILSON, Oak Ridge National Laboratory and Solar on Rural Distribution Systems
A. EKTI, Oak Ridge National Laboratory S. NGUYEN, University of Pittsburgh
B. WARMACK, Oak Ridge National Laboratory J. BAZERQUE, University of Pittsburgh
S. DEBNATH, Oak Ridge National Laboratory M. ABDELHAKIM, University of Pittsburgh
R. KERESTES, University of Pittsburgh
* 25GET0093, Transformer-based Model for Anomaly
Detection and Mitigation in Power Grids with Edge Devices * 25GET0112, Distributed Energy Export Scheduling using
T. ZHAO, Brookhaven National Laboratory Limited Generation Profile Interconnections
 A. YOGARATHNAM, Brookhaven National B. MURRAY, University of Oviedo
Laboratory P. GARCÍA FERNÁNDEZ, University of Oviedo
M. YUE, Brookhaven National Laboratory Á. NAVARRO-RODRÍGUEZ, University of Oviedo
* 25GET0094, Beyond the Drive: Tackling Idling Energy * 25GET0114, XGridDS: An Explainable Data Analytics
Consumption of Heavy-Duty Battery Electric Vehicle Toolkit for Grid Operations
A. KUSHWAH, Calstart M. NGO, Pacific Science & Engineering Group, Inc.
L. K. OSHIRO CALDEIRA, Calstart A. BOONE, Pacific Science & Engineering Group, Inc.

32 ieee power & energy magazine January 2025 Show Issue

 T. PHAM, Pacific Science & Engineering Group, P. OHODNICKI, University of Pittsburgh
Inc. Y-D SU, University of Pittsburgh
I. CHAKRABORTY, Lawrence Livermore National G. S. SUBRAMANIA, Sandia National Laboratory
Laboratory J. PILLARS , Sandia National Laboratory
T. MONSON, Sandia National Laboratory
* 25GET0115, Universal decoupling grid-forming control
N. GURULE, Sandia National Laboratory
for utility-scale distributed energy resources in low-strength
distribution grids <br clear=”all”> * 25GET0125, Integrating Emerging Communication Tech-
S. YU, University of Illinois Chicago nologies and Cloud-Based Microservice Architecture for
L. HE, University of Illinois Chicago the Ingestion of Distribution-Level Phasor Measurement
Units and Low-Cost Sensors
* 25GET0118, Risk Assessment of Transmission Lines
M. MILLER, NRECA Research
Against Grid-ignited Wildfires
M. SERRANO, NRECA Research
S. NEMATSHAHI, University of Denver
M. MADADI, North Carolina State University
A. KHODAEI, University of Denver
L. KONDAPANENI, North Carolina State University
A. ARABNYA, University of Denver
 S. BHATTACHARYA, North Carolina State
* 25GET0119, Resilient Electricity Network Recovery University
Operation Following Seismic Events Y-D SU, University of Pittsburgh
S. ARIF, The University of Auckland H. PHILLIPS, University of Pittsburgh
N-K C. NAIR, The University of Auckland P. OHODNICKI, University of Pittsburgh
* 25GET0120, Who should I trust? A Visual Analytics * 25GET0127, Flexible operation of microgrids through
Approach for Comparing Net Load Forecasting Models operator configurable microgrid controllers
 K. BHATTACHARJEE, New Jersey Institute of P. CURTISS, S&C Electric Company
Technology  K. PRABAKAR, National Renewable Energy
S. KUNDU, Pacific Northwest National Laboratory Laboratory
I. CHAKRABORTY, Lawrence Livermore National D. VAIDHYNATHAN, National Renewable Energy
Laboratory Laboratory
A. DASGUPTA, New Jersey Institute of Technology S. GANGULY, National Renewable Energy Laboratory
S. KAMALINIA, S&C Electric Company
* 25GET0121, GridSweep: Active and Passive Signal
R. JAIN, National Renewable Energy Laboratory
Processing
P. TOP, Lawrence Livermore National Lab * 25GET0132, Detection Algorithm for High Impedance
H. SHEHADA, Virginia Tech Faults on Electrical Edge Device
x. MENG, Eaton Corp
* 25GET0122, Equitable Regional Load Curtailment Frame-
x. MENG, Eaton Corp
work Considering Community Fairness and Line Congestion
S. CHANDRA, Eaton Corp
 N. BATADUVAARACHCHI, Wichita State
D. ISHCHENKO, Eaton Corp
University
M. NOWAK, Eaton Corp
D. ALEXANDER, Wichita State University
S. TALUKDER, Eaton Corp
A. MELAGODA, Wichita State University
A. MANOHARAN, Wichita State University * 25GET0099, Optimal PMU Placement for State Estima-
V. ARAVINTHAN, Wichita State University tion with Grid Parameter Uncertainty
A. TAMIMI, Sunflower Electric Corporation I. ROMERO, University of California, Merced
R. MARCIA, University of California, Merced
* 25GET0123, Distributed Privacy-Preserving Resilient
 I. ARAVENA, Lawrence Livermore National
Control of DC Microgrids Against Unbounded Cyberattacks
Laboratory
M. RAJABINEZHAD, University of Connecticut
N. PETRA, University of California, Merced
Y. ZHANG, University of Connecticut
 Y. YANG, Nanjing University of Posts and * 25GET0004, Reliability Calculation of a Power Con-
Telecommunications version System (PCS) for Electric Vehicle (EV) Charging
S. ZUO, University of Connecticut Stations
M. ARIFUJJAMAN, Southern California Edison
* 25GET0124, Smart Low-cost Sensing Solutions for
Enhanced Behind-the-Meter Visibility of Grid Edge * 25GET0074, Integrating Grid Edge Technologies with
Renewable Energy Sources Grid Operations Technologies to Facilitate Clean Energy
M. MADADI, North Carolina State University Transition
 S. BHATTACHARYA, North Carolina State E. DANIEL, Accenture LLP
University P. CHAKRAVARTY, Accenture LLP

January 2025 Show Issue ieee power & energy magazine 33

 * 25GET0083, Hardware-in-the-Loop Validation of Utility- * 25GET0248, Free-Form Dynamic Load Model Synthe-
Scale Power Plant Controllers for Inverter-Based Resources sis With Symbolic Regression Based on Sparse Dictionary
S. SONI, I2R GRID SOLUTIONS INC. Learning
R. KUMAR, Merit SI Technologies LLC J. WANG, Southern Methodist University
N. MILAM, Merit SI Technologies LLC
* 25GET0249, Imagery and AI-based Digital Transforma-
D. ANICHKOV, Merit SI Technologies LLC
tion for T&D Inspections
* 25GET0104, Modeling Techniques for VSC-Based HVDC J. ZHAO, Eversource Energy
Offshore Wind Farms: A Comparative Evaluation
* 25GET0250, Edgewise: Making Sense of Grid Data
D. SINGH, STONY BROOK
N. CLONTZ, AES US Utilities
S. SINGH, STONY BROOK
F. LUO, STONY BROOK
California Wine and Cheese Reception (reception)
* 25GET0110, Integration of Distributed Energy Resources Wednesday, January 22, 2025 4:00 PM-5:00 PM
for Microgrid Hall AB1
R. KINI, PNNL
N. DRIGAL, Pacific Northwest National Laboratory Enhancing Distribution Grid Resilience through Analy-
Y. JIANG, Pacific Northwest National Laboratory sis, Evaluation and Planning: Challenges and Current
C. VARTANIAN, Sandia National Laboratory Trends (Stage panel)
S. MAPLE, Pacific Northwest National Laboratory Wednesday, January 22, 2025 4:00 PM-5:00 PM - Sustain-
J. KOLLN, Pacific Northwest National Laboratory ability and Resilience Stage
Session Chair: Monish Mukherjee, Pacific Northwest
* 25GET0128, LSTM-Xgboost Model for Cyberattack
National Laboratory
Detection in Grid-Connected Solar PV Inverter System
S. VODAPALLY, The University of Memphis
With the increasing frequency and intensity of extreme
M. H. ALI, The University of Memphis
weather events, many of which are climate-change related,
* 25GET0135, The Unified Theory of Grid Coordination: one of the major modern-day concerns of utilities is dealing
Putting the Customer First with extreme outages and consequently, its repercussions on
B. NORDMAN, Lawrence Berkeley National Laboratory the lives of people in society and social aspects. The ongoing
transformation of distribution systems with the prolifera-
Start Up Lightning Pitch (other) tion of distributed energy resources (DERs), ubiquitousness
Wednesday, January 22, 2025 3:00 PM-3:45 PM of smart metering, and changing regulator environment
provides strategic opportunities for utilities to reduce the
Optimizing the Smart Grid with Data and Analytics: likelihood and consequences of outages. However, planning
Opportunities, Technologies, Applications and Chal- and implementing solutions for enhancing distribution grid
lenges (Stage panel) resilience poses challenges to utilities and their regulators.
Wednesday, January 22, 2025 3:30 PM-4:30 PM - Artificial A host of factor including ambiguity over the definition of
Intelligence Stage resilience, the uncertainty of the benefits from improving
Session Chair: Xuan Wu, AES US Utilities resilience, the difficulty of quantifying resilience and estab-
lishing a baseline, and the optimal arrangement involving
In the past, we used physical and mathematical models to both utility planning and a consumer-driven approach—
analyze and optimize the operation of power systems, achiev- complicate the tasks of both utilities and regulators.
ing automation. In today’s era of big data, we need to intro-
duce analytics methods such as machine learning and deep This panel proposal aims at a structured discussion of
learning for data analysis to reexamine and analyze power experts from distribution utilities across the U.S. and
systems, achieving the intelligence of the power system. national laboratories to present different perspectives
about the some of the key challenges and solution road-
There will be panelists from both the industry and research maps towards enhancing distribution grid resilience. The
entities to envision and demonstrate how data analytics will panel would present some of the state-of-the-art tools for
benefit utilities and customers with both big-picture overview analyzing and evaluating the resilience of distribution
and specific use cases. Machine learning techniques along systems along with recent technical advancements. The
with other emerging technologies will be discussed regarding panel would also highlight current industry practices for
their opportunities and challenges for broader impacts. quantifying the value of service and implementing resil-
ience enhancement solutions. The panel would dissemi-
* 25GET0247, Data Analytics for Electric Utilities: From
nate interesting results and operational approaches from
Vision to Reality
the resilience research and would benefit attendees from
R. SMITH, AES US Utilities
diverse communities of power engineering through shared

34 ieee power & energy magazine January 2025 Show Issue

conventional wisdom along with current practices that are * 25GET0244, Challenges with High Electric Vehicle Pen-
being undertaken by distribution utilities towards enhanc- etration during Climate Emergencies
ing distribution grid resilience. M. ARIFUJJAMAN, Southern California Edison
* 25GET0327, ComEd’s Leadership on Quantification of * 25GET0245, Challenges with High Electric Vehicle Pen-
Distribution Resiliency etration during Climate Emergencies
S. PANDEY, Commonwealth Edison A. SHAHSAVARI, San Diego Gas & Electric
* 25GET0328, Evaluating Consumer-Centric Strategies for * 25GET0246, Challenges with High Electric Vehicle Pen-
Resilience Enhancement etration during Climate Emergencies
S. POUDEL, Pacific Northwest National Laboratory S. CHUANGPISHIT, Quanta Technology
* 25GET0329, Orchestrated situational awareness for auto-
Advanced Synchrophasor Applications for IBR-Domi-
mated restoration and resilience enhancement
nated Power Grids (Stage panel)
F. DING, National Renewable Energy Laboratory
Thursday, January 23, 2025 10:45 AM-11:45 AM - Sustain-
* 25GET0330, SCE’s Efforts to Advance Electric Grid ability and Resilience Stage
Reliability, Resilience and Readiness Session Chair: Babak Enayati, Luma Energy
J. CASTANEDA, Southern California Edison (SCE)
Electric utilities are embracing time-synchronized mea-
surement technology based on phasor measurement units
Thursday, January 23, 2025 (PMUs) distributed across the grid and streaming high-rate
Breakfast and Networking (breakfast) synchrophasor data to centralized and grid-edge applica-
Thursday, January 23, 2025 7:00 AM-8:00 AM tions for wide-area operator situational awareness, along
 Ballroom 6C-F with advanced control and protection. These wide-area
functions are strategically imperative due to increasing grid
Challenges with High Electric Vehicle Penetration dur- reliability demands and operational stress. At the same time,
ing Climate Emergencies (Stage panel) zero-carbon initiatives are driving penetration of renewable
Thursday, January 23, 2025 10:30 AM-11:45 AM - Electri- distributed energy sources (DER) and other inverter-based
fication Stage resources (IBRs) to high levels that vary during the day
Session Chair: Jubair Yusuf, Sandia National Laboratories and will approach 100% of energy supply. High levels of
IBR generation reduce system inertia and stability as they
The frequency of climate-related events has increased sig- yield low or unpredictable fault currents, leading to brittle
nificantly in recent years. A total of 18 separate weather and operating scenarios, along with risk of uncleared faults and
climate disasters (e.g. wildfire, hurricane, tornado, storm, cascading outages. The holistic wide-area and high-speed
flood, etc.) happened in the United States in 2022, and each synchronized data collection and processing will provide
of these disasters cost at least $1 billion. These types of the platform for reliable operation in these new operating
disasters often cause disastrous impacts on the power grid scenarios. Utilities will increasingly rely on synchrophasor-
and require a large-scale evacuation for the people residing based use cases and applications for rapid system state and
in that area. The impacts of the increased penetration of behavior awareness, operational management, and fault and
Electric Vehicles (EVs) in such scenarios may raise addi- swing protection. Advanced data analytic algorithms are
tional concerns because of their charging requirements and extracting critical wide-area observations for proactively
range constraints. Existing evacuation plans will also be managing IBR-dominated power systems, making them
impacted by the increased EV penetration in the transpor- more resilient, flexible, and reliable.
tation network. EVs will encompass many of the vehicles
to be used for both self-evacuees and public transportation This panel provides a forum for discussion of advanced tech-
during climate emergencies, and the incapability of charg- nologies and initiatives in the synchronized measurement
ing them sufficiently can accelerate the catastrophic dam- domain that assist utilities and independent system opera-
ages. This session will analyze the various challenges of the tors (ISOs) in identifying and mitigating real-time system
integration of electrified transport into power systems dur- anomalies so they can operate the system securely. These
ing climate emergencies, considering both grid and mobility technologies empower system operators to mitigate risk
challenges, and discuss solutions to mitigate these impacts. factors before they escalate into widespread disturbances.
The panelists will cover a range of challenges and solutions Additionally, the panel will delve into the challenges of high
for different stakeholders such as power grid, transportation penetration of IBRs, exploring how synchrophasor data can
network, EV owners, and first responders. aid in overcoming these challenges. Specifically, we will
* 25GET0243, Challenges with High Electric Vehicle Pen- explain developments in synchrophasor-based wide-area
etration during Climate Emergencies system protection (WASP) for the zero-carbon grids and
A. MAMMOLI, Sandia National Laboratories how it complements today’s protection schemes. Following

January 2025 Show Issue ieee power & energy magazine 35

 from these discussions, the panel aims to spotlight LUMA utilities – examining these diverse perspectives is crucial to
Energy’s initiatives and plans for deploying synchrophasor- understanding the complete microgrid landscape in North
based tools and techniques across the Puerto Rico power Carolina. This panel will provide a comprehensive under-
grid. LUMA Energy aligns its developments with Puerto standing of the microgrid journey, including the financial
Rico’s goals for a resilient and sustainable grid as it inte- benefits for utilities looking to optimize costs and improve
grates renewable energy resources. The panel will include reliability. You’ll also gain insights into building trust and
presentation of the 10-year LUMA rollout plan for compre- strong partnerships with utilities as a microgrid vendor.
hensive PMU deployment with use cases and applications.
Join us to gain valuable knowledge about designing, testing,
* 25GET0350, Advanced Synchrophasor Applications for
and implementing microgrids that empower communities
IBR-Dominated Power Grids
and contribute to a more resilient and sustainable energy
E. UDREN, LUMA Energy
future.
* 25GET0351, Advanced Synchrophasor Applications for
* 25GET0233, The Microgrid Journey in North Carolina:
IBR-Dominated Power Grids
Design, Testing, and Implementation
M. VAIMAN, V&R Energy
N. BHAGAT, Duke Energy
* 25GET0352, Advanced Synchrophasor Applications for
* 25GET0234, The Microgrid Journey in North Carolina:
IBR-Dominated Power Grids
Design, Testing, and Implementation
F. AMINIFAR, LUMA Energy
T. GUBITZ, North Carolina Electric Cooperatives
The Microgrid Journey in North Carolina: Design, * 25GET0235, The Microgrid Journey in North Carolina:
Testing, and Implementation (Stage panel) Design, Testing, and Implementation
Thursday, January 23, 2025 11:00 AM-12:00 PM - DER J. KEIFFER, OATI
Stage
* 25GET0236, The Microgrid Journey in North Carolina:
Session Chair: Shahab Afshar, North Carolina Electric
Design, Testing, and Implementation
Cooperatives
M. SHEIKHOLESLAMI, Quanta Technology
This panel delves into the collaborative world of microgrid
Conference Lunch (luncheon)
development in North Carolina. We’ll explore the crucial
Thursday, January 23, 2025 11:30 AM-1:00 PM Hall AB1
stages of design, testing, and implementation, emphasiz-
ing the importance of community benefits throughout the
AI/ML at the Edge to inform situational awareness,
process.
control, and protection (Stage panel)
Thursday, January 23, 2025 11:45 AM-12:30 PM - Artificial
Community-Centered Design:
Intelligence Stage
Successful microgrid projects prioritize the unique needs
Session Chair: Abder Elandaloussi, Southern California
of the community they serve. The design phase involves
Edison – SCE
a deep understanding of local challenges, whether it’s
enhancing grid resilience in cities or integrating renewable
Artificial Intelligence could be a driving force in the trans-
energy sources for remote communities.
formation of decision-making processes. When harnessed
correctly, it can equip utilities with the capacity to make
Rigorous Testing and Implementation:
optimal, data-driven decisions. The conventional method
Following a comprehensive design, rigorous testing
of centrally developing and running ML/AI models can be
becomes paramount. Advanced microgrid controllers play
resource-intensive, given the exponential increase in data
a vital role in this stage, simulating various scenarios and
collection. If we further consider the current and future
optimizing energy resource management. This testing
dependence on a robust communication and computational
ensures the microgrid functions seamlessly during outages
infrastructure for data collection and analysis, it becomes
and integrates effectively with the broader grid during nor-
clear that a strictly centralized AI/ML architecture is
mal operation. Once testing is complete, the implementa-
impractical. This panel will discuss different perspec-
tion phase requires close collaboration between utilities,
tives on a decentralized approach to AI/ML deployment,
vendors, and the community. Lessons learned from ongoing
with the aim of reducing pressure on the communication
projects can be applied to ensure smooth integration and
infrastructure and potentially enabling faster, more reliable
maximum benefit for future communities.
decision-making to monitor, protect, and control the grid.
Diverse Perspectives for Success: * 25GET0333,
While different entities prioritize varying aspects – com- A. ELANDALOUSSI, Southern California Edison
munity focus for cooperatives, grid integration for large - SCE

36 ieee power & energy magazine January 2025 Show Issue

* 25GET0334, AI for DER Management and Grid Connec- • 
Do aggregators have a role in power restoration after a
tivity Awareness storm or proactive de-energization to improve overall
M. SAHOTA, Southern California Edison system resilience.
* 25GET0389, Empowering the Grid: Federated Learning * 25GET0274, FERC 2222 Panel
for Decentralized Intelligence in Power Systems A. PORTILLA, PG&E
D. BANDURIN, COMED
* 25GET0275, FERC 2222 Panel
J. TRUMPETTO, ConEd
Start Up Lightning Pitch (other)
Thursday, January 23, 2025 12:30 PM-1:15 PM * 25GET0276, FERC 2222 Panel
Z. POLLOCK, Xcel Energy
FERC Order 2222 implementation: the coming role of
* 25GET0277, FERC 2222 Panel
DER aggregators in coordinated T&D operations (Stage
S. PANDEY, Commonwealth Edison
panel)
Thursday, January 23, 2025 12:45 PM-1:30 PM - DER Stage * 25GET0278, FERC 2222 Panel
Session Chair: Chad Abbey, Quanta Technology A. PACINELLI, PHI

The nation’s regional transmission organizations (RTOs) AI Applications in Asset Management (Stage panel)
are at various stages of implementation of FERC Order Thursday, January 23, 2025 12:45 PM-1:45 PM - Artificial
2222, which aims to remove barriers for participation of Intelligence Stage
DERs in wholesale electricity markets. Although the goal Session Chair: John McDonald, JDM Associates, LLC
is to enable wholesale market participation, this change
* 25GET0337, AI Applications in Asset Management
will have impacts on the distribution system companies
J. MCDONALD, JDM Associates, LLC
to which the DER that makeup the aggregations connect,
T. BIALEK
especially considering the aggregations will typically be
Y. SUN
driven by commercial arrangements (i.e. customer enroll-
U. ZIA
ment) as opposed to technical analysis of distribution grid
and what delivers the most value to the system. Hence, utili-
Addressing Complex Challenges with Digital Twins: A
ties responsible for planning and operation of the distribu-
Big Data Enabled Solution for the Evolving Grid (Stage
tion systems have had to strategically assess these coming
panel)
changes and rethink how DER aggregations, or sometime
Thursday, January 23, 2025 1:15 PM-2:15 PM - Sustainabil-
referred to as Virtual Power Producer (VPP), are considered
ity and Resilience Stage
in system operation and upfront planning.
Session Chair: Abder Elandaloussi, Southern California
Edison – SCE
Following a review of the basic terminology and timelines
for FERC Order 2222, the panel will provide views from sev-
Digital twins hold transformative potential for the electrical
eral different investor-owned utilities across three different
industry, particularly in the operation and maintenance of the
RTOs to compare their perspectives in terms of how they are
electric grid. To fully harness this potential, it’s crucial that we,
planning for the coming changes, what specific initiatives are
as an industry, establish a consensus on the definition of digital
underway, and learn how they see the impacts and growth
twins, including their essential features and components. This
within the medium-term. The moderator will then guide a
panel will explore various perspectives on the concept of digi-
discussion on specific topics around aggregators and the role
tal twins, discuss their potential use cases, and share insights
of third-parties and customers in operation of the distribution
for considerations for successful implementations within elec-
system of the future. Specific discussion points may include:
tric utilities. Our goal is to reach a comprehensive understand-
•  In the absence of any other programs or incentives, how
ing of digital twins by the end of the panel, and to explore how
are DER aggregations expected to operate?
they can be utilized to shape the future of our grid.
•  Does the concept of hosting capacity need to be re-
thought in the context of DER aggregators for both PV * 25GET0332, Digital Twins for Electric Utilities: Defini-
and EV, and customer-owned storage? tion, Considerations, and Applications
•  Can aggregators and VPPs help us to make better use of A. ELANDALOUSSI, Southern California Edison - SCE
the existing capacity?
* 25GET0385, Digital Twins in Oncor
•  How does system operations get situational awareness of
H. HAENTSCH, Oncor
DER aggregation and can they influence the operation of
these DER in any away? * 25GET0387, VX Platform: Enabling a Standards-Based
•  How can DER aggregators help to better align peak solar Digital Twin
production and peak loads? D. KOPIN, Vermont Electric

January 2025 Show Issue ieee power & energy magazine 37

 * 25GET0390, A slice by slice construction of digital twins monitoring and situational awareness. A wide range of topics
from first principles will be discussed, including sensor technologies at grid edge,
N. MATEVOSYAN, COMED benefits and challenges of supporting waveform capture in
smart meters, and applications of synchro-waveforms in asset
Start Up Lightning Pitch (other) monitoring at inverter-based resources at grid edge and wild-
Thursday, January 23, 2025 1:45 PM-2:30 PM fire mitigation at power distribution systems.
* 25GET0228, GridSweep: Using Synchro-Waveforms to
Renewable Energy and Protection Challenges (Stage
Probe Distribution Grid Stability from 120V Outlets
panel)
A. MCEACHERN, Mc Eachern Laboratories
Thursday, January 23, 2025 2:00 PM-3:00 PM - DER Stage
Session Chair: Kamal Garg, SEL Inc * 25GET0229, Improve Grid Edge Awareness with Con-
tinuous Waveform Recording
Renewable generation protection challenges are introduced. R. KIRBY, Schweitzer Engineering Laboratories
The output current of an IBR facility is very different from
* 25GET0230, Benefits and Challenges of Using Smart
a conventional generation using traditional rotating syn-
Meters for Synchronous Waveform Collection
chronous source facility during short circuit conditions.
D. RIEKEN, Hubbell Inc.
This panel session from industry, academia and relay sys-
tem vendors will discuss the challenges, protection issues * 25GET0231, Detecting Anomalies in Waveform Mea-
and proposed solutions. surements for Fire Prevention in Distribution Systems
J-Y JOO, Lawrence Livermore National Laboratory
* 25GET0216, Changing Grid with Inverter Based
Resources
Lessons learnt from implementing EV charging technol-
M. PATEL, EPRI
ogies for residential and fleet use cases globally (Stage
* 25GET0217, IBR Protection using Transient Quantities panel)
N. FISHER, SEL Thursday, January 23, 2025 2:00 PM-3:00 PM - Electrifica-
tion Stage
* 25GET0237, IBR Protection challenges and solutions
Session Chair: Chan Wong, Landis & Gyr
I. VOLOH, GE
* 25GET0372, IBR Protection Expereience PG&E The electrification of the transportation industry will
A. SAEED, PG&E require substantial investments by the energy industry in
new additional generation and delivery capacity. In addi-
Synchro-waveforms at Grid Edge: Technologies and tion V2X will require close communications and bring
Applications (Stage panel) new use cases for utilities and electric vehicle owners and
Thursday, January 23, 2025 2:00 PM-3:00 PM - Artificial users. This panel will discuss the scale of these new invest-
Intelligence Stage ments and the challenges the utility industry has in meeting
Session Chair: Hamed Mohsenian-Rad, University of the requirements of the electrified transportation industry
California while maintaining reliability and resiliency of the grid.
* 25GET0309,
The proliferation of Grid Edge Technologies (GETs) is making
C. WONG, Landis & Gyr
power systems drastically more complex and more dynamic.
Numerous inputs and controls are pushed and pulled from * 25GET0310, Lessons learnt from implementing EV charg-
various GETs; many of which operate outside of the util- ing technologies for residential and fleet use cases globally
ity’s network boundaries and under the control of customers. S. SHEKHAR, Landis+Gyr AG
Therefore, it is critical to establish new and advanced power
* 25GET0311,
system monitoring and situational awareness capabilities to
C. WONG, Landis & Gyr
address these increasing challenges in maintaining reliability
and efficiency at or near the edge of the grid. This panel will
Closing Session & Awards
discuss the state-of-the-art technology and applications of a
Thursday, January 23, 2025 4:30 PM-5:00 PM Ballroom 6AB
synchro-waveforms and its growing applications at grid edge p&e


38 ieee power & energy magazine January 2025 Show Issue

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Digital Object Identifier 10.1109/MPE.2024.3487026
 Distributed
Energy Resources
and Electric
Vehicle Adoption
A Distribution Planning Strategy
and Road Map

By Rick Siepka, Julio Romero Agüero , Marty O’Baker,

Don Hall, and H. Lee Willis

T
THIS ARTICLE DISCUSSES AN INTEGRATED DISTRI- work must be considered within the context of larger trends,
bution planning (IDP) strategy and road map developed by which are described conceptually in Figure 1:
Dominion Energy Virginia to address the needs driven by ✔ Horizontal integration: This involves the integra-
the integration of distributed energy resources (DERs) and tion of distribution planning and distribution en-
significant load growth due to the adoption of electric vehi- gineering activities that utilities have historically
cles (EVs) and data centers. addressed separately, with relatively minor coordi-
nation and, in some cases, as siloes, such as distri-
Introduction bution capacity planning and distribution reliability
The first part of the article discusses the IDP framework, improvement.
and the second part focuses specifically on its expansion ✔ Vertical integration: This relates to the integration of
and application to help Dominion Energy Virginia to pre- generation, transmission, and distribution activities
pare its distribution system for EV adoption. The IDP frame- that have been largely decoupled by deregulation and
practice over the past four decades but have become
Digital Object Identifier 10.1109/MPE.2024.3463588
increasingly interdependent due to the growing im-
Date of current version: 11 November 2024 portance of DERs, electrification, and data centers.

