SUMMER TRAINING REPORT

ON

Electric Vehicle Plug & Smart Charging

Submitted To Maharshi Dayanand University, Rohtak in the partial fulfilment of the

required for award of degree of bachelor’s in business administration (BBA CAM)

SESSION – (2021-2024)

SUBMITTED TO: SUBMITTED BY: Anju Jadon

CONTROLLER OF EXAMINATION BBA CAM Sem- 5th
M D UNIVERSITY, ROHTAK Registration No -

.
 ACKNOWLEDGEMENT

To begin with I would like to offer my sincere thank from bottom of my heart to all the people who
supported me and help me. Due to them only, I got a very God opportunity to express my talent on
project report.

I am. Sincerely grateful to them fir sharing there truthful and illuminating views on a few issues related
to the project.

Also, I am thankful to my facility guide Dr. NEELAM GULATI of my Institute for her valuable guidance and
continues encouragement.

Signature

(Student)

ROLL No:

Signature faculty
 DECLARATION

I, ANJU JADON student of BBA CAM 5 semester of the DAV INSTITUTE OF MANAGEMENT,
Faridabad here by declare that this project work done on "Demonstrating plug in electric
vehicles smart charging and storage supporting the grid".

My worth carried out under the guidance of my faculty guide Dr. NEELAM GULATI. The results
reported in this study are genuine, original and the script is written by me.

Candidate Signature:

..

(Anju Jadon)
Sr.no Topic Page No

1 Acknowledgement

2 Declaration

3 Faculty Certificate

4 Executive Summary

5 Chapter 1- Introduction

6 Chapter 2 - Project Objectives

7 Chapter 3 - Project Approach

8 Chapter 4 - Project Outcomes

9 Chapter 5 - Conclusion

10 Chapter 6 - Recommendation

11 Chapter 7 - Public Benefits

 EXECUTIVE SUMMARY

Introduction

There has been significant growth of plug-in electric vehicles (PEVs) India as a result of
societal awareness of their environmental benefits, substantial improvement in battery
technology and attractive federal, state, and local government incentives. India's goal is to
reduce greenhouse gases to 40 percent below 1990 levels by 2030, and PEVs are expected
to substantially contribute to this reduction. Besides helping to reduce pollutants and
emissions, PEVs also can be used as a unique and essential method for energy storage.

The American Automobile Association and Federal Highway Administration state that a
typical U.S. driver spends less than an hour each day in personal vehicles and drives an
average of 37 miles per day, resulting in the vehicle being stationary for more than 23
hours each day. A modern PEV, when plugged into the electricity grid, could therefore serve
as a supplemental energy storage device by using the battery to provide electricity for peak
demand and congestion. Drawing from the supplemental stored energy of the PEVs would
reduce having the grid operator purchase additional energy storage.

Moving towards India 2050 renewable target of reducing greenhouse gases by 50 percent
below 1990 levels has been helped by large amounts of solar photovoltaic (PV) generation
into indiaelectric grid. Excessive generation in the middle of the day from PV is the ideal
time to charge the PEVs– a service the PEVs can easily provide especially in larger numbers.
Effectively managing the grid with PEVs using sensors, data, modeling, analysis and smart
software-based controls, energy pricing, driver preferences, real-time and historical PEV
charging information, renewable electricity generation on the grid, and grid capacity
information would convert the PEVs into a high value storage asset and help manage
inconsistent renewable energy generation. Therefore, the infrastructure for “smart” electric
vehicle support equipment is essential to the success in growth of PEVs.

Vehicle-to-grid (V2G) and vehicle-to-building (V2B) technology takes grid impacts into
account and may provide additional value to customers, while supporting the grid. For
example, V2G and V2B enable a PEV to discharge energy into the grid or to support building
loads which helps reduce peak loads and associated energy bills or can provide power to
the customer during times of power shortage. While most current PEVs only support
unidirectional charging from grid to vehicle, V2G and V2B technology allows power to flow
in the reverse direction so a PEV can act as a battery energy storage system. Although a
single vehicle may not provide large amounts of power, large numbers of vehicles can be
aggregated into a single resource allowing the grid operator to draw significant amount of
power.
 Grid services help utilities resolve issues of reliability and stability. For example, PEV
charging can be used to take excess energy during times of over-generation (such as
during peak PV generation) or provide energy back to the grid when demand is high. The
challenge with PEVs providing grid services is the ability to aggregate and control multiple
PEVs for a coordinated response.

Project Purpose

This project was designed to develop and demonstrate advanced charging infrastructure
(software and hardware) for smart charging, V2G and V2B, grid services, and cost
recovery validation. Demonstration occurred in a controlled setting at the University of
California, Los Angeles (UCLA) and then expanded into public infrastructure in the City of
Santa Monica.
This project developed and demonstrated a solution for smart charging, V2G and V2B,
and grid services. This solution is helpful for PEV fleet and parking garage owners,
because it can help bring down the cost of adding charging infrastructure and the cost of
charging large numbers of PEVs through coordinated control.
Project Process
The research team conducted the project in four phases: (1) technology development, (2)
infrastructure setup, (3) algorithm implementation, and (4) data collection, analysis, and
system refinement. In the first phase, research was conducted on relevant technologies and
a system was designed. The project team determined that it was necessary to develop a
flexible smart PEV charger, V2G and V2B station, and a communication network to support
demonstrations in the second phase. This step was necessary because the PEV
manufacturers require different V2B connectors and the architecture of PEV chargers are
not commonly standardized.
In the second phase, prototypical smart charging and energy management systems were
developed and tested with the in-house developed instructions and software codes at the
UCLA testing sites and then installed and used at the demonstration site in the City of
Santa Monica. Smart charging was achieved with the developed PEV charging scheduling
algorithms and was validated and improved through the interaction and feedback from a
mobile application, developed and distributed among participating users for this project.
The V2G and V2B system was implemented with a local controller that received commands
to change its power flow and current and communicate this instantaneous information to
the computers since most chargers.

Project Outcomes
Smart Charging: The team developed and refined a flexible smart PEV charger, managed
by a cloud-based software system, and connected to a mobile application. Each smart
charger was equipped with four PEV connections, so that four PEVs could be controlled at
one time. The software controls the charger power output to each PEV simultaneously,
based on inputs from the site, user and grid. A key algorithm within the smart charging
system incentivized users to maximize using solar energy, allowing better grid control, and
reducing the energy cost for the utility consumer by increasing the amount of solar on the
grid. This technology increased the number of charging sessions from 56 sessions (average
monthly charging sessions of a conventional charger) to 108 sessions at Santa Monic Civic
Center parking lot - an increase of about 92 percent use, as compared to a conventional
charger; and also increased the total energy delivered from an average of 588 kWh
(average monthly energy delivered) to 837 kWh - a 42 percent increase in total energy
delivered.

V2G and V2B: Two V2G and V2B systems were used to investigate results from different
types of PEVs. The first system, using a Mitsubishi PEV, provided 1.5 kW for discharging and
3.3 kW for charging. Due to its limited power capacity, a higher power system using
Princeton Power direct current fast charger (DCFC) was installed and used to provide 30 kW
bi-directional power flow. The higher power bi-directional power provided sufficient
controllable load to leverage the PEV charging load and PV generation in the parking garage
at the demonstration site in the City of Santa Monica. It also provided load shaving resource
by supporting building and PEV charging loads, or as an energy source/sink for demand
response at ±30 kW capacity.
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 Grid Services: Grid services were enabled through the smart charging algorithm
residing on the cloud software. The algorithm employed a user-charging pattern
prediction model and an actual building load profile, shifting the peak load by
scheduling the PEV charging load to a time when the building load was decreased to a
certain threshold. Using this algorithm, the peak power consumption was reduced by 35
percent which would allow a site host to avoid a utility’s demand charge and pave the way
for demand response -- a key grid service for the utility.

On certain days, especially during spring and fall when grid demand is lower, PV over-
generation results in demand collapse in the middle of the day and a steeply rising load
curve during the evening hours, making it difficult for the grid operator to balance the
generation and load. Controlling this phenomenon can be achieved by the system using the
Princeton Power DCFC that receives scheduling signals from the cloud software by messages
from the grid operator to charge the PEV at higher power levels in the middle of the day to
mitigate the impact of PV over-generation.

A key service to the site host is the ability to use load shifting based on time-of-use pricing,
which in turn benefits the utility’s load balancing needs. For example, the battery energy
storage system, when used in conjunction with the DCFC, provides potential benefit to the
site by reducing the cost per charging session by 23 percent via exploitation of time-of-use
pricing. In the future, V2G and V2B could itself be used to help PEVs reduce cost through
this opportunity.

Using smart charging in the system without the V2G has benefits when the grid operator
offers time-of-use pricing, and the customer is paying demand charges. By shifting PEV
charging load from peak to off-peak itself has shown a savings of $2,006 for one year of
data collection at the demonstration site consisting of seven level-2 smart PEV chargers (14
plugs) and 16 level-1 PEV chargers.

In addition to the PEV curtailment, the V2G hardware in combination with the stationary
battery storage at the demonstration site provided the system with a total of 117 kW of
demand response capability which is greater than the minimum of 100 kW required by
Southern California Edison (Time-Of-Use-General Service Base Interruptible Program - March
2017). Roughly half of the 117 kW demand response capacity was provided by the V2G
system by going from +30 kW to -30 kW or its inverse, resulting in a total controllable load
of 60 kW (±30 kW). Eventually, V2G is expected to cost substantially lower than what it is
now as the technology gets standardized (there is almost no standardization today) as well
as volume sales, making it far more competitive cost-wise than stationary battery energy
storage system. The team also concluded the following as the result of this research:

The proposed system can be used as a foundation for DER management system in a
microgrid system. With a focus on microgrid (grid-tie and islanding) operation, a
demonstration project can be the next step from this project.
This research finds that based on the type of locations and parking limitations,
customized scheduling algorithms and power management rules may be required.
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 The scheduling algorithms and rules must be reviewed monthly and improved based
on the data obtained.

The technologies and systems developed in this project, specifically those associated with
V2G, are stable and mature. They can be further developed as an energy management
product and be commercialized through startup companies.
For V2G to be scaled up on the grid, advances are necessary in technologies that support
communications, data and control standards between the interfacing equipment involved in
V2G and that includes PEV, charger, infrastructure, and grid. An open and standards-based
approach would enable a much faster development of the existing modules to inter-operate
seamlessly as well as for innovations to occur to lower the cost eventually leading to mass
market adoption.

Benefits to India
This project showed that larger numbers of PEVs can be added to the grid by maximizing
the existing PEV charging infrastructure without the need to add large amounts of power
capacity. Using smart PEV chargers, the system can save on PEV charging infrastructure
cost for site owners. Compared with a single plug electric vehicle support equipment,
UCLA’s smart PEV chargers allow roughly twice the number of charging sessions per day –
benefitting the site host/utility customer by serving a greater number of customers and
serving larger amounts of energy per unit capacity.

Using PEVs to participate in demand charge reduction and demand response, improved grid
reliability. By smart-control of PEV charging and even before using a V2G and V2B system or
storage, the site owner and fleet manager can avoid demand charges, take advantage of
time-of-use pricing through peak reduction, and save money. By adding V2G and V2B and
storage to the electric vehicle support equipment, the site owner or fleet manager can
receive additional rewards annually from demand response incentive programs.
Furthermore, using only the battery energy storage system to shift direct current electric
vehicle charging can result in additional energy cost saving for the site host.

Utilities can increase grid stability by using smart electric vehicle support equipment
systems. These systems would provide various grid services, including load smoothing and
demand response event support. Improved grid stability is also achieved by integrating
V2G and V2B systems and battery energy storage systems to provide power for buildings
and grid support during periods of power shortage. Using an external battery energy
storage system reduces the effect of fast chargers on the grid, which also improves grid
stability. Using smart charging algorithms to charge PEVs during periods of excess solar
generation can also solve the power instability problems caused by over-generation from
the renewable energy source.

Greenhouse gases are expected to decline by using PEVs instead of internal combustion
engines. Based on the 216 new PEV user accounts created during this project, greenhouse
gas emissions can be reduced from 1,241 tons for gasoline vehicles in contrast to the 621
tons of greenhouse gas emissions for PEVs.

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CHAPTER 1:
Introduction

The India vehicle-grid integration (VGI) roadmap [1] identified the vehicle-based grid
services as a key to maximize benefits to the owners of plug-in electric vehicles (PEVs) as
well as of the electric grid operator. The California Independent System Operator (California
ISO) in coordination with the Governor’s Office, India Energy Commission, India Public
Utilities Commission, and India air Resources Board developed this roadmap. It identified
three tracks for determining the value of VGI, developing the enabling technologies, and
developing the associated policies. While bidirectional PEVs offer a promising cost-effective
solution to stabilize the grid, the lack of a proven bidirectional PEV infrastructure that is
standardized and an efficient and ubiquitous smart energy management system in the
market could limit the widespread adoption of PEVs by consumers.

To demonstrate grid resiliency, cost savings to PEV fleet owners, and benefits to
investor-owned utilities (IOUs), the research team developed and deployed an
advanced smart and bidirectional PEV charging infrastructure (Figure 1).

