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Recent Optimization Techniques for Coordinated Control of
Electric Vehicles in Super Smart Power Grids Network: A
State of the Art
Bindeshwar Singh
Kamla Nehru Institute of Technology,
Sultanpur, U.P, India
bindeshwar.singh2025@gmail.com
Pankaj Kumar Dubey
Kamla Nehru Institute of Technology,
Sultanpur, U.P, India
pankajdubeysangamboy@gmail.com
Dr. S. N. Singh
Indian Institute of Technology Kanpur
snsingh93@gmail.com
Abstract- Heavy adoption of alternative power
supplies and e - mobility produces power imbalances and
overload in the current power grid, posing considerable
management and operational challenges. Considering
massive initiatives by electric companies, authorities,
and scientists, smart grids are still in their infancy in
terms of addressing those concerns. The purpose is to
increase a basic layout that can maintain grid stability
while maintaining a requisite quality of services, as well
as to advance the growth of a generic approach for
monitoring the attributes of such stations regarding
traffic character traits, grid storage size, and cost
structure, and main financing. The primary optimization
approaches used in a variable pricing context to attain
goals like energy losses and electric costs mitigation,
peak demand reductions, voltage control, distributing
network overload reductions, and so on are covered in
this study.
Keywords: Coordinated Control, Distribution Network,
Electric Vehicles, Optimization techniques, Smart Grids,
Vehicles to Grids.
Abbreviations
BEV
Battery
-
operated
electric vehicles
CS
Charging system
DQN
Deep Q
-
learning technique
DS
Distribution system
EVs
Electric vehicles
Ex
-
EVs
Extended
-
range electric vehicles
FCEV
Fuel cell electric vehicles
GA
Genetic algorithm
G2V
Grid to vehicle
MIN
Minimization
PHEV
Plug
-
in hybrid electric vehicles
PS
Power system
PSO
Particle swarm optimization
PQ
Power quality
PV
Photovoltaic
SG
Smart grid
V2G
Vehicle to grid
1. INTRODUCTION
In past years, rising environmental consciousness has fueled
the quick adoption of sustainable technological advancements
like alternative power supplies and EVs in current PS to
minimize global carbon emissions. This situation results in
system-level power imbalances as well as localized delays in
DS, posing serious issues for conventional grid planning and
management. Some of the major aspects covered in this study
are flexible electrical tariff regulations are critical in the
energy market for affecting consumer electric use. We looked
at a variety of cost rules from the standpoint of EV recharging
to see how they affect EV charging behavior. This study also
includes the optimization goals achieved throughout the
charging scheduling phase.
EVs obtain electricity from the system by connecting to a
charging point. The charged battery stores power and has been
utilized to activate the motors that turn the tires. Electric
automobiles travel faster than cars with traditional gasoline
engines, helping them feel lighter to drive.
An SG is an electrical platform that facilitates digital
technology to facilitate a two-way flow of electricity and
information, as well as the identification, response, and
avoidance of changes in usage and other problems. The 6
major SG targets are smart meter design, load shifting, electric
automobiles, wide-area spatial consciousness, scattered
energy supplies and storing, and distributed administrations.
After examining all of the previous studies stated in this
research, we can infer that using EVs in the smart grid can
assist to increase system dependability by decreasing losses,
increasing losses, and enhancing smart grid load capability
features.