40 ieee power & energy magazine 1540-7977/24©2024IEEE January 2025 Show Issue
 This includes coordinated or integrated resource, The IDP process informs Integrated/Coordinated
transmission, and distribution (RT&D) planning, Resource, Transmission and Distribution (RT&D)
which includes generation connected to the bulk elec- planning, Integrated Resource Planning (IRP), or other
tric system and DERs as well as holistic investment utility planning processes such as Grid Moderniza-
prioritization. tion and capital budgeting. IDP may consider utility,
✔ Planning and operations convergence: This includes customer, and/or third-party owned DER solutions to
the integration of planning and operations activities accomplish its objectives. IDP also accounts for un-
into a more technically focused whole. This concept, certainties introduced by the dynamic nature of vari-
which is well established in bulk electric system plan- ables impacting grid operation, shifting results and as-
ning and operation, is emerging as an important trend sociated decisions from deterministic to probabilistic
at the local delivery system level. This is due to the outcomes.
proliferation of DERs and the increasing focus to as- True IDP requires changes in planner’s skills,
sure that those assets and resources work well in con- technologies, tools used, and processes. Throughout,
junction with traditional distribution system needs trained professionals are vital to fully leverage the
and capabilities, for instance, via nonwires alterna- technologies and optimize the processes and emerging
tives (NWAs) and microgrids. tool sets. Technologies and communications systems
that provide visibility into the distribution grid to the
IDP Framework customer premises level are foundational to enabling
The framework was developed to address the objectives of IDP. Processes and tools must then be developed to
the specific IDP definition adopted by Dominion Energy incorporate the data gathered, including advanced dis-
Virginia: tribution modeling and analysis tools that consider a
Integrated distribution planning (IDP) is a process to range of possible futures where varying levels of DER
address the capacity, performance, reliability, resil- and emerging technologies are adopted on different
ience, and DER integration needs of the distribution parts of the distribution grid.
grid. IDP uses traditional solutions as well as new Figure 2 shows the proposed integrated planning frame-
solutions offered by DER and other non-traditional work to address these needs. This framework consists of
technologies. IDP relies on utilizing high-resolution three main areas: 1) load and DER forecasting, 2) distribu-
temporal and granular spatial engineering analyses tion planning and selected distribution engineering activities,
to identify systemwide and locational needs and and 3) coordinated/integrated RT&D planning. The focus of
benefits. this article is IDP, which covers the first two components

High-Granularity/Resolution Models and Software Solutions

Spatial/Temporal Modeling, Data and Analytics
Simulation, and Analysis
Telecom and IT/
Operational Capacity
Load
Technology Asset Planning Distribution
and DER
Distribution (OT) Manage- and DER
Fore-
and DERs ment Operations
casting
Planning
NWAs RT&D
Volt–Var and Operations
RT&D DER Generation
Control Microgrids Convergence (e.g.,
Planning and Renewable
Convergence (e.g., Modern DER Voltage and Resources
IRP and Future Operations
"Duck Curve") Frequency
End User and
internet of Value of Ride Through)
Automation DER/Grid
Things Transmission
Transmission Operations
Planning
Protection
and Reliability
Control
DER
Inter- Resilience
connection

Result is Holistic
Investment Prioritization
Transition from deterministic (snapshot) analyses based on annual peak demand of key assets
(e.g., substation transformers) to multihour (time-series) probabilistic and spatial analysis of
demand/production of granular assets (e.g., service transformers).

IDP

figure 1. Trends in the integration of resource, transmission, and distribution (RT&D) planning and operations. IRP: inte-
grated resource planning; NWA: nonwires alternative.

January 2025 Show Issue ieee power & energy magazine 41

 and provides inputs for implementing
the third component.

Management
IDP Road Map

Grid
Figure 3 exhibits the IDP road map

Management
Operations
developed by Dominion Energy Virginia
Planning

Risk
that covers most of the elements shown
in the IDP framework of Fig­u re 2. The
Integrated RT&D five-year implementation time frame
Coordinated /

Forecasting
Planning

Resource
Load and
considers the expected evolution of
IRP

needs and drivers, the typical time frame

for specific activity e­ xecution, the prior-
ity and importance of each component,
Modernization
Reliability, and
Resilience

interdependencies among components,

Capacity,

Planning

Forecasting Distribution Planning and Engineering Analysis Coordinated / Integrated RT&D

Grid

and internal capabilities to execute the

Modeling and
Simulation
Advanced

Software

required activities. Some components are

Tools

labeled as “no regrets” since their imple-

mentation is critical to addressing exist-
Reliability and

ing distribution system needs. Therefore,

Resiliency
Microgrids
NWAs and

they are the highest-priority components

of the IDP road map. The remaining
Telecom

components are also important but are

Value of
DER /
Grid

either enabled by “no regrets” compo-

nents or intended to address emerging or
Distribution Planning
and Engineering

evolving distribution system needs that

Planning

Volt–Var
Capacity

Analysis

Quality
Control

Power

are expected to become more important

and

in the short or mid/long term.

Key objectives of the IDP road map
figure 2. The proposed IDP framework. DR: demand response; EE: energy efficiency.
Automation,
Electrification

Protection,

are to consider DERs intrinsic compo-

Capacity

Control
DER and

Hosting

and

nents of distribution planning, mov-

Interconnection

ing away from snapshot analyses of

Management

Maintenance

IDP

key assets (e.g., annual peak demands

Asset
DER

and

of substation transformers and feeder

mains) and into time-series spatial
analysis at feeder section and service
Forecasting

transformer levels (high-resolution/

Electrification
Forecasting

DER

granularity spatiotemporal analysis).

IDP components are described below:
Forecasting
DR and EE

1) Distribution system model: This

“no regrets” component focuses
Forecasting
Forecasting

on enhancing the quality, ac-

Load

curacy, and data integrity of

distribution system models and
Forecasting
Weather

automating selected distribution

engineering and planning anal-
(Small Area)
Analysis

yses, particularly those involved

Spatial

Temporal
Analysis
(Hourly)

in DER and electrification stud-

ies. It includes the following:
• Primary system models: This
includes primary system assets
(e.g., distribution lines, protec-
tive/switching devices, voltage
regulation/control equipment,
service transformers, utility-
scale DERs, and so on).

42 ieee power & energy magazine January 2025 Show Issue

 • Secondary system models: This includes low-volt- distributed generation, distributed energy storage
age secondary system assets (e.g., radial second- (DES), and demand response (DR)], and demand-
ary lines, service drops, secondary networks, spot side management solutions [e.g., energy efficiency
networks, behind-the-meter DERs, and so forth). (EE)]. It is worth noting that some of these loads are
This area requires greater improvement since, controllable or flexible and may respond automati-
generally, no models are available for radial sec- cally to control signals (e.g., smart thermostats) or
ondary systems and service drops. Experience at to control actions issued by system operators. This
peer utilities (e.g., Hawaiian Electric and PHI) in- is the highest-priority “no regrets” component of the
dicates that this type of capability is needed to plan IDP road map since it will enable the implementa-
distribution systems with high penetration levels tion of additional components (e.g., integrated capac-
of DERs and significant adoption of behind-the- ity analysis) and is explained in more detail later. It
meter DERs. consists of the following:
• Dynamic transient models: This includes 1) • Load and DER adoption: This involves calculating
identifying specific distribution system studies the expected spatial distribution and penetration
that require conducting dynamic transient and levels of DERs and electrification loads. This will
quasi-static analyses (e.g., voltage and frequency help with understanding where in the distribution
ride-through studies for DER integration) and 2) system DERs and electrification adoption (particu-
developing processes and computational models larly EVs) are expected to occur for the forecasting
to conduct these studies. Industry experience indi- period under study.
cates that this type of analysis becomes more im- • Load and DER production: This relates to calculat-
portant as DER adoption grows. ing highly granular (e.g., at service transformer or
2) Load and DER forecasting: This component focuses small area levels) time-series load and DER fore-
on developing new capabilities to perform load and casts for planning scenarios of interest (e.g., specific
DER forecasting with greater spatial granularity years, forecasting horizons, or DER and electrifica-
(e.g., at the service transformer level) and higher tion adoption levels). This will help with understand-
resolution (e.g., 8,760 h) for different types of loads ing projected asset loading and outputs from DER
(organic load growth of existing customers, load facilities for the forecasting period under study.
growth associated to new customers, electrification, 3) Distribution system analysis: This “no regrets”
data centers, and so on), DER technologies [e.g., component includes developing new capabilities to

Component Year 1 Year 2 Year 3 Year 4 Year 5

1. Distribution System Model

2. Load and DER Forecasting

3. Distribution System Analysis

4. Integrated Capacity Analysis

5. DER Interconnection Process

6. NWA Analysis and Process

7. Resilience Metrics, Methodology, and Implementation

8. IT Integration and Data Management

9. Value of DERs and Locational Value Assessment

10. Advanced Analytics

11. Coordinated/Integrated RT&D Planning

figure 3. The proposed IDP road map (brown: no regrets; red: short/medium term; pink: long term).

January 2025 Show Issue ieee power & energy magazine 43

 perform automated detailed analyses for distribu- 7) Resilience metrics, methodology, and implementa-
tion planning (e.g., hourly time-series power flow tion: This component focuses on developing metrics,
analyses) and selected engineering studies (e.g., au- methodologies, and tools for evaluating and improv-
tomated verification of protection coordination for ing distribution system resilience using traditional,
system reconfiguration due to distribution automa- intelligent, and NWA solutions. This includes con-
tion operation, contingency analysis, and so forth). It ducting benefit–cost analyses, prioritizing solutions,
includes the following: identifying interrelationships and dependencies with
• Planning and engineering: This includes develop- other distribution planning activities, and updating
ing capabilities for automating detailed modeling the overall distribution planning process accordingly.
and simulation for distribution planning studies 8) IT integration and data management: This “no re-
and selected distribution protection, automation, grets” component focuses on improving the inte-
and reliability analyses. gration of IT and operational technology systems
• DER impact studies: This includes developing ca- involved in the distribution planning process to
pabilities for automating detailed modeling and implement automation functions. This includes au-
simulation for impact studies of utility-scale and tomating key distribution planning data analyses to
behind-the-meter DERs. increase process efficiency and accuracy.
4) Integrated capacity analysis: This “no regrets” com- 9) Value of DERs and locational value assessment:
ponent focuses on developing modeling and simula- This component focuses on developing and imple-
tion capabilities to consider DERs intrinsic capacity menting a methodology, process, and software so-
planning components. This includes 1) understand- lution to calculate the locational value of DERs for
ing the effect of DERs on load masking and firm specific value streams of interest (e.g., avoided or
capacity contribution, 2) utilizing on-demand DER deferred T&D capacity capital investment, avoided
hosting capacity maps as a distribution planning reliability or resilience costs, and so on).
tool, and 3) assessing the potential value and benefits 10) Advanced analytics: This component focuses on
of DERs as an NWA solution to address distribution identifying and defining advanced analytics applica-
system needs. It includes the following: tions to support distribution planning and selected
• Capacity and reliability: This includes developing distribution engineering analyses. This includes
capabilities to evaluate the impacts and benefits of defining data, infrastructure, and technology re-
DERs on capacity planning, incorporate NWAs in quirements for implementing the selected appli-
the portfolio of solutions available to address dis- cations. It includes the following:
tribution system needs, and calculating annual dis- • Platform: This includes defining and prioritizing
tribution system capacity and energy requirements. data analytics applications of interest, defining re-
• Hosting capacity: This includes enabling capa- quirements, and implementing, testing, and validat-
bilities to develop more detailed DER hosting ing selected applications in a pilot project setting.
capacity analyses (e.g., dynamic hosting capacity • Sensors: This includes identifying sensor technol-
considering volt–var support from smart inverters) ogy requirements and applications for monitoring
and to use DER hosting capacity calculations as DERs, electric transportation, and distribution
an on-demand planning tool available to distribu- grids. This includes testing and validating sensor
tion planners to evaluate system performance for technologies, updating practices and standards,
specific penetration levels. and developing a strategy, including benefit–cost
5) DER interconnection: This component focuses on analysis, for systemwide deployment.
enhancing the DER interconnection process, specifi- 11) Coordinated/integrated RT&D planning: This com-
cally on improving the collection of accurate data for ponent focuses on the following:
behind-the-meter DERs and creating respective geo- • developing individual and consolidated processes
graphic information system and distribution system for the elements of the coordinated/integrated
models as well as automating the process itself, to RT&D planning framework (capacity, reliabil-
the extent possible. ity, resilience planning, load and resource fore-
6) NWA analysis and process: This component focuses casting, integrated resource planning, operations
on developing detailed processes and guidelines for planning, grid management, risk management,
conducting required analyses to identify opportuni- grid modernization, and advanced modeling and
ties of using NWAs to address distribution system simulation)
needs. This also includes identifying suitable ap- • selecting the approach and identifying and deploy-
plications for NWA technologies and developing ing solutions for modeling, simulation, and analy-
prioritization methodologies, including benefit–cost sis of integrated RT&D systems (e.g., co-modeling,
analyses. co-simulation, and so on)

44 ieee power & energy magazine January 2025 Show Issue

 • implementing, testing,
validating, socializing, 1,600,000 9.2%
and approving a pro- 1,400,000 PHEV
posed coordinated/inte-
BEV
grated RT&D planning 1,200,000
approach. Total EV 6.8%

Annual Sales
1,000,000
The IDP concept is not static,
800,000
and further changes are expected 4.3%
in the next decade. The road map 600,000
will also set the foundations to 400,000
1.9% 1.9% 2.1%
facilitate the future evolution of 0.9% 1.1%
200,000 0.6% 0.7% 0.7%
the IDP definition and framework 0.1% 0.4%
to address forthcoming distribu- –

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

2023

2024
tion systems and customer needs.
The road map introduces impor-
tant enhancements to existing
figure 4. Plug-in EV sales as a share of all U.S. light-duty vehicle sales. (Source:
distribution grid planning compo-
Argonne National Laboratory.)
nents, such as load and DER fore-
casting. Moreover, it formalizes
new components that are becoming increasingly relevant and charging patterns. Demand increase can lead to poten-
for distribution grid planning, such as NWAs and resilience. tial asset overloads at all levels of the distribution system.
Implementing the IDP road map is a multiorganization and EV impacts can combine with power flow and voltage
multidisciplinary effort involving Dominion Energy Vir- variations caused by DER adoption and increase the over-
ginia’s distribution grid planning organization and other all dynamic behavior of the distribution grid. Other impacts
stakeholders. Moreover, the execution of the IDP road map include minimum-voltage violations, voltage unbalance, loss
involves existing and new resources (internal and external), of life of service transformers, increased effects of cold load
the implementation of new processes and concepts, and pickup (CLPU), reduction of the reserve capacity of feeders
the adoption of new software solutions and technologies. and substations, reduction of the ability to reconfigure the
Therefore, it is critical to pay special attention to organiza- distribution grid, interaction with protection and automa-
tional, resource, and project management aspects. tion systems, and power quality issues. These impacts may
The following sections explain how the IDP framework occur during normal operation, emergency conditions, and
was used by Dominion Energy Virginia as a foundation to service restoration, which subject the grid to greater stress.
develop a strategy and road map to prepare its distribution Therefore, it is important to perform N-1 contingency analy-
grid for EV adoption. sis to identify substations, feeders, and assets in general with
reduced reserve capacity due to load increases driven by EV
EV Adoption Trends adoption and propose respective mitigation measures. Figure 6
Figure 4 displays U.S. historical sales data for light-duty conceptually illustrates these types of impacts. Scenario 1
EVs, collected by Argonne National Laboratory and the U.S. is the base case (no or minimal EV adoption); scenario 2
Department of Energy. EVs consist of battery EVs and plug- is a normal reconfiguration when there is enough reserve
in hybrid EVs. These results show that annual sales reached
approximately 1.43 million EVs in 2023 and that sales
have increased steadily over the last four years, from 2.1% 220,000
200,000 Medium/Heavy Duty
of all light-duty vehicle sales in 2020 to 9.2% in 2023. As
180,000 Light Duty
described in Figure 5, EV adoption is expected to increase
160,000
over the coming years in the Dominion Energy Virginia 140,000
service territory. These results show that utilities in general
Total EVs

120,000
must prepare and be ready to plan the distribution grid to 100,000
integrate increasing penetration levels of EVs. 80,000
60,000
Impacts of EV Adoption in 40,000
Distribution Grids 20,000
The main impact of EV adoption in distribution grids is the 0
2023 2024 2025 2026 2027
increase in overall system demand. The specific amount,
location, and timing of this demand increase depend on figure 5. The Dominion Energy Virginia EV adoption
multiple factors, including EV location, adoption growth, forecast. (Source: Dominion Energy Virginia.)

January 2025 Show Issue ieee power & energy magazine 45

 capacity and no low-voltage violations (section R2–R3 is centers, new developments, and native loads) with
restored); scenario 3 shows that reconfiguration and restora- hourly resolution (8,760 h) and spatial granular-
tion of section R2–R3 is not feasible, due to significant EV ity at the service transformer and small area levels.
adoption and lack of enough reserve capacity; and scenario Besides the aggregated results, ideally, the forecast
4 shows that there is enough reserve capacity to make the should also include the following individual results
reconfiguration and restoration of section R2–R3 feasible, for the planning horizons of interest (e.g., 1–5 years
but this leads to low-voltage violations for some customers for short-term planning and 20–30 years for long-
in this section, due to significant EV adoption. term planning):
• Peak demand projections and respective profiles for
Strategy and Road Map for EV Adoption typical days (e.g., weekdays and weekends for ev-
This section explains how Dominion Energy Virginia used ery month of the year) for native (organic) and new
the foundation set by the IDP road map to outline a distribu- business (e.g., data centers and electrification) load
tion planning strategy and road map for EV adoption. The growth are included.
company is currently implementing this strategy and road • Projected peak production/demand and respective
map, and lessons learned will be shared in a future article. profiles by DER and electrification technology (e.g.,
An important consideration in developing this strategy is PV, DES, DR, EE, EVs, and so on) are included.
that the required studies and analyses for EV adoption can- • Projected peak production/demand and respective
not be conducted in isolation with respect to DER integra- profiles for typical days for utility-scale and residen-
tion. Both technologies must be considered jointly, along tial DERs; level 1, 2, and 3 EVs; light-, medium-,
with native (organic) and new business load growth (e.g., and heavy-duty EVs; and EV fleets are integral. The
data centers and electrification in general). EV adoption EV fleet forecast is particularly important given the
must consider all vehicle technologies (levels 1, 2, and 3; magnitude, potential impacts, and expected risks of
light, medium, and heavy duty; and fleets). DER adoption these loads.
must include distributed and utility-scale photovoltaic (PV) • The forecast must also include estimates of EVs oper-
and other distributed generation technologies, DES, and DR ating under grid-to-vehicle and vehicle-to-grid (V2G)
as well as EE. The building blocks (BBs) of the proposed modes. Although V2G is currently incipient and lim-
distribution grid planning strategy and their respective ited to pilot implementations, there is increasing inter-
relationships appear in Figure 7 and are explained in more est in the utility industry and among vehicle manufac-
detail below: turers and end users in the adoption and utilization of
✔ Load, EV, and DER forecast (BB1): This is argu- this type of technology in applications like reliability
ably the most critical BB and consists of an accurate improvement and frequency regulation. (According to
and representative load and DER forecast (includ- V2G Hub, there are 142 pilot projects involving over
ing forecasting EVs, electrification in general, data 7,100 chargers in 27 countries worldwide).

S S
Scenario 1 u u
b CB1 R1 b
R2 R3 CB2
Voltage

Scenario 2 S S Vn
u u S1
b CB1 R1 b
R2 R3 CB2 S3 S2
Vmin
S S S4
Scenario 3 u u
b CB1 R1 b
R2 R3 CB2 CB1 R1 R2 R3
Distance
S S (b)
Scenario 4 u u
b CB1 R1 b
R2 R3 CB2
(a)

figure 6. The impact of EVs on distribution system reliability and contingency planning. (a) Contingency scenarios and
(b) voltage profiles.

46 ieee power & energy magazine January 2025 Show Issue

 Notably, generating this type of forecast can be time-con- business, and per capita change due in all other catego-
suming and data intensive, and this level of detail may ries) will be sufficient in the short term while the com-
not be attainable in the short term and can be considered pany develops the internal capabilities required to achieve
aspirational or a vision for mid- to long-term capabilities this mid- to long-term vision. Dominion Energy Virginia
in this area. Therefore, forecasts by aggregated ­categories is currently in the process of adopting a software solution
(e.g., total DERs, electrification, native load growth, new with the capability to produce this type of ­forecast with

Regulatory, Existing
Load, EV, and Distribution
Policy, and Distribution Grid
DER Forecast System Model
Strategy Drivers Planning
(BB1) (BB2)
(BB3) Practices (BB4)

DER and EV
Hosting Capacity
(BB5)

Risk Assessment
and
Prioritization
(BB6)

Short- and
Long-Range
Distribution Grid
Planning Scenarios
(BB7)

Distribution Grid Planning and Engineering Analyses (BB8)

Power Quality
Voltage Overcurrent
Thermal Loading, (Harmonics, Reliability and
Regulation and Protection and
CLPU, and LOL Unbalance, and Contingency
Power Factor Automation
Flicker)

Distribution Grid
Planning Impacts
(BB9)

Mitigation
Measures (BB10)

Benefit–Cost
Analysis and
Prioritization
(BB11)

Short- and Long-

Range Distribution
Grid Plans (BB12)

Updated
Distribution Grid
Planning
Practices (BB13)

figure 7. The relationship between BBs of the distribution grid planning strategy and road map for EV adoption. LOL: loss
of life.

January 2025 Show Issue ieee power & energy magazine 47

 high temporal resolution, granularity, and categorization, at the planning area or substation level). EV adoption
and it has included the activities described in this BB in is expected to become a rapidly evolving area, like
the software integration plan. what has happened over the last decade with DER
✔ Distribution system model (BB2): This BB consists of adoption. Therefore, assumptions and high-level pro-
an accurate primary (medium-voltage) feeder model jections should be reviewed periodically and updated
that includes overhead and underground lines, pro- as needed to understand how they may impact more
tective and switching devices (reclosers, fuses, and detailed EV adoption and load forecasts and distribu-
switches), voltage regulation and control equipment tion planning scenarios.
(line voltage regulators and capacitor banks), and ser- ✔ Existing distribution grid planning practices (BB4):
vice transformers. Additionally, this BB should ide- This BB consists of existing distribution grid planning
ally contain a secondary (low-voltage) system model, practices, standards, guidelines, processes, and targets
including both radial and network systems (e.g., low- pertaining to asset ratings, planning and emergency
voltage secondary networks and spot networks). The loading limits, voltage regulation limits, N-1 contin-
distribution system model must include accurate grid gency planning, reliability targets, power quality (e.g.,
topology and ratings of system components, which harmonics), voltage unbalance, the power factor, and
are key parameters needed to properly evaluate sys- so on. This information is used as a reference to assess
tem performance and its limitations. It is worth noting distribution grid performance, define planning sce-
that having this type of model can be challenging in narios, identify impacts, and verify the effectiveness
the short term since it requires addressing data integ- of mitigation measures. It is expected that as DER and
rity, completeness, and accuracy issues throughout EV adoption increase, existing practices and guide-
the geographic information system databases. These lines will be updated, and new ones will be developed.
models must be updated automatically and periodical- ✔ DER and EV hosting capacity (BB5): This BB con-
ly to include new customers, new equipment, topology sists of an evaluation of existing DER and EV hosting
changes, new EV loads, and new DERs (as generated capacity that is available in the distribution grid to in-
by load and DER forecasting analyses), and they must tegrate these technologies in the future. This evalua-
have the capability to perform annual time-series sim- tion can be conducted initially based on thermal and
ulations with hourly production/consumption curves. voltage regulation limits and expanded in the future
It is worth noting that EV impacts propagate from the (once there is an established process and experience
distribution grid edge through primary lines to sub- conducting this type of joint analysis) to account for
stations. Therefore, it is important to have the ability other parameters. The results from this BB will serve
to perform analyses of these systems using 1) a com- as a baseline for identifying potential distribution grid
bined distribution model that represents substations, components at risk of experiencing impacts due to
primary lines, service transformers, secondary lines, the adoption of these technologies, particularly in the
and customers and 2) software solutions with the abil- short and medium terms. Calculation of DER hosting
ity to conduct time-series analyses in an automated capacity is an established analysis of modern distribu-
fashion. Finally, an important additional consideration tion engineering, while EV hosting capacity remains
is the potential interaction effects between EV and an emerging area that can be used, for instance, to op-
DER operation, which can 1) mitigate or exacerbate timize the installation of EV fast charging stations. To
individual impacts, such as voltage fluctuations, and improve the accuracy and validity of the results, it is
2) create system conditions and scenarios that require important that hosting capacity analyses consider the
detailed studies, such as power quality, switching, and interactions between DER and EV technologies. This
CLPU. Studying these impacts requires having the new paradigm is necessary to accurately estimate the
ability to generate dynamic and/or transient models ability of the distribution grid to adopt these technolo-
and perform respective time-domain analyses. This is gies. Figure 8 provides an example of Dominion En-
usually a new area for most distribution grid planning ergy Virginia’s EV hosting capacity tool to help opti-
organizations, and therefore, it requires a combination mize installation of EV fast charging stations.
of training of existing resources, talent recruitment, ✔ Risk assessment and prioritization (BB6): This BB
and change management. consists of an evaluation of the risk of potential
✔ Regulatory, policy, and strategy drivers (BB3): This impacts (overloads and low- and high-voltage vio-
BB consists of assumptions, targets, and DER and lations) for substations, feeders, and individual as-
EV adoption projections at system and region levels, sets, based on the initial results of 1) DER and EV
which are based on policy, regulatory, and company hosting capacity analysis and 2) load, EV, and DER
strategy goals. These elements can be used as inputs forecasts. DER and EV hosting capacity results will
to define long-range planning scenarios and more be cross-referenced with results from load, EV, and
granular DER and EV adoption rates and trends (e.g., DER forecasts to identify areas of the distribution

48 ieee power & energy magazine January 2025 Show Issue

 grid that are likely to experience significant impacts. take preventive measures if needed, particularly in
These areas may require more specialized planning the short term.
and engineering analysis (e.g., power quality, pro- ✔ Short- and long-range distribution grid planning sce-
tection, and reliability) to assess impacts in detail, narios (BB7): This BB consists of the development of
design respective mitigation measures, and identify distribution grid planning scenarios of interest to study
investments. This evaluation will also use key risk via power flow and specialized analyses, for instance,
factors to identify and prioritize substations, feeders, 1) system performance for expected load, DER, and
and individual assets at risk of experiencing impacts EV forecasts (the base planning scenario); 2) system
due to DER and EV adoption. These assets will re- performance for specific penetration levels of DERs
quire further and more detailed planning and engi- and EVs (some of these penetration levels may exceed
neering analyses to assess impact severity and iden- the limits identified by the hosting capacity analyses,
tify respective mitigation measures. Examples of risk particularly in the long term); 3) system performance
factors include operating voltages (e.g., 5- and 15-kV under contingencies (the failure of key assets like sub-
class feeders have greater risks than 25- and 35-kV station transformers, interruption of critical custom-
class feeders), feeder lengths (e.g., longer feeders are ers, and so on); loading conditions (e.g., the impact of
more vulnerable to voltage regulation impacts), ex- extreme temperatures) and external factors (e.g., the
isting substations, and feeder and asset loading (e.g., impact of major events); 4) system performance under
feeders exceeding 80% or 90% loading with respect special scenarios, such as implementation of EE mea-
to planning limits are likely to experience loading sures, time-of-use rates, and so forth; and 5) regulatory
violations due to EV adoption). The objective of this and policy changes that may accelerate or impact EV
BB is to ensure that utilities identify and are aware of adoption. This BB will define assumptions, simplifi-
high-risk substations, feeders, and assets in general. cations, and objectives for short- and long-range plan-
This will allow utilities to pay special attention and ning scenarios of interest. Respective load, DER, and

figure 8. Dominion Energy Virginia’s EV hosting capacity tool to help optimize the installation of EV fast charging
­stations. (Source: Dominion Energy Virginia.)

