International Journal of

INTELLIGENT SYSTEMS AND APPLICATIONS IN

ENGINEERING
ISSN:2147-67992147-6799 www.ijisae.org Original Research Paper

Intelligent Grid Management for Power and Energy Supply and Distribution
Chandan Kumar Panda 1, Dr. Chittaranjan Panda2
Submitted: 23/04/2023 Revised: 25/06/2023 Accepted: 05/07/2023
Abstract: Modern electrical systems' efficient and reliable distribution of power and energy depend heavily on intelligent grid
management. To optimize grid operations, enhanced control and management systems are needed given the increased integration of
renewable energy sources and the rising demand for sustainable energy options. In order to effectively monitor, regulate, and optimize
power and energy systems, this article suggests an intelligent grid management strategy that integrates smart grid technology, cutting-
edge analytics, and control algorithms. The suggested intelligent grid management system makes use of real-time data collection from
smart metres, sensors, and other grid equipment to facilitate situational awareness and decision-making. In order to analyze the gathered
data and extract insights for grid operation and planning, advanced analytics techniques, including as machine learning and optimization
algorithms, are utilized. Utilizingthis information will improve power production, load scheduling, energy storage use, and system
stability. In order to encourage energy users to modify their energy consumption habits in response to price signals and grid
circumstances, the intelligent grid management system also integrates demand response methods. Demand-side management promotes
grid stability and resilience by balancing supply and demand, lowering peak loads. In addition, the suggested approach incorporates
distributed energy resources (DERs) into the grid management procedure, including solar panels, wind turbines, and energy storage
devices. To maximize their impact on grid performance overall and improve grid resiliency during emergencies, these DERs are
coordinated and controlled in a decentralized way. Case studies and simulation results provide as proof of the efficacy of the intelligent
grid management strategy. The grid efficiency, energy costs, grid reliability, and integration of renewable energy sources are all
improved by the system.

Keywords: Intelligent grid management, Power and energy supply, Power and energy distribution, Smart grid technologies.

(image courtesy of World Economic Forum). The need for

1. Introduction
electricity is also being fueled by the revolution in
Energy is the cornerstone of a sustainable planet, social information technology and other engineering fields in
equality, and economic progress. A significant deficiency industrialised nations. Fossil fuels alone cannot satisfy this
in the economy's supply of energy resources is what is requirement since they are rapidly running out. The world's
known as an energy crisis. Since many recessions are climate goals, in particular the aim of keeping global
brought on by an energy crisis of some kind, the crisis warming to a maximum of two degrees Celsius, are at risk
frequently has an impact on the rest of the economy. as a terrible side effect of the production of energy using
Particularly, if the cost of producing energy increases, the fossil fuels. This conundrum can be resolved by using
cost of producing any good also increases, which has an renewable energy sources to provide power in a manner
impact on the development of the country. For humanity to that is as climate neutral as possible. The G7's commitment
continue existing, access to electricity is both morally to phase out fossil fuels by 2100 gave the development of a
required and economically vital. However, approximately carbon-free global energy system a boost in 2015.
a billion people still don't have access to energy However, this fundamental change presents us with
worldwide. In addition to the lack of resources, the crisis is enormous technical difficulties that call for a whole new
also a result of population expansion. The United Nations approach to controlling our energy system. The three
General Assembly has declared 2012 to be the worldwide trends of decentralization, digitization, and
"International Year of Sustainable Energy for all" in decarbonization are altering how we generate, distribute,
recognition of the significance of energy for sustainable and use power today. The use of renewable energy is
development. Worldwide demand for power is rising in increasing over the world in an effort to follow these
direct proportion to this expansion. By 2040, experts trends. The energy system is being expanded with millions
anticipate a surge in global electricity production of 80% of new tiny generating units like wind turbines, solar
panels, and biogas facilities. For best effectiveness, they
1 Biju Patnaik University of Technology, Odisha need to be effectively organised and managed. It is
Email: chandankumarpanda@gmail.com
technically feasible to obtain approximately 100%
ORCID ID : 0009-0002-4442-0704
2 Sudhananda Engineering and Research Center, Bhubaneswar renewable energy sources over the next forty years,
panda_chitaranjan@rediffmail.com according to a 2011 research by the World Wild Life
* Corresponding Author Email: chandankumarpanda@gmail.com Federation (WWF). This means that even after accounting