An SG test platform is essential in this aspect for developing,
analyzing, and demonstrating different unique SG approaches,
including demand reaction (DR), real-time price, and
congestion control [1]. The primary aim of the research is on
optimizing EV recharging schedules under dynamic power
billing systems such as Real-Time Pricing (RTP), Time of Use
(ToU), Critical Peak Pricing (CPP), and Prime Times Refunds
(PTR). Each payment scheme has been explored in detail, with
key features highlighted [2]. A probabilistic framework was
developed to evaluate the CS results in terms of traffic
patterns, power storage capacity, prices, and main financing
[3]. This study discusses the approach used for energy flow
under the V2G concept, including the GA and PSO [4]. This
study gives a comprehensive fundamental evaluation of the
operation of several programs used inside the power
management system, such as consumption response, demand
management, and energy quality assurance [5]. This study
proposes coordinating the management of EVs, wind farms,
and PV power in a microgrid for microgrid frequency
command [6]. This research will look into and evaluate the
obstacles that electrical systems face when it comes to
improving PQ [7]. This work proposes a
battery/supercapacitor (SC)-powered EV switched-reluctance
2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) | 979-8-3503-3250-6/22/$31.00 ©2022 IEEE | DOI: 10.1109/UPCON56432.2022.9986471
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motor (SRM) operation with grid-to-vehicle (G2V) and
vehicle-to-home (V2H)/V2G features [8]. Using a simulated
SOC prototype and synchronized operation of power flows
amongst some of the hands of the same kind of distinct phase-
units by taking full benefit of circulation current, this article
presents a multi-energy management approach for a modular
multilevel converter (MMC) centered EV facility to be
incorporated into the SG [9]. Throughout this study, we offer
a two-tier energy management scheme (EMS) with a Superior
Command Center at the top and an EV command center at the
bottom [10]. We describe a methodology related to software-
defined networks (SDN) and blockchain (BC) to handle two
difficult concerns in EV-assisted SG ecologies: confidentiality
and energy safety [11]. The real-time V2G control scheme
under price volatility is investigated in this research, where the
power price is adjusted periodically per hour. The DQN
technique, which mixes popular Q-learning with such a neural
network, influenced our approach [12]. This article presents a
day-ahead marketplace paradigm for smart DS congestion
control [13]. To reduce the total daily expense suffered by CS
operators, an intelligent charging scheduling algorithm
(ICSA) with the incorporation of henry gas solubility
optimization is presented [14]. This article evaluates the latest
research on dispersed recharging control techniques, in which
calculations are spread over numerous EVs and/or aggregates
[15]. This study presents AEBIS, an AI-enabled, blockchain-
based EV integrating solution for SG power administration
[16]. Utilizing the differential evolution (DE) optimization
algorithm, this study presents an IoT-based centralized control
scheme for coordinating EV and DES deployment [17]. The
research reveals that regulated recharging and adaptable
production of hydrogen infrastructures can help to minimize
peak load spikes and minimize renewable electricity
attenuation, lowering the operation of the system, generation,
and network expenditures [18]. This work presents a two-stage
optimization to analyze the effect of distribution feeder
reconfiguration (DFR) with various EV scheduling strategies
on the DS in terms of inefficiencies and voltages [19].
Regulations and operating architecture for a G2V-based plug-
in electric vehicle (PEV) aggregators have been developed
[20]. The imperialist competitive algorithm (ICA) and the bat-
inspired algorithm (BIA) were employed as optimization
strategies in this study to tune the model predictive control
(MPC) controllers with PHEV for LFC in the SG system with
RES adoption [21]. This research discusses a novel use of EVs
to swiftly assist reheated thermal turbine units in stabilizing
while load needs vary [22]. The topic of energy trading
network topology control (ENTRANT) for PHEVs is
investigated as a multi-leader multi-follower Stackelberg
game in this research [23]. An upgraded publicly accessible
FCEV supplied 9.5 kW of AC electricity to the network in a
sequence of V2G testing [24]. The FFA-GBDT method
presented for the energy management system (EMS) among
EVs and DS in this publication is a hybrid approach that
combines the implementation of the Fertile Field algorithm
(FFA) and Gradient Boost decision tree (GBDT) [25]. This
research proposes a real-time multilayer energy management
method (RMEMS) for EV recharging. The centralized
repository is a tri-level optimization model (TOM) [26]. Using
a multi-objective multiverse optimization method (MOMVO),
this research presents a charging/discharging technique to
improve the usability of EVs on a controlled timetable by
taking into account optimal operating behavior [27]. This
study looks into the effects of a V2G enabled bilateral off-
board EV battery charger on grid current harmonic
compensation (GCHC) and reactive power compensation
(RPC), as well as how it can be used as both a rechargeable
battery and a generator [28]. An integrative period of use
price-based demand response (TOUPBDR) and dynamic G2V
charge routing of EVA are modeled in this work to optimize
regulating functions and charging costs at the same time [29].
The existing research developments and upcoming projects for
power electronics-based systems that allow the combination of
PV sources of energy and the SG with CS for EVs and PHEVs
are reviewed in this article [30].
2. MATHEMATICAL PROBLEM
FORMULATION
This section covers mathematical modeling for several sorts of
EVs as well as the objective function.
The following are the several kinds of EVs:
a)
BEV: It's a vehicle that gets all of its power from the
battery itself, rather than relying on other engines.
b)
PHEV: It employs batteries to drive an electric motor and
additional fuel, such as gasoline or diesel, to drive an internal
combustion engine or another form of locomotion.
c)
FCEV: An EV having an inbuilt electrical motor that is
powered by a fuel cell, occasionally in conjunction with a tiny
battery or ultracapacitor.
d)
Ex-PHEV: An EV with a tiny ICE present to produce
more electrical energy, but with all intent energy offered by an
electrical motor.