January 2025 Show Issue ieee power & energy magazine 49

 EV forecasts will be generated accordingly for these to verify the effectiveness of the proposed solutions to
scenarios and used by the subsequent BBs to identify mitigate the impacts of interest.
specific impacts and mitigation measures. ✔ Benefit–cost analysis and prioritization (BB11): This
✔ Distribution grid planning and engineering analyses BB consists of an evaluation of the quantitative and
(BB8): This BB consists of performing detailed and qualitative benefits and costs of the proposed mitiga-
specialized analysis on the short- and long-term plan- tion measures, a prioritization based on their cost-
ning scenarios defined in BB7. Examples of analyses effectiveness, and a selection of final solutions to be
include 1) evaluation of thermal and voltage limits; 2) included in the short- and/or long-range plans. Expe-
CLPU and loss of life (LOL) analyses for substations, rience indicates that usually, a combination of vari-
feeders, and power and service transformers; 3) eval- ous solution types (infrastructure, technology, and so
uation of the impact of reactive power flow chang- forth) is the most cost-effective way to address im-
es on voltage regulation; 4) power quality analyses pacts, but the selected mitigation measure will depend
to verify compliance with power quality standards, on the specifics of each type of impact.
such as voltage total harmonic distortion, unbalance, ✔ Short- and long-range distribution grid plans (BB12):
and flicker limits; 5) overcurrent protection and au- The results from the previous analyses are used to
tomation; and 6) reliability and contingency analy- develop short- and long-range plans to address the
sis. Other specialized analyses may be needed on a requirements of the respective distribution grid plan-
case-by-case basis depending on the distribution grid ning scenarios. Short-range plans are tactical, address
planning scenarios of interest. Some analyses are impending needs of the distribution grid, and are usu-
more important from the standpoint of DER adoption ally developed annually, with a planning horizon of
(e.g., flicker is more likely due to output fluctuations one to five years. Examples of projects included in
caused by PV output variations), others are more im- short-range plans are new substations and feeders,
portant from an EV adoption perspective (e.g., LOL substation transformer upgrades, feeder reconduc-
is more likely due to off-peak loading increases), and toring and reconfiguration, deployment of substation
others are important for both PV and EV adoption and distribution automation, and so on. Long-range
(e.g., voltage regulation may be impacted by both distribution plans are strategic and are developed on
voltage rises due to PV interconnection and low-volt- an as-needed basis and certainly not as often as short-
age violations due to loading increases driven by EV range plans (e.g., every five years). Long-range plans
adoption). are intended to assess system performance and readi-
✔ Distribution grid planning impacts (BB9): This BB ness for longer-term time frames (e.g., 10–20 years) in
consists of reviewing results from specialized plan- the future and/or for operation under special condi-
ning and engineering analyses to identify potential tions (e.g., low-probability/frequency and high-impact
impacts (e.g., overloads, high- and low-voltage viola- scenarios) and signal changes in capital expenditure
tions, CLPU, unbalance, voltage total harmonic dis- requirements in the long term. Examples include eval-
tortion, and so on) on selected distribution substations, uating distribution system performance for 100% PV
feeders, and assets at risk. This BB also includes pri- and EV penetration scenarios or under severe weather
oritization based on the type and severity of impacts conditions (e.g., extreme temperatures). Although they
(e.g., severe overloads may have higher priority than may not drive immediate investments, long-range
minor voltage unbalance). The outcome of this BB is plans provide inputs to decision makers regarding
a list of prioritized distribution grid components that overall distribution grid needs and trends, which in
need to be further analyzed to identify potential miti- turn are used to define company strategy. Most im-
gation measures to alleviate impacts. Impact analyses portantly, results from long-term plans must also be
must consider the adoption and interactions of mul- used as a reference in developing short-term plans.
tiple DER and EV technologies, particularly between This will ensure that tactical decisions not only ad-
PVs and fleet EVs. dress emerging challenges but are also aligned and, to
✔ Mitigation measures (BB10): This BB consists of some extent, serve as a foundation to address longer-
identifying and verifying the effectiveness of potential term distribution grid needs.
mitigation measures to address the distribution system ✔ Updated distribution grid planning practices (BB13):
impacts due to load growth and PV and EV adoption. Finally, results from the short- and long-range plans
Mitigation measures may include the deployment of are also intended to be used to review and update
conventional infrastructure, technology and software distribution planning practices. This is a recurrent
solutions, NWAs, and new processes and practices. activity that is expected to become more important
Examples include capacity increases, asset replace- as the pace of change and evolution of the distribu-
ment, intelligent devices, DES, power electronics de- tion grid accelerate. For instance, practices that to-
vices, and so on. This BB includes detailed analyses day are considered standard distribution grid planning

50 ieee power & energy magazine January 2025 Show Issue

 activities, like developing DER hosting capacity maps charging their EVs when needed and will thus have a sig-
or conducting DER impact and interconnection stud- nificant impact in terms of societal cost. Hence, there will
ies, were emergent or did not exist as such 10–15 years be a need to revisit reliability and resilience performance
ago. Therefore, it is expected that over the next 10–20 targets to account for this. Finally, DER and EV adop-
years, new distribution grid planning practices will tion must be planned jointly, given the interactions and
be adopted by the electric utility industry in general. synergies between both technologies. The starting point
Examples include considering both reliability and re- for this is having a forecast with high temporal resolution
silience requirements in distribution grid planning or and spatial granularity and a span of representation for all
developing distribution grid planning guidelines or factors relevant and needed to plan the distribution grid’s
practices to prepare the grid for V2G applications or future well.
microgrids. This BB is intended to capture this peri- The distribution grid planning strategy and road map for
odic (annual) review and update of distribution grid DER and EV adoption and the IDP road map are intended
planning practices. to serve as a reference in addressing these critical issues and
prepare the distribution grid for the future.
Conclusions
Dominion Energy Virginia developed a distribution plan- For Further Reading
ning strategy and road map to prepare for the adoption of “Flexibility for integrated grid planning with distrib-
DER and EVs in its service territory. This is part of a broader uted energy resources,” IEEE Power Energy Soc., Tech.
strategy to implement an IDP approach that accounts for Rep. PES-TR 115, Sep. 2023. [Online]. Available: https://
other important factors, including the adoption of electrifi- resourcecenter.ieee-pes.org/publications/technical-reports/
cation in general and data centers. pes_tp_tr115_itslc_092723
Planning the distribution grid to prepare for DER growth “Task force on comprehensive electricity planning,” Nat.
and EV adoption is an urgent need. For instance, EV fleets Assoc. Regulatory Utility Commissioners (NARUC), Wash-
are likely to create demands that are large enough to signifi- ington, DC, USA. [Online]. Available: https://www.naruc.
cantly impact distribution feeders and substations, regardless org/committees/task-forces-working-groups/retired-task
of operating voltage. Therefore, EV fleet load forecasting -forces/task-force-on-comprehensive-electricity-planning/
and respective distribution grid planning should be regarded home/
as high priorities for utilities. Residential DER and EV L. C. Schwartz et al., “State requirements for electric dis-
adoption is expected to create impacts at the grid edge level tribution system planning,” Lawrence Berkeley Nat. Lab.,
(service transformers, secondary lines, and service drops). Berkeley, CA, USA, Mar. 2024. [Online]. Available: https://
Although these impacts may not be as severe as those that emp.lbl.gov/publications/state-requirements-electric
could affect primary lines due to EV fleet adoption, they are “Integrated distribution planning: A framework for the
likely to occur in large numbers and at multiple locations future,” Smart Electric Power Alliance, Washington, DC,
across distribution grids and thus may overwhelm plan- USA, Nov. 2020. [Online]. Available: https://sepapower.org/
ning organizations (i.e., there may not be enough resources resource/integrated-distribution-planning-a-framework-for
to address all system needs). Moreover, in many cases, the -the-future/
aggregated effect of residential DER and EV adoption may “Integrated distribution system planning,” Office of
accumulate and propagate beyond the grid edge and impact Electricity, U.S. Dept. Energy, Washington, DC, USA.
feeder and substation performance and needs. Therefore, it [Online]. Available: https://www.energy.gov/oe/integrated
is important to prevent or anticipate, to the extent possible, -distribution-system-planning
these issues caused by DER and EV adoption. This requires
switching from a reactive (i.e., run to failure) to a proactive Biographies
approach to manage grid edge assets, including using predic- Rick Siepka is with Dominion Energy Virginia, Richmond,
tive solutions to identify assets at risk and implement preven- VA 23219 USA.
tive measures. Julio Romero Agüero is with Quanta Technology, Hous-
DER and EV adoption will change key aspects of dis- ton, TX 77008 USA.
tribution grid planning and engineering. For instance, the Marty O’Baker is with Dominion Energy Virginia,
value of reliability and resilience will both increase as Richmond, VA 23219 USA.
EV adoption grows since transportation is arguably one of Don Hall is with Quanta Technology, Raleigh, NC 27607
the most essential capabilities our society depends upon, USA.
and it thus adds to the values that other more traditional H. Lee Willis is with Quanta Technology, Raleigh, NC
end uses have long put on electric power availability. 27607 USA.
p&e
Long-duration interruptions can prevent customers from 

January 2025 Show Issue ieee power & energy magazine 51

 Beyond
Demand
Response
Integrating Aggregated DERs in Western
Australia’s Wholesale Electricity Market

By Andrei Costache , Bruce Redmond,

Jean-Philippe Montandon, and Dean Sharafi

T
THE WHOLESALE ELECTRICITY MARKET (WEM) tion, the SWIS has experienced new challenges in balancing
supplies electricity to the South-West Interconnected Sys- supply and demand and maintaining power system security
tem (SWIS), which serves more than 1.2 million businesses due to lower minimums, higher peaks, and steeper ramping
and households across Western Australia. The SWIS is an events. Peak demand drivers are the hot weather resulting in
islanded system, with no connection to the National Elec- load increases for cooling and the volatility of uncontrolled
tricity Market, which supplies electricity to the east coast residential rooftop photovoltaic (PV) generation. Minimum
of Australia. Today, wind and solar generation in the WEM demand rests mainly with rooftop PV generation on mild tem-
account for one-third of the annual energy supply, peaking perature days during spring as ramping events are typically
at about 84% instantaneous generation. caused by large bands of clouds sweeping across the SWIS.
Traditionally, electricity system operators focused more on
Introduction addressing the peak demand issue, but experience from recent
As Western Australia continues to grow and the electric- years shows that low load and volatility of distributed energy
ity system transitions to higher levels of renewable genera- resources (DERs) are risks that need to be managed.
Figure 1 shows that the SWIS minimum demand of
Digital Object Identifier 10.1109/MPE.2024.3457542
595 MW occurred in September 2023, coupled with a peak
Date of current version: 11 November 2024 of 4,233 MW during the recent February 2024 heat waves,

52 ieee power & energy magazine 1540-7977/24©2024IEEE January 2025 Show Issue
and showcases the dramatic load differential that has come were successfully aggregated and orchestrated into a vir-
to define the operational requirements of the network. The tual power plant (VPP). Located in Southern River, an area
supply of clean energy from solar generation needs to be bal- of Perth where 60% of households host rooftop solar, the
anced with the need to mitigate its intermittency and man- VPP enabled aggregation and the dispatch of energy in an
age its adverse security impact on the power system. approach that is more similar to a conventional power plant.
The orchestration effort represents a collaboration
What Are DERs, and Why Should They Be between AEMO as the distributed market operator (DMO),
Aggregated and Orchestrated? Synergy as the aggregator, and the residential retail company,
The 2019 “Integrating Utility Scale Renewables and Dis- Western Power, as the network operator. This was overseen
tributed Energy Resources in the SWIS” Australian Energy by the government’s energy policy maker, Energy Policy
Market Operator (AEMO) report set the foundations for the WA, in a supervisory role. The project received support from
Energy Transformation Strategy and the DER Roadmap. the Australian Renewable Energy Agency (ARENA) as part
Updated in 2021, the SWIS Renewable Energy Integration of ARENA’s Advanced Renewables Program. The orchestra-
report details the challenges of integrating large amounts of tion objective was to provide a better understanding of how
renewable energy in the power system. A policy recommen- DERs can be integrated at scale and provide a safe, reliable,
dation was to incentivize frameworks for DER participation, and efficient electricity system while at the same time provid-
leading the way for the development of Project Symphony, ing value to all customers.
the DER Orchestration Pilot. The 2024 WEM Electricity Statement of Opportuni-
Approximately 900 individual customer DER assets, ties forecasts that the rooftop PV uptake in the SWIS
including rooftop solar, household- and network-connected will increase from 3 GW in 2024 to more than 6 GW
batteries, and appliances across 500 homes and businesses, in 2033. All passive DERs, consisting of rooftop PV,

5,000 5 GW Underlaying Demand

4,500 WEM Highest Operational Emergency State
4,000 Demand Record Issued
4,233 MW
3,500
18 Feb. 2024
3,000
MW

2,500
Low Reserve Condition
2,000
1,500
Due to volatility in distributed PV, system
1,000 frequency deviated outside the normal
band to 49.56 Hz and recovered to
500
normal band within 8 min.
0
(a)
5,000
4,500
4,000
3,500 WEM Minimum Operational
Demand Record
3,000 595 MW
25 Sep. 2024
MW

2,500
2,000
1,500
1,000
500
0
00
00
00
00
00
00
00
00
00
00

0
0
0
0
0
0
0
0
0
0
0
0
0
0
:0
:0
:0
:0
:0
:0
:0
:0
:0
:0
:0
:0
:0
:0
0:
1:
2:
3:
4:
5:
6:
7:
8:
9:
10
11
12
13
14
15
16
17
18
19
20
21
22
23

(b)

Coal Gas Landfill Gas Distillate Wind Solar Rooftop PV Battery Operational Demand

figure 1. (a) and (b) SWIS records. (Source: AEMO).

January 2025 Show Issue ieee power & energy magazine 53

 residential battery energy storage systems (BESS), air will value-stack revenue streams above the basic customer
conditioning, and water heating, are currently operating value proposition to offset purchases from the grid.
unconstrained with the purpose of covering residential
customer needs. Tests have demonstrated that, through How Can Aggregated DERs Provide
market price integration and active dispatch, it is pos- Value? Provide Visibility and Energy
sible to unlock the full benefit of residential DER assets When the Market Needs It
and achieve better alignment between the energy market Through the VPP control, aggregated DERs provided
and household optimization. energy injection and load in response to the market price
One solution that can unlock the potential value of by submitting bids and bi-directional offers (boffers) into a
DERs and enable Australians to access affordable, secure, simulated real-time wholesale market. The DMO received
and reliable energy is, in simple terms, storage of excess PV and cleared these boffers according to market price, subse-
generation at noon for use in the evening peak. Leveraging quently sending dispatch instructions to aggregated DERs to
market prices into the decision chain has the potential to respond as directed.
enable DER aggregators to generate revenue in the energy This demonstrated the requirement for aggregated DERs
market, above the cost savings that customer optimization to submit boffers, enabling its dispatch by the market operator
alone can provide. through participation in the energy market. Figure 2 show-
The following range of capabilities was identified for cases this behavior. BESS assets are charged overnight, in
aggregated DERs to participate in and interact with the mar- anticipation of high morning prices. The price increases dur-
ket and power system: ing the morning peak—but not enough to reach the injection
✔ provide market visibility of aggregated DER genera- offer price, so no injection occurs. Toward noon, the price
tion/load in close to real time becomes negative due to excess PV generation in the network.
✔ provide energy and capacity when the customer needs it The price is low enough to clear the offered load, resulting
✔ provide/curtail energy and capacity in response to in the aggregated BESS being charged from the network. PV
market signals generation is also curtailed due to the negative price. When
✔ provide/curtail energy and capacity when the system the BESS assets are fully charged, only PV remains curtailed.
needs it Once the price increases above zero, PV curtailment stops,
✔ provide/withdraw energy and capacity when the local and uncontrolled injection occurs. High evening prices result
network needs it in the aggregated BESS being dispatched to inject. Due to the
✔ quickly inject/withdraw energy in response to system sustained high evening price, the BESS is depleted of stored
frequency excursions. energy, and the underlying aggregated DERs become a load.
Demonstrating the aforementioned capabilities will By taking advantage of fluctuations in the market price,
inform the transition of DERs from being a passive resource the pilot showed that aggregated DERs could access revenue
to an active orchestrated resource visible to the market, net- sources that had previously been inaccessible to residential
work, and power system. Existing and future market services customers. As aggregated DERs scale up, the benefits of such

3,000 350
2,500 Injection 300
2,000 250
1,500
200
Price ($/MW)

1,000
150
kW

500
100
0
50
–500
–1,000 0
Withdrawal –50
–1,500
–2,000 –100
0

00
00

0
0

0
:0

:0

:0
:0

:0

:0

:0

:0

:0

:0
:
:
00

10
04

06

08
02

12

14

16

18

20

22
07

7
7

7
07

07

07

07

07
/0
/0

/0

/0

/0

/0
/

/
01

01
01

01

01
01

01

01

01

01

01

01

Solar Load Residential BESS FOM BESS Aggregated Telemetry Energy Dispatch Energy Price (rhs)
Instruction

figure 2. Bidirectional energy market order (BMO). FOM: front of meter.

54 ieee power & energy magazine January 2025 Show Issue

 Once market price clearing occurs, the aggregated DER facility is
enabled to provide the service through dispatch and predispatch
instructions from the DMO.

orchestrated actions are addressing both low and peak demand dropped below the 49.975 Hz threshold, the aggregated DER
issues without the need for less flexible time-of-use tariffs. facility responded by injecting energy proportional to the
frequency deviation.
Rapidly Inject or Withdraw Energy The results in Figure 3 show that the network-connected
in Response to System Frequency front-of-meter 1 MW BESS, shown in blue, was idle at the
Excursions time of the frequency deviation. Its inverter responded by
The capability of aggregated DERs to participate in the injecting 0.313 MW in proportion to the frequency devia-
Contingency Reserve Raise (CRR) service represents the tion. Before the frequency event, the behind-the-meter
automatic provision of energy injection in response to a drop 0.25 MW BESS, shown in green, was injecting to cover a
in network frequency. Such contingencies occur when large site load. Its droop response represents the additional energy
generators or network lines trip. Through local control, gen- that is injected on top of the existing discharge rate, maxing
erators and BESS assets need to curtail load and increase out at 0.25 MW. Unfortunately, because the frequency devia-
generation proportionally to the frequency deviation, a capa- tion occurred overnight, no residential BESS had sufficient
bility known as the droop response. state of charge to respond. Overall, the ability of aggregated
The CRR quantity is offered into the market through bof- DERs to participate in essential system services by provid-
fer submissions, similar to the energy scenario described ing CRR responses to credible frequency events from a mix
before. Once market price clearing occurs, the aggregated of BESS assets was successfully demonstrated.
DER facility is enabled to provide the service through dis-
patch and predispatch instructions from the DMO. In con- Provide or Curtail Energy and Capacity
trast to the energy scenario, the CRR response is energy kept When the Distribution System Needs It
in reserve until required due to a system frequency event. Population expansion, housing infill, and access to low-cost
The CRR service was demonstrated on a number of occa- electrical appliances all contribute to the increasing demand
sions, including at 1:14 a.m. on 6 July 2023. A power station on the distribution network. To operate within the distri-
trip resulted in the loss of approximately 260 MW, which bution network safety limits, the network operator needs
depressed the system frequency from its normal operating to augment network capacity by building more poles and
range of ~50 Hz down to 49.66 Hz. Once the frequency wires as well as transformer upgrades. The financial cost of

350 50.1

300 50
250
49.9
200
49.8
kW

150
Hz

49.7
100
49.6
50

0 49.5

–50 49.4
1:10:00
1:10:21
1:10:42
1:11:03
1:11:24
1:11:45
1:12:05
1:12:26
1:12:48
1:13:08
1:13:30
1:13:50
1:14:11
1:14:32
1:14:53
1:15:14
1:15:35
1:15:55
1:16:17
1:16:38
1:16:59
1:17:20
1:17:41
1:18:02
1:18:23
1:18:44
1:19:05
1:19:26
1:19:46
1:20:08
1:20:29
1:20:49
1:21:10
1:21:31
1:21:52
1:22:13
1:22:34
1:22:55
1:23:16
1:23:37
1:23:58
1:24:19
1:24:40

1 MW Front of Meter BESS (kW) 0.250 MW BTM BESS (kW) Frequency (Hz)

figure 3. CRR response.

January 2025 Show Issue ieee power & energy magazine 55

 these upgrades is significant and serves to cover load condi- activation period. The aggregated connection point teleme-
tions that typically occur for a few hours across a handful try response is overlayed with the contribution of residential
of days each year. The increasing adoption of DERs, such and front-of-meter BESS assets. When the NSS deployment
as customer-owned battery energy storage, provides another signal was active, aggregated DERs injected energy at the
option for the network operator to address network con- asset level, which was close to the requested service amount.
straints through the aggregation and orchestration of DERs. The difference between BESS and aggregated con-
Together with demand management, whereby loads are nection point telemetry is consumed by the behind-the-
reduced by controlling devices, such as air conditioning and meter household loads. This statement holds true from
pumps, aggregated DERs can be utilized as a Network Sup- when the NSS was first deployed at approximately 6.15
port Service (NSS) to alleviate local constraints at a lower p.m. until approximately 8.00 p.m. After this point, the
cost than traditional augmentation solutions. VPP response fell, with a large gap between the response
The NSS deployment shown in Figure 4 represents the and the NSS deployment signal as battery storage was
amount of energy that BESS assets need to provide and the depleted.

2,000 8,000

1,500 6,000

1,000 4,000

500 2,000
kW

kW
0 0

–500 –2,000

–1,000 –4,000

–1,500 –6,000

–2,000 –8,000
0

/0 :05
5

/0 :15

/0 :40

/0 :30

5
0

5
/0 :50
5

/0 :20
:2

:2

:5
:0

:2

:5

:1

:4

:0

:3

:5

:4

:1

:0

:4
:3
18

01 22
20

01 21

01 21

01 22

01 22
15

15

15

16

16

17

17

17

18

19

20

23
01 20
19

01 23
7

7
7

7
7

7
7
7

7
/0

/0
/0

/0

/0

/0

/0

/0

/0

/0

/0

/0

/0

/0
/0

/0
01

01
01

01

01

01

01

01

01

01

01

01

01

01
01

Solar Load Residential BESS FOM BESS NSS Aggregated Distribution Feeder
Telemetry (RHS)

figure 4. NSS. RHS: right hand side.

600 700
Price CTZ CTZ CTZ CTZ CTZ CTZ Price
Gross Net Gross Gross Net Gross 600
Solar Irradiance (W/m2)

400 BTM Zero BTM Zero

Assist Assist Assist Assist 500
200
400
0
kW

300
–200 200
–400 100

–600 0
0

20 9:00

20 9:30

00

23 30

0
:0

:3

20 0:0

:3

:0

:3

:0

:3

:0

:3

:0

20 5:3

:0

:3
2:

:
08

08

10

11

11

12

13

13

15

16

16
0

1
23

23

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23

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23

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23
17 202
20

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/2
5/

5/

5/

5/

5/

5/

5/

5/

5/

5/

5/

5/

5/

5/

5/

5/

5/
05
/0

/0

/0

/0

/0

/0

/0

/0

/0

/0

/0

/0

/0

/0

/0

/0

/0
/
17

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17

17

17

17

17

17

17

17

17

17

17

Energy Dispatch Instruction Aggregated Telemetry Average_Solar Irradiance

figure 5. CTZ.

56 ieee power & energy magazine January 2025 Show Issue

 In the bottom part of Figure 4, the distribution feeder actors in the system, including the market and network oper-
load shows a reduction over the BESS injection and NSS ators, aggregators, and residential consumers. An important
service deployment. As such, this example clearly demon- recommendation is the implementation of a DER participa-
strates the ability of aggregated DERs to provide services to tion framework to accommodate the technical capabilities
the distribution network as a potential future value stream of aggregated DERs while enabling access to multiple value
for DER aggregators. streams.
Increased participation from customers and their DER
Curtailing Energy When the Transmission assets via products offered by aggregators and participa-
System Needs It tion in multiple market services will be critical in the sup-
The capability of aggregated DERs to curtail or “turn down” port of power system security and reliability into the future.
residential solar PV output in a controlled manner to support Furthermore, enabling this outcome will deliver substantial
the power system was also tested. This capability reduced the power system savings and generate value for stakeholders
export of electricity into the grid during the middle of and consumers.
the day. Constrain to zero (CTZ) was designed as a back-
stop solution to low operational demand events in the SWIS. For Further Reading
CTZ Net is the direction for residential sites to stop injecting “Integrating utility scale renewables and distributed en-
energy into the grid (reduce export), and CTZ Gross is the ergy resources in the SWIS.” AEMO. Accessed: Apr. 5,
instruction to stop PV generation at the asset. 2024. [Online]. Available: https://www.aemo.com.au/energy
From left to right, the results in Figure 5 show that -systems/electricity/wholesale-electricity-market-wem/
✔ Negative prices dispatch BESS charging from the grid. system-operations/integrating-utility-scale-renewables-and
✔ The CTZ Gross BTM Assist command stops PV gen- -distributed-energy-resources-in-the-swis
eration entirely. AC-connected PV and BESS assets “Distributed energy resources roadmap.” AEMO. Accessed:
systems can achieve net zero export using the BESS Apr. 5, 2024. [Online]. Available: https://www.wa.gov.au/
to cover BTM loads. DC systems have a harder time government/distributed-energy-resources-roadmap
modulating the shared inverter to cover the household “Renewable energy integration – SWIS update.” AEMO.
load. This can result in small over/under-generation at Accessed: Apr. 5, 2024. [Online]. Available: https://
the connection point over the entire aggregation. aemo.com.au/-/media/files/electricity/wem/security_and
✔ The CTZ Net command limits PV export to cover _reliability/2021/renewable-energy-integration--swis-update.
BTM loads, with dc-connected systems exporting a pdf?la=en
small amount of energy. “Western Australia distributed energy resources orches-
✔ The CTZ Gross Zero Assist curtails PV and BESS tration pilot (Project Symphony),” Australian Renewable
completely, thus exposing the BTM load at the con- Energy Agency, Canberra, ACT, Australia, Feb. 10, 2024.
nection point. This additional load is most useful in [Online]. Available: https://arena.gov.au/projects/western
low-load conditions and system restart scenarios, -australia-distributed-energy-resources-orchestration-pilot/
where a stable load is needed to re-energize the grid. “2024 wholesale electricity market electricity statement
of opportunities.” AEMO. Accessed: Apr. 5, 2024. [Online].
Conclusion Available: https://aemo.com.au/-/media/files/electricity/wem/
AEMO’s WEM Electricity Statement of Opportunities fore- planning_and_forecasting/esoo/2024/2024-wem-electricity
cast identifies highly distributed PV, battery storage, and -statement-of-opportunities.pdf?la=en&hash=6B9DD8B889
electric vehicle uptake over the next 10 years in Western C7EE8B280475DEC8F655FA
Australia. The cost-benefit analysis identified that without “Project symphony – Cost benefit analysis and recommen-
action, the operation of the electricity market in Western dations report,” Australian Renewable Energy Agency, Can-
Australia will become more expensive for all consumers if berra, ACT, Australia, 2024. [Online]. Available: https://arena.
DERs are not effectively integrated and operated into the gov.au/knowledge-bank/project-symphony-cost-benefit
system. Without DER integration, a larger number of grid -analysis-and-recommendations-report/
storage assets and firming services will be required so that
the grid operates “around” or “in spite of” distributed resi- Biographies
dential PV generation. The analysis showed that, for aggre- Andrei Costache is with AEMO, Perth 6164, Australia.
gated DERs providing several market and network services, Bruce Redmond is with AEMO, Perth 6050, Australia.
the combined cashflows for all actors increase year on year Jean-Philippe Montandon is with AEMO, Perth 6163,
with a net present value of US$450 million over 10 years. Australia.
A framework is required to encourage and enable aggre- Dean Sharafi is with AEMO, Perth 6152, Australia.
gated DERs to be orchestrated to participate in the electric-
p&e
ity market. This will maximize and distribute value to all 

January 2025 Show Issue ieee power & energy magazine 57

 Data
Fusion and
Interoperable
Control
Engineering the Grid of the Future

By Gowtham Kandaperumal, Bo Chen, Keith DSouza,

Ruoxi Zhu , and Brooks Glisson

A
AT THE INTERSECTION OF CUSTOMER INTER- architecture that fosters the integration of variable renew-
est in energy efficiency and increased electrification and able energy resources at the distribution level and the inte-
utility interest in distributed energy resources (DERs) gration of electric vehicles (EVs), electrified loads, and
lies a prospective landscape featuring the “utility of the other grid edge devices.
future.” This vision encapsulates a grid that not only sup- The current state of the art approaches this problem in a
ports decarbonization goals, electrification, and adoption piecemeal fashion. Incentives are in place for customer-side
of distributed and renewable energy resources but also adoption of load and vehicle electrification. Centralized
improves grid reliability and resilience while ensuring that decision support is provided at the distribution control cen-
customers evolve into consumption-conscious prosumers. ter level by state estimation, event, and fault detection func-
As utilities navigate this transition, they encounter several tions. Controls are established at a utility program level by
roadblocks that must be managed carefully. Utilities need use cases and their supporting actuating devices. Advanced
to manage this communication-enabled, data-intensive, metering devices stream interval data to a centralized data
and grid edge-controllable grid through an interoperable repository upon which analytics and visualization applica-
tions are developed. This prevailing strategy that involves
Digital Object Identifier 10.1109/MPE.2024.3470729
deconstructing the issues into manageable tasks that can
Date of current version: 11 November 2024 be tackled in a step-by-step manner, though demonstrating

58 ieee power & energy magazine 1540-7977/24©2024IEEE January 2025 Show Issue
operational success, does not address the issues of integra- conditions of the grid, improving visibility and opening
tion and scalability of big data applications, cybersecurity, avenues for multiple cross-functional use cases. Large data
and interoperability in the rapidly changing grid environ- streams from high-resolution PMUs were piloted through
ment. cloud-hosted real-time sensor data services, and applica-
This article introduces Commonwealth Edison’s (ComEd’s) tions, such as distribution linear state estimation (DLSE),
approach to identifying a solution to the roadblocks. The were validated through field deployments.
approach, as shown in Figure 1, utilizes commercially avail- Technological advancements in distribution system oper-
able technologies to foster distributed intelligence (DI) ation through data fusion and interoperable control herald a
through grid edge sensors and controllers. The stage for this new era of economic opportunities and customer benefits, in
demonstration project is the city of Rockford, IL, USA. These addition to promising a more robust and cleaner grid. The
sensors and controllers are designed to measure, optimize, transition is poised to create a new class of smart grid jobs,
and automate the operations of various DERs, such as vehi- stimulate innovation, transform customers into sophisticated
cle-to-grid-enabled EVs, grid-interactive efficient buildings energy users, and, most importantly, promote sustainability
(GEBs), distributed behind-the-meter (BTM) solar installa- and clean energy technology adoption by bridging the gap
tions, and energy storage systems. Incorporating intelligent between utilities and their decarbonization goals.
sensors and advanced data fusion techniques, the framework
enhances data interoperability. This, in turn, facilitates pre- The Need for Data Fusion and an
dictive analytics, augments grid maintenance, and advances Interoperable Control Framework
system reliability and resilience. Ultimately, it paves the way There are no standard established templates that utilities
for increased integration of EVs, solar power, and other DERs can adopt for their evolving grid. Variables, such as geog-
into the energy ecosystem. raphy, population metrics, infrastructure design and health,
ComEd serves more than 4 million customers and a pop- extreme weather, and other high-impact events, play a role
ulation of 9 million people, and its 11,400-mi2 service region in how grid modernization efforts are implemented. For
showcases a plethora of emerging technologies. Advanced Rockford, a city of over 150,000 people, these challenges
controls supported by its Distributed Energy Resource Man- represent only a part of the story. There has been a marked
agement System (DERMS) program support DER integra- increase in DER adoption in the city, as charted in Figure 2,
tion, community microgrids, EV fast charging, and energy along with the resurgence of Rockford as an industrial center
storage solutions. Advanced sensors, such as phasor mea- in Illinois, in part due to city and state efforts to increase
surement units (PMUs) and power quality (PQ) meters, have investments that strengthen equitable access to affordable
been installed and provide valuable insights into operating power, cleaner technologies, vehicle electrification, and

Advanced Communication Line Sensors Distribution Automation

• Fiber Optic • PMU • Point-On-Wave Controller
• Cellular • PQ Meter • Fault Interrupters

Grid Edge Devices

• DI Meter • EV Chargers
• BTM Controller • Mobile Battery Energy Storage
System

Analytics Platform Interoperable Control Platform

• Data Fusion and Visualization • Interoperable Control for Grid and Edge Devices
• Use Case Development • System Integration

figure 1. An overview of the framework. PMU: phasor measurement unit; PQ: power quality; DI: distributed intelligence.