International Journal of Intelligent Systems and Applications in Engineering IJISAE, 2023, 11(3), 238–245 | 238
for variations in supply and demand, the energy produced performs well, and as compared to conventional
by the sun, wind, water, sea, and earth's heat has the controllers, tweaking requires less work.
potential to more than meet the world's electrical needs.
2. Literature Review
1.1. Intelligent controllers for power system
A brand-new procedure for lessening glint and improving
applications
power quality for a frail grid associated with a breeze
In all power system applications, the control mechanism ranch utilizing DFIGs was introduced by Hossein Mahvash
controllers are frequently implemented utilising analogue et al. (2015). The Rotor Side Converter (RSC) of the DFIG
control with PI controller. Since the parameters of the utilizes the proposed strategy to direct the result receptive
power system are constantly changing, this calls for power. The stator voltage control circle with the hang
extremely detailed mathematical modelling of the system. coefficient is introduced to change the grid voltage level in
Because of this, maintaining the stability and reliability of each functional circumstance to deliver the reference
the system requires more effort than using typical PI responsive power. By utilizing the Stator Voltage
controllers. The previous efforts concentrated on creating Direction Control (SVOC) strategy, the dynamic power is
intelligent control techniques to regulate electrical autonomously controlled in the d-hub. The post
characteristics in the power system to improve its dynamic cancelation technique is utilized to make PI regulators with
behaviour. In all engineering disciplines, artificial foreordained bandwidth. While dissecting the gleam
intelligence (AI), in the form of computer algorithms, has discharge at different mean breeze paces, choppiness and
aided in producing the desired results (Tanya et al. 2018). changes to the grid qualities are considered.
The automatic analysis of operating data, environmental
The STATCOM is analysed by Dinesh Shetty et al. (2017)
factors, and component characteristics is superior to human
utilising a model-free methodology and a fuzzy logic
analysis. Artificial intelligence approaches have
controller for reactive current regulation. A thorough
significantly influenced engineering applications,
STATCOM model is part of the test system. The novel
especially in power systems where the control parameters
controller has outstanding dynamic responsiveness for step
are constantly changing. Heuristic controls based on
changes in the reactive current reference, it is observed.
artificial intelligence provide high levels of resilience and
adaptability, which are necessary for the power system to A brand-new UPFC with a dual proportional Integral (PI)
operate reliably. One of the AI methods that incorporates controller is suggested by Narayan Nahak et al. (2018). To
the fundamentals of human thought is the fuzzy logic ensure improved performance, the gains are tweaked
controller (FLC). According to S. Mishra et al. (2009), the utilising the Improved Grey Wolf Optimizer (IGWO),
notions of linguistic variables are defined in this controller Particle Swarm Optimisation Technique (PSO), and DE
as a fuzzy set. Membership functions (MF) are used to technique. The dual PI controller controls the phase angle
describe fuzzy sets. The process of turning traditional, or of the shunt converter and the UPFC modulation index of
crisp, data collected by sensors and measuring devices into the series converter in order to reduce oscillations in the
a fuzzy set is known as fuzzy set conversion. The power system. The findings demonstrate that IGWO
membership function and control rules are combined to approach outperforms PSO & DE technique and that dual
create the fuzzy interface, which is what produces the PI controller performance is superior to single PI
fuzzy output. The process of turning fuzzy data into clear controller.
data is called defuzzification. According to applications
A fuzzy logic-based MPPT controller can improve the
involving the power system, the fuzzy controller is found
overall performance of the microgrid under partial shade
to provide higher damping performance under altered
situations, even while the reconfigured PI-based control
system operation conditions (Mansour, D. O. et al. 2009).
provides sufficient output parameters (Alajmi et al. 2013).
Because it employs function approximation rather than a
Another body of literature (El Khateb et al. 2014) models a
typical mathematical model, the Artificial Neural Network
fuzzy logic-based SEPIC converter for MPPT. There is a
(ANN), also known as Neural Networks (NNs), is another
mismatch between voltage and frequency when non-linear
AI technology that is frequently employed. Among many
loads predominate in a power network, especially if solar
other application areas, these include pattern recognition,
PV penetration is high. In order to achieve voltage
function approximation optimisation, simulation, and
regulation, a DSTATCOM is therefore included. The
automation. Three fundamental characteristics of ANN
effectiveness of the system increases when FACTS devices
may be to blame for the growing interest in them: first,
are utilised (Indumathi et al. 2012). When charging and
there are no prerequisites or presumptions; second,
discharging are governed by fuzzy rules and power
forecasts are produced by extrapolating from historical
shortfall is addressed, fuzzy based battery management
data; and third, complicated nonlinear problems are solved
systems also demonstrate their effectiveness
sequentially (Rasit Ata 2015). as properly taught, it
(Venkateshkumar, 2016). Due to its flexibility and