Figure 1: Examples of PEV Charging Infrastructure

(a) V2G Station; (b) PMU/PQA meter; (c) Battery cart; (4) Civic Center Level 2 EV charger; (5) BESS integration testing.
Credit: UCLA SMERC © 2014-2018

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This infrastructure enabled smart charging (SC) [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12]
[13] [14]
[16] [17] [18], vehicle-to-building (V2B) [19], vehicle-to-grid (V2G) [20] [21], and other
promising applications for providing grid services with demand respond (DR) [22] [23] [24]
participation. The smart charging hardware and software followed the Society of Automotive
Engineers (SAE) J1772 standard (a North American standard for electric vehicles’ electrical
connectors). The research team tested these advanced technologies in Santa Monica
demonstration sites in public settings. Moreover, UCLA’s Smart Grid Energy Research Center
(SMERC) deployed smart charging and micro-grid technologies at the Southern California
Edison (SCE) territory to address the integration of PEVs into the electric grid. Figure 2 shows
an example of SMERC’s control center that monitors and controls all energy flow of PEV
charging sessions.

Figure 2: Example of SMERC Control Center

SMERC’s control center monitors and controls all energy flow of PEV charging sessions.
Credit: UCLA SMERC © 2014-2018

SMERC’s research team developed the backbone technologies [1] [2] [3] [4] [5] [6] [7] [8]
[9] [10]
[12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24], which include WINSmartEVTM
[2] (an intelligence-based bidirectional PEV infrastructure), and WINSmartGridTM [25] (a
wireless communication network). These technologies determined the optimized charging
and/or backfill operations based on daily operations, PEV profiles, user preferences, grid-
service events, and grid capacities, which maximized benefits to PEV fleet owners and
utilities. To predict the grid impact and potential benefit from a large number of V2G
capable PEV fleets in the state, demonstrations of grid services were scaled up using
various simulations.

The SMERC team collaborated with an advisory board and project partners that supported
the project with additional resources, including technical and commercial consultations,
guidance and contributions to fleet management, demonstration sites, simulation
platforms, and V2G-enabled Nissan and Mitsubishi PEVs, to ensure a successful and cost-
effective project.
CHAPTER 2:
Project Objectives

This project developed and demonstrated the functions of smart charging (SC)
technology, including:

Smart charging based on energy price, user preference and grid capacities.
Vehicle-to-grid (V2G) focuses on two-way communication and energy flow.
Vehicle-to-building (V2B) uses EV fleet storage to support building loads.
Grid services—mitigate the PV duck-curve with focus on V2G and grid-to-vehicle
infrastructure to smooth the over-generation from renewable sources.
Grid services—automated demand response demonstration that involves both actual
and simulated real-time grid services, including demand response program
participation by using V2G technologies.
Grid services—load leveling for Level-3 (fast charging) with battery energy storage
system (BESS).
Grid services—bi-directional EV fleet infrastructure with renewable solar generation and
BESS.
Grid services—peak shaving through SC, V2G, and BESS.
Grid services—local power congestion minimization through SC, V2G and BESS.
Cost recovery validation—to maximize the fleet owner's benefits by enabling the SC,
V2B, V2G and/or grid-service technologies.

The proposed project aimed to provide a smart comprehensive bidirectional-charging

infrastructure that enables PEVs to have a dual-function as energy loads and distributed
energy resources. This two-way charging system improves the efficiency, stability, and
reliability of the power grid by balancing and leveling the load, as well as enabling grid
services, such as peak shaving. This PEV charging system provides three different
technologies (SC, V2G, and V2B), which will benefit PEV owners and utility companies.

The SC technology provides power demand optimization and electricity costs reduction by
controlling line currents, correcting voltage deviation, and flattering load curves. By investing
more into this technology, smart charging could alleviate energy inefficiencies and losses.

The V2G technology allows PEVs to discharge power from their batteries into the grid
during periods of high demand to stabilize the grid, reduce overall costs, and reduce the
emissions by maximizing the use of renewable sources. In fact, this technology integrates
PEVs with smart-charging schedules using low energy consumption at off-peak periods,
which will enhance the power system’s efficiency, reduce CO2 emissions, and improve
integrating short-term PEV power into the renewable portfolio. Moreover, V2G can help
flatten the “duck curve” [26] (a graph of the net power demand during the day, which
demonstrates the timing imbalance between the renewable energy production and the
peak demand times as shown in Figure 3) by acting as an incentive to improve the
consumer behavior.

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 Figure 3: Net Load Curve with Renewable Energy Product and Load

Credit: California ISO [26].

The V2B technology provides storage capacity that benefits vehicle and building owners
by allocating reliable emergency backup power services during outages or blackouts,
lowering energy costs for buildings, and offsetting the higher costs of PEVs during off-peak
power use. This technology focuses on the time-of-use (TOU) pricing, peak shaving, and
demand charge avoidance to generate, store, distribute, and consume the energy smartly.

The proposed smart bidirectional charging system will maximize the benefits for utilities,
and meet charging requirements of fleet owners by enhancing the grid services as well as
the different technologies including SC, V2G, and V2B. In fact, the V2G concept will smooth
energy demand peaks and provide balanced power generation by supplying the grid with
energy when it needs it most. Use of this technology by fleet owners allows them to charge
their PEVs during off peak hours and sell the energy back to the utility during peak hours,
providing fleet owners with economic incentives. The bidirectional charging system will
provide management of the buying and selling decisions to ensure the achievement of
these benefits.

Developing a smart bidirectional charging system presents macro as well as micro level
challenges. Macro level challenges include alleviating the PEV owners’ anxiety over battery
usage and range reduction, convincing PEV manufacturers to make V2G-ready vehicles,
and convincing utilities to provide signaling services of energy needs. These macro
obstacles may require government, utility and regulatory agency interventions. Micro level
challenges include the technical obstacles to the development of a bidirectional charging
system.

In addition, the research team aims to prepare large-scale adoption of V2G by using grid-
service simulations based on collection of data. This data, obtained from pilots on fleet
vehicles that are charging in predictable locations, along with simulations, predict the grid-
wide effects of V2G. The results could be useful for utilities to control rates and provide
incentives for V2G, which will reduce carbon emissions and enhance grid stability, energy
efficiency, and grid economics.

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CHAPTER 3:
Project Approach

Based on the project objectives, UCLA SMERC studied state-of-the-art technology in smart
EV charging, renewable integration, power grid impact and integration, available V2G
solutions, BESS, demand response, and communication network to create system design,
implementation and deployment plans. Experts from various smart grid related fields and
the Technical Advisory Board were consulted throughout this project. Feedback from site
owners, EV drivers and facility management were considered to implement a UCLA
microgrid test bed and Santa Monica demonstration sites. The following sections describe
the approaches and tasks conducted to achieve the project goals.

Software and Algorithm Development

UCLA SMERC created a user friendly, grid friendly, and garage friendly EV charging system
using technology from WinSmartEV™ program. With the vast amount of data captured
through WinSmartEV™ Platform, vital information such as, power consumption, grid impact,
and user preferences are inputted into this system using which various EV charging
algorithms within the system are tested to determine optimum charging scheduling.

The WINSmartEVTM EV charging network uses a centralized control system to monitor and
regulate the network for real-time smart charging services. This smart charging
infrastructure uses standard technologies to create a network that facilitates charging
services for the end user and provides monitoring and control tasks for site
maintainers/operators. The charging services are completely adaptable by way of local or
remote charging algorithms. In addition, the architecture incorporates multiplexing
capabilities with a unique safety system that integrates safety at all levels of control. Fleet
drivers use mobile devices or a kiosk tablet installed at nearby locations to login and
activate the chargers. Charging status can be obtained from the mobile app. The drivers
can check on the total energy received by their vehicle, expected fully charged time and
disconnect the charger remotely if necessary.

User Friendly EV Fleet Charging Management Interface

UCLA SMERC studied and investigated the operations of the PEV fleet by interviewing and/or
conducting surveys with fleet drivers. Based on the interview result, mobile apps are
implemented to allow smart charging/discharging operations on Android and iOS platforms.
The users may start/stop charging, review charging record and specify their charging profiles
and preferences shows some screen shots from the mobile app and kiosk system developed.

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 Figure 4: Screen Shots from EV Mobile Apps and Kiosk System

Credit: UCLA SMERC © 2014-2018

Personalized EV Charging/Discharging Management

SMERC understands that it is not only what the system is capable of doing, but also how the
system is presented and used. WINSmartEV™ is distinctly focused on a system that is
appealing to modern culture and provides its users with the right incentives. The
Personalized EV Charging/Discharging Management Console (EV Control Center) was
created to manage EV fleets. Details about this console was included in the deliverable
report, Task 2.2 Personalized EV Charging and Discharging EV Fleet Management System
User Manual.

Modeling and Simulation

To study benefit of smart charging, V1G, V2G operations and conduct cost-revenue
analysis, some modeling and simulation tools were used to simulate the scale up
impacts.

Grid-Scale Impacts, Opportunities and Predictions

V2G-SIM [27] from Lawrence Berkeley National Laboratory (LBNL) was used to evaluate
case studies to predict grid impacts and opportunities for using PEVs. The following five
use cases were discussed and proposed for simulation and analysis:

Peak Shaving/Valley Filling

As substantial renewable generation (especially solar) is deployed, four important issues
attract more and more attention. They are: 1) Low daytime net load or over-generation,
2) High evening net load 3) Sharp mid-morning down-ramps 4) Substantial evening up-
ramps. PEVs can maximize its power consumption during the daytime over-generation
period (9 am to 2:30 pm) and limit charging or even feed energy back to the grid (V2G)

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 during the evening peak (around 5 pm) to limit renewable curtailment and
mitigating the peak load.
Ramping Mitigation
PEV charging load can be adjusted to minimize the ramping rates of the net load
profile. For sharp ramp-down periods (7 am to 9 am), this typically results in PEVs
transitioning from generating electricity to charging during the hours adjacent to the
sharp ramp-down. For sharp ramp-up periods (2 pm to 5 pm), PEVs transition from
consuming energy to discharging during the hours adjacent to the sharp up-ramp.
With this control objective, ramp-down and ramp-up rates are mitigated to the
greatest extent.
Emergency Demand Response
PEV is a flexible load which can provide demand response services. It can stop
charging when receiving emergency demand response call from the utility company.
Demand Charge Mitigation
For business owners, their electricity bill is comprised of two components: energy
consumption and demand charges. With electricity consumption on the rise and
utilities struggling to keep pace with market and regulatory changes, demand
charges can account for a significant portion of business users’ utility bills – at times
between 25–50 percent. Furthermore, in contrast to rates outside of peak periods,
demand charges have been rising steadily, year after year. In this use case, the team
studied how smart charging can reduce the peak of charging load.
Ancillary services (spinning reserve, frequency regulation, etc.)
PEVs have high charging and discharging flexibilities, which enable them to work as
“battery storages” to provide ancillary services to the grid. The provision of ancillary
services must be decided on in advance, with a certain lead-time. Lead-times are
necessary to organize available resources (such as PEVs) or to respect current
market structures (for example ancillary services co-optimized with day ahead
wholesale markets). Once a service is contractually agreed upon, the total power
consumption of the collection of vehicles must follow a prescribed reference signal
that depends on the nature of the commitment made. In this use case, the benefits
of providing ancillary services including frequency regulation up/down and spinning
reserve were analyzed. To maximize the total benefits, the team designed the
bidding strategy for aggregator to participate into the wholesale electricity market.

Scale Up Emulation of EV Use Cases to Benefit Fleet

Owners in IOU Territories
This project developed and used an advanced smart and bidirectional PEV charging infrastructure
to demonstrate grid resiliency, cost savings to PEV fleet owners, and benefits to investor-owned
utilities (IOUs). The grid services capability is scaled up in simulation to predict the grid impact
and potential benefit from a large number of V2G capable PEV fleets in the State. To this end,
ETAP® [28] software is deployed for scaled up emulation of EV Use Cases. ETAP® offers an
integrated distribution network analysis, system planning and operations solution on the testbed
platform to simulate, analyze, operate and scale the EV use cases. To evaluate the grid impact
and benefits from a large number of PEVs under managed bidirectional

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charging systems, three different base load and EV load profiles are applied to load buses
#5, #7 and #9, as shown in Figure 5.

Figure 5: ETAP Power Grid Simulation with EV Load

Credit: UCLA SMERC © 2014-2018

For base load modeling, hourly power consumption historical data is retrieved from UCLA
facility management and scaled up to the level of a small city with about a population of
50,000, which is comparable to the cities in Los Angeles area such as Culver City and
Beverly Hills. Predicting EV user daily traveling schedule and energy demand is carried out
based on the historical data in UCLA SMERC smart charging infrastructure collected during
the past four years. The EVs assigned to each load bus is 15,000, consisting of 35 percent
light duty vehicles market shares as predicted for the year of 2040 [29]. This simulation
optimal case showed how EV charging demand scheduling mitigated overloading conditions.

When no charging control is implemented there is the potential that EV charging demand
peak overlaps with the grid peak demand (time interval~20), which can propel the system
toward intentional load shedding conditions or instability (Figure 6).

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 Figure 6: Uncoordinated EV Charging Demand

EV load peak overlaps with grid peak demand

Credit: UCLA SMERC © 2014-2018

However, the coordinated charging arranges as much V2G discharging sessions as during
grid peak hours, and then coordinates full capacity charging activity over its control network
during low load demand time intervals from 35 to 55 (Figure 7). The total load profile also
reveals that the peak load after coordinated charging implementation is 125MW, while it is
180MW in the worst case without EV load coordination

Figure 7: Coordinated EV Charging Demand

Optimal load profile with shifting EV charging demand.