Table 1 (a), shows the comparative analysis of different kinds
of EVs while Table 1 (b); shows the different kinds of electric
vehicles have different features.
The central substation's apparent power in MVA excluding
EVs is provided in eqn. (1).
22
GGWODG QPS (1)
Where, PG = Active power generating at generating station in
MW and QG = Reactive power generating in MVAR.
The primary substation's apparent power in MVA with BEV
is shown in eqn. (2).
2 2
( )
WBEV G BEV G
S P P Q
(2)
Where PBEV= real power delivered by BEV in MW.
The major substation's apparent power in MVA with PHEV is
depicted in eqn. (3).
2
2
( )
WPHEV G PHEV G PHEV
S P P Q Q (3)
Where, PPHEV = real power delivered by PHEV in MW and
QPHEV = reactive power delivered by PHEV in MVAR.
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The apparent power of the main substation with FCEV in
MVA is given in eqn. (4).
2
2
WFCEV G FCEV G
S P P Q
(4)
Where PFCEV = reactive power delivered by FCEV in MVAR.
The apparent power of the main substation with Ex-PHEV in
MVA is given in eqn. (5).
2
2
( )
WEx PHEV G Ex PHEV G Ex PHEV
S P P Q Q
(5)
Where PEx-PHEV = real power delivered by Ex-PHEV in MW
and QEx-PHEV = reactive power delivered/consumed by Ex-
PHEV in MVAR. This purposed objective function is to
minimize the system’s total real power loss (PL) for optimal
DG planning is given by eqn. (6).
_ _
2 2
_
2
,
_
ij bus ij bus
L
loss mn bus
m n N
m bus
P Q
P r
V
(6)
The PL is a variable for all system bus voltage and line
resistances
_
mn bus
r.
Table 1 (a): Comparison between different kinds of EVs
S. No. Parameter HEV PHEV BEV FCEV
1 Battery Size (kWh). 1.0 - 2.0 5.0 - 10.0 15.0 – 100.0 1.0 -2.0
2 Driving on a severely
depleted battery.
Possible Possibly Never feasible Feasible
3 With full tanks,
maximum reach.
400.0 - 700.0 km on
fossil-fuel.
30.0 – 100.0 km battery
electric driving range,
400-700 km on fossil fuel.
100.0 –
400.0 km battery
electric
maximum range.
400.0 – 700.0 km on
hydrogen fuel.
4 Fuel economy Higher than ICEVs. Higher than ICEVs and
HEVs.
Higher than ICEV, HEV, and
PHEV.
Higher than
ICEVs.
Table 1 (b): Different kinds of electric vehicles have different features
Structure of operations
Start & stop Traction powered by
electricity
Stopping with regenerative
energy
The only mode of transport is
electric.
Charge your
battery from
the outside
BEV
Slow
True
Moderate
True
True
HEV
Moderate
Possible
Fast
True
False
PHEV
Fast
True
Fast
True
True
FCEV
Very fast
Possible
Quick
True
True
EREV
Quick
True
Quick
True
True
This paper is organized as follows: Section 1 presents the introduction part; Section 2 presents the mathematical problem
formulation; Section 3 shows the taxonomical review on coordinated control of EVs in super-smart grid planning. Section 3 depicts
the conclusion and future scope as well as describes the summary of the work. Table 2 (a) – (c); show the taxonomical review on
coordinated control of EVs in smart grid planning.
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Table 2 (a): Coordinated control of EVs in smart grid planning
Ref. No.
Title
Outcomes
Objectives
Limitations
Performance
Method Utilizing
Future scope
ROC
1 In a scaled-down SG testbed,
optimizing EV synchronization
over a heterogeneous mesh
topology.
Instead of employing time-
based sensors, coordinated
sensors can effectively
enhance measurement
synchronization
.
Charging cost
MIN.
Effect of high
traffic in SG re
missing.
Cost MIN, network
congestion, and local
voltage enhancement.
Heterogeneous Ethernet-
based mesh network.
Examine the
communications system for
highly congested loads and
faulty communications
networks
.
6
2 A look at the best charging
technique for electric vehicles in
the distributed CS under variable
fee structures.