International Journal of Intelligent Systems and Applications in Engineering IJISAE, 2023, 11(4s), 238–245 | 239
reliability, hierarchical control in microgrids is gaining this, the MPPT technology is applied to wind turbine (WT)
importance recently. Battery storage is employed as a generators to maximise power delivery to the DC bus by
backup power source in the literature cited. According to adjusting the rotor speed. The pair (Cpopt, opt) determines
Wandhare et al. (2014), the interfacing of PV resources is the WT's ideal point, and the speed regulation is changed
investigated under a variety of circumstances, including to produce the ideal amount of power. A cascade control
changes in irradiation level, MPPT, centralised control of mechanism that controls battery current and DC bus
actual and reactive powers, and coordinated control. energy maintains the microgrid's power flow balance
However, sophisticated methods are not used. The through controlled currents on the DC bus. It is assumed
intelligent hybrid microgrid is modelled in this paper using that the bidirectional converter, which joins the microgrid's
the IEEE 1547 standard. This standard is essentially parts to the DC bus, is voltage-controlled. Based on a set of
developed to satisfy the technological specifications that criteria, the central controller, such as an Energy
can be used globally. Instead of concentrating on the many Management System (EMS), chooses the suitable energy
DR technology types, it focuses on the technical source (DG or batteries).
requirements and testing of the interconnection itself.
Although this standard recognises that the technical
characteristics of DR and the many types of EPSs do have
an impact on the connectivity requirements, it nonetheless
tries to be technology-neutral. The system and its response
will change in some way when DR is added to an EPS
(Basso and Friedman 2003). Researchers are concentrating
on alternate methods for power generation with decreased
environmental pollution and carbon footprint because
fossil fuel-based power generation currently does not
satisfy consumer power demand due to its availability and
pollution. The suggested self-sustaining intelligent
microgrid would undoubtedly pave the way for using the
technical breakthroughs in the field of renewable
production and improving the grid.

3. Methodology
In a microgrid, there are two layers of hierarchical control:
local control and central control. Utilizing multiple control
algorithms unique to each converter, the local control layer
oversees the operation of dispersed power generation
sources like photovoltaic (PV) and wind turbine (WT). The Fig 1: PV generator-boost converter schematic
local control layer's goals include maximizing power
Overall, the MPPT control methods for PV and WT
extraction from dispersed generating sources, restricting
systems as well as the coordinated regulation of power
WT speed, controlling bidirectional converters, regulating
flow help to maximise the performance and power
AC voltage and frequency, and extending battery life.
production of the microgrid components.
The central control layer improves microgrid stability and
3.2. Central Control
optimizes energy flow through the use of an EMS with
fuzzy logic. In addition to batteries and a dump load, it FLC is superior to standard mathematical methods because
distributes power among various sources. This power it can handle language knowledge. FLC development relies
distribution procedure is supervised by the central on the designer's skill rather than the system's
controller. mathematical modelling, which requires researchers to
solve difficult equations. "Fuzzy sets," Pr. Lotfi Zadeh's
3.1. Local Control
1965 article, founded fuzzy logic. An intelligent EMS
The graphic shows how to maximise electricity from a based on Mamdani's Fuzzy Inference System (FIS) builds
photovoltaic (PV) system by using MPPT (Maximum the central controller.
electricity Point Tracking) control. Due to its low cost and
reliable performance, the P&O (Perturb-and-Observe)
approach is frequently employed for MPPT control. To
maximise power delivery to the DC bus and optimise PV
system performance, a boost converter is used. Similar to