Credit: UCLA SMERC © 2014-2018

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As another case study, Santa Monica Civic Center charging infrastructure is modeled in
RSCAD/RTDS real time simulator (Figure 8), to study the impact of V2G and SC on total
load demand of the charging infrastructure.

Figure 8: RTDS Simulation Model of Santa Monica Civic Center Charging Infrastructure

Credit: UCLA SMERC © 2014-2018

The case study includes 213 kW solar panel, seven level-2, 6.6 kW EV chargers, and one
30 kW DC fast charger (DCFC). Through the simulation, it is shown how SC in combination
with V2G can provide load leveling (Figure 9).

Figure 9: Comparison of System Load With and Without Smart Charging and V2G

Load with (blue) and without (red) smart charging and V2G.
Credit: UCLA SMERC © 2014-2018

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SC takes effect at 14:00 wherein the charging rate of the EVs is throttled by 20 percent from
6.6 kW to 5.3 kW. At 14:30, it is assumed that the fast charger begins charging a vehicle at
30 kW, which is more realistic than assuming that a fully charged vehicle is already available
to provide V2G services. Consequently, the system load increases from 46.2 kW to 76.2 kW.
At this point in time, the price of electricity is still at $0.68 and is most economical to supply
the 30 kW of power. At 15:00, the EV charging rate is reduced to the minimum value of 2
kW. This counteracts the increase in load due to the 30 kW fast charging but not entirely. At
15:30, V2G is engaged and the system load drops drastically from roughly 54 kW to zero.
The charging rate is simultaneously increased from 2.0 kW to a larger value to avoid having
reverse power flow back to the grid. To avoid any confusion, it should be noted that
immediately before 15:30, +30 kW is being supplied to a vehicle, and after 15:30, -30 kW is
being supplied. Therefore, there is a change of 60 kW in the system load at 15:30. At 16:30,
V2G is deactivated and the charging rate is returned to 2.0 kW to maximize energy cost
savings at this time. Load leveling using V2G and SC results in considerable energy cost
reduction by shifting the loads from highly-priced time intervals to the periods when
electricity price is low.

Developmental Testing in the UCLA Micro-Grid

As of December 31, 2017, the UCLA Micro-Grid Testbed consists of 115 smart EV
charging stations, two DC fast chargers, 35 kW PV system, a 76 kWh of Battery Energy
Storage System (BESS) and 343 active EV users.

The following project tasks are performed and tested on UCLA Micro-Grid Testbed before
their deployment on the demonstration sites.

V2G and V2B Technologies Based on SAE J1772 and CHAdeMO

Standards
Currently there are three major standards that support bi-directional power flow charging,
also known as V2G technology. They are ISO 15118 [30], SAE combined charging system
(CCS) [31] and CHAdeMO [32]. Two V2G systems have been successfully used in the current
project Mitsubishi MiEV V2G system and the Princeton Power V2G fast charger. The direct
current (DC) fast charger from Bosch has also been installed in UCLA parking structure,
which is mainly for testing of SAE CCS standard.

Figure 10 shows the Mitsubishi and Princeton Power V2G system. All of the V2G charging
stations are connected with SMERC smart charging network. Charging session data are
uploaded to the central server every minute. V2G charging stations can be controlled
remotely by a command released from control center. The stations can perform V2G during
a demand response event or grid service request.

17
 Figure 10: Mitsubishi and Princeton Power V2G System Setup

The Mitsubishi V2G charging system connecting with a Mitsubishi MiEV and performing bi-directional charging (left).
A Nissan Leaf connected to the Princeton Power V2G charging station (right).
Credit: UCLA SMERC © 2014-2018

Safety and Reliability Analysis of the V2G and V2B System

Mitsubishi and Princeton Power V2G systems have provided several safety features. The
V2G system has a grid tie inverter which can convert DC current to AC current and
synchronize the phase of AC current to the phase of power grid. It is a UL 1741 (2005) [33]
requirement for a grid-tie system to have approved surge protection. If the AC source is
irregular or unreliable with power surges (a lower quality generator or inconsistent utility
power, for instance), a surge protector is necessary. The surge protector used in Mitsubishi
V2G system is FLEXware surge protector FW-SP-ACA.

The Princeton Power V2G system has charge cable electro-mechanical lock and ground fault
detection for safety consideration. Before any voltage is applied to the terminals of the
charge plug an electronic lock is closed on the plug. This prevents accidental or intentional
removal of the plug while dangerous voltages are present at the output. When the plug is
inserted properly there is a mechanical indicator window that is green. If the plug is not
inserted properly, the indicator is yellow. When the lock is electronically activated, there is a
red indicator light on the handle that illuminates to signal that the plug is secured and a
charging session is in process. During the shutdown process the lock is only disengaged
when the output voltage drops below 10V. The ground fault detection system is capable of
measuring 0 to 50k Ohms ground fault. The Princeton Power V2G charger will shut down if
the impedance between any of the DC terminals and earth drops below 50k Ohm.

As shown in

Figure 11, the Mitsubishi and Princeton Power V2G charger are enclosed in cabinet with locking
and grounding mechanism, preventing any unauthorized access and possibly electric shock.

18
 Figure 11: Exterior Cabinet Enclosure of V2G Charging Station

Mitsubishi V2G Cart (lef); Princeton Power V2G DC charger (right)

Credit: UCLA SMERC © 2014-2018

API Deployment of V2G/V2B System Integrated into the Control

Center
Integrating the V2G/V2B system into the control center is based on CHAdeMO protocol.
Since CHAdeMO is a proprietary protocol, the Mitsubishi V2G system added a layer of
DC/AC and AC/DC to allow current control. This reduced the overall efficiency. The
Princeton Power can charge and discharge a 2013 (or later) Nissan Leaf with a DC fast
charging port. The first benefit of this system is that it uses the CHAdeMO V2G protocol so
it could control the on/off status of the system and also control the current of the
input/output power.

UCLA SMERC integrated the Princeton Power V2G system with our EV control center to
allow remote monitoring and control. The monitoring of the system is accomplished
through the power meter that provides energy/power data at 1-minute sampling rate. This
is accomplished through TCP/IP protocol and HTTP POST method. The data will first be
stored in a MySQL database and then pulled by the web application for presentation on the
control center or mobile app. For controlling functions by the users, it is accomplished via a
mobile app developed by UCLA SMERC. Princeton Power provides API for control purposes.
The control signals are sent through TCP/IP to first pin out a router and use port forwarding
to locate a Raspberry PI. On the PI, a python script will be executed to control the system
output.
User Incentives for SC, V2G and V2B
To achieve SC, the UCLA SMERC Level-2 smart charger and Princeton bi-directional DC
fast charger was used. The UCLA SMERC Level 2 smart charger allows one dedicated
circuit to be shared among four EV charging sessions simultaneously.

Figure 12: UCLA SMERC Level 2 Smart Charger

Credit: UCLA SMERC © 2014-2018

On the software side, the project team implemented the smart charging scheme to
encourage smart charging of users by observing real-time solar energy generation and
other demand response signals, the following incentive scheme is published and
implemented.

Regular Operation
In general, PEVs will share the 30 Amp circuit when they are plugged in on the same
charger and the users do not need account registration to activate the charger. However,
registered users can associate their account with a smart plug to accumulate Solar Score
and build up their solar use profile. A registered user with higher solar score will receive
higher charging power.

The amperage distribution is shown in Table 1.

20
 Table 1: SC Amperage Distribution Under Regular Operation

Source: UCLA SMERC © 2014-2018

Solar Use Profile

A user will have an overall solar use profile based on his/her use of solar energy and
overall energy use as shown in equation (1).
(1)
=
ℎ

Accumulate SMERCOINSTM by Using Solar Energy

Users will receive 0.25 SMERCOINSTM per kWh when the vehicle is charged with solar energy.

Using SMERCOINSTM to boost Charging Power

User can use their SMERCOINSTM to boost their charging power to 24 Amp on a level 2 EV
charger when both plugs are used. A fixed amount of 25 SMERCOINSTM will be deducted
during a boost session. The amperage distribution is shown in Table 2.

Table 2: SC Amperage Distribution Under Boost Operation

Number of Vehicles plugged in Vehicle # 1 (Boosting Mode) Vehicle # 2

2 24 Amp 6 Amp

SMERCOINSTM rewards during a DR event

Source: SMERCOINSTM

A registered user can choose to manually and completely turn off their charging session
from the mobile app during that 30 min and receive SMERCOINSTM Reward during a DR
event. A registered DCFC user can also choose to discharge their battery to help the
power grid and receive SMERCOINSTM rewards.

21
Communication Network for EV Fleet
As shown in Figure 13, a sample of EV Communication Network is illustrated. The DR event
is received through SCC. The SCC communicates with EV Control Center through Ethernet,
the EV Control Center communicates with each individual Communication Gateway (CG)
through either Ethernet, 3G/4G, or WiFi, and the CG communicates with each individual EV
charging stations.

Figure 13: A Sample of EV Communication Network

Credit: UCLA SMERC © 2014-2018

Integrating IEC 61850 Standard into Distributed Energy Resources

System
Create a communication system where the control center and end devices communicate
with an IEC 61850 gateway by exchanging status information. The IEC 61850 Gateway and
Client communicate with each other via the Manufacturing Message Specification (MMS)
protocol that is required by IEC 61850. The communications between the mobile app,
control center and EVSE are standardized by IEC 61850 interface and communicating using
MMS protocol which is specified by IEC 61850 (Figure 14). In SMERC’s current EVSE
communication design, Zigbee protocol was used between smart meters and the
communication gateway inside the EVSE. Powerline Communication (PLC) via control pilot
line connection is used between EVSE and the connected EVs.

22
 Figure 14: Smart Charging Infrastructure with IEC 61850 Interface

Credit: UCLA SMERC © 2014-2018

IEC 61850 Modeling

The smart charging data and parameter such as user id, charging current, starting
timestamp, etc., are mapped into the IEC 61850 system framework. Steps to integrate the
IEC 61850 data model into the smart charging infrastructure are:

Summarize the charging parameters and data transmitted in the Mobile app –
Control center – EVSE communication.
Design an IEC 61850 system framework to describe the components in the smart
charging infrastructure.
Design IEC 61850 data set to map the charging parameters and data into the
system framework.
Write Substation Configuration Language (SCL) file for the system.
Write C# web service to manipulate variables in SCL file, integrate the IEC
61850 system framework into existing control center program, serving as the
communication interface.

IEC 61850 Integration

With the charging data and parameters mapped into IEC 61850 abstract data framework, an
SCL file is then written to carry the data. The SCL file is used in the IEC 61850 standardized
communication between multiple smart grid devices. The IEC 61850 gateway communicates
with the hardware using proprietary protocols and translates the information into the
appropriate IEC 61850 format using SCL files. The IEC 61850 client then communicates with
the gateway via MMS and provides the data to the control center using proprietary protocols.

Demand Response Participation with Bi-Directional EV Fleet

Infrastructures
To implement Automatic Demand Response, the project team adopted a two-layer
management structure. Super Control Center receives PV energy data directly from PV
panel. EV Control

23
Center works for direct control of charging stations, including energy data collection,
command to start/stop/suspend charging. EV Control Center, as an intermediate media,
reports aggregated power data on the PS level to the Super Control Center. The Super
Control Center can act as a VEN in openADR standard 2.0.

Battery Energy Storage System Integrated Level 3 Charging

Infrastructure
The DCFC, C1-30 V2X is a 30 kW, 480 VAC, 3-phase charger [34] manufactured by Princeton
Power has V2G capability supporting CHAdeMO type connector and is used for level-3
charging station.

UCLA SMERC designed and created a mobile battery storage system (MBSS) which provides
a portable modular battery storage system for the EV chargers in this project. MBSS can
supply the EV chargers when, due to a problem in the distribution system, the charger gets
disconnected from the grid. It can also support grid services such as peak load shaving and
load leveling which result in load variance minimization from the grid point of view and
energy cost reduction from customer’s perspective. To facilitate transportation of the
battery and make it modular, the battery modules and their circuit breaker are installed in a
compact configuration (Figure 15). Such a portable storage system eliminates the need for
costly site inspection, installation and commissioning.

Figure 15: Modular Battery Storage System

Left 2.2 kWh system and right 8.7 kWh system

Credit: UCLA SMERC © 2014-2018

24
Field Installations
Hardware Installations
As of December 31, 2017, the following hardware was installed in SCE territory:

To conduct research and perform proof of concept, the existing UCLA microgrid
testbed composed of the following has also been used.