January 2025 Show Issue ieee power & energy magazine 59

 sustainable jobs for the community at large. This forecast enable safe, rapid, and cost-effective distribution automa-
increase makes it imperative to evaluate optimal solutions tion implementation.
that support continued growth while concurrently enabling
grid modernization benefits. Fast Two-Way Communication Network
This effort can serve as a template for other utilities seek- Another critical element of a smart grid is the communica-
ing to deploy advanced grid edge technologies with the aim tion backbone to enable low-latency data for advanced ana-
of maintaining balance between generation and load during lytics functions. The smart grid’s two-way communication
uncertainty, resulting in increased grid efficiency, resiliency, network, enabled through the deployment of advanced and
and the integration of variable renewables into the grid. secured communication infrastructure, allows the secure
Broadly, the deployments of advanced grid edge technolo- interaction of various stakeholders in the grid. Smart grid
gies are envisioned to enable the following functions and domains and subdomains will use a variety of private and
improve grid flexibility accordingly. public communication networks, both wireless and wired.
This variety of networking environments is foundational
Data Fusion to identifying performance metrics, maintaining appro-
A data fusion platform integrates and processes data from priate security and access controls, and validating core
multiple sources to provide a unified and comprehensive operational requirements of various applications, users,
view. In the context of power systems, a data fusion plat- and domains.
form is needed for several reasons, including integration of The two-way communication network of the smart grid
DERs, enhancing grid visibility and control, and improving puts the end users back at the heart of energy decision mak-
reliability and resilience. On the one hand, the proposed data ing and manages the technical structure of the grid in the
fusion platform will integrate data from DERs with tradi- most effective way. This creates the conditions for connect-
tional grid-synchronized data, providing a coherent view ing new users while maintaining the stability and continuity
that helps in managing generation and load balancing effec- of supplies. The bidirectional communication capabilities
tively. On the other hand, enhanced grid edge intelligence, will unlock the ability to provide interval data and real-
powered by data fusion, will provide unprecedented grid time pricing information to customers and enable advanced
visibility through advanced sensors, smart meters, and other demand response programs and strategies that characterize
devices, and it will facilitate the integration of managed EV basic smart grid objectives for utilities as well as industrial,
charging into the real-time network. business, and residential customers. Furthermore, demand
response promotes the responsiveness and interaction of
Interoperability customers and may offer a broad range of potential benefits
Interoperability defines the open architecture of tech- for system expansion and operation. The benefits include
nologies and their software systems to allow their inter- customer savings, reliability, improved customer choice,
action with other systems and technologies. To realize market performance, and system security.
the capabilities of a smart grid, technology deployments
will connect large numbers of smart devices and systems Enhancing Smart Grid Visibility With Advanced
involving software and hardware. Advanced metering Sensor and Controller Deployments
strategies, distributed generation, DERs, and EVs are Utilities can deploy high-resolution sensors to report real-time
essential parts of a smart grid, and their interoperability conditions along feeders and monitor essential components
is a requirement. Improved cybersecurity and interoper- that enable rapid diagnostics and precise solutions appropri-
ability standards, protocols, tools, and techniques will ate for any event. This will enhance the smart grid’s visibility
beyond substation assets and improve features, such as resist-
ing cyberattacks and self-healing, and provide higher-quality
1,000
power that will save money lost on outages, motivate consum-
100 ers to actively participate in grid operations, and accommo-
10 date all generation and energy storage options.
MW

To implement such a framework, deployments that include

1
line and grid edge sensors, such as micro-PMUs and PQ
0.1 meters; DI smart meters; and controllers, associated com-
0.01 munication infrastructure, the data fusion platform, and the
2008 2010 2012 2014 2016 2018 2020 2022
software solutions that coordinate and integrate grid and
Interconnected Interconnected customer-side functions are quintessential. These devices
Applications MW will enable automatic or minimal interaction, monitoring,
and control of the grid and grid edge devices, which will
figure 2. The exponential growth in photovoltaics in improve the reliability, safety, security, flexibility, and
Rockford. resiliency of the grid and unlock additional DER growth

60 ieee power & energy magazine January 2025 Show Issue

potential. Utilities and regulators endeavoring to investigate A Data Fusion-Empowered Interoperable
and implement such frameworks would reap a wide range of Control Framework
benefits, including but not limited to the following: To pave the way for next-generation power grid planning and
✔ Integration of renewables and electrification: This operation while maximizing community benefits, a frame-
addresses the complexities and uncertainties brought work that leverages data fusion and interoperability may be
on by the widespread adoption of DERs and EVs. the ideal solution. The framework will track recent devel-
✔ Scalability: This addresses the need for enhanced data opments in emerging technologies in the distribution grid
handling and storage capabilities that are necessitated domain and be developed on the deployment of a portfolio
by the advanced sensors and controllers installed across of grid edge technologies and supporting communication
the distribution network. It highlights the significant and data infrastructure to facilitate both utility-side and
challenges in big data management and the concerns customer-side functions.
arising from the increasing number of variables. The technical solution architecture, described in Fig-
✔ Empowerment: This enables customers to be sophis- ure 3, includes four fundamental pillars, which cover the
ticated energy consumers by providing visibility deployment of the following:
and insights into energy use and to pursue energy-­ ✔ The physical pillar: hardware sensors and grid edge
efficient solutions. controllers
✔ Security: This preserves cybersecurity amidst an ex- ✔ The communication pillar: multitechnology multipro-
panding network of sensors and controllers, thereby tocol communication architecture
reducing the risk of cyberthreats. ✔ The information pillar: associated data infrastructure
✔ Visibility: This offers enhanced system monitoring and platforms
for a comprehensive understanding of the grid’s state, ✔ The control pillar: where software solutions for grid and
aiding operators in anticipating and managing service customer functions are integrated and coordinated.
disruptions. The technical solution traces the trajectory of the modern
✔ Reliability: This supports advanced local voltage con- smart grid evolution, where visibility and control of all com-
trol through new deployments, addressing PQ issues ponents is not only possible but is ultimately expected.
and enhancing grid reliability and service availability.
✔ Resilience: This improves the grid’s response to major Pillar 1: Advanced Sensor and Grid Edge
disruptions by facilitating efficient system disturbance Device Deployment
diagnosis, robust during-event operations to prevent Grid visibility is essential to grid operators to respond in a
fault cascades, and expedited postevent recovery. timely and efficient manner in the face of abnormal conditions.
✔ Safety: This utilizes a data fusion platform for real- The community impacts of extended power outages and severe
time analytics, enabling predictive maintenance of PQ issues can be mitigated by enhancing grid visibility and con-
system health; reducing risks to repair crews, custom- trollability. However, with a variety of grid edge technologies
ers, and the public; and minimizing emergency work procured from different vendors that are expected to be ubiq-
and its associated costs and safety hazards. uitously deployed in utility territories and customer premises,
✔ Flexibility: This aims to enhance the grid’s capacity to interoperability is imperative. The foundation of the interop-
integrate DERs, providing economic and environmen- erability framework lies in the deployment of sensors that
tal benefits, reducing carbon intensity, and supporting enhance grid visibility and controllability. In pillar 1, advanced
decarbonization through the adoption of electrifica- grid sensors and grid edge devices will be deployed to provide
tion solutions like heat pumps and induction stovetops. both improved grid visibility and flexible operation solutions.

Grid Visibility Grid Resilience and Reliability Energy Efficiency

• PQ Monitoring • Fault Detection and Location • Virtual Power Plant
• Anomaly Detection • Asset Health Monitoring • EV Charging Management
• BTM Detection • Flexible Operation • Renewable Integration

Pillar 1 Pillar 2 Pillar 3 Pillar 4

Sensor and Grid Edge Advanced Data Fusion and Interoperable Control
Devices Communication Analytics Platform Framework

figure 3. A block diagram of the framework.

January 2025 Show Issue ieee power & energy magazine 61

 The sensors on the line and grid edge, depicted in Fig- ✔ GEB controller and open home energy management
ure 4, to be deployed are as follows: systems: These are typical smart building technolo-
✔ Dual-use PQ meters and distribution PMUs: gies to improve energy efficiency and flexibility. They
• PQ meters are capable of accurately measuring can collect and analyze energy usage data (from the
high-frequency power phenomena and interactions building level to the device level) to provide more vis-
in electrical systems. Harmonics may be emerg- ibility as well as to provide controllability over build-
ing as a significant PQ issue due to the incorpora- ing heating, ventilation, and air conditioning (HVAC);
tion of inverter-based resources, such as DERs, EV lighting; plug load controls; managed EV charging;
chargers, electronic lighting and loads, and so on. and behind-the-meter DERs.
Excessive harmonics may cause severe damage to ✔ EV charging management and vehicle-to-everything
both grid and customer assets, which could hinder controllers: Bidirectional charger hardware, includ-
renewable integration and EV adoption. PQ meters ing home hub technology, bidirectional inverters, and
can aid in identifying and locating devices that are dark start batteries for each bidirectional system, will
not only injecting harmonics but driving PQ issues be deployed.
on the grid. ✔ Point-on-wave controllers: These are sophisticated
• Micro-PMUs can measure multiple electrical sig- control devices that are capable of disconnecting from
nals (magnitude and phase angles of currents and (or connecting to) the grid at a precise point on the
voltages and frequency) with high accuracy and sine wave. These devices greatly help in managing
speed across all three phases. A broad deployment and mitigating transients, impulses, ringing, and other
of these devices can provide rich time-synchronized switching-related phenomena, thereby improving the
data sources that improve grid visibility. quality of power in the system. As a result, these de-
✔ DI-advanced metering infrastructure (AMI): This re- vices help improve and extend the life of distribution
lates to next-generation smart meters with high-perfor- system assets like transformers, capacitor banks, and
mance onboard processing that can run applications at other switching devices, promoting the reliability and
the grid edge (edge computing capabilities) and provide longevity of these assets.
decision support analytics or assist with making opera-
tional decisions. DI-AMI can support several applica- Pillar 2: Communication Infrastructure
tions, such as active power and voltage monitoring, Deployment
dynamic hosting capacity calculations, BTM photovol- A secure and rapid communication network, encompassing
taic and EV detection, and high-impedance and neutral technologies such as optical fiber, 4G/5G LTE, and Wi-Fi,
fault detection, and directly improve grid reliability and will facilitate the efficient collection of data from the sen-
customer safety. sors and the management of control systems. PMUs generate
The following list of grid edge controls will be deployed to high-resolution distribution data and require robust fiber
facilitate smart grid functions, such as renewable energy inte- networks for relaying data to the data fusion platform, while
gration, electrification, and customer engagement through other technologies that do not require stringent latency and
smart building and metering technologies: bandwidth will be connected via LTE. At the customer level,

Vehicle-to-Everything
Micro-PMU PQ Meter Smart Building
Controller
Controller DI Meters

EV Fleet Charger GEBs DERs Smart Appliances with

Home Energy
Management System

(a) (b) (c)

figure 4. Pillar 1: Advanced sensor and grid edge device deployment. The (a) distribution system, (b) electrification, and
(c) community assets.

62 ieee power & energy magazine January 2025 Show Issue

home/building area networks operating via Zigbee or Wi-Fi a wide range of devices, including smart meters, sensors,
need to be established. The overall communication archi- DERs, EVs, and demand response systems, the data gen-
tecture will support bidirectional communication, real-time erated by each device and the data’s associated pipeline
monitoring, and control. will vary in format and protocol, respectively. Also, the
The envisioned architecture, reviewed in Figure 5, will need to process data in real time for real-time decision
enable grid modernization efforts by providing avenues for making is critical. This requires robust and scalable data
data analytics, advanced control schemes, visualization, fusion architectures capable of handling high-volume
energy monitoring algorithms, system diagnostics, and even data streams with low-latency data processing. Also, cur-
customer outreach. Along with the advent of the Internet of rent data fusion technologies often require custom soft-
Things, the multitechnology, multiprotocol, and multidevice ware, which hinders integration with other, especially
smart grid communication scheme will allow coordinated legacy, systems.
responses starting at the residential level with, say, a cus- The new data fusion strategy will enable utilities to
tomer’s smart refrigerator and extending all the way to the align software solutions for data storage, edge comput-
utility’s medium-voltage substation. ing, and local analytics more systematically while also
increasing system flexibility with DER and EV penetra-
Pillar 3: Data Fusion Infrastructure tion. The proposed data flow platform will process and
and Grid Edge Analytics Platforms analyze data in a unified manner and help with optimiz-
Pillar 3 encompasses the deployment of a unified data ing resource use, reducing operational costs, and improv-
fusion architecture and associated analytics platforms, as ing asset management. Also, it will consolidate data from
seen in Figure 6. Data fusion allows for the utilization of various sources to ensure accurate compliance reporting.
the extensive data generated by field sensors, inverter- This initiative will bolster the interoperability and data
based DERs, and customer-side devices (DI-AMI and architecture of systems that support the bidirectional flow
GEBs). The deployment of data fusion platforms in mod- of electric power and localized analytics, delivering valu-
ern power distribution systems presents challenges. The able information between electricity system operations
dynamic nature of power systems, the diversity of data and consumers. Advanced applications utilizing machine
sources, and the need for robust integration increase the learning and artificial intelligence (AI) models can be
complexity of data integration. Considering the proposed enabled for predictive maintenance, demand forecasting,
deployment of grid edge technologies, which encompass and more.

Data Fusion Architecture

and Analytics Platform
Cloud Network
The data fusion architecture and its
accompanying analytics platform
represent a transformative approach
Core Network Fiber Backbone LTE to integrating diverse data sets from
various sensors. This integration
creates a robust data foundation that
surpasses the capabilities of single-
Field Area Network Home Area Network source data processing, offering
superior compatibility, interopera-
figure 5. Pillar 2: Communication infrastructure deployment. bility, and enhanced data processing

Data Mart
Extraction Central Data
Transformation Repository
Loading
Data Mart Use Case Applications

GEBs and Open Home

Data Fusion and Functional Integration Distributed AI-Based Functions Energy Management Systems

figure 6. Pillar 3: The data fusion infrastructure and grid edge analytics platform. AI: artificial intelligence.

January 2025 Show Issue ieee power & energy magazine 63

 and storage. The platform leverages this architecture to deliver vices to be developed and deployed on the meters. Over time,
cutting-edge functionalities, including DLSE and distribution the platform will enable AI models to be trained with granu-
hybrid state estimation as well as harmonic state estimation lar edge data, which can precisely predict demand, detect
utilizing PQ meter data. These features contribute to a com- anomalies, and issue complex optimization instructions.
prehensive real-time situational awareness, facilitate seamless Updates for applications and the platform may be issued
intertemporal data integration, and enable thorough assessments over the air, reducing the time spent by field technicians.
of asset health and PQ. Moreover, the platform offers advanced To successfully integrate a data fusion platform within
data visualization tools, enriching the user experience, and pro- a utility network, it is imperative to select robust technolo-
vides opportunities to leverage energy insights toward the bet- gies that seamlessly align with the grid’s operational frame-
terment of customers. It also includes data quality assurance work. The following structured plan outlines the initial
tools that function as an interface with established operational steps toward deployment, ensuring a seamless transition and
systems like the advanced distribution management system enhanced operational efficiency:
(ADMS) and DERMS. ✔ Identify use cases: Through discussion with internal
technical teams, specific use cases for the data fusion
GEB and Open Home Energy Management System platform will be analyzed. Meanwhile, data sources,
Analytics Platforms for Smart Buildings integration needs, and real-time capabilities can be
Building loads are the primary controllable asset providing determined.
flexibility to grid operations. GEB and open home energy ✔ Evaluate existing infrastructure: Current IT, opera-
management system platforms can collect historical load and tional technology, and security systems, including
behind-the-meter DER information and provide data analyt- supervisory control and data acquisition, AMI, and
ics, such as load disaggregation, load and behind-the-meter legacy systems, need to be assessed. Gaps between
DER predictions, HVAC fault detection and diagnostics, and the current and proposed systems will be identified.
optimal control of HVAC and behind-the-meter DERs for ✔ Design the architecture and technology stack: The
grid services. This will be established through a data fusion architecture for the data path, data processing, and
architecture that can leverage DI-AMI meter data. To dem- storage solution will be chosen. The data fusion tech-
onstrate this capability, open source platforms supporting nologies that support the integration and analysis of
building functions will be deployed. Customer participation multisource data will be determined.
is paramount in this regard; ComEd, with support from its ✔ Implement data management: The governance model
community partners, will enlist participants. for data management, covering data ownership, ac-
cess control, data quality, and compliance, will be
Next-Generation DI-AMI Metering Platform established.
The next-generation metering platform will leverage the
capability of DI-AMI, introduced in pillar 1. The platform Pillar 4: Interoperable Control Framework
can support AI-based applications that offer additional ser- Integration is the most critical task before utilities can
commission a solution that contains multiple innovative
technologies. It is imperative that these innovative tech-
Grid Functions nologies can talk with one another without hiccups and
Capacity/DER Planning DLSE
process data effectively. In pillar 4, the interoperable
coordination framework and the grid edge intelligent con-
ADMS/DERMS DER/Load Prediction
trollers to achieve interoperability between grid-side and
Voltage/Var Control FLISR customer-side applications while maximizing benefits for
both communities and utilities will be deployed, as pre-
sented in Figure 7.
Grid Edge Intelligent
Controller/Coordinator Grid Edge Intelligent Controller
Grid edge intelligent controllers will enable a comprehen-
sive integration strategy for integrating grid edge devices
Edge Functions
and DERs into a centralized distribution control/commu-
PQ Monitoring Virtual Power Plant
nication network. These controllers will create intelligent
Asset Health Monitoring EV Management connections between a utility and its devices at the grid
BTM Detection Building/Home edge, including assets installed on either side of meters.
Energy Management Utilities can leverage a wide range of communication pro-
tocols, such as International Electrotechnical Commission
figure 7. Pillar 4: The interoperable control framework. 61850, Open Field Message Bus, Modbus, and Distributed
FLISR: fault location, isolation, and service restoration. Network Protocol 3, to network the controllers with a variety

64 ieee power & energy magazine January 2025 Show Issue

of grid edge devices while ensuring interoperability. The grid decentralized control that is both responsive to changing
edge i­ ntelligent controller interfaces with the GEB and open conditions and flexible in the face of system disruption.
home energy management system controller and DI-AMI Technology development is advancing in this direction, and
meters on the customer side; with capacitor banks and other numerous challenges still lie ahead, as deliberated in the fol-
DERs, including EV charging infrastructure, on the grid lowing section.
side; and with the DERMS/ADMS on the upper level of the The interoperable control framework will enable multiple
control hierarchy and data management. use cases:
By integrating grid edge intelligent controllers across ✔ automated DER and capacity planning
critical feeder assets, a suite of advanced functionalities ✔ hosting capacity analysis with DERs and electrification
to enhance grid performance can be unlocked. These ✔ DERMS operation integration
controllers are pivotal in facilitating a range of use ✔ voltage optimization with the support of DER, GEB,
cases, including and vehicle-to-everything technologies
✔ distributed voltage regulation ✔ EV charging management
✔ energy optimization during normal operation ✔ edge visibility analysis
✔ coordination between grid edge devices/resources and ✔ load disaggregation and prediction
DERMS ✔ automated high-impedance fault detection.
✔ fast cloud–edge communication In addition, a grid resilience modeling, simulation, and
✔ interoperability with grid devices, such capacitor assessment platform can be developed to streamline the
banks and distribution automation devices workflow and to support the implementation of above-men-
✔ inverter control during grid outages through grid- tioned functions in a lab testing environment as well as in
forming inverter-based resources the field.
✔ renewable-enabled local system restoration
✔ autonomous orchestration at the grid edge, including zon- Challenges in Implementing
al forecasting, federated model validation and calibra- Interoperable Control
tion, situational awareness, and distributed optimization. Implementing interoperable control for grid of the future
These grid edge intelligent controllers open avenues for faces several challenges from technical, organizational, reg-
the development of algorithms and applications that can ulatory and compliance, and financial perspectives.
perform more niche functions, such as outage boundary Technical challenges identified by the authors include but
detection, EV and DER discovery, load disaggregation, are not limited to the following:
and identification of feasible restoration paths to support ✔ Diverse protocols and standards used by various de-
emergency operations during extreme weather events. In vices and systems: A main objective of interoperable
essence, these controllers signify the transformative step in control is to enable data integration and coordinated
enabling grid edge control, where management of energy control across different devices and systems, which
distribution and consumption can be revolutionized by may use different protocols and standards. In addi-
optimally harnessing the power of advanced algorithms tion, technology providers may use different data
empowered by data. As DER integration increases, these formats and semantics that may prevent devices and
controllers could facilitate the bypassing of traditional util- systems from exchanging information correctly. To
ity models to more prosumer-involved models, such as vir- achieve interoperability, it is essential to either adopt
tual power plants, peer-to-peer energy exchange, demand common protocols and standards among devices and
response, and more. systems or deploy adaptors/converters to bridge dif-
ferent protocols and semantics.
Interoperable Control Framework ✔ Data fusion of multiple data sources: Data fusion
All things considered, the ideal control framework must of multiple data sources may present different kinds
be affordable, portable, and reliable. Its structure needs to of challenges due the heterogeneity of data sources,
be dynamic to stimulate the grid transformation while also poor data quality, overwhelming data volume, and
maintaining grid reliability and equitable service. At the temporal and spatial misalignment. As mentioned
same time, this framework needs to be flexible enough to above, sensors and grid edge controllers could gen-
reap the maximum grid benefits from grid edge DERs, such erate data streams in various formats, structures,
as grid-forming capability, demand response, and vehicle-to- and types that cannot be readily integrated. It is
grid technology, via grid edge control architectures. There inevitable that the data platform will integrate vari-
is a promise of additional reliability and resilience that can ous inconsistent data, missing/duplicate data, inac-
yield significant benefits for customers and their neighbors curate data, and misaligned data with wrong special
with DERs (including battery energy storage systems) on and temporal tags. In addition, the sheer volume of
the grid edge. The crowning achievement of the advanced data poses significant challenges for data process-
control framework would be a balance of centralized and ing and storage.

January 2025 Show Issue ieee power & energy magazine 65

 ✔ Architecture to support interoperable control para- Acknowledgment
digm: Interoperable control can be implemented in The authors would like to extend their deepest gratitude to
various architectures, depending on the requirements all the individuals whose contributions made this article
of communication, integration, performance, secu- possible. Special thanks to Marianna Vaiman, from V&R
rity, and so on. Specifically, interoperable control can Energy; Andrew Rodgers, from ACE IoT Solutions; Dongbo
be implemented either on the edge in a decentralized Zhao, from Eaton; and Sean Murphy, from PingThings, who
manner or as a function module of centralized sys- shared their insights and perspectives, shaping this article.
tems (e.g., ADMSs and DERMS). However, it is chal- This work is supported by the Grid Resilience and Innova-
lenging to design the architecture to enable seamless tion Partnerships Program, which is administered by the
integration with heterogeneous and legacy systems U.S. Department of Energy’s Grid Deployment Office.
while ensuring the requirements of scalability, secu-
rity, and cost. For Further Reading
✔ Cybersecurity: Cybersecurity is crucial for the grid N. Gurung et al., “Use of PMU-based software platform to
of the future, especially in the context of an in- provide real-time situational awareness for Bronzeville com-
teroperable control framework designed to integrate munity microgrid,” presented at the IEEE PES T&D Conf.
various systems and devices, which may inevitably Expo., Chicago, IL, USA, 2020, p. 2020TD0343.
enlarge the attack surface on the grid edge. The ar- S. Pandey et al., “PMU-based distribution linear state es-
chitecture must be carefully designed to ensure com- timation to improve data quality and application reliability,”
pliance and satisfy requirements to manage assets, presented at the IEEE PES T&D Conf. Expo., New Orleans,
threats, and vulnerabilities; supply chain and third- LA, USA, 2022, p. 2022TD0183.
party risks; and identity and access and to ensure A. Alvi, T. Alford, M. Y. Vaiman, and M. M. Vaiman,
continuous operations with efficient event response “Pioneering real-time control: The deployment of a distribu-
and incident mitigation strategies. Guardrails must tion linear state estimator at commonwealth Edison,” IEEE
be in place to protect the data privacy of both utility Power Energy Mag., vol. 22, no. 2, pp. 67–77, Mar./Apr.
and customer data. 2024, doi: 10.1109/MPE.2024.3353476.
As the industry moves forward, collaboration and con- F. Ding, W. Liu, U. Kumar, and Y. Yao, “Unleash values
tinuous technological advancement will be key in overcom- from grid-edge flexibility: An overview, experience, and vi-
ing these obstacles. sion for leveraging grid-edge distributed energy resources
to improve grid operations,” IEEE Electrific. Mag., vol. 10,
Conclusions no. 4, pp. 29–37, Dec. 2022, doi: 10.1109/MELE.2022.3211017.
Proprietary data formats and protocols proposed by ven- A. Satchwell et al., “A national roadmap for grid-inter-
dors complicate the system integration process and delay the active efficient buildings,” Lawrence Berkeley Nat. Lab.,
renewable interconnection from the customer side. The lack Berkeley, CA, USA, 2021. [Online]. Available: https://
of mature integration guidance for new technologies (some gebroadmap.lbl.gov/
cloud hosted) creates headaches for utilities on how to inte- “Supercharging the electric grid edge for an integrated
grate and analyze data. The capability to integrate and ana- energy system,” Office of Energy Efficiency & Renewable
lyze data is critical for utilities to better plan and visualize the Energy, U.S. Dept. of Energy, Washington, DC, USA. [On-
system and, in turn, provide better serve to customers, with line]. Available: https://www.energy.gov/eere/supercharging
prompt support. -electric-grid-edge-integrated-energy-system
The comprehensive and transformative technology
deployment presented here stimulates the needed grid Biographies
transformation to support decarbonization, electrifica- Gowtham Kandaperumal is with the Smart Grid Emerging
tion, and DER adoption. The field demonstration of an Technology Department, Commonwealth Edison, Chicago,
interoperable grid edge management architecture will IL 60181 USA.
foster the integration of distributed generation at the Bo Chen is with the Smart Grid Emerging Technology De-
distribution level and facilitate the aggregation and inte- partment, Commonwealth Edison, Chicago, IL 60181 USA.
gration of advanced sensors, distributed and AI applica- Keith DSouza is with the Smart Grid Emerging Tech-
tions, EVs, electrified loads, and other grid edge devices. nology Department, Commonwealth Edison, Chicago, IL
This technology will improve monitoring and detection 60181 USA.
to enable proactive maintenance and improve reliability. Ruoxi Zhu is with the Smart Grid Emerging Technology
The challenges in implementation can be overcome by Department, Commonwealth Edison, Chicago, IL 60181 USA.
collaboration among utilities, vendors, and academia to Brooks Glisson is with the Smart Grid Emerging Tech-
accelerate innovation and promote adaptation in the grid nology Department, Commonwealth Edison, Chicago, IL
modernization effort. 60181 USA. p&e

66 ieee power & energy magazine January 2025 Show Issue

Empowering a
Proactive Grid
With Power
Quality Visibility
Taking Advantage of Advanced
Power Quality Monitoring

By Nick Nakamura , Luis Vega,

and Kamron Tangney

T
THE EVOLUTION OF THE POWER GRID IS PRO- factors that may threaten reliability and empowers moving
gressively shifting to a more dynamic and complex sys- to a proactive grid.
tem. The introduction of decentralized inverter-based This article discusses how advanced power quality moni-
resources (IBRs) on the generation side in addition to the toring provides critical visibility into the health of the power
increasing demand for new electrification on the load side system, providing grid operators with diagnostics tools that
is making a significant impact on the grid, introducing enable the immediate correction and mitigation of emerg-
new challenges and vulnerabilities to grid stability. To ing issues. Power quality analyzers equipped with real-time
keep up with these changes while maintaining a reliable event notifications and reports that detect noncompliant con-
system, grid operators must adapt. Continuous visibility ditions can provide early warning notifications on potential
on power quality equips operators with critical insights on system faults or equipment failure before they develop into
Digital Object Identifier 10.1109/MPE.2024.3466121
permanently faulted conditions that result in costly system
Date of current version: 11 November 2024 outages and downtime.