When all of the pricing
methods are compared in
terms of economics,
equity, and risk
motivation, RTP emerges
as the clear winner.
Electricity cost
MIN.
A higher degree
of billing
instability.
Power loss MIN, peak load
reduction, and voltage
regulation.
Dynamics pricing
schemes, fuzzy logic, and
GA.
EV arriving and departing
behavior can be predicted.
10
3 Difficulties, perspectives, and
applicability to CS layout for
smart vehicles in the SG.
The infrastructure we
propose is capable of
storing excess grid
electricity.
Stability,
controlling as
well as pricing
enhancement.
Theoretical
analysis.
Smoothly accommodate
peak loads.
Storage Modeling for
EV/PHEV CS.
Upcoming CS will benefit
from this information.
6
4 A current state-of-the-art
analysis of electrical V2G
technologies.
The PSO approach is used
to enhance the answer to a
specific issue, whereas the
GA technique is used to
control HEV.
Eco friendly
and PS cost
MIN.
GA is expensive
And PSO has a
low convergence
rate.
At peak usage, provide
reactive power assistance
and discharge stored power
back to the network.
PSO, and GA. On the distribution grid, EV
embedded PS architecture,
i.e. peak shaving and load
shifting, has indeed been
examined.
8
5 State-of-the-art and future
projections in power
administration in the SG.
Enhance the supply and
demand equilibrium, and
reduce peak load during
unscheduled periods.
Cost MIN. Performance
needs further
enhancement.
Electrical costs are reduced
by 20–30% when power
regulation is done.
Demand-side
management, EMS, and,
energy storage systems.
Probabilistic modeling that
is accurate and quick is an
area that has to be explored
more.
0
6 In microgrids, frequency
management is achieved by
coordinating the control of EVs
with renewable power supplies.
Enhance the effectiveness
of frequency control.
Improvement
in frequency
stability.
Large time delay. - Modified proportional-
integral (PI) controllers.
It is recommended that can
utilize sophisticated PID
controllers.
0
7 A study of PQ enhancement in
SG employing EVs.
End users and EV
aggregators devices should
work together to enhance
PQ.
PQ
enhancement.
The main
challenge in PQ
improvement is
to develop EV
chargers
Improve the voltage profile
and reliability indices.
Advanced Metering
Infrastructure using
Supervisory Control and
Data Acquisition systems.
Solutions for renewable
energy intermittency issues
using V2G technologies.
9
8 An EV SRM drivetrain with
G2V and V2H/V2G abilities that
is driven by rechargeable
batteries.
Enhance your high-speed
driving ability.
Real power
enhancement.
Expensive. It is possible to get good
acceleration/deceleration,
bidirectional operating, and
stopping properties.
Single-phase three-wire
(1P3W) inverter, SRM,
and bidirectional front-
end buck-boost DC/DC
converter.
It's worth looking at further
regulations for V2G
operations with fleets of
EVs.
9
9 Multi-objective power regulation
for EVs frigates with MMC-
based SG integration.
MMC's three-phase output
power is balanced, and the
circulating current is
regulated.
Power
management.
There is no cost
assessment or
peak current
evaluation.
The percent of circulating
current in the arms flow is
less than 13%.
Modular multilevel
converter, and circulation
current control.
To incorporate EVs better
securely, a cost-effective
isolation DC converter is
worth researching.
6
10 A Solar-To-Vehicle (S2V) study
focused at UCLA: A 2-Tier
EMS for smart EV recharging.
The superior central
controller can minimize the
EV load by up to 73
percent.
Energy
management.
The cost issue is
missing.
Increase the RES
utilization from 0.504 to 1.
EMS, Priority Sharing
and Priority Round
Robin, and queuing
algorithms.
Further work on pricing. 2
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Table 2 (b): Coordinated control of EVs in smart grid planning (Continue)
Ref. No.
Title
Outcomes
Objectives
Limitations
Performance
Method Utilizing
Future scope
ROC
11 EV assisted the SG environment with
blockchain-based cyber-physical
security.
The recommended scheme's
usability in the SG environment.
From the perspective
of communications
and computing
expenses, it is cost-
effective.
Less efficient. Power and data
security.
SDN, BC, Elliptic
Curve Cryptography,
and Ethereum
anonymously.
- 6
12 Deep reinforcement learning is used to
integrate EVs into the SG.
Performing efficiently in the real-
time power markets can help you
make more money.
Control
charge/discharge
operation.
- Maximum profit,
and charge/
discharge
enhancement.