Total of 117 plug points, 2DCFC, 135.2 kW PV, 128.5 kWh BESS
Source: UCLA SMERC © 2014-2018

25
Software Setup and Installation
The software associated with the demonstration is developed and set-up in-house by
SMERC. The software can be divided into three components: front end, back end and data
analysis. The front-end software is a mobile app with the interface between the users and
server and has five main functions:

Submit user charging request: As the main function of the mobile application, the user
interface facilitates the process for user to request charging service from the server.
The request information includes user id, charger id, time of request, user’s estimate
energy demand.
Receive charging status updates: The mobile interface also serves to update users
with their charging status, including power, energy charged and, if there is an
algorithm in action, their place in the queue.
Real-time solar energy indicator: In the front tab of the app, it is clearly indicated the
current level of solar generation so that users can make informed decision to
charge according to the solar generation level, see Figure 16.
Real-time solar credit trading system [35]: The app also provides easy-to-use interface
for users to trade their energy credits with each other described in the previous
section. If multiple solar credits with different prices are offered on the market, the
app automatically sort the unit price from low to high and provides user a
straightforward interface in which user only needs to indicate how much energy
he/she wishes to buy then the app automatically computes the lowest price to
purchase (assuming people always prefer to buy same credit with less coins). If no
user is offering energy credit, buyer can also choose to purchase from system whose
price can be higher than peer users’ price. When putting credit on the market, users
can also choose to sell directly to system without having to wait for other users to
buy their credit, but the purchase price of the system can be lower than those peer
users pay.
Ancillary services, information and configurations: The application provides ancillary
information related to the charging service including locations of the charging
station, station occupancy status and personal consumption information.

Some of the iOS interface snapshots are shown in Figure 16. In addition to iOS app, the
project team also provide a similar web-based interface for users.

The back-end software deals with data collection, hardware control and user
communications. The main components, SCC and EV Control Center, are introduced in the
deliverable report under “Task 5.2 Software Installation Report”.

The data analysis is performed using Matlab, Python, and Microsoft Office.

26
 Figure 16: Mobile iOS Interface

(a) Charging Tab; (b) Solar Power; (c) Station Status and (d) Credit Trading
Credit: UCLA SMERC © 2014-2018

Field Demonstrations
UCLA SMERC has been conducting field testing and systematic data collection since January
1, 2017. Field demonstration was first held on January 25, 2017. Subsequent field
demonstrations have been held monthly or as needed.

27
CHAPTER 4:
Project Outcomes

Smart Charging
This section considers smart charging from EV drivers’ point of view. The smart charging
and peak shaving results based on the needs from utility and garage are presented in the
”Grid Service” and “Cost Recovery” sections.

To assess smart charging based on energy price and user preference, the SMERCOINSTM
virtual currency was used. Details about this system are discussed in “User Incentives for
SC, V2G and V2B” section under “Project Approach.” This system encourages smart
charging by observing real-time solar energy generation and other Demand Response
signals to accumulate SMERCOINSTM, which can be used to boost charging power in an
urgent situation.

Based on current data collection and interaction with EV users, the following results were
obtained. In UCLA micro-grid test bed, all active EV drivers are willing to participate in smart
charging by delaying their charging session or reduced charging power. Out of the seven
level 2 smart EV chargers (28 plug points), the project team have charged 486 sessions
and total of 3,323 kWh energy in June 2017. Only 13 sessions and 83.65 kW was
charged under power boosting because of the urgent demand of increased charging power.

In Santa Monic Civic Center Parking Structure, since there is a 4-hour parking limitation,
all users request maximum charging power when connected. The power distribution to
the four simultaneous charging sessions (specifically. 1.56 kW), are not acceptable for
most users and on December 2016 were converted to two plugs per box to deliver 3.12
kW. Table 3 shows EV charging sessions and energy consumed from eight months of data
collection.

Table 3: SC Charging Sessions and Total Energy by Month

Month SessionTotal Energy Average kWh per

s session
January 643 4876 7.58
February 649 5341 8.23
March 707 5197 7.35
April 362 2736 7.56
May 500 3998 8.00
June 532 4634 8.71
July 563 4679 8.31
August 575 5022 8.73
Septembe 622 5090 8.183
r
October 595 5056 8.50
 November 579 5223 9.02
December 444 4101 9.24
January 339 3013 8.89
Colorado fleet yard – 16 dedicated level 1 chargers
January 82 222.971 2.72
February 142 236.8851 1.67
March 134 280.4667 2.09
April 98 312.1009 3.18
May 93 297.8318 3.20
June 118 347.2627 2.94
July 83 271.9637 3.28
August 98 272.3768 2.78
Septembe 67 292.7597 4.37
r
October 93 325.1912 3.50
November 80 307.4175 3.84
December 17 78.1669 4.60
January 105 324.6214 3.09
SM Hospital – One level 2 smart charger with 2 dedicated 40 Amp
Circuits
January 200 1493.025 7.47
February 179 1458.278 8.15
March 187 1388.603 7.43
April 212 1945.325 9.18
May 250 2279.737 9.12
June 219 2036.493 9.30
July 215 2033.376 9.46
August 263 2164.368 8.23
Septembe 253 2246.714 8.88
r
October 267 2387.961 8.94
November 187 2353.34 12.58
December 235 2440.016 10.38
January 293 3455.236 11.79

Source: UCLA SMERC © 2014-2018

V2G Focusing on Two-way Communication and Energy

Flow
The architecture of the V2G system using Princeton Power charging station is shown in
Figure
The V2G system is integrated into the SMERC smart charging infrastructure to share data
and receive aggregated control signals. The Princeton Power V2G charging station is an

29
advanced equipment with remote control communication interface and integrated with
V2G capability.

Figure 17: Princeton Power V2G System Architecture

Control Center
Power Demand Response

Reading Energy Price Control Center

Control
Commands

Charging / V2G

Princeton Power V2G Nissan Leaf

Charging Station EV

Credit: UCLA SMERC © 2014-2018

The Princeton Power V2G charging station has one CHAdeMO charging port. This charging
port can perform regular DC fast charging to any vehicle using CHAdeMO EVPS-002 V1.0
standard. V2G can also be performed by the same charging port but currently only limited
to Nissan Leaf with V2G technology enabled (model year 2013 and later). Communication
devices are built within the charging station so that it can be reached via the Internet. The
energy price and demand response signal are coming from the power grid service providers
to the SMERC control center in real-time. The local solar panel power generation reading is
also recorded. The decision will be made in the control center based on these incoming data
to determine how and when to perform V2G. The control commands will then be distributed
to the Princeton Power V2G charging station through internet.

The Princeton Power V2G charger uses a Modbus TCP protocol. To realize remote control and
data collection, the project team installed a router as a gateway for the network
communication and a smart meter to measure power consumption data. The router
equipment is installed in the SMERC labeled box next to the charger shown in Figure 18.

30
 Figure 18: Network Communication Equipment Box with Princeton Power V2G Charger

Princeton Power V2G DC charger (left); Router equioment and smart meter within a SMERC labeled box
(right). Credit: UCLA SMERC © 2014-2018

Power consumption data from this V2G fast charger is collected in two ways: 1) Smart
meter records the power flow and uploads metering data to the control center via gateway
every minute; 2) Charger controller reads status data from the charger API and store the
data in a local database in the controller every 1 second. The data uploaded into the
control center can be plotted for visualization in web browser and is shown in Figure 19.
Based on onsite testing results, the V2G fast charger was able to respond to change of
charging current and change of power flow direction commands within 1-2 seconds after
the commands are received by the charger.

Figure 19: V2G Power Consumption Data Collected in Local Controller Database

The V2G fast charger was able to respond to change of charging current and change of power flow direction
commands within 1-2 seconds after the commands are received by the charger.
Credit: UCLA SMERC © 2014-2018

Initially, when the Princeton Power DCFC was installed, it generated high pitch noise that
was
unacceptable to the city staff in the office building attached to the parking structure in which
the charger is installed. The charger had to temporarily be turned off due to the noise issue.
A

31
noise abatement kit was purchased through Princeton Power and it was installed in August
2017. The noise reduced significantly, however, when charging/discharging at full power
(30 kW), the noise was still not acceptable to the city staff. A micro controller was added to
reduce the charging/discharging power to be 10kW during office hour and ramp up to 30
kW during evening and weekend hours.

Table 4 shows the testing sessions performed.

Table 4: Charging and Discharging Sessions on Princeton Power DCFC

Total Charging Total Discharging Starting Ending
Date/Time Testing Type Energy (kWh) Energy (kWh) SoC SoC

2/25/2017 12:25 charging/discharging/ 8.8 6.85 30% 40%

incremental

3/20/2017 11:00 charging/discharging/ 5.87 4.49 64% 52%

incremental

4/28/2017 9:28 charging/discharging 21.45 13.6 39% 55%

5/31/2017 11:00 charging/discharging 8.81 4.08 59% 69%

6/28/2017 19:05 charging/discharging 12.31 7.12 76% 77%

7/26/2017 18:14 charging/discharging 8.3 8.2 97% 77%

8/8/2017 15:18 charging 7.91 0 60% 90%

8/24/2017 13:18 charging/discharging 13.5 0.51 30% 90%

9/29/2017 17:05 charging/discharging 6.79 3.76 71% 84%

10/13/2017 18:37 charging/discharging 7.16 1.25 43% 80%

11/30/2017 10:42 charging/discharging 4.87 0.99 57% 78%

12/22/2017 16:24 charging/discharging 5.84 3.95 88% 93%

Source: UCLA SMERC © 2014-2018

V2B—Using EV Fleet Storage to Support Building Loads

UCLA Engineering IV building load profile on September 20, 2016 from 7 am to 7 pm is chosen as
the baseload for V2B control algorithm. The 12-hour time span is divided into 60 time slots. The
historical data stored in the database of the UCLA SMERC smart EV charging system, including 30
EV drivers on UCLA campus, is extracted for user behavior modeling and prediction. Three
examples of the user charging record are shown in Figure 20. There are power usage peaks and
valleys in the load profile corresponding to the operation of some heavy energy consuming
devices in the building. The fleet of 30 EVs will provide grid service to flatten the load under the
control of control algorithm. Such grid service can be potentially scaled up

32
to support a microgrid or a utility service area with enough EV participation—this is a
key benefit of EV load control.

Figure 20: Data Example of User Charging Record

Blue dots indicate starting time and red dots indicate ending time
Credit: UCLA SMERC © 2014-2018

Before implementing the V2B scheduling algorithm, EV user behavior predictions are based
on the randomly selected historical charging record data from 30 EV users in the past four
years. Charging start time, end time and energy demand are predicted for September 20,
2016 (picked randomly among typical load profiles) and then incorporated in the control
algorithm. The algorithm converges to an optimal bi-directional charging strategy which
effectively flattens the original base-load by peak shaving and valley filling at 35 percent
(Figure 21).

33
 Figure 21: Base Load Profile Flattened by V2B Scheduling Algorithm

September 20, 2016, 7 am to 7 pm, 30 EV charging sessions + 1 V2G station

The algorithm converges to an optimal bi-directional charging strategy which effectively flattens the original base-load by
peak shaving and valley filling at 35 percent.
Credit: UCLA SMERC © 2014-2018

The baseload has power consumption peak from time slot No. 17 to time slot No. 27, and power
use valley from time slot No. 33 to No. 50. The optimal decentralized bi-directional charging
algorithm precisely tunes charging rate of each EV in the network, changing from high speed
charging to discharging according to the trend of baseload profile. Almost all EVs are
performing V2G discharging at baseload peak time, making the peak power
consumption
drop 35 percent, from a high of 140 kW to a low of 90 kW. The Optimal total load
curve, which is the combination of baseload and EV load profile, has been flattened. The
optimal distributed bi-directional charging algorithm demonstrates the capability to
integrate EVs into the power grid as DERs, providing various grid services to benefit the
power grid.

Mitigating the PV Duck Curve

As PV output usually peaks around noon which does not typically coincide with the peak
load, over-generation from this renewable source occurs and could cause problems. By
using grid-to-vehicle, also known as G2V or V1G, the project team could mitigate this over-
generation. This over-generation is typically known as the Duck Curve. The results from the
G2V approach are demonstrated using data from Santa Monica Civic Center PV system and
charging stations. The PV output profile of a typical day and a typical load is presented in
Figure 22. For the PV data, the project team choose the data collected on a random date
(February 1,2017) as an example for discussion. For the typical load, exclusive of the Santa
Monica Civic Center building load profile, the project team used the regular EV charging
loads in that building as the proxy of the building load. Between 11 am -2 pm the energy
generated by solar PV is greater than the
building load. The project team focused on this region and added more EV fast charger load to
demonstrate how EV fast charging loads could mitigate the over-generation. This EV fast charging
load is with a constant load of 28 kW and significantly eliminates the gap between PV generation
and original building load.

Figure 22: PV Generation Against Building Load and Duck Curve Mitigation

Smart Charging Algorithm to solve Duck Curve Problem (February 2, 2017, 7 EV chargers, 213 kW PV ). PV output profile of
a typical day and a typical load (left); Adding additional EV fast charger load to demonstrate how EV fast charging loads
could mitigate this over-generation (right)
Credit: UCLA SMERC © 2014-2018

For the entirety of 2016, the project team collected 325 days of data and 237,001 kWh
energy harvested, with a daily average of 729 kWh. The reasons there are 40 days without
data input include monitoring system firmware/software upgrades, on-site metering system
running other energy applications, and PV system switched turn-off by the site-host.

Figure 23 presents the maximum instance power by month. Observations can be made
that it follows the variation of how much sunshine the PV system is exposed to.
Throughout this period, the project team performed 12 sessions of monitored V1G/V2G
operations. A typical load profile of V1G/V2G can be found in Figure 24.

The V1G has the ability to rapidly increase consumption to 28 kW in a short amount of time
(two minutes) and is capable of covering the period when the solar output reaches its
maximum output. In a typical summer day when the PV system outputs a maximum of 120
kW, this fast charging session accounts for 23.3 percent of the total generation, while in a
typical winter day when the maximum is halved, or 60kW, the fast charging session could
consume a total of 46.7 percent of the total generation.