International Journal of Intelligent Systems and Applications in Engineering IJISAE, 2023, 11(4s), 238–245 | 240
 When regulating the system based on the batteries' state of
charge (SoC) and the stability of the microgrid, the fuzzy
logic controller is essential. The FLC follows a set of
regulations. Using membership functions, inputs like SoC,
net power, load power, and power source are fuzzified into
linguistic variables. Then, based on a set of rules, fuzzy
operators are applied to these variables to decide the proper
course of action. The outputs of all the rules are combined
during the aggregation phase, and defuzzification is then
carried out to produce precise control signals. The FLC
generates three control signals from load, source, net, and
battery SoC inputs: Kbat, Kdump, and Kload. The system's
behavior is determined by these control signals.
Fig 2: Goals and standards for the central controller. Figure 4. Defuzzification variables for SoC (a), net power
The EMS controls the power stream among generators and (b), load and source power (c), and switches (d).
burdens, protects the batteries against profound
charge/release, and keeps up with the DC transport voltage
to guarantee the exhibition of the MG. The proposed FLC,
displayed in Figure 2, is established on the accompanying
standards and objectives: The utilization of DGs for load
supply is liked. By keeping the SoC somewhere in the
range of 20% and 80% and abstaining from cheating or
over depleting, battery duration can be delayed. Both the
recurrence and the voltage should stay inside a resilience
of 0.05 Hz and 10 V, separately.
3.3. Management Algorithm
The microgrid's focal regulator directs a few functional Fig 4 shows defuzzification findings.
modes and controls power stream all through the
FLCs create control signals from inputs and regulations.
framework. The management calculation should be visible
The microgrid's relays are controlled by these control
in Figure 3. At the point when the batteries are completely
signals. There are various modes that can be used,
energized and the power delivered by the dispersed
including turning off all relays, dumping energy through a
generators (DGs) surpasses the demand, the reference
load, feeding loads while dumping the extra, charging
power for the batteries is changed in accordance with
batteries just partially, or using batteries to supplement
nothing and a dump load is gone on to gobble up the
DGs in order to satisfy load demand.
additional power. If the DGs provide insufficient energy
and the batteries are empty, the piles are immediately 3.4. Methods to meet the peak demands
disengaged and the battery reference power is adjusted to
There are a few strategies that can be employed to meet
zero.
peak demands in a microgrid:

• Distributed Generation (DG) Optimization: The central

controller, equipped with an Energy Management
System (EMS) and fuzzy logic control, optimize the
power flow from distributed generation sources such as
photovoltaic (PV) systems and wind turbines (WT). By
intelligently allocating power from these sources, the
microgrid can ensure that sufficient energy is available
to meet peak demand periods.
• Battery Energy Storage: The microgrid incorporates
battery energy storage systems to store excess energy
during periods of low demand or high generation. These
stored energy reserves can then be utilized during peak
demand periods to supplement the power generated by
Fig 3: Flow chart of the study

International Journal of Intelligent Systems and Applications in Engineering IJISAE, 2023, 11(4s), 238–245 | 241
 DG sources, thereby meeting the increased load sensors and meters to the central control system. This
requirements. can involve technologies like Ethernet, Modbus, Wi-Fi,
• Load Management: The central control layer or Zigbee, depending on the specific requirements and
implements the load management strategies to prioritize constraints of the microgrid.
and optimize the allocation of power to different loads • Energy Management System (EMS): Utilize an EMS,
within the microgrid. By identifying and categorizing which serves as the central control layer of the
loads based on their priority and criticality, the central microgrid, to collect and manage data. The EMS can
controller can ensure that essential loads receive power integrate data from various sources, including sensors,
during peak demand periods while temporarily reducing meters, and SCADA systems, to monitor and control the
or shedding non-essential loads. microgrid's operation. It can also store historical data
• Demand Response: The microgrid can participate in for analysis and optimization purposes.
demand response programs, where consumers are • Data Logging and Storage: Implement data logging
incentivized to reduce their electricity consumption systems to record and store the collected data over time.
during peak demand periods. The central control layer This can involve using databases or dedicated data
can coordinate and implement demand response logging devices. Historical data is valuable for trend
strategies by communicating with end-users and analysis, performance evaluation, and decision-making.
adjusting their power consumption patterns to align • Remote Monitoring and Telemetry: Employ remote
with the available power generation capacity. monitoring and telemetry systems to collect data from
dispersed generation sources and remote components of
3.5. Data Collection the microgrid. This allows for continuous monitoring
and control even in remote or inaccessible locations.
To collect data in a microgrid, various methods and
techniques can be employed depending on the specific • Data Analytics and Visualization: Utilize data analytics
parameters and variables of interest. There are some tools and visualization techniques to process and
common approaches for data collection in a microgrid: interpret the collected data. This can involve using
algorithms, statistical analysis, machine learning
models, and data visualization software to gain insights,
detect anomalies, and optimize the microgrid's
performance.