DQN learning. Artificial neural network
with grey wolf
optimization.
1
13 A marketplace architecture for
distributed load mitigation in
intelligent DS that considers EV
aggregator coordination.
In the suggested architecture,
DG/EV aggregates are motivated
to express their pricing to increase
their clients' profits.
Maximum profit. Expensive and
complex.
Enhance the
localized power
market and clear
major bottlenecks in
the network.
Congestion
management, data
traffic operator, and
decentralized
submarket.
Modified hybrid
congestion control
protocol.
22
14 EV charging routine incorporating
G2V and V2G techniques in SG.
In the case of unclear factors, 2m-
PEM is preferable.
Maximize profit
margin.
Complex. - ICSA, and 2m Point
Estimation Method.
Galaxy search
algorithms.
12
15 In the SG, a review of techniques for
decentralized recharge management of
EVs.
Administering supplementary
services and maximizing income
through dispersed charge control
techniques.
Improved efficiency. Coordination
difficulty.
Increased
efficiency, higher
revenues, and
reduced the cost of
power generation
and supply.
Decentralized control,
distributed
optimization, and
hierarchical control.
Distributed error
management with
observers backstepping
has been improved.
13
16 AEBIS is an AI-enabled blockchain-
based EV integration system for the
SG platform’s power administration.
At the price of reasonable storage
and latency, a secured and
accessible application is achieved.
Cost-efficient
performance.
Expensive. The reliability of
the collaborative
learning strategy
was reduced by
only 1.7 percent.
AEBIS. Nature-based
optimization techniques.
4
17 Internet of a things-based decision
support tool for improving SG
effectiveness with distributed energy
sources and EVs.
The management technique
requires fewer communications
networks and is more comfortable
for EV customers.
Performance
enhancement.
Less accurate. Enhanced voltage
by up to 18.35%,
while voltage
unbalances were
decreased by less
than 2%.
Differential evolution
optimization
algorithm.
Grey wolf optimization. 0
18 Using an emphasis on Germany, the
influence of EVs on upcoming
renewables PS in Europe.
Incorporation of renewable
electricity to power EVs in a cost-
effective manner.
Reduce cost. It is not practical
data,
10% of the PEV's
electricity use is
saved as a result of
increased
effectiveness.
Renewable energy mix
and Mobilität in
Deutschland
(Germany-wide
mobility survey).
Modified survey data. 7
19 SG real-time power loss minimizing
with EVs via distribution feeder
rearrangement.
Among the EV routing solutions,
DFR leads to significant
reductions in system losses and
lower voltage levels.
Voltage profile
improvement.
Time-consuming. - Genetic algorithm-
based approach.
AI-based neural network. 6
20 Optimum routing of aggregators
energy and regulatory operations for
PEV based on G2V.
It can improve the aggregator's
revenues while simultaneously
lowering PEV owners' billing
costs.
Maximize aggregator
earnings.
Complex and
time-consuming.
Cost minimize,
improve regulation
and flexibility.
The regulation
algorithm, demand
dispatch, and optimal
scheduling.
Deep and machine
learning algorithm.
1
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Table 2 (c): Coordinated control of EVs in smart grid planning (Continue)
Ref. No.
Title
Outcomes
Objectives
Limitations
Performance
Method Utilizing
Future scope
ROC
21 In an SG, model predictive
management of PHEV is used to
regulate frequency.
The frequency variation can be
reduced using an ICA-based MPC
with PHEV.
Improve system
response.
Difficulties in
operation.
Normalize the
oscillatory patterns
of the systems.
Model predictive
control, ICA, and BIT.
Grey wolf optimization. 5
22 EV incorporation for load frequency
output feedback H∞ management of
SG.
Time delays hurt the system's
efficiency and efficacy.
Performance
enhancement.
Time-consuming. Stability
improvement.
Refined Jensen-based
inequality, 2D
searching method, and
H
∞ Control.
3D searching method. 3
23 Configuration management of a game-
theoretic energy trade route for EVs in
a mobile SG.
What is the best method of
distributing electricity across
PHEVs in a coalition with
multiple micro-grids?
Performance
enhancement.
Little
practicability.
High quality of
service.
ENTRANT, Nash
equilibrium,
Stackelberg game, and
game theory.
How the structure of the
energy trade route may be
managed in advance
depending on the PHEVs'
predicted trajectory.
4
24 Practical assessment and operating
effectiveness of a fuel cell electric
V2G as a balanced power station.