36
 Figure 23: PV Maximum Instance Power by Month

Credit: UCLA SMERC © 2014-2018

Figure 24: V2G Operation Load Profile

Credit: UCLA SMERC © 2014-2018

From these statistics, simulation is performed. Assuming that the fast charging has the
capability ranges from (0, 28) kW. Two fast charging systems are considered in this
simulation. The maximum instant power by day for 2017 is extracted, with base load
subtracted from it, the distribution of load consumption difference and load balancing can
be observed in Figure 25. As could be observed from the histogram, the number of days
when the difference between

35
generation and load being near zero increase dramatically as the number of fast
charging system increases.

Figure 25: Over-Generation Damping with Fast Charging

Distribution of load distribution (blue) and load balancing 1 and 2 fast charging systems in orange and green.
Credit: UCLA SMERC © 2014-2018

A conclusion could be drawn that, since the base load is only around 90 kW as shown in
Figure 22, the V2G system with proper number of instances being installed (in this case, 2
systems are sufficient), the system should be able to fill the gap between load and
generation not just during most of the peak hours, but also most of the time throughout a
day.

Automated Demand Response Demonstration

There are two types of DR considered in this analysis: Pure DR and DR with V2G. For pure
DR, the power from the charger to the vehicle is reduced to an absolute minimum. For DR
with V2G, the discharging power is used to perform reverse power flow and provides
effective V2G capability that is more than the pure DR.

The result for pure DR is shown in Table 5.

38
 Table 5: Pure Demand Response Power Reduction

14031 3629.63 3303.370787 14:12 14:21

11805.04 2052.17 1636.363636 18:50 18:57

17278.4 4195.05 3854.389722 15:15 15:30

6565.74 3425.46 7092.537313 13:58 14:03

5557.98 2602.64 3904.411765 13:38 13:48

Source: UCLA SMERC © 2014-2018

Begin power is the power when DR is initiated (in units of Watts). Minimum power is the
minimum total power consumption for the Civic Center during the DR. Average power is the
average consumption power of all stations summed together, calculated from main energy.

The result for DR with V2G is shown in Table 6.

Table 6: DR with V2G Support

Consumed Energy Back feed Energy Start Time Stop Time Pure (kWh)
(kWh)
(kWh)

0.76 -2.13 2/25/17 12:10 2/25/17 12:18 -1.37

0 -0.944 2/25/17 12:22 2/25/17 12:24 -0.944

0.6 -4.239 2/25/17 12:40 2/25/17 12:49 -3.639

0.81 -4.49 3/20/17 11:00 3/20/17 11:09 -3.68

1.72 -4.658 4/28/17 9:17 4/28/17 9:26 -2.938

2.14 -4.122 4/28/17 9:40 4/28/17 9:48 -1.982

2.46 -4.815 4/28/17 10:00 4/28/17 10:09 -2.355

0.72 -2.581 5/31/17 11:10 5/31/17 11:16 -1.861

0.56 -1.498 5/31/17 11:22 5/31/17 11:25 -0.938

1.67 -4.545 6/28/17 19:07 6/28/17 19:16 -2.875

0.89 -2.571 6/28/17 19:22 6/28/17 19:27 -1.681

1.64 -4.65 7/26/17 18:21 7/26/17 18:30 -3.01

0.69 -1.979 7/26/17 18:35 7/26/17 18:39 -1.289

0.5 -1.583 7/26/17 18:46 7/26/17 18:49 -1.083

0.09 -0.515 8/24/17 14:01 8/24/17 14:03 -0.425

37
 1.31 17:22 1.31

2.41 14:03 2.41

0.6 11:48 0.6

4.1 0.001 12:14 4.099

0.2 16:37 16:40 0.2

0.31 16:50 17:10 0.31

17:22 17:34
0.7 1.739 16:41 16:49 1.039

3.14 1.547 10/30/17 10/30/17 1.593

18:41 19:12
0.1 0.573 10/30/17 10/30/17 0.473
18:54 18:56
2.71 11/30/17 11/30/17 2.71
10:43 11:04

0.76 11/30/17 11/30/17 0.76

11:15 11:20

Source: UCLA SMERC © 2014-2018

Consumed energy is the energy the Civic Center boxes use during V2G session as kWh.
Back feed Energy is the energy that the V2G session discharges in total as kWh. Pure is
the total power flow of the charging power and discharging power as kWh.

To compare the effectiveness of V2G, Table 7 shows the power reduction of the V2G
sessions.

Table 7: DR with V2G Support (Power Reduction)

Begin Power (w) Min Power (w) Ave. Power Start Time Stop Time
(w)

9072.49 4085.57 8341.608739 2/25/17 10:41 2/25/17 11:32

5786.65 -2887.91 3329.153605 2/25/17 12:28 2/25/17 12:39

18096.87 8938.09 11978.83131 2/25/17 13:44 2/25/17 13:59

23444.26 10723.14 19838.83249 3/6/17 11:29 3/6/17 11:55

15335.33 11369.15 14915.49296 5/1/17 15:30 5/1/17 15:58

5783.82 10980.29 18150 8/24/17 14:01 8/24/17 14:03

3330.18 -4562 2503.184713 9/2/17 17:22 9/2/17 17:53

5741.09 1293.63 5505.076142 9/3/17 14:03 9/3/17 14:29

8885.14 -1497.22 7200 9/5/17 11:48 9/5/17 11:53

8860.78 2749.16 11624.88189

38
9/5/17 12:14 9/5/17 12:35
 5843.03 2363.99 3977.900552 9/29/17 16:37 9/29/17 16:40

5849.7 -31.8 922.3140496 9/29/17 16:50 9/29/17 17:10

0.24 -8229.8 0 9/29/17 17:22 9/29/17 17:34

5852.99 11021.99 18254.46985 9/29/17 16:41 9/29/17 16:49

12186.42 15997.68 8956.050955 10/30/17 18:41 10/30/17 19:12

12945.99 16048.7 20023.1405 10/30/17 18:54 10/30/17 18:56

11513.25 8038.89 7694.006309 11/30/17 10:43 11/30/17 11:04

18073.93 8016.38 9059.602649 11/30/17 11:15 11/30/17 11:20

Comparing Table 5 and Table 7, it is evident that with the integration of V2G power
reduction can be achieved more effectively.

Load Leveling for Level-3

The high power demand by the Level-3 DCFC can affect the power quality and stability
locally and this could get exacerbated in large-scale deployments. High power demand can
increase the power loss and voltage drop in the distribution feeders. Integrating BESS in the
distribution feeder and close to the end customers has the capability to mitigate these
negative effects. In this case, BESS can charge when distribution feeder is experiencing low
load demand, and subsequently, support the power grid by supplying the load when the
overall load demand is at the peak value. BESS integration not only decreases power loss
and voltage drop, but also reduces end customers’ electricity bill by avoiding demand
charges.

In this project, a portable, mobile and low capacity BESS was set up to create and
demonstrate a flexible infrastructure where it can provide flexible and on-demand storage
capability which is also mobile and can be connected to different points or different phases
within a commercial customer’s grid site. Although a single low capacity BESS cannot
provide load leveling by itself for the whole charging infrastructure, it still shows how BESS
can reduce power demand from the grid and reduce electricity cost. Employing larger
numbers of these devices would allow simple scale up at the site. The collected data for the
8.7 kWh mobile BESS is shown in Figure
The scale of this battery is similar to the size of the Tesla Power wall, with the following
differences:

The system is mobile

Multiple batteries can be connected within the same infrastructure and logically
connected by a software program to aggregate their capability in a flexible
manner.
The software to control and connected the batteries to each other and to other DERs is
completely customizable.
The algorithms driving the controls for multiple batteries would be flexible and
adaptable.
 Figure 26: Grid Service and Islanding by 8.7 kWh Mobile BESS

Credit: UCLA SMERC © 2014-2018

The bi-directional converter interfacing BESS is charging the battery from the first to 500
measurement sample. At that time, level 2 EV charger charges the EV so the bi-directional
converter charges BESS and EV. At 590 sampling time, because of a fault in the distribution
feeder, bi-directional converter is disconnected from the grid. As BESS has enough energy, it
forms an islanded or independent microgrid with the EV charger and starts charging the EV.

Assuming the bi-directional converter as the interface of a microgrid (including EV charger

and BESS), when the electricity price is low, EV and BESS are charged from the power
supplied by distribution system. However, when the electricity price is high or distribution
system is experiencing peak load, supplying EV charging load by BESS can significantly
contribute to peak load shaving and demand charge reduction. This can be realized by
partially supplying EV by BESS or charging the EV only using BESS, depending on the peak
load amount and energy price. In addition, BESS can supply the EVs in the emergency
situations where power from grid is not available due to faults in the distribution system. In
this situation, BESS, due to islanded operation capability of the bi-directional converter, can
charge the EV.

To show how the BESS can provide load leveling for DCFC two components are considered:

As shown in
Figure 27, consider a typical DCFC charging profile. The peak value of the DCFC load
demand is 30 kW, assuming it takes 50 minutes to fully charge the vehicle, the
DCFC will deliver 25 kWh energy for the EV.

41
 Figure 27: Princeton Power DCFC and Load Profile

(left) Princeton Power DCFC (left); Load profile of DCFC (right).

Credit: UCLA SMERC © 2014-2018

Consider using the BESS developed in this project to support this DCFC charging
session. The BESS has a capacity of 8.7 kWh with an average of 7.2 kW charging and
discharging power. The BESS and its charging/discharging profile is shown in Figure
28. To support the DCFC charging session (50 minutes specifically 5-6 hrs at 30 kW),
a total 7.2 kW * 5/6 = 6 kWh energy will be discharged from the BESS to support
this charging session.

Figure 28: BESS Charging Profile

The BESS system (left); charging/discharging profile (right).

Credit: UCLA SMERC © 2014-2018

Figure 29 illustrates load leveling using BESS, where the red line represents the DCFC
demand profile without BESS, the green line represents the power output of the bi-
directional converter supplying DCFC, and blue line represents the power from the grid
supplying DCFC.

42
 Figure 29: Grid Service-Load Leveling for DCFC by BESS

Credit: UCLA SMERC © 2014-2018

Assuming that this energy is delivered to the EV when the energy price is
experiencing its maximum value (0.48131 $/kWh [36]), EV charging costs without
BESS support is :
(2)
= × .= $ .

With the support of BESS, the BESS can be charged when the electricity price is very
low, 0.01969 $/kWh [36], BESS charging costs for this 6 kWh is
(3)
= × .= $ .

And, the remaining 19 kWh (25kWh – 6 kWh = 19 kWh), will still need to be supported by
the power grid. So, the total cost of this charging session with BESS support would be:
(4)
=∗ .+ .= $ .

This shows a reduction of the charging energy cost by 23 percent (12.03275-

9.2630)/12.03275), per charging session.

Bi-Directional EV Fleet Infrastructure

With large numbers of V2G enabled EVs, the grid can be dynamically balanced with a
combination of V2G and G2V. The team considered a building with solar generation and a
base load, sample result from the data collection on February 1, 2017 is shown in Figure 30.
Ideally, this difference between generation and base load should be constantly at zero—no
power flow to and from power grid. With a fleet of EVs, the peaks can be reduced to fill out
the valleys to a greater extent. In this section, the EV fleet sample data obtained from
Santa Monica Colorado Fleet Yard on February 2017 was used.
Preliminary data analysis and simulation results are shown in Figure 31 and

Figure 32.

43
 Figure 30: Difference Between Generation and Load

Solar generation and a base load (left); Difference between generation and load (right).
Credit: UCLA SMERC © 2014-2018

Figure 31: Mitigation Through Manipulation of a Fleet of EVs

Credit: UCLA SMERC © 2014-2018

44
 Figure 32: Mitigation Through Manipulation of a Fleet of EVs With a BESS System

Credit: UCLA SMERC © 2014-2018

With one year of data collection (January 1, 2017 – December 31, 2017), the previous
simulation results could be extended. For this part, the same data collected from Santa
Monica PV system is used, accompanied with EV charging data from Colorado Fleet Yard
station. To determine the mismatch between load and generation, the absolute value sum is
used. For example, for the day shown in

Figure 30, the total mismatch is 633.3 kWh. If five V2G systems are used for integration into
the system operating at a maximum of 7 kW, a total of 145.2 kWh mismatch still exists. With
an addition of a battery system capable of 20 kW instant power at both direction, this
mismatch reduced to 25.1 kWh per day. As such, the distribution of mismatch of data
collection period is shown in

Figure 33. The team observed that the combination of V2G plus battery significantly
alleviates the overall mismatch between load and generation. However, more of this
system could be introduced to further reduce such mismatch and balance the load, as
shown in

Credit: UCLA SMERC © 2014-2018

Figure 34, which clearly shows that mismatch is reduced.

45
 Figure 33: Mismatch Distribution with Different Levels of Mitigations

Credit: UCLA SMERC © 2014-2018

Figure 34: More Mismatch Distribution with Different Levels of Mitigations

Credit: UCLA SMERC © 2014-2018

46
Peak Shaving Through SC, V2G, and BESS
V2G capability to perform peak shaving depends upon the availability of the EVs. That is, if EVs
are plugged in for an extended period of time, such as fleets used by the City of Santa Monica
Transportation Division, they can play the role of BESS and provide peak shaving. In this case, the
plugged-in EVs with V2G capability are charged when the load demand in the grid, and
consequently energy prices, are low. When the load demand in the grid is high and energy prices
are higher, the EVs feed electricity back to the grid to supply the loads close by. However, the
EVs should be charged to a minimally acceptable level by the time their owners are ready to
drive. Typical charging and discharging sessions as measured in Santa Monica from the EVs with
V2G capability are shown in Figure 35 and impact the peak shaving capability.