January 2025 Show Issue 1540-7977/24©2024IEEE ieee power & energy magazine 67
 What Is Power Quality Monitoring available is a digital fault recorder (DFR). DFRs record and
and Why Do We Need It? trigger on various conditions in the power system and can be
Power quality is defined as the influence that voltage and cur- used to ascertain anomalies if programmed correctly. These
rent anomalies have on end-use equipment. Good power quality are typically installed at the bus level and enable diagnos-
is an optimal level of electrical health and is the ideal condition tics from the head end of the feeder in the substation. The
the grid and its connected assets are designed to operate in. In deployment of power quality monitors at various portions
contrast, poor power quality occurs when a disturbance inter- downstream of the feeder enables further diagnostics. For
feres with the normal operation of equipment or the electrical example, power quality monitoring at solar interconnections
system and involves deviations from the generated sine wave at allows the measurements at that specific location, and the
the fundamental frequency. Disturbances such as voltage sags, magnitude of these measurements could significantly differ
voltage swells, harmonics, high-frequency (HF) transients, if monitored from a DFR, based on the distance from the site
and imbalance are examples of poor power quality. This arti- and the feeder characteristics.
cle will provide case study examples of the effects these power
quality issues can have on equipment and systems. How Is Power Quality Monitoring Typically
Power quality monitoring is typically deployed either Applied From a Utility Perspective?
permanently or on a temporary basis. Traditionally, when a Utilities install permanently mounted power quality monitors
power quality disturbance complaint is reported, the utility to provide visibility and data on grid conditions, and remote
may deploy a temporary portable power quality measure- communication allows long-term data retrieval without hav-
ment system to the problem site for troubleshooting. The ing to physically be on site to collect measurements. Some
communication to this system may be done locally to the utilities are working with regulators and/or commissioning
power quality monitor or remotely through a communication entities to expand power quality monitoring initiatives, which
device, such as a cellular modem. After the analysis has con- allow for the permanent installation and fleet expansion of
cluded and the complaint has been addressed, the measure- power quality nodes to provide visibility and determine grid
ment system is typically removed from the site. Permanent health in real time. These initiatives further exemplify the
installations are typically installed in pole-mounted configu- acceptance and rising importance of power quality to regu-
rations, wall-mounted configurations (e.g., on the outside of lating entities. Historically, power quality monitors have been
a pad-mounted transformer), within the electrical cabinets of installed at feeder sources or substations and have recently
switching devices (e.g., switchgear), paired with other intel- been extended downstream to critical customers (e.g., data
ligent electronic devices (e.g., protection relays and smart centers, hospitals, and airports), generation sites (e.g., cogen-
meters), or inside substations. These installations ideally eration and IBR interconnections), and grid edge locations.
have remote communications enabled and provide continu- Power quality monitoring is becoming more common for
ous measurements and reporting. One of the drivers of per- compliance verification (e.g., IEEE 519-2022 for harmonics
manent installations is new generation sources, and loads are control). These monitors are typically connected to a commu-
often nonlinear devices and continuously being deployed, nications network (either via an intranet or via a 4G/5G cel-
causing potential seasonal changes to the load and harmonic lular modem) to provide remote access and enable real-time
profiles in the grid. These changes over time can be detected grid condition monitoring. This enhanced visibility saves
with permanently installed monitors in near real time to time and improves operator efficiency in diagnosing grid
assist with assessing grid health, mitigation measures, and issues, detecting changing trends (e.g., harmonics increasing
ultimately, planning and decision making. over time), and identifying grid design limitations.
“Power metering” and “power quality monitoring” are From this visibility, utilities can observe baseline condi-
often interchanged. However, they are different in nature tions, allowing them to set notifications when the system is
regarding the granularity of measurements, alarms, and com- performing outside of the normal operating conditions. This
pliance with the respective industry standards. Power quality is often brought to the attention of someone familiar with
monitors provide high-fidelity information that allows the user power quality issues, typically power quality engineers, and
to uncover issues that often go unseen by traditional power then it is decided if further action is required.
metering systems. Smart meters are power metering systems For larger system deployments, attempting to routinely
that remotely communicate data and can provide voltage inspect each power quality monitoring site for potential
trending data or the detection of sustained high/low voltages; issues is very time consuming. In this case, automatic analy-
however, they typically lack the resolution to be considered sis and event notification becomes a necessity for utilities.
a power quality monitor (e.g., common metering intervals Automation allows for power quality engineers to effectively
are based on 15-min data). Typically, power quality monitors utilize their time by focusing on critical issues and events
adhere to an international standard on how the measurements rather than manually filtering through data. These actions
are taken, the most common being IEC 61000-4-30 Ed3, include compliance verification, event frequency tracking,
while power meters typically adhere to the ANSI C12 series and sustained power quality issues (e.g., voltage imbalance
of standards. Another type of high-resolution measurement is sustained above 2% for a predetermined amount of time).

68 ieee power & energy magazine January 2025 Show Issue

Power Quality Disturbances and Case that an issue or potential weak spot in the electrical system
Studies That Empower a Proactive Grid is emerging. Identifying these vulnerabilities early empow-
The visibility of power quality and the various electromag- ers utilities to schedule targeted activities, thus reducing the
netic phenomena associated with it can serve as the eyes over likelihood of premature equipment failure and the resulting
the electrical system and empower proactive decision mak- impacts on customers.
ing, resource optimization, and continuous improvements to
resiliency initiatives. In today’s electrical systems, there are Case Study: HF Impulse Prefault Events
many different phenomena that can degrade resilience. With at Primary Metering
the proliferation of generation from IBRs and the electrifica- The HF impulse measurement is a highly granular voltage
tion of new loads, the occurrence and variety of these phe- transient event trigger that can be leveraged for predic-
nomena are increasing. This section will focus on real-world tive analysis and proactive maintenance. The HF impulse
case studies involving the detection, analysis, and resolution alarming condition emphasized in this case study is a
of problems arising from transient, harmonic, and imbalance transient capture that samples the waveform up to 4 MHz
issues observed in the distribution grid. (single voltage channel, 250-ns resolution) or 1 MHz (four
voltage channels, 1-ms resolution). This high sampling rate
Transients and HF Impulse Applied is critical in the detection of voltage anomalies in the elec-
to Prefault Detection trical system as such anomalies are typically invisible to
According to IEEE 1159-2019, there are two categories of traditional metering equipment. The causes of HF impulse
transients: impulsive and oscillatory. Impulsive transients events are typically attributed to the switching, lightning, or
are unidirectional in polarity and have either a positive arcing of equipment.
or a negative magnitude surge effect, whereas oscillatory A utility site composed of pole-mounted primary meter-
transients are bidirectional and create a ringing effect in ing voltage transformers paired with a permanently mounted
which the positive and negative polarities are rapidly fluc- power quality monitor had the HF impulse trigger config-
tuating. The characteristics of both transients are sudden ured to a sensitive threshold of just over two-and-a-half times
momentary changes from the nominal voltage or current the nominal secondary voltage. These transient events are
and can be used to detect large magnitude transients such generally not conducted far from the source of where they
as lightning strikes to smaller magnitude transients such enter the power system; therefore, it can be presumed that
as disturbances from switching and/or the degradation of the source is equipment on the pole. Over time, the electrical
electrical assets. behavior of the site changed from relative inactive occur-
There are many factors that need to be considered when rence events to a high volume of intermittent HF impulses
using transients to diagnose issues, including the monitoring observed over a brief period, as shown in Figure 1.
equipment, transient thresholds, and operational influences In addition to the high concentration of events, the voltage
that may be present on the source and load being monitored. signature observed in the data was consistently low in mag-
Providing visibility of transient events and monitoring how nitude, oscillatory in nature, and on the same phase. This
frequently they occur can be vital for the early detection of site resulted in a catastrophic failure on the voltage trans-
developing faults or prefault conditions. When the occur- former correlated with the phase the events occurred on,
rence of these events increases over time, it may be a sign resulting in a costly outage, damage to nearby infrastructure,

119-HF Impulse Events Anomaly “Prefault”

Observed in a One-Month Period Events Catastrophic Event

figure 1. HF impulse “prefault” events and a catastrophic failure.

January 2025 Show Issue ieee power & energy magazine 69

 and customer downtime. This recurring event pattern was Industry standards such as IEEE 519-2022 and IEC
recognized within the utility, and they subsequently used 61000-2-2 provide guidance for harmonics compliance. The
it to trigger the proactive replacement of the voltage trans- guidelines set forth in these standards are crucial for the lon-
former equipment at sites with similar behavior. The trig- gevity of electrical health and include a set of compliance
ger to this proactive maintenance is based on HF impulse limits that provide a quantifiable methodology to determine
events increasing to an unacceptable threshold over a short if a site is in violation and at risk. This method involves
duration of time. The utility considers a high concentration running a report based on a set of voltage and current mea-
of these event patterns in a predetermined time duration as surements over a predetermined time (e.g., one week or one
probable prefault conditions that may result in a permanently month) while utilizing a capable measurement device such
faulted condition. An example of power quality event data as an advanced power quality analyzer. This report should
that prompted the utility to initiate proactive maintenance indicate if the measurement point complies or not, providing
can be observed in Figure 2. a dataset of the respective harmonic orders, the measured
A high volume of recurring HF impulse events was observed values versus the limits, and a pass/fail status. A noncompli-
at a critical load, similar to the site referenced in Figure 1 that ance condition can be used to drive proactive harmonic miti-
experienced the failure. Prior to this increase in events, there gation activity that ensures the uninterrupted operation of
were no observed HF impulse events. Additionally, a simi- critical infrastructure, asset reliability, and preventing inter-
lar event magnitude and duration signature were observed, ruptions in the power system. Oftentimes, the noncompliant
as shown in Figure 3. All events had the same characteristics harmonic data in this report are used to specify mitigation
involving triggering on the same phase with a low-magnitude strategies and/or the design of harmonics mitigation equip-
oscillation observed. After the suspect voltage transformers ment, such as harmonic filters, to suppress the problematic
were replaced at this site, the HF impulse events stopped. harmonics out of the system.

Harmonics and Compliance Reporting Case Study: High-Order Harmonic Observations

to Monitor Grid Health Involving a 20-MW Utility-Scale Solar Application
Harmonics are generated by nonlinear loads that are preva- A residential customer complaint was reported to the local
lent in today’s grid, and source examples include pulse recti- utility about lighting switching on and off erratically, fre-
fiers and variable frequency drives on the load side and IBRs quent tripping of ground-fault circuit interrupters, and
on the generation side. The nature of harmonics involves the intermittent appliance issues, notably on sunny days during
distortion of the voltage and current waveforms and results generation hours. The configuration of the system can be
in a variety of issues that can degrade the electrical per- observed in the one-line diagram shown in Figure 4. In this
formance of connected equipment over time. Unchecked configuration, the utility installed a bonding conductor to
noncompliant harmonics accelerate the wear and tear on the common ground of each skid in the solar site, creating a
electrical infrastructure, thus shortening equipment lifespan pathway for HF leakage current and harmonics propagation.
and increasing replacement frequency. Some of the observ- A utility representative visited the site, confirmed the
able symptoms include the overheating, misoperation, and reported behavior, and consulted with the power quality
failure of electrical equipment and inefficiencies such as engineers to further investigate the issue. A permanently
noncompliant power factors. mounted power quality monitor installed at the point of

383-HF Impulse Events Observed

in a One-Month Period

figure 2. HF impulse “prefault” events prompting proactive maintenance.

70 ieee power & energy magazine January 2025 Show Issue

interconnection (POI) of the solar site had stored the data with the levels specified by IEEE 1453-2022, and there were
needed to immediately respond to the customer complaint by no voltage variation disturbances such as voltage sags. When
running power quality and harmonics compliance reports. A observing the IEEE 519 harmonics compliance report, all
temporary power quality monitor was also installed at the of the odd and even harmonics were compliant; however,
residence and confirmed the measurements taken at the POI. the IEC 61000-2-2 harmonics compliance report indicated
During the investigation, it was observed that the long- noncompliant harmonic limits at odd harmonic orders H33,
term flicker (Plt) and short-term flicker (Pst) were complying H39, and H45 (1.98–2.7 kHz), as shown in Figure 5.

figure 3. HF impulse event characteristics.

*Typical Inverter Skid Arrangement

Utility Meter Utility Recloser
Y

20-MW M R
Solar Arrays
~2 mi

*Utility Installed Bonding Conductor

PQ
Meter

Customer Issue

figure 4. The harmonics case study single-line diagram. PQ: power quality monitor.

January 2025 Show Issue ieee power & energy magazine 71

 Conducted emissions (also known as supraharmonics) in a heat map format with the timescale along the horizontal
were also monitored to further investigate the reported com- axis, frequency scale along the vertical axis, and the voltage
plaint. These are frequency distortions in the range of 2–150 magnitude of the frequency distortion as the color intensity,
kHz, and the measurement methodology and general guid- as shown in Figures 6 and 7.
ance are published as an informative reference in the indus- The high concentration of distortion at 3.4 kHz in Figure 6 is
try standard, IEC 61000-4-30 Annex C. The steady-state depicted by the solid dark blue band, and the 10- and 12-kHz
conducted emissions measured in this case are beyond the distortions shown in Figure 7 are depicted by the solid red
harmonics compliance limits represented in IEEE industry bands. These solid bands represent a steady state of con-
standards, which are up to the 50th harmonic or 3 kHz at ducted emissions while the solar site was generating from
the time of this writing. A high concentration of steady-state approximately 7 a.m. to 11:30 a.m. Additionally, there were
distortion was observed at 3.4 kHz in the 2–9-Khz spectrum 106- and 142-kHz emissions present; however, they appear
(200-Hz bin resolution) and at 10 and 12 kHz in the 9–150- to be intermittent throughout the day and unrelated to the
kHz spectrum (2-kHz bin resolution). The data are displayed characteristics of the other emissions observed. To further

Noncompliant
Harmonics Observed

figure 5. The voltage harmonics compliance chart (H33, H39, and H45 noncompliant).

Steady-State Distortion
at 3.4 kHz (During
Generation Hours)

figure 6. A conducted emissions 2–9-kHz heat map (steady-state distortion at 3.4 kHz).

72 ieee power & energy magazine January 2025 Show Issue

troubleshoot the customer complaint, the utility isolated the compliance before the POI is closed and at post-generation
solar site for a period of one week, and the disconnection to validate voltage and current compliance after the POI is
can be observed when the steady-state distortion character- closed. Harmonics and power quality compliance reports
istics abruptly stopped in the conducted emissions graphs at are being automatically generated monthly at all sites that
approximately 11:30 a.m. Upon isolating the solar site, all have the most recent standard of power quality monitoring
reported symptoms and equipment issues at the residential installed. These efforts provide the utility with tools to proac-
customer site ceased. Based on these observations, the util- tively address power quality issues and to continually moni-
ity consulted with the solar inverter manufacturer to further tor compliance under dynamic load conditions, particularly
investigate. Upon analysis of the observed measurements, those resulting from electric vehicles, data centers, and IBR
the inverter manufacturer provided an update to the inverter technology.
firmware, involving pulse shifting the switching frequen-
cies. The solar site operations (contracted by the utility) Imbalance
applied the firmware update in conjunction with the inverter Detecting imbalance issues is critical to ensuring reliable
manufacturer engineering team, and a reduction of the operations in the grid, particularly to operations utilizing
steady-state frequency distortion magnitudes was observed. motor-driven systems. Imbalance in electrical systems refers
The residential customer site that initially reported the prob- to an uneven distribution of voltage or current among the
lem was contacted after reenergizing the solar site with the phases, which can lead to a variety of issues, such as inef-
new firmware update, and no further issues were reported ficiencies, premature failure of equipment, and safety haz-
or observed. ards. Increasing visibility on this measurement early on can
The utility applies permanently mounted power qual- help mitigate the resulting electrical stresses and equipment
ity monitoring at all utility-scale solar interconnections and degradation by scheduling corrective actions before an inter-
is running combined harmonics and power quality compli- ruption or safety hazard occurs.
ance reports during the commissioning process. This report- Voltage imbalance is the ratio of negative or zero-sequence
ing is taken in two steps: at pregeneration to validate voltage to positive-sequence components, per IEEE 1159-2019 Annex

12 kHz Steady-State Distortion at

10 kHz and 12 kHz (During
10 kHz Generation Hours)

figure 7. A conducted emissions 9–150-kHz heat map (steady-state distortion at 10 and 12 kHz).

January 2025 Show Issue ieee power & energy magazine 73

 Solar Array 1
Inverter 1
MAIN ac Panel Step-Up Step-Down
POI Customer Utility Transformer
Switchgear Transformer Transformer ac
Switchgear
Disconnect

Solar Array 2
Inverter 2

figure 8. An imbalance case study single-line diagram.

C. This standard provides reference values on typical char- step-up transformer was installed due to the long cable run
acteristics for imbalance. Measurements exceeding those distance between solar and the POI.
typical values may be harmful to the electrical systems and Upon the initial inspection of the site, the insulation of
equipment assets. Real-time alerts assist in getting ahead of the neutral cable in the utility transformer was observed to
the issue to diagnose and proactively plan for mitigation. have melted away from the conductor due to excessive heat,
as shown in Figure 9. A power quality monitor was installed
Case Study: Imbalance at Net Metering Application to investigate the issue and a significant amount of zero-
A net metering site customer with 250 kW of ground- sequence current was observed, with approximately 430 A
mounted solar arrays reported power quality issues to the of current flow through the transformer neutral, as shown
local utility. The system configuration included a grounded in Figure 10.
wye-delta step-up transformer and a delta-grounded wye When observing the phasors’ behavior, the current phase
step-down transformer to the POI, as shown in Figure 8. The angles measured were in a state of significant imbalance,
which was also reflected in the zero-sequence current, as
shown in Figure 10. This imbalance created the overheat-
ing and melted condition observed on the secondary cable.
Depending on the position of the delta transformation, it
can result in temporary overvoltage that can damage equip-
ment or become a zero-sequence path. Grounding banks
are zero-sequence sources that can lead to other factors
that impact grid operation, such as incorrect meter read-
ings, protective relays, and the potential for ferroresonance.
The transformation in question created a grounding bank,
resulting in the observed imbalance condition and zero-
sequence source.
After the site analysis and recommendations, all secondary
cables to the POI switchgear were replaced due to extensive
damage to the neutral and adjacent conductors. A dissolved
gas analysis test was performed on the utility transformer and
indicated acceptable compliance levels. The analysis showed
the utility transformer was exposed to sustained electri-
cal stress that could’ve resulted in the degradation of useful
life. These factors, combined with safety considerations (the
device was located near pedestrian traffic), prompted the pro-
active replacement of the utility transformer. The customer
ultimately replaced the delta-wye step-up and step-down
transformers with grounded wye-grounded wye transformer
configurations per the utility recommendations.
Unfortunately, a power quality monitor had not been ini-
tially installed at this site to provide system visibility. If data
figure 9. Melted neutral on transformer. from an appropriate power quality meter had been available,

74 ieee power & energy magazine January 2025 Show Issue

 Overloaded Neutral,
430-A Observed

239% Current Zero

Sequence Imbalance

figure 10. Overloading on the neutral and high zero-sequence current.

the problem could have been identified early with action For Further Reading
taken to resolve the issue before it impacted the customer “IEEE Recommended Practice for Monitoring Electric
and damaged the connected equipment. The utility is now Power Quality,” IEEE Std 1159-2019, Jun. 2019.
applying permanently mounted power quality monitors at “IEEE Standard for Harmonic Control in Electric Power
net metering sites ranging from 250 kW to 3 MW of genera- Systems,” IEEE Std 519-2022, May 2022.
tion to provide the visibility needed to proactively identify “Electromagnetic Compatibility (EMC) - Part 4-30:
and mitigate issues. Testing and Measurement Techniques - Power Qual-
ity Measurement Methods,” IEC 61000-4-30:2015 Ed3,
Conclusion 2015.
As power system infrastructure and equipment continue to “Electromagnetic Compatibility (EMC) - Part 2-2: En-
age and evolve, there are opportunities to get ahead of devel- vironment - Compatibility Levels for Low-Frequency Con-
oping electrical problems with advanced tools that provide ducted Disturbances and Signalling in Public Low-Voltage
actionable and proactive information. Throughout this article, Power Supply Systems,” IEC 61000-2-2:2002, 2002.
there have been example case studies showing how new grid G. Singh, T. Cooke, J. Johns, L. Vega, A. Valdez, and G.
dynamics are presenting unique challenges for grid operators Bull, “Telephone interference from solar PV switching,”
to solve. The power quality monitors with remote communica- IEEE Open Access J. Power Energy, vol. 10, pp. 373–384,
tions used in these applications provided the system visibility 2023, doi: 10.1109/OAJPE.2023.3239854.
that enabled shifting from a costly and resource-consuming M. Tefferi, N. Nakamura, B. Barnes, and N. Uzelac, “Su-
reactive system to a system with actionable data streams that praharmonic measurements in distributed energy resources:
empower a proactive grid. Continuous monitoring and com- Power quality observations in a microgrid,” IEEE Electrific.
pliance reporting at critical infrastructure locations provide Mag., vol. 11, no. 2, pp. 88–96, Jun. 2023, doi: 10.1109/MELE.
actionable data to help develop mitigation strategies before 2023.3264929.
unforeseen issues develop into costly outages.
As we look forward in the electric power industry, more Biographies
high-fidelity data provided in an intuitive and timely manner Nick Nakamura is with Powerside, Alameda, CA 94501 USA.
are critical to understanding grid health. These are essential Luis Vega is with Dominion Energy, Richmond, VA
factors in executing the reliability and resiliency initiatives 23294 USA.
needed to address the dynamic grid conditions of today and Kamron Tangney is with Powerside, Alameda, CA
into the future. 94501 USA. p&e

January 2025 Show Issue ieee power & energy magazine 75

 Impact of
Medium- and
Heavy-Duty
Electric Vehicles
and the Grid
An Address-Level/Bottom-Up
California Case Study

By Richard Fioravanti, Lisha Sun , Robert

Mushet, Anderson Bolles, Alex Moffat,
and Matt Belden

A
AS TRANSPORTATION ELECTRIFICATION EXPANDS TO
include medium- and heavy-duty (MHD) vehicle classes, new chal-
lenges begin to emerge. MHD electrification is accelerating under state
and national rules and regulations. At the state level, California has
been leading this initiative. The state has incorporated regulations to
help with goals of achieving 100% zero-emission for all on-road MHD
vehicles in the state by 2045, where feasible, and even sooner for spe-
Digital Object Identifier 10.1109/MPE.2024.3456042
cific vehicle classes such as drayage trucks, which have a target of 2035.
Date of current version: 11 November 2024 From a national perspective, the U.S. National Blueprint for Transportation

76 ieee power & energy magazine 1540-7977/24©2024IEEE January 2025 Show Issue
­ ecarbonization by the Department of Energy includes a
D (LD) versus MHD analysis. The differences in characteristics
national plan to reach at least 30% of MHD sales being zero- between LD and MHD vehicle loads provide insight into dif-
emission by 2030 and 100% of sales by 2040. Although ficulties in applying the approaches used in assessing the LD
zero-emission vehicles (ZEVs) include hydrogen and fuel cell market to the MHD analysis. These potential differences can
vehicles, electric vehicles (EVs) make up the majority of the be seen in the following areas:
expected ZEV growth. ✔ Location: LD vehicles tend to be located at residential
Utilities face significant challenges when trying to assess homes, which are fixed and can be found from utility
the impact of MHD electrification on the grid. MHD vehicles customer data.
often use high-power chargers, are concentrated into fleets, ✔ Size of charger load: While the charging load for a
and are clustered into industrial zones. This unique combina- MHD vehicle can range between 20 kW and 1 MW, the
tion of attributes will result in large spot loads for the grid, charging loads for LD vehicles are essentially fixed be-
potentially resulting in significant long-term transmission and tween 7 and 10 kW.
distribution system investment. ✔ Adoption models and drivers: There are a number of
Additionally, most MHD fleet locations and their corre- adoption models that target consumers and LD vehicle
sponding charging loads are not uniformly distributed across owners. Few models exist for MHD vehicles. Regula-
utility territories. From a planning perspective, such significant tions can drive adoption; however, most fleet operators
spot loading creates unique challenges for utilities and neces- will first focus on maintaining the operations.
sitates the need for utilities to understand the locations of the ✔ Vehicle datasets: California Department of Motor Ve-
loads. When analyzing the potential charging load of MHD hicles data, demographic data, and consumer behavior
vehicles and their corresponding impacts on the electricity data exist and are relatively easy to obtain for consumer
grid, there is a need for utilities to focus on three questions: 1) LD vehicles. Facility data, mileage data, the number of
Where will the load appear? 2) When will the load appear? 3) vehicles, and the fleet address are often more difficult to
How big will the load be? obtain for MHD fleets. Fleet operator tendencies of reg-
Along with the State of California, San Diego Gas & Elec- istering vehicles at a corporate headquarters rather than
tric1 (SDG&E) is actively working to assist its customers with where the vehicles are used can also lead to undercount-
the transition to ZEVs through several transportation electrifi- ing from reliance on data that may have been accurate
cation programs targeting MHD EVs. However, it is essential for LD vehicles.
to accurately estimate the MHD charging load for grid plan- These issues were recognized in an Electric Reliability
ning efforts. Hence, as part of this effort, the team worked Council of Texas EV allocation study, which noted that “unlike
together to develop an address-level, bottom-up approach to with LD vehicles, we do not have data on where MHD vehicles
estimate the impact of fleet electrification on the system. (across all fuel types) are currently located.” In addition, the
The initial case study area focused on the Otay Mesa com- study noted that “historical electric MHD vehicle adoption
munity, which contains two of the most active land ports of has been extremely limited, as a result, empirical relationships
entry between the United States and Mexico, representing between economic variables and electric MHD vehicle adop-
US$60 billion in annual trade, according to California–Baja tion levels have not been established.”
California border crossing and trade data from the San Diego These issues are at the root of the challenges stakeholders
Association of Governments, supported by significant num- face in examining MHD electrification. The inability to find
bers of MHD vehicles and distribution centers. Utilities have a dataset that indicates the locations and adoption propensity
traditionally not followed fleet centers as key accounts, cre- of MHD vehicle operators leads to the need for an address-
ating the challenge of now needing to understand where the level, bottom-up approach from novel data sources. Traditional
MHD charging loads will form. The proposed approach over- approaches can fail to address some of the main questions of
comes this hurdle and allows utilities to identify critical hot MHD impact analysis, such as “Where will the load occur?”
spots and estimate where the loads will occur, as well as the It is essential to allow utilities to determine larger concerns
impacts on feeders and substations to accommodate new elec- such as “Will there be enough capacity to accommodate this
trification demand. spot load?” The address-level approach provides the necessary
This article discusses the benefits of an address-level fidelity to answer these questions, allowing planners to prop-
approach to estimating the impact of MHD charging loads erly assess impacts on substations and feeders.
and how the approach is implemented to identify and estimate
MHD EV charging loads in the Otay Mesa area. Challenges and Gaps in Estimating
MHD Loads
Light-Duty and MHD Market Assessments To effectively estimate the MHD fleet load on the grid, the
These challenges facing utilities are exacerbated due to dif- utility needs to answer three questions: 1) Where will the load
ferences in understanding the charging load of light-duty appear? 2) When will the load appear? 3) How large will the
load be? The first question of “where” drives the main chal-
1
Registered trademark. lenge in understanding the impacts of MHD fleets and the

January 2025 Show Issue ieee power & energy magazine 77

 motivation to utilize an address-level/bottom-up approach. zero vehicles due to vehicle registrations associated with
The geocoding of facility locations allows utilities to link and a different address from where the vehicles are being
then determine the actual feeder- or substation-level impacts. used. Multiple data sources are needed in this step, in-
For many load forecasting circumstances, examining cluding purchased data.
MHD loads from an aggregate level can provide an estimate 2) Categorizing facilities and adoption curves: Not all
for load for planners to anticipate capacity needs across a ter- fleets and their vehicles operate in the same manner.
ritory. However, that information needs to be disaggregated to Hence, in this step, the vehicles are separated into fleet
the feeder level to allow utilities to properly plan how to miti- categories such as buses, drayage, refrigerated trucks,
gate or accommodate the projected load. and trucks supporting distribution warehouses. Spe-
Fleet business operations and routes tend to be well estab- cific adoption rates can also be customized for each
lished. Electrifying fleets will not likely drive a need to change fleet category to ensure an appropriate rate of adop-
their operational routes. However, even so, fleets tend to create tion based on minimum regulatory requirements and
significant loads in very localized areas for two main reasons: policy drivers.
1) the number of vehicles that make up a typical fleet and 2) 3) Creating facility load shapes: With the facilities identi-
the tendency of fleets to “cluster” in specific areas. Data from fied, vehicles counted, and estimated adoption factors
an aggregate-level approach will still need to be disaggregated understood, vehicle operations are examined to estimate
to determine which feeder node or substation a projected load the energy requirements for each vehicle (charging pro-
is linked to and whether the specific feeder/substation can file kW demand and total energy kWh). These estimates
accommodate that load. are based on miles driven and conversions to energy
Utilities also need more information than simple peak loads needs. Once determined, load profiles can be created for
in an area. Aggregate data are not granular enough and prevent each facility and be made into aggregated profiles for an
utilities from developing facility load profiles that are neces- entire region. Key to this step is to understand the time
sary to create projections of future impacts of MHD charging charging is expected to occur and whether new peaks
on the grid at the feeder and substation level. Utilities need for a region are being created.
charging profiles and 8760 data to understand whether the 4) Mapping facilities to the grid: The geocoded data for the
additional charging load is contributing to the existing peak facilities allow the load profiles to be linked to specific
load or not. The majority of charging for MHD vehicles is substations or feeders. These data on individual vehicles
projected to occur in the evening and during off-peak hours. and fleets can be aggregated together and measured
However, long-haul vehicles passing through areas or short- against the current capacities of feeders or substations.
haul vehicles running low on their battery charging level may Once linked, the impact that MHD electrification has
need daytime charging. Hence, it is important to understand on the grid can be determined, and grid upgrades can
charging patterns and load profiles for facilities or charging be identified and constructed to enable the future fleet
centers over a 24-h period. electrification plans in those areas.
To overcome these challenges, the proposed approach will The process and data allow for utilities to consider changes
answer the three main questions of where, when, and how across vehicle categories, changing adoption rates, charging
large these loads will be when they appear on the grid. This profiles, and charging habits to establish a more accurate fore-
approach also allows the utility to understand exactly where cast. The case study below describes how the process was used
the facilities are located and which feeder nodes and substa- to assess Otay Mesa and how big data not only can be used to
tions those facilities are connected to. Often, in industrial or help find individual facilities but also can provide additional
urban areas, multiple substations can be located nearby, mak- information that allows utilities to better understand and model
ing it difficult to easily link an area of load to a specific grid the charging loads.
asset without this additional guidance.
A Case Study for Otay Mesa
Address-Level Approach to Estimate Due to its seaport, major east–west highways, and busy U.S.–
MHD Loads Mexico border crossings, San Diego has a high concentration
The overall approach and process are highlighted in Figure 1 of MHD vehicles in specific areas. This is especially true in
to show how the process answers the main questions around the Otay Mesa area, an industrial area that houses the third
MHD electrification: 1) where it will occur, 2) when it will busiest commercial port of entry along the U.S.–Mexico bor-
occur, and 3) how big the projected load is. der with over 1 million freight crossings annually. This area
The process begins as follows: has significant potential for fleet electrification and was cho-
1) Finding fleets and vehicle counts: This would appear to sen to illustrate the proposed approach, where MHD facilities
be a simple step but can be quite complicated. Publicly are identified and categorized based on the facility type, vehi-
available data help but often undercount due to where cle type, and regulation requirements. After finding facilities,
vehicles are actually registered. A large distribution fa- 8,760-h load profiles are created for each facility and aggre-
cility may be in plain view, but the databases may show gated to evaluate the impact on the grid.