4. Results
The inquiry uses a MG Hardware-in-the-Loop platform
based on RT-LAB real-time simulation. The HIL platform
includes an OP1400 simulator, FPGA controller, and
monitoring workstation. The OP4510 simulator receives
precise mathematical models of the isolated MG from C-
code. The suggested control method operates on the Xilinx
FPGA Kintex-7 325T, which generates PWM at 300 kHz
with a 4 ns resolution. The OP1400 real-time digital
simulator tests power electronics and control systems. The
Fig 5: Procedure of Data Collection
electrical model of the simulated microgrid, local and
• Sensor and Metering Systems: Install sensors and central controllers, and control plan are included. The
meters at relevant points within the microgrid to Master Subsystem (SM) and Console Subsystem (SC) of
measure and monitor key parameters such as voltage, the Sim Power Systems model recreate the MG in real time
current, power output, battery state of charge, and utilising the OPAL-RT platform. The SC contains data
environmental conditions. These sensors and meters can exchange blocks for the simulator, whereas the SM
be connected to a data acquisition system that records contains microgrid component electrical models.
and stores the data for analysis.
4.1. Description
• Supervisory Control and Data Acquisition (SCADA)
System: Use a SCADA system to monitor microgrid The usefulness of the suggested hierarchical control
data. RTUs and PLCs collect sensor and metre data for strategy will be shown through the use of three
SCADA systems. Data is sent to a central control experimental examples. The first instance involves
station for analysis and control. evaluating local controllers under challenging load profiles
• Communication Networks: Establish a communication and a range of meteorological variables, including wind,
network, either wired or wireless, to transmit data from irradiation, and temperature. In the second scenario, the
core fuzzy logic controller that manages power distribution
International Journal of Intelligent Systems and Applications in Engineering IJISAE, 2023, 11(4s), 238–245 | 242
across sources, batteries, and dump loads is assessed. Last
but not least, the microgrid control is assessed under
failure scenarios that include various defects including
short circuits and excessive voltages. In order to assess the
results' conformance, IEEE 1547 and IEC 61727 are also
compared to international standards.
Table 1: FPGA and converter operating frequencies
Frequency
Xilinx FPGA kintex Up to 300kHz, resolution 4
TM-7 325T ns
Fig 7: PV panels and WT generator electricity
PV converters 25 kHz
4.3. Central Controller Test -2
Battery converter 10 kHz
Regardless of wind speed and irradiance, air conditioner
voltage remains constant. DC transport, which is directly
Throughout the test, the temperature is kept at 8 °C while related to the inverter, directly affects heap voltage and
the wind blows at a speed of 7 m/s. The datasets for this frequency. The recommended EMS can provide a stable
investigation were taken from a 2018 original publication. voltage and current to the heap that meets waveform and
The NASA Surface Meteorology and Solar Energy symphonious contortion specifications. The SoC's safe
programmes were used to acquire meteorological range is 20%–80%. This also illustrates that the microgrid
information, including solar radiation, wind speed, and can handle sustainable energy and feed the heap with
ambient temperature for the microgrid under study, which continuous and high power despite climate variations and
is situated in a remote region of Morocco. fluctuating burden demand. As shown in Figure 7, the heap
power and its reference are the same, indicating that the
The 20 kHz PV converter and 10 kHz battery converter
microgrid can maintain a predictable power progression
can be controlled using the Xilinx FPGA KintexTM-7
even when the DGs are not producing enough energy. As
325T's superior PWM generation. It operates at 200 kHz
seen in Figure 7, energy storage devices like batteries can
with 5 ns resolution. The OP1400 real-time digital
power the heap during power outages. When charged,
simulator can test and model control, power electronics,
batteries have positive power; when released, they have
and other embedded real-time systems. The simulator now
negative power. This means that the MG can efficiently
includes the replicating microgrid's electrical model, local,
direct power between the DGs and the heap by storing
and central controllers. Controllers manage system
extra energy in the batteries and distributing it during
operations and implement the electrical model's
shortages.
representation of component electrical activity.