Figure 35: DCFC With V2G Capability Power Profile

G2V (left), V2G (right).

Credit: UCLA SMERC © 2014-2018

To evaluate the effectiveness of peak shaving by V2G and energy cost reduction, SCE real-
time electricity price [36] was used (Figure 36).

Figure 36: SCE Real-Time Energy Price

Source: SCE [36]

Using BESS to support peak shaving, since there is no restriction on charging the battery by
a given time, the potential profit from the stationary BESS is higher. For this case, the
mobile battery storage system (MBSS) with the capacity of 8.7 kWh and charging and
discharging of 7.2 kW was used. The charging and discharging profiles are shown in Figure
37

47
 Figure 37: MBSS Power Profiles

Charging left, Discharging (right)

Credit: UCLA SMERC © 2014-2018

As the battery is connected to the power grid for the entire time, it can be charged
between 3 am and 4 am when the energy price is the lowest, and discharged to provide
peak load shaving and energy cost reduction between 4 pm and 5 pm. The profit is
evaluated as follows:

BESS charging cost, 3 am- 4 am

= .× .$/ =$.
(5)

BESS discharging cost reduction. 7 pm- 8 pm

= .× .$/ =$.
(6)

Therefore, even with current small BESS (8.7 kWh @ 7.2 kW), $3.93 can be saved daily,
resulting in $1,435 saving per year.

The same analysis can be done for V2G, depending on the availability and stay duration of
V2G-capable EV (V2GEV). That is, if V2GEV is plugged in during off-peak and on-peak load
hours when energy price is low and high, respectively, energy cost saving can be achieved
through SC. However, in V2G case, in addition to the uncertainty in availability, stay
duration, and available battery capacity of V2GEV, SC should consider the desired departure
time of the V2GEV owner, which means V2GEV must be fully charged by a deadline when its
owner wants to leave the charging station.

In SC case for Level 2 EV chargers, the cost saving depends on the stay duration of the EV
owner. To show how SC can result in reducing costs, it is assumed that an EV, which
requires 6.6 kWh energy, is plugged in at 3 pm, with a stay duration of four hours.
Considering the power rating of Level 2 EV chargers installed in Santa Monica Civic Center
(which is 6.6 kW), it takes 1 hour to fully charge the EV before 7 pm (departure time). If
the EV is charged at the time it is plugged in, the total charging cost is:
ℎ ℎ = 6.6 ℎ × 0.474 $/ ℎ = $3.13 (7)

However, if it is charged from 6:00 pm to 7:00 pm, the total charging cost is:
ℎ = 6.6 ℎ × 0.474 $/ ℎ = $1.11
ℎ (8)

48
This equates to reducing the charging costs by 64 percent.

Minimizing Local Power Congestion

When electricity demand exceeds the system capacity, congestion occurs. Grid congestion
not only impacts reliability, it also decreases energy efficiency. During periods of high
demand, line losses increase. When lines are congested and operating at or near their
thermal limits, they are subjected to significant line losses. A congested system may also
lead to a violation of network security limits such as thermal, voltage and/or angular
stability. One solution is to upgrade the transmission and distribution system, which is
costly, however, BESS, SC, and V2G can defer such investments by providing an alternative.
BESS and V2G deployed downstream of congested corridors can be discharged during
congested periods to reduce the load burden on the system. SC can also reduce the
charging load during congested periods.
In this project, three technologies including BESS, SC, and V2G are used to demonstrate
the congestion relief in real testbed platform.

Figure 38 shows the integrated BESS, SC and V2G at the host site.

Under high load conditions, the battery energy storage system can discharge and supply the
load locally. It can maximize the local energy utilization and reduce the transferred power
from the main grid. The results obtained from the experimental case study are
demonstrated in Figure 39. In this experiment, the battery energy storage system is
integrated to the distribution circuit in parallel with DC fast charger. DC fast charger is one
of the high demand load in this testbed. The results show the battery energy storage system
can automatically discharge to reduce the peak demand of DC fast charger.

49
 Figure 38: V2G, SC, and Mobile BESS Integrated to the Grid for Congestion Relief

The figure shows the integrated BESS, SC and V2G at the host site.
Credit: UCLA SMERC © 2014-2018

Figure 39: Peak Load Shaving by BESS

The experiment results show the battery energy storage system can automatically discharge to reduce the peak demand of
DC fast charger to mitigate the local power congestion.
Credit: UCLA SMERC © 2014-2018

50
Cost Recovery Validation
EV Charger with SC, V2B, V2G, and grid-service technologies capability has economic
benefit in following components to the fleet owners under SCE grid service. In this section,
the cost recovery validation of two locations, Santa Monica Civic Center and the Fleet Yard,
will be analyzed from January to December 2017.

Load Smoothing to Avoid Higher EV Charge Prices

Since these locations are under Southern California Edison (SCE) territory, SCE EV charges,
TOU-EV-4 Rate Schedule [37] for Civic Center and TOU-EV-3-B rate for the fleet yard [37],
are applied for an efficient economic benefit. Figure 40 shows the TOU-EV-3-B and 4 rate
schedules from SCE, which are referenced in calculating the cost benefit for using Smart
Charging system and avoiding higher charge. SCE Demand Charge rate for 3-B is 20 kW and
for 4 is 500 kW.

Figure 40: SCE TOU-EV Rate Schedule

[37]
Credit: Southern California Edison (SCE)

Smart charging allows the EV load to shift from peak price periods to lower price period
within defined flexible time, 6 am to 8 pm for Civic center and 12 am to 12 pm for the fleet
yard. Then, Civic Center EV chargers can avoid On-Peak charge rate, and the fleet yard can
avoid Mid-Peak price in the morning. Applying this algorithm allows these savings shown in
Table 8 and Table 91..

1 Estimates based on available information. Actual saving may be different based on total building load which is
not available at this time.

51
 Table 8: Santa Monica Civic Center Cost Saving from Smart Charging

Santa Monica Civic Center

Month Energy Cost Without SC Energy Cost with SC [$] Savings
[$] [$]
January 583.04 522.68 60.36
February 620.85 553.41 67.44
March 668.71 594.18 74.53
April 451.58 410.43 41.15
May 566.35 505.83 60.52
June 975.43 623.12 352.31
July 1010.07 622.03 388.04
August 1040.65 647.94 392.71
September 1007.85 636.14 371.71
October 624.03 568.88 55.15
November 644.54 578.83 65.71
December 544.18 496.33 47.85
Source: UCLA SMERC © 2014-2018

Table 9: Santa Monica Fleet Yard Cost Saving from Smart Charging

Santa Monica Fleet Yard

Month Energy Cost Without SC Energy Cost with SC Savings [$]
[$] [$]
January 16.61 16.09 0.52
February 17.91 16.96 0.95
March 21.12 19.73 1.39
April 26.12 22.43 3.69
May 22.77 20.99 1.78
June 25.64 21.74 3.90
July 20.56 16.48 4.08
August 19.24 16.53 2.71
September 22.23 17.44 4.79
October 24.97 22.62 2.35
November 23.65 21.40 2.25
December 6.59 5.93 0.66
Source: UCLA SMERC © 2014-2018

A total of $2006 savings (from January 1, 2017 to December 31, 2017).

52
SCE Demand Response Program Incentives
Table 10 shows SCE TOU General Service Base Interruptible Program Reward Rate for meter
between 2kV and 50kV [38]. The event is limited to one per day, 10 per calendar month, up to 6
hours each for a maximum of 180 hours a calendar year and it must commit to curtail at least 15
percent of the Maximum Demand, which is not less than 100 kW, per period of interruption.

Table 10: SCE TOU General Service Base Interruptible Program Reward Rate
Reward Rate
[$/kW/Month]
Summer Season – On- 22.43
Peak
Summer Season – Mid- 6.75
Peak
Winter Season – Mid-Peak 1.4
Source: UCLA SMERC © 2014-2018

In this project, all EV chargers used are smart chargers and can be turned off during a DR
event and the DCFC and Battery system can stop charging and back-feed their power to the
power grid. This provides 43.68 kW (Level 2 chargers) + 30 kW (DCFC G2V) + 30 kW (DCFC
V2G) + 6.8 kW (Battery charging) + 6.8 (Battery discharging) = 117.28 kW. The demand
curtailment at Colorado Yard (28.8kW) and Medical Center (12.48 kW) does not qualify for
this reward as their curtailment capacities are less than 100 kW.

For this project. the maximum benefit (for all hardware/equipment installed) will be
$11,835.90 a year [($22.43/kW/month * 4 summer months/year) + ($1.4/kW/month *8
winter months/year)] * 117.28 kW = $11,835.9/year]2. If mid-peak summer season is used,
$6.75 instead of $22.43, the reward will be $7,646.66/year [($6.75/kW/month * 4 summer
months/year) + ($1.4/kW/month *8 winter months/year)] * 117.28 kW = $7,646.66/year.

Based on optimum and maximum benefit scenarios.

53
CHAPTER 5:
Conclusions

Through the current project, UCLA SMERC has successfully developed and demonstrated
the advanced technologies to achieve the goals of the project: PEV Smart Charging and
Storage in Supporting Grid Operational Needs. This section describes the conclusions
from the project, the lessons learned and key obstacles encountered through the project.

Major Take-aways
While most current PEVs only support unidirectional charging from grid to vehicle, V2G
and V2B technology allows power to flow in the reverse direction so a PEV can act as
a battery energy storage system.
The software developed through this agreement can aggregate large numbers of
vehicles into a single load and allow the grid operator to draw significant amount of
power. For Example, if 10 percent of California’s 35 million vehicles are PEVs and
were used for vehicle-to-grid and vehicle-to-building, when aggregated they would
provide support for roughly 50 percent of the California’s peak load of 50 GW.
PEVs can provide support to the utilities for reliability, stability, renewable portfolio
standards, etc. The research showed that PEVs can be used to mitigate over-
generation by using vehicle-to-grid, vehicle-to-building, and grid-to-vehicle smart
infrastructure during the early afternoon periods to mitigate the “duck curve”.
Integrating smart charging, vehicle-to-grid, vehicle-to-building, and battery energy
storage system, PEVs can help to shave peak loads and minimize local power
congestion by participating in automatic demand response or other utility
programs.
This agreement also provided validations in cost recovery for using smart chargers,
vehicle-to-grid and vehicle-to-building and battery energy storage systems. When
PEVs are used in fleets, installing additional EV charging stations is essential to
support PEV fleet operations, but can also become a financial burden to fleet
owners if PEV charging schedules are not properly managed.

The next section describes the lessons learned.

Lessons Learned
The lessons learned can be categorized into the following:

1. Technology issues

Challenge of lack of standardization in V2G: Due to lack of commonplace standards, V2G

technologies were found complex and difficult to implement. The additional work was as a
result of the researchers having to build communications and controls interfaces at multiple
levels. As a conclusion, while the V2G technology did function effectively and successfully
at a

54
technical level, for it to get scaled up, it needs to be supported by interface technologies
that are standard and rich in their ability to carry information on status, control, safety,
etc.

V1G Technology Matured with Potential for Commercialization: The technologies and
systems developed in this project for V1G and their integration with other DERS in a
microgrid were found to be relatively stable and mature at the level of control and
reliability. They could be further developed as an Energy Management Product and be
commercialized through the process of starting a company.

2. Site host, fleet and microgrid considerations

The needs of the site hosts or microgrid operator (application) require customization of
algorithms: This research finds that based on the type of locations and parking limitations,
customized scheduling algorithms and power management rules provide best value for the
site host. For example, if the functionality is that of a fleet, the rules would be somewhat
different to that of workplace charging or even public charging. Fleets are more predictable
from a scheduling standpoint, and homogeneous from the technology and user
understanding standpoint, and therefore, can be integrated with the grid needs more easily.
Most workplaces, being controlled environments, tend to be closer to fleet conditions as
compared to public charging.

Fleet Operations: The researchers learned that fleet drivers look for simplicity in use. At
project initiation, the project required the fleet drivers to log a user id and password into a
kiosk so that the energy consumed by driver could be tracked and algorithms could be
customized based on needs of specific drivers. Over time, the team realized that the fleet
drivers became increasingly unwilling to use the kiosk and eventually had to remove the
kiosk. Extrapolating this behavior implies that in the future if customized algorithms by
driver are required, the ID of the individual driver must be determined directly from the
connection established between the vehicle and the charger. In the future, this would mean
that the car manufacturers must be willing to provide either user ID or VIN number through
the charging connector's communications port. Open architecture between the charger, the
car, and the grid operator would enable such a capability and our team is planning to work
on that actively as the next major area of research - an outcome of the findings of the
current project.

Stationary Battery Technology expensive to deploy in commercial environments: The cost

of integration, commissioning, and installation of batteries with inverters and BESS is still
expensive and therefore as V2G becomes standardized and open architecture based, and
therefore more economically deployable, there will be little need for stationary battery
storage in parking structures across California.