78 ieee power & energy magazine January 2025 Show Issue

Finding Fleets and Vehicle Count regulations impact the adoption rate for that category
In this step, databases for commercial truck fleets, business of facility. For example, fleet operators whose business
registration websites and databases, geographic information revenues were US$50 million or greater or operated 50
systems, and vehicle registration data are all used and cross- trucks or more are all required to adhere to the California
validated. Multiple datasets are used to help pinpoint gaps zero-emission mandates; their electrification can be
within individual datasets, as well as provide additional predicted by the schedules outlined in the regulations.
information on the fleets at each location. Vehicle registration ✔ A second factor is vehicles that are not impacted by the
data are often used as the primary data source. However, that regulations.
data source is not always reliable for determining the size ✔ A third factor is whether the facility has a unique op-
and working location of fleets. It is common for a business to erating profile that is conducive to electrification (i.e.,
register their fleet at a different address, such as a company school buses).
headquarters, rather than where the trucks are operating, With address-level data, facilities can be categorized to
leading to vehicle registration data underestimating the vehicle allow adoption curves to be customized. In this case study,
counts at the operating location. Hence, fleet and business adoption curves were determined with California zero-emis-
specific databases are used as the primary data source to sion regulations including innovative clean transit regulations,
identify trucks that are in the target area. Fleet and business zero-emission airport shuttle regulation, advanced clean fleets
databases also provide information such as mileage, business regulation, and zero-emission transport refrigeration unit reg-
characteristics that can be used to help determine energy ulation, as well as Governor’s Executive Order N-79-20, which
needs, adoption curves, and charging load curves. aims to reach a 100% zero-emission drayage truck and off-
road equipment population by 2035 and a 100% zero-emission
Categorizing Facilities and Adoption Curves MHD vehicle population by 2045, where feasible.
With facility locations and vehicle counts finalized, catego- Using a process that customizes adoption curves also allows
rizing fleets into groups with different operating characteris- for different adoption rates to be considered when developing
tics is important to account for differences in adoption speeds the forecast, adding the ability to examine different scenarios
or charging load profiles. Adoption speed is typically driven when examining that area. The case study benefited from the
by local regulations, technology availability, and economics. California Energy Commission’s Integrated Energy Policy
California is leading in MHD electrification with aggressive Report (IEPR), which has MHD vehicle demand forecasts
ZEV regulations applied to different industry sectors and through the year 2040.
business categories. In the case study, multiple adoption scenarios were uti-
In this specific case study, the factors considered to create lized. Evaluations of the case study showed that as facilities
categories include the following: are examined at a granular level, assumptions on adoption
✔ One factor is whether fleets are impacted by specific scenarios and other factors such as charging windows can cas-
California zero-emission regulations and how these cade into varying charging load estimates, particularly when

Answers Estimates Determines

1 Where 2 When 3 How Big! 4

Categorizing Creating Mapping Facility

Finding Facilities Facilities and Facility
and Vehicle Counts Locations to
Adoption Curves Load Shapes Substations

Data Analytics Facility Requirements Charging Needs Impact Analysis

• Purchase Fleet and • Vehicle Class and Type • Miles Per Day • Load Profiles are
Business Data for • Charging Window • Miles/kwh Aggregated for
Best Fidelity Regions
Adoption Curves Load Profiles
• Public Data (e.g., • Geocoded Data
• Regulations, Economics • Scenario Analysis
Zoning, DMV) Links to Feeders
• Facility and Vehicle Types • Create 8760 Profile
and Substations
• Fit into Required Format

figure 1. The address-level process to estimate the load impact of MHD fleet electrification.

January 2025 Show Issue ieee power & energy magazine 79

 assumptions are carried forward 15–25 years into the future. considered and used to create additional scenarios. These three
Hence, the proposed approach is coupled with scenario analy- charging windows are as follows:
sis as well. The scenario analysis provides a range of estimate ✔ whole window: the whole dwelling time from when
loads. The variables were used to create high, medium, and trucks return to the facility until they depart
low projected charging load estimates. Stakeholders typically ✔ off-peak: from 9 p.m. to 4 p.m. the next day
choose the most likely scenario and then track year on year ✔ super off-peak: from 12 a.m. to 6 a.m.
which scenario the market is following. These factors are closely related to the type of business,
Hence, in addition to the adoption scenario used in the case fleet operation logistics, and vehicle class. Using the pro-
study, two additional cases for MHD EVs were utilized from posed approach provides the flexibility to determine the actual
the California IEPR, a baseline adoption scenario study and the energy needs per facility, with consideration of various fleet
Additional Achievable Transportation Electrification 3 (AATE operation schedules, vehicle classes, and geographical address
3) adoption scenario, which has greater adoption assumptions where this charging will happen.
compared with the baseline study due to consideration of Cali-
fornia Air Resources Board regulations. The IEPR also con- Grid Impact Assessment and Scenario Analysis
sidered the adoption of hydrogen and fuel cell EVs starting in In Figure 2, a heat map of the Otay Mesa area shows where
the year 2035. In summary, the case study considered three the concentrations of MHD EV fleets are located within the
sets of adoption curves: studied area through the year 2045. Individual facility load
✔ follow regulation adoption curves: described in the profiles are shown in Figure 3 as a sample of load profiles of
paragraph above, considered various California regula- four different facilities within the Otay Mesa zip code. The
tions and linked to each category differences in the magnitudes of the profiles are due to the
✔ moderate adoption curves: adoption penetration ob- differences in the number of vehicles and the miles per day for
tained from the IEPR AATE 3 scenario and applied to vehicles, while the type of vehicle has subtle differences in the
all categories profiles as well. The starting and ending times of the charging
✔ baseline adoption curves: adoption penetration ob- load are based on the charging window, which is related to
tained from the IEPR baseline scenario and applied to vehicle logistics of when they return and leave the facility and
all categories. potential EV tariff impacts.
Figure 4 shows the example of the aggregated 24-h profile
Creating Facility Load Profiles for the entire study area using the “medium” scenario com-
The categorized facility lists provided the information needed prising the moderate adoption curve and off-peak charging
to create the charging load profiles based on the facility’s window. The maximum peak occurs at night, between 12 a.m.
needs. Three main inputs are used when creating the facility’s and 2 a.m., and reflects vehicles charging at night due to the
load profiles for MHD fleets: 1) off-peak tariff and 2) vehicles operating on a single shift.
✔ miles per day for each facility The load profile also shows a daytime “bump” between the
✔ energy efficiency, kWh/mile hours of 10 a.m. and 3 p.m. This load represents the additional
✔ charging window. daytime charging expected for vehicles that have daily miles
The miles per day are important data in estimating the facil- driven exceeding the vehicle range limits or above the over-
ity’s energy needs for day-to-day operations. The commercial night charging capability. This daytime load reflects the load
fleet dataset provided these data at the facility level, and they from a truck stop in the Otay Mesa area where it is assumed
were incorporated. The vehicle energy efficiency supports the that additional charging needs will occur.
conversion from daily miles driven to required energy kWh.
The charging window determines when the charging will Consideration of Varying Adoption Curves
occur and the charge rating needed to finish charging within The three main adoption curves that were used in the assess-
the targeted charging window. Three charging windows, ment were “follow regulation,” a curve that incorporates Cali-
informed by the SDG&E EV time-of-use tariff structure, are fornia Regulations; a “moderate case”; and a “baseline case”
defined in the “Categorizing Facilities and Adoption Curves”
section. The chart in Figure 5(a) shows the aggregated demand
Load (kW)
Loa MW for different scenarios, and the chart in Figure 5(b) shows
the EV penetration percentage for the study area.
10 k For the EV penetration percentage, the “follow regula-
tion” and “moderate case” curves follow relatively closely.
5k
However, the two curves have differences in early years
0 (2025–2030) and later years (2037–2045). The reason for
these differences is that the “moderate case,” which is based
figure 2. Charts for the heat map of the concentration of on the AATE 3 scenario, includes hydrogen vehicle adoption
the fleet. in the later years, which the “follow regulation” scenario does

80 ieee power & energy magazine January 2025 Show Issue

not. The early years’ divergence is
likely because the “moderate case”
3,000
applies an average adoption curve
for the whole California Indepen- 2,500
dent System Operator area, while
the “follow regulation” scenario 2,000
applies the adoption curves by cat-

kW
egory with specific ZEV regula- 1,500
tions. The separation in the early
1,000
years exists because a higher per-
centage of mandated vehicles exist 500
in the pilot area.
0
1 11 21 31 41 51 61 71 81 91 101 111 121 131 141 151 161
Scenario Analysis by Varying
Hour of the Week
Charging Window
A case analysis around varying
the length of the charging window figure 3. Individual load profile examples.
is also performed. In this case, the
adoption cases in the “moderate
case” scenario were varied across three charging windows: 400
super off-peak, off-peak, and “whole charging window,” as
shown in Figure 6. 300
It can be seen from Figure 6 that there is not a significant
MW

200
difference between the off-peak and whole-window charging
windows, and these two curves track closely. However, the 100
compressed charging period of super off-peak results in sig-
0
nificantly higher peaks, with approximately 60% more peak 0:00 4:00 8:00 12:00 16:00 20:00 0:00
load when the charging window is compressed compared with Hour Of Day
the base case EV load.
figure 4. An example of the aggregated load profile of
Conclusions Otay Mesa.
Assessing the charging load impact of MHD vehicles poses
a challenge for any organization. Location plays a significant Large concentrations of vehicles, oversized chargers, and
role in determining projected loads as fleet facilities are not dense facility clustering all have the potential to create loads
uniformly distributed across an area. Assumptions on factors exceeding hundreds of MW in localized areas. Hence, the
such as adoption rates and charging windows can lead to vari- need to understand the potential impact is significant. Know-
ances in project loads, providing motivations for utilities to ing this information is essential to properly plan for MHD EVs
utilize an address-level/bottom-up, scenario-based approach and will ensure utilities can serve as an enabler of MHD elec-
when trying to project MHD charging loads. trification rather than an inhibitor.

600 100%

80%
400
60%
MW

40%
200
20%

– 0%
2020 2025 2030 2035 2040 2045 2050 2020 2025 2030 2035 2040 2045 2050
Follow Regulation Moderate Baseline Follow Regulation Moderate Baseline
(a) (b)

figure 5. A comparison of different adoption curves. (a) Aggregated demand MW: varying adoption curves. (b) EV pen-
etration percentage.

January 2025 Show Issue ieee power & energy magazine 81

 Knowing these challenges, the team created the proposed
approach, which takes advantage of available big data to locate
600
MHD fleet facilities and identify their corresponding vehicle
counts. Data can be grouped in any manner desired, and adop-
400 tion levels can be customized depending on the facility type
MW

or vehicle type. In addition, the ability to geocode the facility

200 data allows loads to be linked directly to feeder nodes or sub-
stations to assess the impact on the electricity grid and effec-
tively plan for the transition.
2020 2025 2030 2035 2040 2045 2050

Whole Window Offpeak Super Offpeak For Further Reading

“National zero-emission freight corridor strategy. Prioritizing
investments, planning, and deployment for medium- and
figure 6. The impact of varying the charging window on heavy-duty vehicle fueling infrastructure to advance zero-
aggregated demand. emission freight along our nation’s corridors,” Joint Office
of Energy and Transportation, Mar. 2024. Accessed: Mar.
Key Learnings From the Methodology 2024. [Online]. Available: https://driveelectric.gov/files/
and Process zef-corridor-strategy.pdf
✔ Importance of location: The density plot shows that “California’s plan for zero-emission vehicles,” Califor-
fleets are not uniformly distributed across a territory. nia Air Resources Board. Accessed: Jan. 2024. [Online].
Hence, to understand the impact of the MHD fleet Available: https://ww2.arb.ca.gov/our-work/programs/
charger loads on a feeder or substation, identifying truckstop-resources/zev-truckstop/zev-101/californias-plan
the location of the charging sites is essential to sup- -zero-emission-vehicles
port utility planning activities. The importance of “SDG&E clean transportation initiatives,” SDG&E. Ac-
location is a main driver for the need for an address- cessed: Feb. 2024. [Online]. Available: https://www.sdge.com/
level, bottom-up approach when examining MHD residential/electric-vehicles/electrification-projects-overview
fleet impacts. “California Zero-Emission Regulations,” California Air
✔ Variability in projected loads: Significant variability Resources Board,” Accessed: Jan. 2024. [Online]. Avail-
on the total EV load occurs based on different charging able: https://ww2.arb.ca.gov/our-work/programs/truckstop
window assumptions. Scenario analysis is helpful for -resources/regulation-overviews
utilities to understand the variability in impacts. Utili- “2023 integrated energy policy report,” California En-
ties may not need to plan for each scenario but should ergy Commission, Feb 2024. Accessed: Feb. 2024. [Online].
track year-on-year results to ensure the market activity Available: https://www.energy.ca.gov/data-reports/reports/
is tracking their selected scenario. This issue is exacer- integrated-energy-policy-report/2023-integrated-energy
bated due to limited adoption data available on MHD -policy-report
markets or limited recommendations on how to opti- “ERCOT EV Allocation Study. Methodology for deter-
mize charging profiles. mining EV load impact at the substation level,” Aug. 2023.
✔ Utilizing multiple data sources: It is important to use Accessed: May, 2024. [Online]. Available: https://www.
multiple data sources when examining a target area. In- ercot.com/files/docs/2023/08/28/ERCOT-EV-Adoption
dividual data sources can have gaps, such as the Depart- -Final-Report.pdf
ment of Motor Vehicles tending to undercount vehicles
due to listing vehicles where they are registered rather Biographies
than where they are utilized. Important data such as Richard Fioravanti is with Quanta Technology, Raleigh, NC
miles driven may also only be available in specific data 27607 USA.
sources. Hence, using multiple data sources is often nec- Lisha Sun is with Quanta Technology, Raleigh, NC 27607
essary in the MHD market to obtain full, accurate data USA.
necessary to assess the impact. Robert Mushet is with Quanta Technology, Raleigh, NC
✔ 8760 load profiles: It is not enough to have peak load 27607 USA.
information. Fleet charging typically occurs at night; Anderson Bolles is with San Diego Gas & Electric,
however, due to opportunity charging and routes being San Diego, CA 92123 USA.
longer than battery capacity, there may be loads occur- Alex Moffat is with San Diego Gas & Electric, San
ring in the daytime as well that may contribute to a new Diego, CA 92123 USA.
system peak. Hence, it is important to list charging loads Matt Belden is with San Diego Gas & Electric, San
over an 8760 time frame. Diego, CA 92123 USA. p&e

82 ieee power & energy magazine January 2025 Show Issue

Detecting
Anomalies for
Fire Prevention
in Distribution
Systems
Challenges and Analytical
Techniques

By Jhi-Young Joo , Christabella Annalicia,

Apoorv Pochiraju, Ozgur Alaca , Ali Riza Ekti ,
Michael Balestrieri, Hamed Valizadeh Haghi,
and Abder Elandaloussi

Digital Object Identifier 10.1109/MPE.2024.3462306

Date of current version: 11 November 2024

January 2025 Show Issue 1540-7977/24©2024IEEE ieee power & energy magazine 83
E
ELECTRIC UTILITIES HAVE HISTORICALLY CON- degrades over time, especially in high-wind or high-
tributed to the causes of up to 10% of all wildfires in vegetation conditions, air gaps in conductors and other
California, including some of the most devastating confla- materials such as insulation create arcing and heat, which
grations in the state’s history. California electric utilities become high risk and result in faults, such as arcing, that can
like Southern California Edison (SCE) work to combat the cause wildfires. The premise of this work is that when there
risk and impact of wildfires from many angles; SCE alone is an air gap in the path of conductance that emits heat, the
has invested over US$4.5 billion toward addressing wildfire electric signature of this equipment is different from the nor-
challenges as noted in their Wildfire Mitigation Plan (see mal conditions, and this can be detected from system mea-
“2023–2025 Wildfire Mitigation Plan” in the “For Further surements. This work focuses on identifying and detecting
Reading” list for more details). high-resolution point-on-wave [(POW) or waveform] arcing
One focus of the wildfire risk reduction efforts is pre- signatures in system measurements in order to detect arcing
venting energized high-voltage equipment from contacting conditions and prevent potential fire hazards.
foreign objects via undergrounding cables, replacing bare The most fundamental challenge of detecting arcing is in
metal conductors with covered conductors, trimming or quantitatively defining what arcing is and what it looks like
removing vegetation around equipment, and even deenergiz- in system measurements. As shown in Figure 1, many arcing
ing powerlines in areas of high risk during dry windstorms. signatures show subtle transients in currents that are diffi-
The utilities also increased the frequency and vigilance of cult to distinguish from noises in load currents. This makes
equipment inspections in high-fire-risk areas to replace them inadequate for threshold-based detection by their root-
aged and damaged assets like structures, insulators, and mean-square (rms) values. Moreover, arcing in distribution
pins before they fail. systems is even more difficult for several reasons. Longer
While these efforts have led to proven success, they circuit miles with more equipment components in distribu-
require significant workforce and infrastructure. To sup- tion systems mean more candidates for arcing, including at
plement this approach, SCE is testing and deploying new the secondary. In addition, fault currents at the secondary
data-driven technologies that can detect when equipment will be even less visible at substations due to the service
issues on the distribution electric grid may arise before transformers. Also, distribution systems typically have more
they ignite a fire. complex topology (such as unbalanced phases and single- or
As the causes of these fires are multifaceted, so must
be the solutions. Current fire prevention methods directly
address the source elements of fire (fuel, heat, and oxygen)
and fire conditions. However, this work tackles prevention
from a different angle: through detecting the heat element
of fire within a distribution system. Electrical equipment is
always emitting heat but, in healthy and normal conditions,
does not pose a significant fire risk. However, as equipment

70

60

50
Current (A)

40

30
Phase A

20 Phase B
Phase C
10
0 10 20 30 40 50
Time (s)

figure 1. One example of arcing signatures measured at

an anonymized utility distribution substation. Shown are
rms values of three phase currents where phase B showed
arcing signatures, one of them highlighted in red. (Source: figure 2. RF sensors deployed on SCE’s system. (Photo
U.S. Department of Energy Grid Event Signature Library.) courtesy of SCE.)

84 ieee power & energy magazine January 2025 Show Issue

two-phase parts), and the records and data are often incom- rial, such as vegetation, where contact, by nature, does not
plete or inaccurate. Furthermore, changes in end users’ produce a high-fault current, making it difficult for traditional
equipment and loads such as distributed energy resources time–overcurrent trip elements to detect. These high-impen-
and electric vehicles may not be readily incorporated in dence fault trip elements perform targeted signal processing
the utility’s data, resulting in added noises and harmonics analytics that detect signal characteristics associated with the
that can make measurements more convoluted, yet untrace- excitation of odd harmonics. One challenge with this type
able of their sources. This article will discuss some of these of protection, however, is the subtle and sensitive nature of
challenges and present promising analytical techniques to faults that makes performing a protection action difficult
address them that leverage signal processing and artificial without the settings being overly sensitive and accidentally
intelligence, specifically in distribution systems. taking customers offline.
Another noteworthy technology for detecting degrading
Current Technologies for electrical equipment is RF emission monitoring (Figure 2),
Arcing Detection which is particularly good at detecting issues like contact
Several technologies that leverage signal processing have with vegetation, damaged insulators, and loose cable strands,
been developed in response to the need for arcing detection, all of which could be precursors to arcing. The dispersed
differing depending on the application. From a protection placement of these sensors also benefits from synchronized
standpoint, new relays provide off-the-shelf trip elements GPS time and location, which can be used to pinpoint the
specifically aimed at detecting high-impedance faults. These locations of issues, aiding in faster remediation.
types of faults are commonly associated with arcing or a Another area of arc detection that this article discusses
downed conductor in contact with a low-conductivity mate- is the monitoring of voltage and current waveforms, which
is much like protection relays,
although the output is not intended
to perform a protection action but
rather monitor for incipient con-
ditions that may be indicative of
faulty equipment. This technology
has several benefits over RF sen-
sors: it can utilize existing sensing
instrumentation, such as system
relays and fault recorders, and is
more flexible in capturing anoma-
lies beyond one specific type. The
higher-resolution measurements
appropriate for capturing transient
anomalies come from waveform
measurement units (such as sub-
station digital fault recorders [Fig-
ure 3] and power quality meters)
and involve numerous data pro-
(a) (b) cessing steps to achieve a precise
detection of the system anomaly.
figure 3. (a) A front and (b) a rear view of a digital fault recorder. Devices and systems using this

Point-on-Wave
(POW)
Measurements Preprocessing Feature Classification Identification
of Data Extraction • Binary Versus • Arcing type,
• Digital Fault Recorders
and Other • Segmentation • Seconds-Level Multi-Class Phase
Meters/Relays • Noise Removal Detection • Neural Network • Other
• Real-Time Digital • Milliseconds- Disturbance
Simulations Level Detection Events
• Grid Event Signature
Library

figure 4. An overview of the arcing detection algorithm with feature extractions in two different time scales.

January 2025 Show Issue ieee power & energy magazine 85

 Devices and systems using this concept are becoming more
commercially available, and utilities and researchers alike are
teaming up to enhance solutions.

concept are becoming more commercially available, and at the grid edge and centralized systems aggregating the data
utilities and researchers alike are teaming up to enhance into applications that inform grid operators of incipient fail-
solutions. Due to the volume of data produced and the devel- ure conditions.
opmental stage of this concept, data pipelines are employed
to bring vast amounts of time series waveform data to cen- Data-Based Arcing Detection
tralized big data systems where data scientists can then test in Distribution Systems
and build analytical models before deploying them. This Because of the unique challenges in detecting arcing sig-
technology is leading toward a hybrid of analytics running natures, it is critical to understand the characteristics of

150 400

300
100
200

50 100
Dimension 2

Dimension 2

0
0
–100

–50 –200

–300
–100
–400

–150 –500
–200 –150 –100 –50 0 50 100 150 –400 –300 –200 –100 0 100 200 300 400
Dimension 1 Dimension 1
(a) (b)
100 300
80
200
60
40
100
Dimension 2
Dimension 2

20
0 0
–20
–100
–40
–60
–200
–80
–100 –300
–150 –100 –50 0 50 100 150 –300 –200 –100 0 100 200 300 400
Dimension 1 Dimension 1
(c) (d)
Blown Fuse Line Switching Low Amplitude Arcing Transformer Energization

figure 5. The t-SNE plot of the feature vectors from the various signal processing techniques applied to the real-world grid
disturbance measurements from GESL. The techniques used are (a) SCF, (b) FFT, (c) amplitude and phase, and (d) raw data.

86 ieee power & energy magazine January 2025 Show Issue

 For example, a feature defined for step voltage change, when used
in conjunction with other features, can also help detect capacitor
bank operations.

arcing in system measurements. These characteristics are are also being conducted on SCE’s representative distribu-
captured in machine learning algorithms as features, and tion systems. The parameters in an arcing module and the
the challenge is to effectively extract these features in order locations of arcing and sensing/measurement equipment are
to successfully identify anomalies. By crafting features that varied in simulations to capture diverse, potential scenarios
capture the essence of raw grid data, models can extract of arcing and to assess how different settings impact the per-
meaningful patterns and relationships from the data, lead- formance of the detection algorithm.
ing to more accurate detections and insights. The features Once POW measurements from actual distribution sys-
ultimately dictate the success or failure of an anomaly tems, simulations, and/or data repositories (in this case the
detection model. GESL) are obtained, the data are processed to remove noise
and break the raw measurements into time durations (about 2
Applied Feature Engineering to 20 s) that are computationally manageable. Features from
for Anomaly Detection these data segments are then extracted. Using these features,
There are several key considerations for feature extraction the segments are categorized against other types of events,
that apply to distribution grid data as follows: such as line switching and blown fuse events, to determine
1) Improving Model Interpretability: Features should instances of arcing. The results showed good accuracy,
be interpretable and enable transparency for building which was determined using classification techniques. The
trust in anomaly detection systems and making in- details of this approach and results are discussed in the fol-
formed decisions based on model outputs. For exam- lowing sections.
ple, a feature defined for step voltage change, when
used in conjunction with other features, can also help Seconds-Level Arcing Detection:
detect capacitor bank operations. Spectral Correlation Function
2) Handling Complex Data: Distribution grid data are Signal processing techniques are essential for feature engi-
rarely clean. They often contain missing values and neering, especially for waveform measurements. Well-
outliers that can hinder model performance. Feature known methods, such as the fast Fourier transform (FFT)
extraction can address these challenges by transform- and amplitude-phase, etc., have been widely used to dis-
ing raw data into a more suitable representation. An tinguish anomalies in these measurements. However, cur-
example of this is combining imperfect grid-edge data rent studies reveal that these conventional signal processing
and substation waveform recordings to extract mean- techniques are not effective in capturing intermittent tran-
ingful insights despite missing values. sient behaviors in signals, such as arcing. The spectral
3) Minimal Need for Labeled Data: Accurately labeling
power grid and equipment data often requires highly
trained experts and can be a time-consuming process. 1
Well-engineered features minimize the requirement
0.75
for labeled signature libraries as the hidden patterns
0.5
Normalized Current

become more salient for the model to make accurate

predictions. 0.25
Given these considerations, an arcing detection algorithm
0
was developed based on existing signal processing tech-
niques using a set of well-curated labeled arcing signatures –0.25
from the U.S. Department of Energy (DOE)’s Grid Event –0.5
Signature Library (GESL). Figure 4 outlines the overall pro- –0.75
cedure. For the first step in this procedure, actual POW mea-
–1
surements from digital fault recorders and other sensors from 0 200 400 600 800 1,000 1,200
the SCE distribution systems are captured as input to the Time Step
subsequent steps. In addition, since arcing of equipment can
be affected by various factors and is difficult to create and figure 6. An example of an arcing segment over about five
control in a real environment, real-time digital simulations cycles or 83.3 milliseconds.

January 2025 Show Issue ieee power & energy magazine 87

 c­orrelation function (SCF)-based approach was used to To ensure the efficacy of this approach for identify-
extract hidden periodicities in the frequency domain that ing arcing within data segments, the SCF was compared
are not stationary, i.e., the statistical characteristics of with conventional methods (FFT, amplitude-phase,
which do not vary over time. and raw data) by visualizing the features of each using

0.0010

Detail Coefficient Value

0.0005

0
Level 1
Decomposition –0.0005

–0.0010

–0.0015
0 100 200 300 400 500 600
Coefficient Index

0.002

0.001

Detail Coefficient Value

0

Level 2
–0.001
Decomposition
–0.002

–0.003
0 50 100 150 200 250 300
Coefficient Index

0.015

Raw Waveform Data 0.010

Detail Coefficient Value

0.005
1
Level 3 0
0.75
Decomposition –0.005
0.5
Normalized Current

–0.010
0.25
–0.015
0 0 20 40 60 80 100 120 140 160
Coefficient Index
–0.25 Feature Vector
0.10
–0.5
Detail Coefficient Value

0.05
–0.75

–1
0 200 400 600 800 1,000 1,200 Level 4 0

Time Step Decomposition

–0.05

–0.10

0 10 20 30 40 50 60 70 80
Coefficient Index

0.4
Detail Coefficient Value

0.2

0
Level 5
Decomposition –0.2

–0.4

–0.6
0 5 10 15 20 25 30 35 40
Coefficient Index

0.6

0.4
Detail Coefficient Value

0.2

Level 6 0
Decomposition
–0.2

–0.4

–0.6
0 2.5 5 7.5 10 12.5 15 17.5
Coefficient Index

figure 7. A diagram showing six levels of decomposition of waveform data into their wavelet coefficients. The greatest
energy differential value from consecutive segments at each decomposition level constitutes an element of a six-dimensional
feature vector for the original waveform data.