Fig 6: View of Climate condition

4.2. Local Controllers Test-1
Figure 7 illustrates solar and wind turbine electricity. The Fig 8: power consumption and batteries power
Microgrid (MG) responded correctly to extreme weather
and variable load demand. With a +/1 V overrun, the DC
bus voltage matches the reference voltage and stays at 500
V.

International Journal of Intelligent Systems and Applications in Engineering IJISAE, 2023, 11(4s), 238–245 | 243
4.4. Performance of Microgrids under Defective Table 2: performance of the micro grid
Conditions-Test 3
Healthy Regime Faulty Regime
The efficiency of the suggested hierarchical control
Time of responses 0.05s 0.3s
method in managing short circuits is assessed in the final
test. A three-phase short circuit occurs at the Point of Changes in ±0.004 Hz ±0.079 Hz
Common Coupling (PCC) in the interval 10-10.1 s, and the Frequency
DG-side breakdown occurs in the interval 15-15.1 s. This
Changes in voltage ±0.8% ±30%
test's objective is to look at how the central controller acts
and reacts to these failures. Even though faults were Clearing time 0.04 s 0.152 s
simulated, after the fault was fixed the system was still
THD 0.42% 6%
able to stabilise. The DC bus voltage lowers dramatically
and fluctuates noticeably while the short circuit takes
place. However, after the problem is fixed at 12.2 seconds,
5. Conclusion
it returns to normal, demonstrating the robustness and
effectiveness of the suggested hierarchical control strategy An MG Hardware-in-the-Loop (HIL) platform built on the
for handling short circuits. When the fault happens, the RT-LAB system is used for the study. It consists of an
frequency fluctuates just 0.2%, which is very little. Once FPGA controller, OP1400 simulator, and monitoring
the issue has been resolved, the frequency stays at 50 Hz as workstation. The C-code version of the MG's mathematical
it was originally. THD fluctuation over time, which is still model is posted to the OP4510 simulator. The control
very low in the defective regime, can reach 2.5 during 100 algorithm is executed at a high frequency of up to 300 kHz
ms. To assess the efficiency of the strategy in the fault with a resolution of 4 ns using the Xilinx FPGA Kintex-7
regime, results must adhere to international standards. 325T. With the MG's electrical model and local/central
Recommendations for voltage overshoots and response controllers, the OP1400 simulator functions as a real-time
time are outlined in IEEE 1547 and IEC 61727. System digital simulator. Three test cases—local controller
safety is decreased when the PCC is overvoltaged over an performance, central controller assessment, and microgrid
extended period of time. According to IEC 61727, an behaviour under fault conditions—are examined in the
isolated microgrid's overvoltage can occur in one of four paper. For precise load consumption estimation,
ranges: meteorological data from NASA programmes is employed.
Test 1 demonstrates the microgrid's precise responsiveness
to load variations and weather changes. Test 2 shows how
well the Energy Management System (EMS) works to
manage battery charge and provide a steady power supply.
Test 3 certifies the fault handling and voltage and
frequency stability of the hierarchical control method. The
study's outcomes meet the requirements of IEEE 1547 and
IEC 61727, demonstrating the viability of the control
strategy. Overall, the study emphasises how reliable local
and central controllers are and how the microgrid can
Fig 9: DC bus voltage before and after a problem
continue to provide stable power in difficult circumstances.
The greatest allowed travel time is 0.1 seconds when the
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