3. Utility Considerations

PEV Smart Charging Technology as a basis for microgrids distributed energy resources
(DER) needs: The proposed system - PEV Smart Charging and Storage - can be used as a
foundation
for DER management within a microgrid. With a focus on microgrid (grid-tie and
islanding) operation, a scaled-up project involving multiple microgrids forms the potential
next level of technology demonstration in the future.

4. User Issues

Constant refinement of algorithms based on driver needs: The team determined that
scheduling algorithms and rules need to be periodically (monthly) reviewed and improved
based on the data obtained. The reason for this is several fold and includes the following:
(1) the constant increase in the number of vehicles in California was resulting in
continuously greater pressure on the PEV charging infrastructure, and therefore, rules that
were developed at a given point in time were not as applicable at a later time; and (2) the
users themselves were becoming more sophisticated as they learned from each other and
also developed an understanding of algorithms and adapted their behavior based on the
scheduling algorithms. The understanding of a user’s capability to act in a certain manner
was no longer valid later in the project.

Obstacles Encountered
The team also concluded that in a complex project such as the current one, outcomes are
not always predictable and encountered several obstacles.

Lack of acceptable V2G standards by car manufacturers. Few PEV manufacturers

supported or openly supported V2G capability. This resulted in a challenge in
selecting PEV models.
Lack of open architecture or open standards for enabling bi-directional charging. A lack
of open architecture and open standards results in compatibility and limitations
when integrating bi-directional chargers with DER for energy management.

Multiple DCFC standards (Combo CCS versus CHAdeMO): Since there continued to be two
separate DCFC standards prevailing during the project, Combo CCS and CHAdeMO, there was
lack of maturity of tools in either one resulting in additional efforts to develop and integrate V2G
capability into the solution. While Combo CCS had been gaining traction in the duration of the
project, existing vehicles with CHAdeMO ports and newer vehicles with the port continued to
come into the market, making DCFC standardization a challenge for the industry as a whole.

Risk management. Risk management involved trying to determine and manage risks
associated with DCFC, V2G/V2B, and using the algorithms for smart charging with
V1G. Getting sign off by all parties with respect to risk management given that there
was new and untested equipment resulted in challenges in deployment and
participations from users. While risk management departments are often
conservative, it was found individual PEV drivers and owners are much more risk
taking just so that they can participate in advanced research and technological
innovation. Perhaps, one reason is that they would get free electricity for doing so.
This implies that people are willing to take risks provided they are incentivized and
this therefore may help regulators and rule makers when setting policies on
incentives.

56
 Procurement and installation delay for battery systems due to complexity in determining
installation site within a parking structure and high cost of installation. The
complexity in finding a location within a parking structure as well as the high cost of
installation of stationary battery within a parking garage resulted in an innovation in
this project—the development and deployment of a portable mobile battery enabling
DER integration in our site. The approach to having mobile battery energy storage
systems within parking structures is a potential area for future investigation.

Lack of integrated communications and controls standards between batteries, BMS,

inverters, and other DER assets. While the team investigated a variety of standards
as outlined in the project reports, the actual hardware assets of batteries, inverters,
solar, PEV, etc., were rarely supported by the same standards required to build
interfaces between the various DER assets. DER integration is, therefore, expensive
and cumbersome, and is an area for future research and investigation.

Lack of adequate V1G capabilities by car manufacturers. Certain vehicles did not
support current control. This resulted in certain vehicles starting to beep when
subjected to a control modulating signal to initiate V1G. This is yet another area
where the car companies can enhance the battery and charging managements
systems within their vehicles.

Project outcomes
Smart Charging. Flexible smart PEV charger technology was developed and managed
by a cloud-based software system connected to a mobile app used by the PEV driver.
The smart charger controls the charger power based on inputs from the site, user
and grid. The charger uses one input power circuit and shares the power with four
output circuits. The four output circuits are controlled simultaneously. The power was
shared dynamically and based on smart charger’s algorithms. A key algorithm within
the smart charging system was one that incentivized users to maximize using solar
energy allowing better grid control and reducing the energy cost for the utility by
increasing the amount of solar on the grid. This technology increased the number of
charging sessions to about 92 percent as compared to conventional charger and
almost twice the number of PEVs charged at any site.

Vehicle-to-Grid/Vehicle-to-Building. Two vehicle-to-grid and vehicle-to-building

systems were used to investigate results from different types of PEVs. The first
system, using a Mitsubishi PEV, provided 1.5 kW for discharging and 3.3 kW for
charging. Due to its limited power capacity, a higher power system using Princeton
Power DCFC was installed and used to provide 30kW bi-directional power flow. The
higher power bi-directional power provided sufficient controllable load to leverage
the PEV charging load and PV generation in the parking garage at the demonstration
site in the City of Santa Monica. It provided grid services as a load shaving resource
by supporting building and PEV charging loads or as an energy source/sink for
demand response.

57
Grid Services. Grid services were enabled through the smart charging algorithm
residing on the cloud software. The algorithm employed a user-charging pattern
prediction model and an actual building load profile, shifting the peak load by
scheduling the PEV charging load to a time when the building load was decreased to
certain threshold. Using this algorithm, the peak power consumption was reduced by
35 percent which would allow a site host to avoid a utility’s demand charge and pave
the way for demand response – a key grid service for the utility.

On certain days, especially during spring and fall when grid demand is lower, PV over-
generation results in demand collapse in the middle of the day and a steeply rising
load curve during the evening hours making it difficult for the grid operator to
balance the generation and load. Controlling this phenomenon can be achieved by
the system using the Princeton Power DC fast charger that receives scheduling
signals from the cloud software by messages from the grid operator to charge the
PEV at higher power levels in the middle of the day to mitigate the impact of PV over-
generation.

A key service to the site host is the ability to use load shifting based on time-of-use
pricing, which in turn benefits the utility’s load balancing needs. For example, the
battery energy storage system when used in conjunction with the DC fast
charger, provides potential benefit to the site by reducing the cost per charging
session by 23 percent via exploitation of time-of-use pricing. In the future,
V2G/V2B could itself be used to help PEVs reduce cost through this opportunity.

Using smart charging in the system without the V2G has benefits when the grid operator
offers time-of-use pricing and the customer is paying demand charges. By shifting
PEV charging load from peak to off-peak itself has shown a savings of $2006 for one
year of data collection at the demonstration site consisting of 7 level-2 smart PEV
chargers (14 plugs) and 16 level-1 PEV chargers.

In addition to the PEV curtailment, the V2G hardware in combination with the stationary
battery storage at the demonstration site provided the system with a total of 117 kW
of demand response capability which is greater than the minimum of 100 kW
required by Southern California Edison (Southern California Edison, "Time-Of-Use-
General Service Base Interruptible Program," March 2017). Roughly half of the 117
kW demand response capacity was provided by the V2G system by going from +30
kW to -30 kW or its inverse, resulting in a total controllable load of 60kW (±30kW).
Eventually, V2G is expected to cost substantially lower than what it is now as the
technology gets standardized (there is almost no standardization today) as well as
volume sales, making it far more competitive cost-wise than stationary battery
energy storage system.

58
CHAPTER 6:
Recommendations

Additional scaled up demonstration and tests should be performed to validate the

results obtained in this project.

The following is a summarized list of major project outcomes:

Developed and deployed a flexible smart PEV charging system along with the mobile app to
allow smart charging. Each input plug of the PEV charging system was interfaced with
four outputs leading to 92 percent greater number of charging sessions.

Developed two smart charging algorithms that can prioritize PEV charging cost
minimization or renewable energy usage maximization.

Used novel incentive concepts such as virtual currency and priority charging to
encourage users to charge PEVs during time periods of higher solar energy. It was
determined that using incentives resulted in increase of the local solar consumption
by 37 percent.

Designed and installed the V2G systems to support bi-directional power flow which can
be a very effective asset to support demand response and other power grid services
such as PV “duck curve” reduction or demand charge avoidance, etc.

Designed and installed a mobile battery storage system which is portable, low-cost, and
modular to charge PEVs and support grid services such as peak shaving and load
leveling. This is a unique innovation with potential for scale in commercial buildings
to connect with PEV chargers and to offer flexibility in power management and cost
savings.

Integrated mobile battery storage system with DC fast charger to mitigate voltage drop
problems and reduce electricity bill of the site host by $2.77 per charging session.

Installed DC fast chargers to charge PEVs during times of over-generation and mitigate
the PV duck-curve for load smoothing.

Automated demand response via the use of V2G demonstrated that V2G can offer
demand response (DR) which can be a substantial fraction of the typical load
of a parking structure.

Designed and implemented a local controller for the V2G station with fast power
ramping (1-2 seconds) and ±30 kW of power flow.

Demonstrated that V2G, battery energy storage system and smart charging can be
used for peak shaving, load shifting, and cost reduction. ($2.77 per direct current
fast charging session for a Nissan Leaf).

59
Designed and deployed centralized control center with demand response interface and
various scheduling algorithms to support grid services.

Validated cost benefits for PEV fleet owners within investor-owned utility (IOU)
territories – annual savings of $2,006 through exploiting time-of-use (TOU) pricing
and maximum of $11,836 rewards annually for demand response incentive
program.

Collected 12 months of data in the City of Santa Monica and supported more than 216
PEV users in their daily PEV charging needs.

Filed three patents on the smart charging algorithm and control of battery energy
storage system, published five journal/ conference papers, and held two
technology workshops on April 13, 2016, and September 26, 2016.

60
CHAPTER 7:
Public Benefits to California

Increase EV Charging Infrastructure

Based on comparison of regular commercial PEV chargers with the hardware and software
developed in this project, current UCLA SMERC PEV smart chargers provide additional
flexibility with control of charging power and allowing for additional PEV charging sessions
simultaneously within a single charger connected to multiple vehicles. Based on initial tests
when the first smart PEV charger was installed in January 2016, SMERC’s smart PEV chargers
provide an average of 92.17 percent more charging sessions and 43.26% more energy
delivered than a traditional charger (Figure 41). SMCCP01 is the first UCLA SMERC Level 2
smart PEV charger as compared to Level 2 regular Clipper Creek PEV chargers (SMCCP02 to
SMCCP07) at same parking lot and same time frame.

Figure 41: Comparing Regular EV Chargers and SMERC’s Smart EV Charger

SMCCP01 is the first UCLA SMERC level 2 smart EV charger as compared to level 2 regular Clipper Creek EV chargers
(SMCCP02 to SMCCP07) at same parking lot and same time frame.
Credit: UCLA SMERC © 2014-2018

Energy Cost Reduction

When the City of Santa Monica chooses to participate in SCE’s demand response incentive
program, they can avoid SCE demand charge by peak saving of $2006.55 (from January 1,
2017 to December 31, 2017) and $11,835.9 annually. Using the BESS (8.7 kWh), during
peak demand times can save energy costs of up to $1,435 annually.

61
Stable Power Grid
In this project, the project team achieved these technical outcomes providing a more
stable power grid.
Load smoothing, which helps avoid peak power impact.
Interface to the grid by an integrated control center to support grid­originated DR
events.
Capability for power quality monitoring and initiate peak shaving as necessary.
Time shifting to address the Duck Curve phenomenon.
The ability of V2G and BESS in providing emergency power for building and grid
support.
Reduce the peak power from DCFC with a BESS.

GHG Emission Reduction

Benefits to California include reductions in greenhouse gas emissions and air emissions such as
oxides of nitrogen from using PEVs. For this project, there were publicly accessible smart
chargers to accommodate an increase of 216 PEVs users between 2015 and 2017 during this
project. Argonne’s alternative fuel life-cycle environmental and economic transportation (AFLEET)
Toll
estimated life-cycle greenhouse gas emissions and vehicle air-pollutant emissions for these
additional 216 PEVs. This tool estimated emissions from gasoline light duty vehicles and
compared them to that of light duty EVs. The following assumptions are used:

Annual average mileage per vehicle of 12,400.

Fuel economy of 26.2 mpge for gasoline, 72.1 (miles per gallon equivalent) mpge for
PEVs.
Los Angeles, California as the location for emissions basis.
Average passenger car lifetime of 15 years.
One EV increase per user account request.
For each new user account created in current project, it is assumed that a new PEV is
purchased and used.

The results as presented in Figure 42 and Figure 43. They show substantial reductions
in petroleum use, GHG, and air pollutant emissions by using additional 163 PEVs in
place of gasoline passenger cars:

The annual petroleum use is 2,133 barrels, and GHG emissions is 1,241 short tons for
gasoline vehicles. In contrast, the annual petroleum use is 17 barrels and GHG emission is
significantly reduced to 621 short tons for PEVs. Being scaled up to the lifetime cycle for
the petroleum use, 216 gasoline passenger cars have up to approximately 31, 990 barrels
compared with the PEVs' 251 barrels.

The calculated annual air pollutant emissions include CO (9,564 lb), NOx (489 lb), PM10 (195
lb), PM2.5 (45 lb) and VOC (531 lb) for gasoline vehicle operations, however, the PEV can
reduce the pollutant emissions to PM 10 (177 lb) and PM 2.5 (24 lb). Further, the PEV has
zero-emission of CO, NOx and VOC.

62
The PEVs can substantially reduce the lifetime air pollutant emissions to PM10 (2,657 lb),
PM 2.5 (354) compared with the PM10 emission (2,918 lb) and PM 2.5 (663 lb) emissions
of 216 gasoline cars.