88 ieee power & energy magazine January 2025 Show Issue

 This work obtained around or over a 90% accuracy of binary
classification in milliseconds-level arcing identification, depending
on the metrics calculated.

t-distributed stochastic neighbor embedding (t-SNE), on provides validity for the effectiveness of a distance-based
the measurements of grid disturbance events from the classification algorithm.
GESL. Figure 5 shows the t-SNE plots of the feature vec-
tors obtained from SCF, FFT, amplitude-phase, and raw Classification Results
data. SCF was the only method that was able to create To evaluate the effectiveness of DWT-based feature vectors
perimeters around different event types without overlaps, extracted from each segment, a k-nearest neighbors model
proving the effectiveness of its feature engineering for was trained and tested, to predict either a positive (arcing) or
arcing identification. a negative (nonarcing) label for each feature vector. Classi-
fier training and testing was repeated for 200 iterations, with
Milliseconds-Level Arcing Detection: the feature vectors randomly split into training and testing
Discrete Wavelet Transform subsets with a 9:1 ratio. The performance of classification
After classifying seconds-level waveform measurements (with the testing dataset) was then evaluated by calculating
as arcing, the milliseconds-level time scale was analyzed the Matthews correlation coefficient (MCC) and F1 score
to identify arcing occurrence(s) within the seconds period. metrics. The results are summarized in Figure 9. In Figure 9,
Segmentation at the milliseconds level enables the extrac- we see that all the MCC values are positive with an average
tion of frequency characteristics within a narrower window, of 0.92 across 200 iterations, indicating good classification
which enables greater differentiation of arcing and nonarc- with no tendency toward randomness or inverse correla-
ing components of the signal and detection of arcing events tions; the F1 scores promote a similar result, with most val-
on a shorter time scale. This step was performed by decom- ues being greater than 0.85 and having an average of 0.96
posing the milliseconds-level signal using discrete wavelet across 200 iterations. With no negative values observed for
transform (DWT), which provides a comprehensive repre- the MCC in any of the 200 iterations and with both the MCC
sentation that captures both the time and frequency informa- and F1 scores having averages around 0.90, the proximity-
tion of the signal. based classification performed well with the DWT-based
Each arcing current phase signal was segmented into five- feature vectors.
cycle windows, which was observed as the optimal window
length for capturing arcing behavior regardless of the nature Conclusions
of the waveform distortion and the given sampling rate of Arcing is a precursor to fire hazards in power systems, but
15,360 Hz. Each individual five-cycle segment that contained detecting it in distribution systems is difficult. This article
an arcing fault was labeled as an arcing segment. The arcing addressed relevant efforts and technologies to detect ­arcing
segments were identified based on the data provider’s anno-
tations, and every individual segment that did not contain an
arcing event was labeled as a nonarcing segment. An exam-
Arcing Segment
ple of an arcing segment is shown in Figure 6. 15 Nonarcing Segment
From each segment, detail coefficients were obtained for
six decomposition levels, as observed in Figure 7. From each 10
decomposition level, the greatest energy change differential
t-SNE Value 2

5
among cycles (represented by red boxes and dotted lines in
Figure 7) was computed. These values were then concat- 0
enated to form a six-dimensional feature vector for each
waveform segment. –5
Figure 8 shows the distribution of arcing and nonarc-
–10
ing feature vectors in the reduced feature vector space after
applying t-SNE. The feature vectors belonging to arcing
–20 –10 0 10 20
segments show a visible similarity to one another and vis- t-SNE Value 1
ible proximity. Feature vectors belonging to nonarcing seg-
ments have an observable separation from those belonging figure 8. The distribution of t-SNE values of arcing and
to arcing segments. The distribution of these feature vectors nonarcing feature vectors.

January 2025 Show Issue ieee power & energy magazine 89

 Acknowledgment
This work was partly performed under the auspices of the
1 DOE by Lawrence Livermore National Laboratory under
Contract DE-AC52-07NA27344. This article has been
authored by UT-Battelle, LLC, under Contract DE-AC05-
0.95 00OR22725 with the DOE. The publisher acknowledges
the U.S. government license to provide public access under
the DOE Public Access Plan (https://energy.gov/doe-public
-access-plan).
Value

0.90
For Further Reading
“2023–2025 wildfire mitigation plan,” Southern Califor-
0.85 nia Edison, Sacramento, CA, USA, Apr. 2024. Accessed:
Sep. 23, 2024. [Online]. Available: https://www.sce.com/sites/
default/files/AEM/Wildfire%20Mitigation%20Plan/2023
-2025/SCE%202023%20WMP%20R2-clean.pdf
0.80 M. Kistler, F. Heleniak, and T. Varshney, “Practical ex-
perience with high-impedance fault detection in distribution
MCC F-1 Score systems,” in Proc. 46th Annu. Western Protect. Relay Conf.,
Spokane, WA. USA, 2019, pp. 1–12.
figure 9. The MCC and F1 score statistics over 200 ran- “Grid event signature library,” Oak Ridge Natl. Lab.
domized sampling and iterations of arcing detection classi- Lawrence Livermore Natl. Lab. [Online]. Available: https://
fier training and testing. gesl.ornl.gov/
O. Alaca et al. “Detection of grid-signal distortions us-
in distribution systems and focused on detecting arcing sig- ing the spectral correlation function,” IEEE Trans. Smart
natures in waveform measurements that can be obtained from Grid, vol. 14, no. 6, pp. 4980–4983, Nov. 2023, doi: 10.1109/
system measurement units such as digital fault recorders, TSG.2023.3309532.
protection relays, and power quality meters. Two different I. Chakraborty and J. Y. Joo, “Data-driven detection of
signal processing techniques were applied, SCF and DWT, low-current arcing events in power distribution systems,”
for extracting features in different time scales and identify- in Proc. IEEE/PES Transmiss. Distribution Conf. Expo.
ing arcing within these waveform measurements. Using the (T&D), 2022, pp. 1–5, doi: 10.1109/TD43745.2022.9816885
curated signatures in the GESL from anonymous utility field P. M. Bentley and J. T. E. McDonnell, “Wavelet trans-
measurements, this work obtained around or over a 90% forms: An introduction,” Electron. Commun. Eng. J., vol. 6,
accuracy of binary classification in milliseconds-level arcing no. 4, pp. 175–186, 1994, doi: 10.1049/ecej:19940401.
identification, depending on the metrics calculated.
With this encouraging result, the algorithm is being tested Biographies
against larger datasets of unlabeled measurements from the Jhi-Young Joo is with Lawrence Livermore National Labo-
field at SCE, as well as measurements from hardware-in- ratory, Livermore, CA 94550 USA.
the-loop simulations performed on an SCE distribution sys- Christabella Annalicia is with Lawrence Livermore Na-
tem. One interesting aspect of the application is making the tional Laboratory, Livermore, CA 94550 USA.
algorithm robust against different temporal resolution mea- Apoorv Pochiraju is with Lawrence Livermore National
surements. As the POW measurement systems are not stan- Laboratory, Livermore, CA 94550 USA.
dardized, the resolution of the datasets from systems vary, Ozgur Alaca is with Oak Ridge National Laboratory,
and the detection algorithm’s performance may be inconsis- Oak Ridge, TN 37830 USA.
tent depending on the anomalies of interest, the distance/ Ali Riza Ekti is with Oak Ridge National Laboratory,
impedance between the measurement and the anomaly, etc. Oak Ridge, TN 37830 USA.
Considering this, pathways for implementing the algorithm Michael Balestrieri is with Southern California Edison,
in various utility monitoring, operation, and protection set- Westminster, CA 92683 USA.
tings are being investigated along with variations in arcing Hamed Valizadeh Haghi is with Southern California
signatures depending on different factors with real-time dig- Edison, Irvine, CA 92618 USA.
ital simulations to help gain a better understanding of arcing Abder Elandaloussi is with Southern California Edison,
characteristics themselves and how they manifest in sensor Westminster, CA 92683 USA.
measurements.  p&e

90 ieee power & energy magazine January 2025 Show Issue

Integrating
Behind-the-
Meter Grid Edge
Technologies
Into Wholesale
Electricity
Markets
A Novel Methodology Using
Virtual Power Plants

By Alex Papalexopoulos , Shmuel Oren,

and Hung-po Chao

Digital Object Identifier 10.1109/MPE.2024.3473852

Date of current version: 11 November 2024

January 2025 Show Issue 1540-7977/24©2024IEEE ieee power & energy magazine 91
 R
RECENT TECHNOLOGICAL ADVANCES AND POLICY The VPP Optimization Platform for
initiatives have led to the massive penetration of distributed Energy Market Participation
energy resources (DERs) into the distribution grid. DERs This section presents a cloud-based VPP aggregation plat-
include Internet of Things (IoT) smart devices, renewable form for creating VPPs and harnessing their capacity for
energy source generation, battery energy storage systems, participation in the wholesale and retail energy markets.
electric vehicles (EVs), demand response, and other fuel- The VPP platform aggregates, monitors, and controls IoT
based distributed generation connected behind the meter devices and other BTM DERs installed in residential and/or
(BTM) in many power systems around the world. The pro- commercial buildings, transforming them into grid-interac-
liferation of DERs creates significant opportunities but also tive efficient buildings (GEBs). The objective is to develop
poses new challenges to the power system from an opera- energy offers for the created VPPs that are offered as a sin-
tional and reliability perspective and from an economic and gle resource participating in energy, capacity, and ancillary
market perspective. services markets.
The core hardware technology underlying the VPP aggre-
Introduction gation platform consists of the following subcomponents:
The key challenges concern the coordination of such het- ✔ the cloud-based backend system (the cloud), which is a
erogeneous energy resources and their harmonization with closed secure cloud-based architecture segmented by
grid operations while creating opportunities for the DER microservices
aggregators (DERAs)/owners for the participation of such ✔ the gateways at the GEBs
resources in hierarchical energy market structures. DERs, ✔ smart thermostats for all heating, ventilation, and air
which come in all shapes and forms, could participate, conditioning systems in the GEBs
individually or through aggregations, in the form of vir- ✔ smart switches for all 220-V hot water heaters and
tual power plants (VPPs) in peer-to-peer markets, distri- other DERs in the GEBs
bution-level markets, and wholesale energy markets. The ✔ smart mesh network repeaters for areas needing im-
landmark Federal Energy Regulatory Commission (FERC) proved device connection reliability
Order 2222 has created a regulatory framework for foster- ✔ redundant Internet modems with remote power cy-
ing DER market participation and allowing DERs to bypass cling and data packet load balancing between the pri-
the distribution grid and directly participate in wholesale mary and secondary virtual private network (VPN)
energy markets. paths to the cloud
Just as different types of conventional power plants offer ✔ long-term evolution (LTE) cellular modem for third-
to the grid different types of services (e.g., nuclear plants party VPN access to the cloud
provide baseload generation), so can different VPP configu- ✔ a mobile app for tenants and a reporting portal system
rations. For example, VPPs can integrate distributed solar for the building owners and managers.
and storage. The majority of VPPs today strictly shape the The gateway and component devices are built to cre-
grid demand by orchestrating load-consuming-based DERs ate a GEB-wide closed network for all the common areas
and/or DERs that generate and store electricity for on-site while also providing user interfaces and individual control
use (e.g., demand-shaping VPPs). Other VPPs inject energy per each apartment unit for its occupants without requir-
back into the grid (exporting VPPs). VPPs can be used stra- ing a gateway device in each apartment. The result is
tegically to shed demand on the grid during shortages, shift fewer components to fail, lower hardware costs, less main-
demand from peak to off-peak hours, or provide ancillary tenance, and faster installation time. The gateway in the
and other grid services to satisfy the needs of the distribution GEBs communicates live with the cloud computing back
and/or transmission grid and reshape baseload consumption. end and, from there, over the Internet to the wholesale and
This article highlights a process for aggregating hetero- retail energy markets. The networked control adapters
geneous BTM DER smart devices into VPPs for whole- measure energy that flows through the wire (and through
sale energy market participation. It also presents a novel the adapter) to measure energy and thus, also energy cur-
methodology representing VPPs in the wholesale market, tailment at each energy-consuming device. The gateway
like conventional resources, through a supply function that supports, in addition to IoT smart devices, EV chargers,
determines the optimal VPP energy offers as a function of rooftop and parking lot solar, and battery energy storage
the wholesale price. The supply function is designed so as systems. The system has been tested in actual realistic
to harness maximum DER value from market participation configurations and is able to become the energy logic con-
while controlling availability risk so as to meet the indepen- troller for the GEBs; in a nutshell, it can be used as a build-
dent system operator’s (ISO’s) compliance standards. Supply ing “microgrid starter kit.” The local area network (LAN)
functions-based energy offers for wholesale energy market within the building is based on a mesh architecture among
participation are data driven and are determined based on all the devices in the GEBs (Figure 1); all the thermostats,
the offline sampling of actual DER usage data from actual hot water heater adapters, and other DER assets are con-
installations of DER assets. nected in a mesh topology.

92 ieee power & energy magazine January 2025 Show Issue

 All the communication between the VPP optimization platform
and third-party service providers follows zero-trust and end-to-end
security principles.

While the architecture is capable of supporting any ISOs for market participation (“northbound” signaling) are
mesh network technology, the VPP platform currently sup- architected to pass through the Ethernet port or the LTE
ports the Z-Wave protocol over low-frequency networks cellular connection. For enhanced security, the VPP aggre-
(908 MHz in the United States), which are much better at gation platform offers virtual firewall services available on
penetrating walls and floors compared to higher frequency the controller at the Ethernet port level as it is expected
signals, such as those used in Wi-Fi. All the IoT smart that certain advanced grid services will have heightened
devices and other DERs are connected together in this grid security requirements. In this meshed network, DER
Z-Wave meshed network of curtailment-capable and gen- devices can communicate with one another by using inter-
erating DER devices. Communications out from the GEBs mediate nodes to actively route around and circumvent
over the Internet to the cloud and eventually to all the GEB obstacles or radio dead spots that might occur in
the multipath environment of the
GEB units.
The VPP aggregation platform
and controllers are networked
together on a GEB site using
ethernet and other wireless tech-
nologies to create a cluster of hub/
controllers that can share the wide
area communications infrastruc-
ture at a site to communicate
securely to the cloud using VPN
connections. The networking
architectures used in a GEB and
among the apartment units use
Wi-Fi only for inter-hub network-
ing. All GEB tenant communica-
figure 1. Looking “inside” a GEB at its meshed network for DER devices. tions are over Z-Wave (Figure 2).

Low-Frequency Building Z-Wave Wi-Fi (High-Frequency) Tenant’s

Infrastructure Network Mesh in each Personal Wi-Fi Network
Network Apt. Unit

Across the Many but

Property Private

Tenant’s Router

Smart
Thermostat Z-Wave Wi-Fi
Tenant’s
Cabled Broadband
Z-Wave Broadband
ata
ar D
C ellul Mobile
Cloud
Smart On/Off Cellular Application
Switch, Hot Water Backup
Gateway

figure 2. A networking diagram showing cellular communications with the cloud.

January 2025 Show Issue ieee power & energy magazine 93

 The energy yield of each DER asset is the energy consumed/
produced out of one KW nameplate capacity; it is treated as an
uncertain random variable.

The VPP Optimization Platform access to their smart devices and manages communications
Architecture to dashboards and other web services. The artificial intel-
The technology operates on a DER device level (not aggre- ligence (AI)-based data enrichment microservice is based
gated VPP level) and deploys priority tranches and stochastic on a classical machine learning (ML) algorithm, called Iso-
distributed computing to coordinate and control the demand lation Forest (iForest), which is an unsupervised algorithm
shaping of various groupings of devices that are assigned to dif- and works on the principle that usage data anomalies are
ferent activation tiers. The BTM DER devices switch between rare and different and hence, easier to isolate in a decision
various states according to a modified Markov model. The tree. Based on our experience, this assumption is considered
customers are not directly exposed to dynamic spot energy to be realistic. The advantages of the iForest ML algorithm
market prices but instead are represented by intermediary are as follows:
DERAs, employing the VPP paradigm to participate in 1) It is an unsupervised algorithm that can be trained
wholesale energy market trading activities, consistent with with or without anomalies.
the FERC Order 2222. Individual agreements between 2) It can work with high-dimensional data.
DERAs and individual end users determine the demand 3) It is efficient with respect to time and space complexity.
flexibility in terms of the tier of service provided by each 4) The execution of this process requires the application
DER device. The determination of which DER device in the of the following methodology steps: 1) data normal-
VPP aggregation will consume/produce power and at what ization and preprocessing to convert the raw DER
times is implemented through aggregation and virtualized usage data into a form that can be consumed by the
network control. algorithms and 2) data enrichment where normalized
The cloud-based BTM DER optimization platform archi- data are further enriched by a) historical ML indices,
tecture is based on a software-as-services (SaaS) framework, b) the latest configuration and operational settings of
as shown in Figure 3. It deploys application programming devices (e.g., settings of a given device on a given day
interfaces (APIs) for all functionalities and microservices and or week), and c) human advisories (e.g., taking out
is agnostic to DER device appliances; it also provides a gate- anomalous behavior of one particular day).
way digital twin and digital lake capability. It is very flexible The device management microservice keeps track of the
and can scale at negligible incremental costs. The cloud ser- device state and modifies its state as needed. The device
vices in the SaaS optimization platform architecture run asyn- management microservice coordinates device communi-
chronously from each other in the VPP platform, including the cations, including making sure the device state is current,
cloud, gateway, and other applications. The SaaS platform pro- ensuring that the device is available, logging commands to
vides multiclient support for all internal system components the device, and updating the device state to the rest of the
(such as the gateways) and external stakeholders [such as secu- system. The energy modeling microservice keeps track of
rity, third-party services, web applications, ISOs, transmission DER usage data and their past activations, past performance,
system operators, distribution system operators (DSOs), and and other metadata for predicting future DER energy use
local energy markets, etc.]. All the communication between estimates. This microservice also collects data from exter-
the VPP optimization platform and third-party service provid- nal services like weather forecasting services and property
ers follows zero-trust and end-to-end security principles. and real estate data that could impact usage and tenants.
The Internet-Connected Devices Microservice group DER device states can change on a minute-by-minute basis,
extends the cloud control and management to devices out- allowing the cloud to modify their state for curtailment/pro-
side of the VPP optimization platform hub’s managed duction activations.
devices. This group is separate from the others in that it has The cloud-based BTM DER optimization platform archi-
additional security firewalls and encryption requirements tecture is designed to unlock the value of a very large number
that distinguish connections only to authorized equipment of heterogeneous small DER assets at the edge of the grid,
and servers. This group has hooks ready to support addi- make them visible to energy markets at scale to improve
tional devices and equipment to support future growth. The the resilience of the grid, and offer several grid ancillary
External Actors group is a general category for all the other services. The created VPP, based on numerous field tests,
services and functions that are not hardware devices. This is can emulate dispatchable conventional generators, as shown
a collection of microservices that handles end-user remote in Figure 4. In particular, we focus on the curtailment of

94 ieee power & energy magazine January 2025 Show Issue

 Cloud
Micro-Services

January 2025 Show Issue

Third-Party
Grid
Services
AI Device Energy Data VPP Load and Ctrl. Operator
(e.g. Security Auth. Enrichment
Mgmt. Modeling Forecasting Optimization
Weather,
Real Third-Party API GW
Estate Grid API GW

Energy Demand/
Producers Consumers Trading
Storage Generation
WebApp DSO API GW Distribution
Operator
Digital Twins
UI API GW

DER API GW

Hub LoRaWAN Hub Z-Wave

FDEMS
Cloud

Facility

VPP Across Multiple Deployments

figure 3. The SaaS VPP optimization platform. Auth.: authorization; Mgmt.: management; Ctrl.: control ; FDEMS: Facility Energy Management System; WebApp:
web application.

ieee power & energy magazine

95
 devices such as heating, ventilation, and air conditioning, default. In Figure 4, demand-side resources consisting of
EV chargers, water heaters, etc., which can be controlled load segments within GEB buildings are controlled and
through the VPP optimization platform. dispatched through the cloud algorithms. The dispatch
A key aspect of controllable load segments through frequency of each device is based on subscription con-
device curtailment is the uncertainty of the energy yield tracts with the customer in accordance with the customer’s
of each curtailed device (DER asset) for which only the selection from a menu of contracts specifying the proba-
nameplate capacity is known. The energy yield of each bility of curtailment or priority of curtailment versus com-
DER asset is the energy consumed/produced out of one pensation. The customer selections are aggregated into a
KW nameplate capacity; it is treated as an uncertain ran- portfolio comprising the VPP underlying asset.
dom variable. The design architecture is based on aggre-
gating a large number of such devices of different priority Supply Function Derivation for
tranches (different colors) into a demand resource port- Wholesale Energy Market Participation
folio controlled by a proprietary dispatch algorithm in The implemented methodology for determining the supply
the cloud to produce a single VPP that can emulate a dis- function-based energy offers for wholesale energy mar-
patchable conventional generator that can be offered into ket participation is based on the offline sampling of actual
wholesale energy markets by a DERA through a supply DER usage data from actual installations of DER assets.
function (Figure 4). The aggregation of many small con- This methodology includes 1) the conditional energy yield
trollable load segments, along with the dispatch algorithm estimation using offline sampling techniques by deploying
governing their activation, controls the aggregated uncer- actual minute-by-minute DER consumption data in vari-
tainty of energy yield, resulting in a VPP with supply reli- ous locations, 2) the energy yield distribution represented
ability within conventional ISO norms. The implemented by beta probability functions, 3) the energy yield joint beta
approach is analogous to creating financially managed distribution functions, and 4) the supply function derivations
portfolios underlying high-quality bonds by aggregating determining the integrated VPP energy and reserve offers
large numbers of “junk bonds” with a high probability of for wholesale energy market participation.

Supply Function Offer

Market Management System
Price $/KWh.
Dispatch
Instructions Bids/Offers

Cloud
Supply KWh.

Curtailment .2% Curailment

Prob.
Pay $/KW/Year

Instructions .5% Curailment

Contract Menu
Prob.
1% Curailment
Prob.

10% Curailment Prob. of Curtail.

Prob.
Aggregator
Demand Resource Portfolio

figure 4. The VPP demand resource portfolio creation for market participation. Prob. of Curtail.: probability of
­curtailment.

96 ieee power & energy magazine January 2025 Show Issue

 The California ISO allows zonal offers (with 23 distinct zones) in
its compliance proposal to the FERC Order 2222, while other ISOs
allow only nodal offers.

Energy Yield Estimation year. The data are classified into different classes based on
The methodology focuses on small loads at the scale of consumers’ preferences. For this purpose, a double filtering
individual appliances, households, or GEB buildings approach is deployed to cluster the data using two algo-
that would otherwise not be deployable as demand-side rithms (K-means and hierarchical). The K-means algorithm
resources by the ISO due to their large number and infor- partitions the dataset into K-predefined distinct nonover-
mational requirement or failure to meet availability criteria lapping subgroups (clusters) where each data point (i.e.,
for market participation due to the uncertainty of the active individual normalized load profile) belongs to only one
energy used at any particular point in time. Controllable group. The algorithm assigns data points (i.e., individual
demand is characterized in terms of capacity increments normalized load profiles) to a cluster. The hierarchical
and accounts for the uncertainty of harvested energy result- approach is then applied to reduce the number of clusters
ing from the curtailment of such capacity segments. These derived from the first stage to a desirable number based on
DER devices are typically BTM, but the design methodol- a similarity metric.
ogy can also accommodate devices in front of the meter
to allow the full or partial curtailment of loads through Energy Yield Beta Probability
fuse limiters based on demand subscription protocols. A Distribution Functions
key aspect of controllable load segments is the uncertainty The reduced energy yield data are then fit to conditional beta
of energy yield resulting from the curtailment of known probability distribution functions, using the ambient tem-
nameplate capacity. perature, the location, and the hour of the day as the three
The energy yield of various smart IoT devices and DER independent observable conditioning variables for each
assets is an important element in calculating the demand- DER device. Temperature data are grouped in predefined
based supply function of an integrated VPP for market temperature bins, resulting in a small number of clusters for
participation. The demand-based supply function takes each temperature value within each bin. It is evident that
the form of energy reduction as a function of the market by increasing the number of clusters, less information is
clearing price per KWh. Based on the composition of the expected to be included in each of them. Hence, the meth-
contract VPP portfolio and the energy yield statistics of odology is applied in a way that the full information coming
the curtailable load segments, the DERA can construct the from the actual DER usage dataset is included in the clus-
demand-based supply function by maximizing expected ters. For different city locations, the segmentation analysis
returns. The resulting supply function emulates a conven- based on the temperature binning is different due to the dif-
tional generator offer curve. The awarded quantity is then ferent temperature profiles in each location.
realized through the deployment of load control by the Figure 5 illustrates a typical beta distribution for the
cloud algorithm. energy yield for bin range from 55 °F to 60 °F at a specific
In general, the DERA may also integrate and submit to location in the Electric Reliability Council of Texas, Inc.
the wholesale market nondispatchable variable resources, (ERCOT) region. As the ratio beta/alpha increases, the
such as rooftop solar panels, that are deployed as a part of energy yield distributions are tilted to the left and get closer
the VPP. The output of such resources is also a random vari- to the intersection of the two axes. In other words, as the ratio
able represented by a probability distribution. In such a case, increases, the probability of a consumption point lying out-
the VPP dispatch amount will depend on the realized out- side the interval [0,0.1] constantly decreases.
put of the noncontrollable variable resources, and the supply
commitment is a composite of these resources’ output and Energy Yield Joint Beta Probability
the demand curtailment. Distribution Functions
The energy yield estimation that is adopted for each To the extent that the derived supply function represents
device type, such as hot water heaters, is data driven based multiple time segments, temperature bins, or locations,
on 1-min actual usage data for an entire year from a large the corresponding conditional energy yield beta distri-
number of GEB buildings in various locations (to capture bution functions need to be combined into joint distribu-
the location city dependency). These data are first aggre- tions that will be used in the supply function derivation.
gated into hourly resolutions to create normalized load This is achieved in accordance with the laws of condi-
profiles for each day for every customer and for the whole tional probabilities.

January 2025 Show Issue ieee power & energy magazine 97

 The penalty does not necessarily represent the actual monetary
penalty imposed on the DERA but can be interpreted as a means
of controlling the shortfall risk.