Figure 42: Annual Well-to-Wheels Petroleum Use and

GHGs for 216 Gasoline/EV Passenger Car Fleets

Credit: Argonne National Laboratory

63
 Figure 43: Annual Vehicle Operation Air Pollutants for 216 Gasoline/EV Passenger Car Fleets

Credit: Argonne National Laboratory

64
 GLOSSARY
Word/Term Definition
API Application Programming Interface is a set of clearly defined methods of
communication between various software components
BESS Battery Energy Storage System
CAISO California Independent System Operator
DCFC Direct Current Fast Charging supersede Level-1 and Level-2 charging, and are
designed to charge electric vehicles quickly with an electric output ranging
between 50 kW – 120 kW.
DER Distributed Energy Resources
DR Demand Response is defined as: “Changes in electric usage by end-use
customers from their normal consumption patterns in response to changes in
the price of electricity over time, or to incentive payments designed to induce
lower electricity use at times of high wholesale market prices or when system
reliability is jeopardized.”
EV Electric Vehicle
EVSE EV Supply Equipment. It is commonly called as charging station or charging
dock. It is built into the EV charging standard for electrical safety
G2V Grid to Vehicle describe the power from the grid to a plug-in EV.
GHG Green House Gas is a gas in an atmosphere that absorbs and emits radiant
energy within the thermal infrared range.
IEC International Electrotechnical Commission
IOU Investor-owned utility
LBNL Lawrence Berkeley National Laboratory
MBSS Mobile Battery Storage System
PEV Plug-in Electric Vehicle
PV Photovoltaic
SAE Society of Automotive Engineers
SC Smart Charging is the intelligent charging of EVs, where charging can be
shifted based on grid loads and in accordance to the vehicle owner’s needs.
SCE Southern California Edison
SMERC Smart Grid Energy Research Center
V2B Vehicle to Building describes a system in which EV can communicate with a
building to sell demand response services by either delivering electricity into
the building or by throttling their charging rate.
V2G Vehicle to Grid describes a system in which plug-in EV communicate with the
power grid to sell demand response services by either returning electricity to
the grid or by throttling their charging rate.
VGI Vehicle-Grid Integration

65
APPENDIX A:
Technical Transfer Plan

As the project continued to progress by way of showing the technologies to key

stakeholders including utilities, industry, regulators, researchers, etc., and, as additional
data was collected and the systems were modified based on data collection, analysis and
feedback from stakeholders, the tech transfer plan needed to be updated. UCLA has
updated the plan and submitted a final updated version at the end of the project.

Various technologies have been developed and they are being used to gather data. Based
on this data, the technology would be continuously refined. The technologies resulting in
systems been discussed in various reports already submitted and other reports to be
submitted.

These systems would, prior to project completion, either by copyrighted or patented

through the UCLA intellectual property (I.P.) office. The I.P. would then be either licensed or
sold by UCLA or it would be spun off by way of a startup company. The team (Dr. Gadh and
Dr. Peter Chu) has significant experience spinning off startups from a university. The most
recent startup that was spun-off was the smart EV charging technology called
WINSmartEVTM and it was based on technology developed at UCLA (via three patents) and
funded in part by the Department of Energy/Los Angeles Department of Water and Power
(LADWP) Smart Grid Demonstration Regional Program (SGRDP) program (DOE funded
LADWP SGRDP to the tune of $60 million and another $60 million was provided as cost
share by LADWP and its partners on this project including UCLA). Dr. Gadh and his team
went through the various steps and were able to successfully spin off the startup company
two years ago.

The startup team worked closely with the UCLA’s Anderson School of Management, UCLA
Office of Intellectual Property and the UCLA’s Institute for Technology Advancement – who
collectively helped develop the business plan and introduced the team to potential
commercial partners. This was critical to the success of the startup.In addition, the
startup team worked with Silicon Beach entrepreneurs here in the Los Angeles
region as well as the Los Angeles Cleantech Incubator (which Dr. Gadh is an advisor to) to
take
the business plan developed by UCLA business team and make it practically executable. The
support structure within UCLA and from the business community has been very helpful. Most
recently, SMERC technology was picked as a Finalist for the Los Angeles Business Journal
Patrick Soon-Shiong Awards Innovation Awards in November 2016 (news item appeared on
Los
Angeles Business Journal News -
http://www.cbjonline.com/a2labj/supplements/InnovationAwards_20161128.pdf.

The startup is now in operation and installing its technology in Southern California and
Northern California in the territories of PG&E and SCE. Dr. Gadh and Dr. Chu are advisors
to this startup.
By virtue of having the technology installed, tested and demonstrated at various sites and
within the territories of different utilities, the team was able to understand the strengths
and weaknesses of the technology and so were able to refine it constantly. The team was
able to work with the utilities in understanding the value of the technology to utilities. The
team was able to work with site hosts and understand how to customize the technology to
maximize value to site host and the EV drivers. The following were the test sites in
Southern California where the technology was installed prior to the commercialization:

UCLA
Los Angeles Department of Water and Power (LADWP) headquarters in Los Angeles
Southern California Edison (SCE), Pomona EV Test Labs (SCE signed an agreement with
UCLA to test the system in their test labs).
Port of Los Angeles (LADWP)
City of Pasadena (Pasadena Water and Power)
City of Santa Monica (SCE)

With the success and excitement of the first startup, a second technology that’s being
investigated for commercialization that also came out of the SGRDP is that of software-
based battery control. One patent has been filed from this technology and it is expected that
additional patents will be filed. SMERC has been selected as a winner of the NSF I-Corp
award with funding of $50,000 entitled: "Software/Hardware Controller for Real Time Control
of Battery Energy Storage System in a Grid" to work with utilities and energy companies and
investigate the market potential of the software-based battery control. The award will enable
SMERC to meet and interview 100 experts across California and the USA – to determine how
to position the product in the market. The primary goal of NSF I-Corps is to foster
entrepreneurship that will lead to the commercialization of technology that has been
supported previously by NSF-funded research. I-Corps prepares scientists and engineers to
extend their focus beyond the laboratory, and broadens the impact of select, NSF-funded,
basic-research projects. This program teaches NSF grantees to identify valuable product
opportunities that can emerge from academic research, and offers entrepreneurship training
to participants by combining experience and guidance from established entrepreneurs
through a targeted curriculum.

Given that the team has substantial experience now in commercializing UCLA technology
using the ecosystem in UCLA and the surrounding communities that exists to create
startups, the project team is confident about creating new technology that’s valuable (by
engaging the utilities via the TAC and the site host, i.e., the city of Santa Monica, that also
serves on the TAC).

65

A-2
APPENDIX B:
Questionnaire
DEVELOPMENT STATUS QUESTIONNAIRE

California Energy Commission

Energy Innovations Small Grant (EISG) Program Questionnaire

PROJECT DEVELOPMENT STATUS

Answer each question below and provide brief comments where appropriate to clarify status. If you are
filling out this form in MS Word the comment block will expand to accommodate inserted text.

__

Questions Comments:
1) Do you consider that this research project Yes, various technologies were successfully
developed
proved the feasibility of your concept? and demonstrated with quantitative results.

2) Do you intend to continue this development Yes.

effort towards commercialization?

3) What are the key remaining technical or The prototypical system needs refinement by way
of
engineering obstacles that prevent product a larger deployment and verification and UL
demonstration?
certification before it becomes a commercial
product.
4) Have you defined a development path from Yes, the developmental path requires a scaled up
where you are to product demonstration? demonstration within a larger microgrid site that
combines EVs, V2G, Solar Generation, BESS and
building loads – all connected to our software
control
system.

5) How many years are required to complete Approximately two years

product development and demonstration?
6) How much money is required to complete $500,000
engineering development and
demonstration?
 Marketing
7) What market does your concept serve? Parking Facility Owner, Workplace EV charging,
Multi Unit Dwelling (MUD) management, Fleet
operator, Distribution utility.

8) What is the market need? The need of EV charging infrastructure is fast

growing, garage owners hesitate to invest due to
high infrastructure cost and impact to electric bill.

9) Have you surveyed potential customers for The team had informal conversations with potential
interest in your product? customers about the product.

10) Have you performed a market analysis that No, the official market analysis has not been
takes external factors into consideration? performed yet.

11) Have you identified any regulatory, Yes, UL certification is needed. Also, regulatory
institutional or legal barriers to product markets need to be created at the intersection of
acceptance? V2G, G2V, BESS and solar – today these markets
and
regulations are separate and independent.

12) What is the size of the potential market in Qualitatively, the customers that constitute the
California for your proposed technology? market include: Parking Facility Owner, Workplace
EV charging, Multi Unit Dwelling (MUD)
management, Fleet operator, Distribution utility.

13) Have you clearly identified the technology Yes.

that can be patented?

14) Have you performed a patent search? Yes.

15) Have you applied for patents? Yes, the following patent disclosures have been
filed:
(i) UC-2017-213-2FP, Gadh R., Zhang T., Chung C-

.C., Chu C-Ch., AUTOMATED EV CHARGING

STATION IDENTIFICATION PROCESS WITH MOBILE
PHONES AND OTHER AUTOMATION PROCESSES.
Provisional submission of patent document to US
patent office.
(ii) R. Gadh, H. Nazaripouya, P. Chu, “Plug and Play

Battery Energy Storage Control System for Voltage

Regulation”, Sep 10, 2017, disclosure for patent
submitted to UCLA Office of Intellectual Property
and
Industry Sponsored Research (OIP-ISR). Document
available for review.

16) Have you secured any patents? Not yet

65
Have you published any paper or publicly Several papers have been published. The list of
disclosed your concept in any way that
these papers is available from UCLA. These
would limit your ability to seek patent
protection? papers do not limit our ability to see patent
protection.

Commercialization Path
17 Can your organization commercialize your Our organization can commercialize and has on
product without partnering with another numerous occasions commercialized technology
organization? developed by it by way of either licensing the
technology to a company or by way of assisting
and
supporting the technology generators to form
startup
companies. This requires partnering with various
types of organizations including technology
companies, entrepreneurs, investors and venture
capitalists, government agencies offering grants,
electric utilities and others.

18 Has an industrial or commercial company Two of our graduate students have expressed an
expressed interest in helping you take your interest in commercialization by way of doing a
technology to the market? startup company.

19) Have you developed a commercialization The commercialization plan has not been
developed
plan? as of yet, but it is planned.

20) What are the commercialization risks? Commercialization risks include the following
1. Potential elimination of the automotive and

energy storage rebates.

2. Potential risk in getting the UL certification
of
the entire system as this technology is very
new.
3. Inability to get funding in the near future.

Financial Plan
21) If you plan to continue development of your Not yet, but it is planned to do so.
concept, do you have a plan for the required
funding?
22) Have you identified funding requirements Yes, we have an approximate idea of the funding
for each of the development and requirements for development and
commercialization phases? commercialization.
23) Have you received any follow-on funding or Yes, we received a $50,000 NSF I-CORP grant to
commitments to fund the follow-on work to investigate commercialization of the battery energy
this grant? storage component of our system -
https://nsf.gov/awardsearch/showAward?AWD_ID=1
700775&HistoricalAwards=false. This grant is
currently active and through this we have
interviewed dozens of companies that are in the
energy storage space. We plan to continue
pursuing
further funding – especially from the NSF SBIR
program.

24) What are the go/no-go milestones in your Go/no-go milestones are:
commercialization plan? 1. Getting a real pilot site with a pilot customer

that is willing to pay some money

Obtaining certification for the system

25) How would you assess the financial risk of Given the reaction to our installed system and our
bringing this product/service to the market? conversations with the site host, we firmly believe
that the financial risk is medium to low.

26) Have you developed a comprehensive We do not have a comprehensive business plan as
of
business plan that incorporates the yet.
information requested in this questionnaire?
Public Benefits
27) What sectors will receive the greatest Commercial sector, Multi-unit dwelling, large
benefits as a result of your concept? buildings with parking structures.

28) Identify the relevant savings to California in See details in section “Public Benefits to California”
in
terms of kWh, cost, reliability, safety, this report.
environment etc.
29) Does the proposed technology reduce Yes, see details in section “Public Benefits to
emissions from power generation? California” in this report.

30) Are there any potential negative effects Not at this time.
from
the application of this technology with
regard to public safety, environment etc.?
Competitive Analysis
31) What are the comparative advantages of Maximized EV infrastructure utilization through smart
your product (compared to your competition) EV chargers developed.
and how relevant are they to your customers? Flexible and scalable EV charging infrastructure to
include various components in the power grid.
Cloud based centralized high-level management system
with local intelligence to manage immediate and offline
power needs for a commercial facility/site.
32) What are the comparative disadvantages of • The need of communication network as
your product (compared to your compared to standalone EV chargers.
competition) and how relevant are they to
your customers? • Customers need to be educated on the
system and its capabilities for managing
charging and storage in an intelligent
manner using software-based controls.
• May take a longer time and additional costs to
deploy as compared to standalone chargers.

Development Assistance
The EISG Program may in the future provide follow-on services to selected Awardees that would
assist them in obtaining follow-on funding from the full range of funding sources (i.e. Partners,
PIER, NSF, SBIR, DOE etc.). The types of services offered could include: (1) intellectual property
assessment; (2) market assessment; (3) business plan development etc.
33) If selected, would you be interested in Yes, market assessment and business plan
receiving development assistance? development.