It is also important to highlight that the framework can Supply Function Derivation
be scaled up accordingly depending on the number of inde- This section presents the methodology of the VPP supply
pendent external conditioning variables that need to be con- function derivation along with a sensitivity analysis and a
sidered. This will depend on the granularity of the supply numerical illustration that will guide the selection of risk
function offers, which may vary across ISOs. For example, control parameters in actual market implementations. For
the California ISO allows zonal offers (with 23 distinct the purpose of illustration, a single time period (hour) is
zones) in its compliance proposal to the FERC Order 2222, considered, and it is assumed that the DERA holds a port-
while other ISOs allow only nodal offers. folio of N contracted demand-side resource types that are
The possibility of reducing the number of condition- sorted into virtual pools or tranches, which are available
ing variables as much as possible without substantial loss for curtailment at that time period and denoted by an index.
of information to produce the least possible number of The assignment of specific physical devices over time to
the supply functions based on possible scenarios was the different tranches is handled by a separate algorithm
evaluated. Specifically, the conditioning on temperature that accounts for the device’s nominal priority, the alloca-
subsuming the conditioning on location and on season tion of dispatchable capacity over the hours of the day, and
was evaluated. Unfortunately, the results of the analysis the time since the last activation. In particular, a device that
suggested that conditioning on temperature cannot sub- has been curtailed is moved to the top priority tranche (last
sume the conditioning on location (for most locations) to be activated) and brought down gradually to its nominal
and season. Hence, the location is crucial and cannot be tranche so as to reduce the probability of a device being
omitted in the derivation of the supply functions. This curtailed too frequently.
conclusion is of major importance because it highlights In each bidding interval, a priority tranche is character-
the complexity of the problem due to the many different ized by the following parameters: 1) marginal cost (MC) per
factors that can influence the results. Location is impor- KW capacity per hour of curtailment, 2) available KW capac-
tant, and along with the hour and the temperature indi- ity, 3) curtailed KW capacity, and 4) energy yield distribu-
ces, it constitutes one of the main parameters that have a tion. The curtailment cost is an approximate cost, calculated
strong influence on the consumption patterns and there- offline, that captures all the cost components associated
fore on the supply functions. with the curtailment of a device, including amortized equip-
ment cost, compensation paid to
the customer, and lost opportu-
Beta Distribution nity cost. The supply function
needs to specify the total cur-
10 Temp_id = 5 tailed energy as a function of the
Alpha = 4 wholesale energy market price.
Beta = 43 Thus, the DERA needs to decide
8
how much capacity to curtail at
each price and how much energy
Probability

6
to offer, taking into consideration
the tradeoff between potential
4 revenue for offered energy ver-
sus shortfall penalty, character-
2 ized in terms of a penalty factor,
for energy reduction that does not
0 materialize due to uncertain yield.
The penalty does not necessarily
0 0.2 0.4 0.6 0.8 1
Values of Random Variable X (0, 1)
represent the actual monetary
penalty imposed on the DERA
figure 5. The beta distribution for the temperature bin (from 55 °F to 60°F) for a loca- but can be interpreted as a means
tion in the E­ RCOT region. of controlling the shortfall risk; it

98 ieee power & energy magazine January 2025 Show Issue

can be adjusted to meet confidence interval requirements on It is also used in the hospitality industry and in the financial
deliverable energy. sector for setting interest rates based on credit ratings. In
The DERA’s two-step decision problem can be decom- the VPP supply function application, it can be shown that
posed and performed separately for each priority tranche. the optimal policy dictates an all-or-nothing activation of
This is a classic revenue management problem often referred curtailable tranche capacity with an activation price that
to in the operations research literature as the newsboy prob- depends on the penalty factor, the curtailment cost, and the
lem. Revenue management has been widely used in the airline yield distribution. The optimal offered energy to curtailed
industry for the optimal allocation of seating capacity to fair capacity ratio also depends on these parameters. The offered
classes given demand uncertainty and optimal overbooking. energy for the different tranches of different MC available
capacity yield distributions can
then be stacked according to the
Resources Tiered Priority Tranches Supply Function
(Offered Energy/Clearing Price) activation prices to form a stair-
MC Avail. Cap. Yield Dist. Q case supply function, as shown in
Figure 6.
MC Avail. Cap. Yield Dist. As indicated previously, the
penalty factor controls both
MC Avail. Cap. Yield Dist.
the activation price and offered
MC Avail. Cap. Yield Dist. energy quantity for each prior-
ity tranche and subsequently the
P
Yield Distribution (Beta) P1 P2 P3 PN implied shortfall probability and
4
3 expected shortfall quantities. Fig-
2 Shortfall Probability ure 7 illustrates these dependen-
1 cies and shows how the delivery
Offline Sampling 0
–1.000
0 0.2 0.4 0.6 0.8 1 reliability requirement could
guide the penalty factor selection
Conditioning: Device, Tiem, Temp, and the resulting supply func-
Location
tion. For simplicity, a specific
figure 6. Risk-based VPP supply function based on curtailed tiered capacities and yield distribution represented by
offline energy yield estimation. Avail. Cap.: available capacity; Yield Dist.: yield the beta (3,10) probability den-
distribution. sity function is considered. The

4
3.5
3 0.25
Expected Shortfall/K

2.5 0.2
2
0.15
1.5
1 0.1
0.5 0.05
0
0 0.2 0.4 0.6 0.8 1 0
–0.5 0 0.2 0.4 0.6 0.8 1
(a) Gamma
(b)
25
0.5
20
0.4
15
0.3
p/c

Q/K

10 0.2
5 0.1
0 0
0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1
Gamma Gamma
(c) (d)

figure 7. Offer parameters and delivery risk sensitivity to the penalty factor gamma. (a) Yield distribution (beta 3,10).
(b) Expected shortfall/capacity as a function of gamma. (c) The trigger price-to-cost ratio (p/c) as a function of gamma.
(d) Offer quantity/capacity (Q/K) as a function of gamma.

January 2025 Show Issue ieee power & energy magazine 99

 DERs could participate, individually or through aggregations, in the
form of VPPs BTM in wholesale energy markets consistent with the
landmark FERC Order 2222.

penalty factor penalizing energy shortfall is assumed to U.S. Department of Energy, under the ETA Award
be the wholesale price divided by a parameter gamma. DE- AR0001281. The views and opinions of the authors
Figure 7 shows the sensitivity to gamma of the expected expressed herein do not necessarily state or reflect those of
energy shortfall to the curtailed capacity ratio, the opti- the U.S. Government or any agency thereof.
mal energy offers to the curtailed capacity ratio, and the
tranche activation point expressed as the ratio of activation For Further Reading
price to curtailment cost. To achieve an expected shortfall A. Papalexopoulos, J. Beal, and S. Florek, “Precise mass
of 5% of curtailed capacity, we would choose gamma = market energy demand management through stochas-
0.6, which implies the curtailment of the tranche capacity tic distributed computing,” IEEE Trans. Smart Grid,
if the wholesale price exceeds about five times the curtail- vol. 4, no. 4, pp. 2017–2027, Dec. 2013, doi: 10.1109/
ment cost per KW capacity and offering about 25% of the TSG.2013.2263396
curtailed capacity as energy reduction. A. Papalexopoulos, “The evolution of the multitier
hierarchical energy market structure and the emergence of
Summary and Conclusion the transactive energy model,” IEEE Electrific. Mag.,
The massive penetration of DERs and IoT devices in the vol. 9, no. 3, pp. 37–45, Sep. 2021, doi: 10.1109/MELE.
grid is creating significant opportunities but also poses 2021.3093598.
new challenges to the power system from an operational C. Clay, and S. Oren, “Firming renewable power with de-
and market perspective. The key challenges concern the mand response: An end to end aggregator business model,”
coordination of a very large number of small heteroge- J. Regulatory Econ., vol. 50, no. 1, pp. 1–37, May 2016, doi:
neous energy resources at the edge of the grid and their 10.1007/s11149-016-9301-y.
harmonization with grid operations while creating oppor- H.-p. Chao, “Competitive electricity markets with con-
tunities for the DERAs/owners for the participation of such sumer subscription service in a smart grid,” J. Regulatory
resources in hierarchical energy market structures. DERs Econ., vol. 41, no. 1, pp. 155–180, Jan. 2012, doi: 10.1007/
could participate, individually or through aggregations, in s11149-011-9179-7.
the form of VPPs BTM in wholesale energy markets con- S. R. Schiller, L. C. Schwartz, and S. Murphy, Perfor-
sistent with the landmark FERC Order 2222. This order mance Assessments of Demand Flexibility From Grid-
has created a regulatory framework for allowing DERs Interactive Efficient Buildings: Issues and Considerations,”
to bypass the distribution grid and directly participate in Berkeley, CA, USA: Lawrence Berkeley National Labora-
wholesale energy markets. tory, 2020.
This article presents a process for integrating heteroge- E. Kardakos, C. Simoglou, and A. Bakirtzis, “Optimal
neous BTM DER smart devices into VPPs for wholesale offering strategy of a virtual power plant: A stochastic Bi-
energy market participation. It also presents a VPP offer level approach,” IEEE Trans. Smart Grid, vol. 7, no. 2, pp.
methodology via supply functions determining the optimal 794–806, Mar. 2016, doi: 10.1109/TSG.2015.2419714.
VPP energy offers maximizing DER value from market
participation. The supply functions are determined based Biographies
on offline sampling of actual DER usage data from actual Alex Papalexopoulos is with ECCO International and
installations of DER assets. ZOME Energy Networks, San Francisco, CA 94104 USA.
Shmuel Oren is with the Graduate School at the Univer-
Acknowledgment sity of California at Berkeley, Berkeley, CA 94720 USA.
The work presented in this article was funded in part by Hung-po Chao is with Energy Trading Analytics,
the Advanced Research Projects Agency-Energy (ARPA-E), Phoenixville, PA 19460 USA. p&e

100 ieee power & energy magazine January 2025 Show Issue
The Red Sea
Microgrid
A 100%-Renewable Grid
for the New City

By Hongwu She and Hua Zheng

T
THE RED SEA PROJECT STANDS AS A CORNERSTONE Furthermore, the nature reserve within the Red Sea Proj-
of Saudi Arabia’s ambitious Vision 2030, encompassing an ect is a sanctuary for rare wildlife, like Arabian leopards,
expansive 28,000 km2 along the Red Sea coast. This vision- Arabian wolves, Arabian wildcats, and falcons. Emphasiz-
ary project aims to establish a premier sustainable luxury ing sustainability, the project aims to have a net positive
tourism destination on the west coast. Central to its design impact on biodiversity, setting new benchmarks for environ-
are 50 luxurious hotels boasting 8,000 guest rooms, spread mental stewardship in luxury tourism development.
across 50 pristine natural islands along 200 km of pic- To achieve its sustainability goals, the Red Sea Proj-
turesque coastline. The location of the Red Sea Project is ect plans to cap annual tourist visits at 1 million, ensur-
shown in Figure 1. ing responsible management of resources and conserva-
tion efforts. This commitment underscores its aspiration
Introduction to lead global standards in sustainable tourism, thereby
A focal point within the project is the Al Wajh Lagoon, encom- elevating Saudi Arabia’s profile on the international tour-
passing 92 islands over 2,081 km2. This ecologically rich area ism stage.
supports diverse habitats, including coral reefs and mangroves, A distinctive feature of the Red Sea Project is its energy
and endangered marine species, such as hawksbill turtles. infrastructure, which operates independently from Saudi
Notably, the region also features dormant volcanoes with a Arabia’s main electric power grid. It utilizes a pioneering
historical record of activity dating back to the 17th century approach with a grid-forming (GFM) battery energy stor-
AD. Located in Al-Ula, approximately 200 km from the Red age system (BESS) and a 400-MW solar photovoltaic (PV)
Sea Project, the UNESCO World Heritage Site of Hegra, also system to deliver 100% renewable energy across what is the
known as Mada’in Salih, s­ howcases remnants dating back to world’s largest microgrid project of its kind. The 1.3-GWh
the Nabataean Kingdom (the first century AD). BESS ensures a continuous power supply, with biodiesel
generators reserved for emergency backup.
Digital Object Identifier 10.1109/MPE.2024.3433318
Unlike similar large-scale GFM BESS projects deployed
Date of current version: 11 November 2024 globally (see Badrzadeh et al.), the Red Sea Project’s BESS

January 2025 Show Issue 1540-7977/24©2024IEEE ieee power & energy magazine 101
 operates in permanent islanding mode, maintaining grid sta- services, such as seawater desalination plants, sewage treat-
bility without linking to a bulk power system. The microgrid ment plants, and solid-waste treatment plants.
supports a variety of loads, including commercial and indus- The addressing of technical challenges, such as autono-
trial facilities, the Red Sea International Airport, and utility mous operation, black start, large transformer energization,
and fault ride-through, was the key focus during the sys-
tem’s design and commissioning phases, which concluded
in October 2023. Since then, the microgrid has been fully
operational, setting new standards for sustainable energy in
large-scale utility infrastructures.

Red Sea Microgrid System

To provide tourists with a varied landscape view, the Red
Sea Project includes several strategically placed resorts, such
as Shura Island, Desert Rock, and Southern Dunes, among
others. The project’s microgrid electrical system has been
meticulously designed to consider the diverse geographical
locations of power generation plants, including the solar PV
and BESS plants, and the resorts’ locations. An overview of
one such solar PV plant and BESS plant layout is depicted
in Figure 2.

Microgrid Electrical System

The Red Sea Project’s electrical infrastructure features a
main islanded grid (the main grid) comprising five substa-
tions connected through a 110-kV underground cable net-
work, forming a ring with a total length exceeding 100 km.
figure 1. Location of the Red Sea Project at Saudi Arabia. Additionally, three smaller islanded grids (small grids)
(Courtesy of the University of Texas Libraries, University of have been developed to manage local loads through 33-kV
Texas at Austin. Project location link: https://maps.app.goo. ­underground cables. The main grid with the three small grids
gl/dzV49RhMixpKXBxj6.) operate as a four-islanded electric power system w ­ ithout

figure 2. A solar PV plant and BESS plant at the Red Sea Project.

102 ieee power & energy magazine January 2025 Show Issue
electrical interconnections. A simplified schematic of the main The microgrid employs a hierarchical control structure to
grid and one of the small grids is presented in Figure 3. manage power distribution among different substations effec-
The main grid includes two solar PV farms (Solar Farm 1 tively throughout the day and night. This system includes
and Solar Farm 2) with a combined capacity of 324 MWac. three levels of control structure, as shown in Figure 4.
There are three BESS plants with a total capacity of 225
MW/1.3 GWh located at Solar Farm 1, Solar Farm 2, and The Primary Control
Coast Village. Furthermore, biodiesel emergency backup The GFM control strategy is used as the primary control
generators with a total capacity of 102 MW are situated for the BESS, which operates the BESS as a virtual syn-
at Solar Farm 1, Solar Farm 2, and Shura Island. Each of chronous generator (VSG) and provides autonomous droop-
the three small grids—Desert Rock, Southern Dunes, and based frequency and voltage regulation. The primary
Sheybarah Island—features a 5-MWac solar PV plant, a control responds to transient active power and reactive
5-MW/18-MWh BESS, and two 1.9-MW emergency bio- power disturbances, such as loss of generation and/or load,
diesel generators. Altogether, 25 biodiesel generators with cut-in/cut-out of the shunt reactors and the underground
a combined capacity of 112 MW are configured to use envi- cables, energization of the transformer, start-up/shutdown
ronmentally friendly B100 biofuel. of the induction motors, etc. The primary control character-
Despite not being connected to Saudi Arabia’s national istics ensure automatic frequency and voltage recovery by
grid, the electrical system of the Red Sea Project adheres autonomously balancing active power and reactive power in
to the grid codes of Saudi Arabia, ensuring compliance the steady state and transient states, given that there is suf-
with power quality standards, including voltage and current ficient spinning reserve for both active power and reactive
harmonic distortions, flicker, and variations in voltage and power in the microgrid.
frequency.
The Secondary Control
Microgrid Operation Strategy After the activation of the primary control due to a disturbance,
Solar PV power serves as the primary energy source for the the frequency and/or voltage may deviate. The ­secondary
Red Sea microgrid during the daytime. Excess solar energy control, once the primary control has brought the system to
is stored in the BESS, which then discharges to supply power a new steady-state operation, establishes new set points for
overnight. In cases where the BESS depletes because of the primary controls to regulate the microgrid’s active power
adverse weather conditions or other factors, biodiesel gener- and reactive power flow and adjusts the frequency and volt-
ators are activated to maintain an energy balance until solar age to the desired values. As shown in Figures 3 and 4, the
PV generation resumes. active power sources of the system consist of PV generation

222 MWac 75 MW 4*18.5 MW 2*20 Mvar 101 MWac 75 MW 9*1.9 MW 2*20 Mvar 5 MWac 5 MW 2*1.9 MW
PV BESS Gensets SVGs PV BESS Gensets SVGs PV BESS Genset

Solar Farm 2 33 kv 33 kv Solar Farm 1 SD/DR/SHEY 33 kv

110 kv

Shura Coast
33 kv Airport 33 kv Island 33 kv Village 33 kv

Airport SVGs Reactors Hotels Gensets SVGs Hotels BESS SVGs Hotels
6 MW 2*10 Mvar 2*40 Mvar 24 MW 6*1.9 MW 2*20 Mvar 84 MW 75 MW 2*20 Mvar 1.8 MW
(a) (b)

figure 3. Simplified diagrams of the Red Sea Project microgrids. (a) A schematic diagram of the main grid. (b) A schematic
diagram of one small grid. SVG: static var generator; SD: Southern Dunes; DR: Desert Rock; SHEY: Sheybarah island.

January 2025 Show Issue ieee power & energy magazine 103
 units, BESS units, and diesel generator units, while the reac- power and the biodiesel generator power to ensure
tive/voltage sources include a static var generator (SVG, or sufficient charging and discharging power capability
STATCOM), solar PV generation units, and shunt reactors. (spinning reserve)
The frequency is a global signal in the microgrid, and 4) starting up and shutting down appropriate combina-
the secondary frequency control is implemented through the tions of biodiesel generators based on overall efficien-
dispatch of all of the active power sources in different loca- cy optimization in low-SOC scenarios of the BESS.
tions of the microgrid. This includes Figure 5 shows a typical operation profile of the
1) curtailment control of the solar PV generation systems microgrid:
based on spinning reserve requirements and BESS 1) During the daytime, the solar PV generation power is
state of charge (SOC) limits during the daytime partially consumed by the load and charges the BESS
2) frequency set-point regulation of the GFM BESS with 175 MW, leaving about a 50-MW charging pow-
plants er margin for grid transient stability.
3) power generation set-point regulation of the biodiesel 2) After sunset, the BESS transitions from the charging
generators when they are online. state to the discharging state. Figure 5 also highlights
The voltage is usually a local signal, especially for the that the BESS SOC only reaches about 70%; thus, it can-
main grid with more than 100 km of underground 110-kV not provide continuous power supply for the entire night.
cables. The secondary voltage control is a multiobjective 3) After midnight, the BESS SOC drops to the lower
regulation issue, considering the reactive power flow and the limit, and the diesel generator starts up to supply suc-
locally available reactive power sources in the microgrid. cessive power.
4) When the solar PV system resumes power generation
The Tertiary Control the following day, the diesel generator shuts down and
The tertiary control is performed by the energy manage- the BESS resumes charging.
ment system to provide economic and reliable dispatch of 5) Because of a high level of irradiation, the BESS is
the microgrid. The functionality of the energy management charged to an SOC of 90% and can continuously sup-
system includes the intraday energy balance dispatch of the ply power to the load during the following night with-
power resources based on the solar PV forecasting system out the need for diesel generator assistance.
and the load power forecasting system. This includes
1) charging the BESS with surplus solar PV generation GFM BESS
power during the daytime and discharging the BESS Despite being an emerging technology, the GFM BESS
to the load at night serves as a foundational element in the Red Sea Project’s
2) maintaining sufficient loading capability (spinning re- microgrid, primarily used for frequency and voltage stabiliza-
serve) for the GFM BESS of the microgrid by limiting tion. Biodiesel generators are relegated to emergency backup
the solar PV generation power during the daytime roles, while the GFM BESS actively manages and regulates
3) maintaining the SOC of the BESS within the upper frequency and voltage to address disturbances and ensure
and lower limits by regulating the PV generation continuous, stable operation.

Energy
Economic Dispatch
Management Tertiary
and Safety Operation
System Control
Margin Management

Microgrid Secondary Coordinated Voltage

Controller Control and Frequency Control

Distributed Voltage
Primary and Frequency Droop
Control Control with
GFM BESS
PV BESS Gensets SVGs BESS SVGs PV BESS Gensets SVGs
Solar Farm 1 Coast Village Solar Farm 2

figure 4. The hierarchy of the control structure of the Red Sea microgrid.

104 ieee power & energy magazine January 2025 Show Issue
 350
PV Generation Capability
300 PV Generation Power
250 Load Power
Genset Power
Power (MW)

200

150

100

50

0
5 15 25 35 45 55
Time (Hours)
(a)

200 BESS Charging 100

Power
180 90
BESS Discharging
160 Power 80
140 SOC 70
Power (MW)

120 60

SOC (%)
100 50
80 40
60 30
40 20
20 10
0 0
5 15 25 35 45 55
Time (Hours)
(b)

figure 5. An operation profile of the Red Sea microgrid. (a) Power of the PV, Genset, and load. (b) Power and SOC of
the BESS.

In the main grid, the Red Sea

Project incorporates a substantial
225-MW BESS, consisting of
MV Tran
sform more than 1,200 power conver-
Each 9 M er, Battery PCS, sion system (PCS) units operating
VA
Each 185 kW in parallel. Each unit can deliver a
rated power of 185 kW at an ambi-
ent temperature of 50 °C. Figure 6
provides a visual representa-
Bat tion of one of the BESS plants.
tery
Eac Conta Throughout the commissioning
h2 i
MW ner, and operational phases, critical
h
functionalities, such as black start
capability, transformer energiza-
tion, and fault ride-through, have
been rigorously evaluated and
successfully implemented using
figure 6. A view of the GFM BESS plant in the Red Sea Project. the GFM BESS.

January 2025 Show Issue ieee power & energy magazine 105
 The primary challenge in the synchronized black-start process
involves managing the synchronization and circulating current
control for more than 400 PCS units.

Significant differences in GFM capabilities between the or partial shutdown without depending on external systems.
traditional diesel generators and the GFM BESS are out- In the case of the Red Sea microgrid, which is a sizable
lined in Table 1. Figure 7 illustrates the conceptual control electrical system, the reactive power demand due to cable
systems for both the diesel generator and the GFM BESS. capacitance is notably high. Therefore, a synchronous ramp-
The virtual governor and virtual exciter are the two princi- up black start is implemented using the GFM BESS. During
pal control components within the GFM BESS, based on the the black-start procedure, a BESS plant with a capacity of
VSG control algorithm. These components are designed to 75 MW and more than 400 PCS units acts as the starting
emulate the traditional systems found in synchronous gen- source, ramping up the entire grid, including all 110-kV
erators—the virtual governor mimics a rotating governor for transformers and cables. The primary challenge in the syn-
frequency regulation, while the virtual exciter replicates a chronized black-start process involves managing the syn-
conventional excitation system for voltage regulation. The chronization and circulating current control for more than
energy for the GFM BESS, as shown in Figure 7, is primarily 400 PCS units. Figure 8 displays the voltage and current
sourced from solar PV power and other renewable resources. waveforms observed during the black-start process.

Black Start Transformer Energization

A black start is the procedure used to recover an electric Transformer energization is a routine operation within any
power plant or a section of an electric grid from a complete electric power system, necessitated by reasons such as fault

table 1. A comparison of the diesel generator and the GFM BESS in the microgrids.

Diesel Generator GFM BESS

Energy sources Diesel engine PV or other renewable sources

Frequency regulation Governor Virtual governor of VSG

Voltage regulation Exciter Virtual exciter of VSG

Time constant of inertia 2–9 s 0.5–10 s

based on physical rotating inertia based on software configuration
Short circuit/inrush 6–8 pu rated current ≤1.5 pu rated current
current capability based on electromagnetic design based on power converter design

Battery PCS Grid

Prime Mover
Generator Grid
H
Fuel V, I

ω V Power Electronics F/V

If Controller
Ffbk Vmag

Governor ωref Exciter Vref

Virtual Governor Fref Virtual Exciter V
ref

(a) (b)

figure 7. Conceptual control system structures of (a) the synchronous generator and (b) the GFM BESS.

106 ieee power & energy magazine January 2025 Show Issue
trips or scheduled maintenance.
30
The inrush current generated Ua
when a transformer is energized 20 Ub
can be six to eight times greater

Voltage (kV)
10
than the nominal transformer cur-
0
rent, potentially triggering over-
current protection in the GFM –10
BESS and causing a blackout in –20
the microgrid. In large microgrids –30
with multiple transformers, a phe- 10 20 30 40 50 60
nomenon known as sympathetic Time (s)
(a)
inrush can occur, where the ener- 200
gization of one transformer affects la
150
others that are already active, lb
100
leading to even higher peak cur-
Current (A)

50
rents. These sympathetic inrush 0
currents can persist for several –50
seconds, unlike the typical inrush, –100
which decays within a few cycles. –150
The major challenge during trans- –200
10 20 30 40 50 60
former energization is ensuring
Time (s)
even distribution of the inrush cur-
(b)
rent and rapid current limitation to
maintain synchronization of the figure 8. Black start with capacitive 110-kV cable load. (a) Voltage waveforms.
GFM BESS without disrupting (b) Current waveforms.
grid stability. Figure 9 illustrates
the transformer energization wave-
Voltage (kV)

25 VanMV
forms during the commissioning of
VbnMV
a 90-MVA ­transformer within the 0
VcnMV
main grid of the Red Sea Project. –25
0.4 0.5 0.6 0.7 0.8 0.9
Time (s)
Fault Ride-Through (a)
Short circuit faults, caused by con-
1.2
Voltage (pu)

ditions such as lightning, extreme VanRMS

1
wind, or equipment insulation fail- VbnRMS
0.8 VcnRMS
ures, are common in any power sys-
0.6
tem. A fault ride-through capability 0.4 0.5 0.6 0.7 0.8 0.9
is crucial for maintaining stability; Time (s)
(b)
it allows a power source, like the
5,000
Current (A)

GFM BESS in the Red Sea Project, la

to stay connected and maintain its 0 lb
electrical characteristics—current, lc
–5,000
voltage, and frequency—within 0.4 0.5 0.6 0.7 0.8 0.9
specified limits during and after Time (s)
(c)
a fault. Unlike grid-following 1,000
Current (A)

resources, such as solar PV invert- la1 lc3

ers, which only need to limit their 0
lb2 la4
output during faults, the GFM
–1,000
BESS is also tasked with recon- 0.4 0.5 0.6 0.7 0.8 0.9
structing the microgrid’s voltage Time (s)
(d)
postfault. This involves complex
dynamics depending on the fault’s figure 9. Large transformer energization in the Red Sea microgrid. (a) Medium-­voltage
location and affects the voltage bus instantaneous voltage waveforms. (b) Medium-voltage bus root-mean-square
and phase angle across the BESS voltage waveforms. (c) Transformer instantaneous current waveforms. (d) Phase A
units connected at different nodes instantaneous current of different BESS feeders.

January 2025 Show Issue ieee power & energy magazine 107
 Voltage (kV)
fault ride-through, demonstrate
25 Van the microgrid’s advanced techni-
0 Vbn
Vcn cal achievements and robustness
–25
in handling diverse operational
0.48 0.5 0.52 0.54 0.56 0.58 0.6 0.62 0.64 0.66 challenges.
Time (s)
(a) Acknowledgment
Voltage (pu)

VanRMS The authors would like to thank the

1
VbnRMS partners, including Red Sea Global,
VcnRMS ACWA Power, and the Marafiq Red
0
0.48 0.5 0.52 0.54 0.56 0.58 0.6 0.62 0.64 0.66
Sea for Energy Company, among
Time (s) others, for their invaluable contri-
(b) butions to this article. The authors
1,000 la
would also like to thank Dr. Reza
Current (A)

lb Iravani for his great help in organiz-

0
lc ing and drafting this article.
–1000
0.48 0.5 0.52 0.54 0.56 0.58 0.6 0.62 0.64 0.66
Time (s)
For Further Reading
(c) “Red Sea Global is pioneering luxu-
ry destinations along the west coast
la
5,000
Current (A)

0
of Saudi Arabia.” Public Investment
lb
lc
Fund (PIF). Accessed: Aug. 1, 2024.
–5,000
[Online]. Available: https://www.pif.
0.48 0.5 0.52 0.54 0.56 0.58 0.6 0.62 0.64 0.66
gov.sa/en/our-investments/giga
Time (s) -projects/red-sea-global/
(d) “The red sea.” Red Sea Global.
figure 10. Fault ride-through transients of the Red Sea microgrid. (a) Instantaneous Accessed: Aug. 1, 2024. [Online].
voltage waveforms of the medium-voltage bus. (b) Root-mean-square voltage wave- Available: https://www.redseaglobal.
forms of the medium-voltage bus. (c) Current waveforms of the non-fault feeder of com/en/our-destinations/the-red-sea
the BESS plant. (d) Current waveforms of the fault feeder of the BESS plant. “The Saudi Arabian grid code.”
Saudi Electricity Company. Ac-
(as depicted in Figure 3). The fault ride-through procedure cessed: Aug. 1, 2024. [Online]. Available: https://www.se.
includes the following two critical steps: com.sa/-/media/sec/About-Us/National-Grid-SA/Saudi
1) During the fault, the GFM BESS injects short circuit -Arabian-Grid-Code-Guide/English/SAGC-July_2022
current to the fault location, enabling protection re- -Update.ashx
lays to identify and clear the fault while preventing an “ACWA power and Huawei to spur innovation in local re-
overcurrent trip of the GFM BESS. newable energy and storage development.” Huawei. Accessed:
2) After the fault is cleared, proper resynchronization of Aug. 1, 2024. [Online]. Available: https://solar.huawei.com/
all GFM BESS units is necessary to reconstruct the en/news-room/en/2023/news-20230616
grid’s overall voltage. X. Wang, M. G. Taul, H. Wu, Y. Liao, F. Blaabjerg, and
Figure 10 illustrates experimental waveforms recorded L. Harnefors, “Grid-synchronization stability of converter-
during a short circuit event on a medium-voltage bus within based resources—An overview,” IEEE Open J. Ind. Appl.,
the Red Sea main grid, showing a fault current approximately vol. 1, pp. 115–134, 2020, doi: 10.1109/OJIA.2020.3020392.
nine times the rated current that was cleared within 60 ms. B. Badrzadeh et al., “Grid-forming inverters: Project
The voltage of the medium-voltage bus was restored to 70% of demonstrations and pilots,” IEEE Power Energy Mag.,
its rated value within 20 ms and stabilized at the rated voltage vol. 22, no. 2, pp. 66–77, Mar./Apr. 2024, doi: 10.1109/MPE.
shortly after the fault clearance. 2023.3342766.

Conclusions Biographies
Commissioned in October 2023, the Red Sea microgrid Hongwu She is with Huawei Digital Power Technologies
is currently the largest renewable microgrid in operation Co. Ltd., Shanghai 201206, China.
worldwide. It incorporates a 400-MW solar PV generation Hua Zheng is with SEPCOIII Electric Power Construc-
system, a 1.3-GWh BESS, and backup biodiesel genera- tion Co., Ltd., Power China, Tsingtao 266061, China.
tors. Key features, such as hierarchical control architec-
p&e
ture, black-start capability, transformer energization, and 

108 ieee power & energy magazine January 2025 Show Issue
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