FCEVs can be utilized for both
movement and electricity
generation when parked.
Efficiency as well as
feasibility
enhancement.
For this research,
we did not look
into the possibility
to schedule autos.
Direct current to
alternating current
efficiency was 95
%.
Balancing Power
Plant, Efficiency,
Electrical Energy
Services, and spinning
reserve.
The possibility of self-
driving, cloud-connected,
and grid-connected future
cars may make car
planning easier.
2
25 Energy coordination between EVs and
the electricity DS proposed hybrid
optimization.
By less calculation, it's possible to
find the near-global optimized
value.
Cost minimization. Prone to
overfitting.
The method
employs 720.34
kilojoules of
energy.
FFA-GDBT. AI-based neural network. 1
26 In an intelligent electrical power
distribution network, an RMEMS for
EV charging has been developed.
In 3 phases, RMEMS is important
in managing active and reactive
power usage.
Active and reactive
power enhancement.
The infrastructure
for trading
administration is
unavailable.
- RMEMS, and TOM. In other nations, RMEMS
for EV charging in the
domestic and urban areas
will be explored.
1
27 G2V and V2G in an intelligent DS for
optimizing revenues utilizing
MOMVO are enabled by EV
administration.
The V2G functioning of EVs can
yield enormous economic and
societal benefits.
Cost minimization. Inaccurate. Flattening of load
profile, and peak
load shaving.
MOMVO. Grey wolf optimization. 6
28 A VG2 activated bidirectional off-
board EV battery charger improves
grid PQ.
It cuts down on the number of
incremental charging and
discharging cycles, extending the
life of the battery.
Power quality
improvement.
- The THD of grid
current is limited to
less than 5% with
an EV charger.
RPC, GCHC, adaptive
notch filter, and phase-
locked loop.
- 1
29 Integrating TOU price-based demand
response and variable G2V charge
routing of an EVs aggregator to help
maintain the stability of the grid.
The findings reveal some
significant patterns in terms of EV
charging costs, EV aggregator
profit, and system operator
regulations.
Reduces EV charging
cost.
Not suitable for
large-scale data.
Profit increase. TOU‐PBDR, and
agglomerative
hierarchical clustering
method.
Natural-based
optimization techniques.
2
30 State-of-the-art and upcoming
opportunities for intelligent power
electronics-based approaches to
integrate solar PV, SG, and electric-
powered
transportation.
The effectiveness of most circuit
topologies compromises the cost.
Efficiency
enhancement.
Controlling cost
parameters is not
available.
- DC-DC converters,
DC–AC inverters, and
maximum power point
tracker techniques.
One of the goals for
future projects is to
reduce element costs.
3
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3. CONCLUSION AND FUTURE SCOPE
In this paper, a study has been done about the coordinated
control of Electric vehicles in super-smart grids. Various
techniques have been proposed in this literature such as
residue-based methods, Heterogeneous Ethernet-based mesh
networks, liner, and non-linear optimization programming
algorithms, genetic algorithms (GA), artificial neural networks
(ANN) based algorithms, swarm optimization (PSO)
techniques, fuzzy logic-based approach, adaptive neuro-fuzzy
inference system (ANIS) techniques, 2D searching method,
and model predictive control.
A state-of-the-art review of the current optimization methods
for synchronized control of EVs in super-smart power grid
networks was conducted in this article. Several points will be
examined, including optimum charging-discharging of EVs,
optimized coordination of EVs, power control of EVs with the
SG, losses minimizing of EVs with smart grid cooperation,
and energy performance improvement solutions for EVs with
smart grid synchronization. The following are a few of the
conclusions:
It is proved that the proposed centralized and
decentralized control solution for EV charging addresses
charging costs, network problems, and local voltages at
the same time.
Administering supplementary services and maximizing
income through dispersed charge control methods.
When all of the costing methods are compared in terms of
economics, equality, and hazard motivation, RTP
emerges as the clear winner.
Promotes operating effectiveness at higher velocities
while also improving frequency regulation ability.
Across all EV planning solutions, DFR results in
substantial reductions in system losses and minimum
voltage levels.
Considering the price, operation time, precision, durability,
and velocity of numerous optimization methods, some
limitations can be solved by applying new natural-based
optimization methods. The following are a few of the potential
scopes of this review article:
In the future, RMEMS for EV charging in the domestic
and urban areas of additional countries will be tried.
How the design of the energy trade route may be regulated
in advance depending on the predicted PHEV trend.
To incorporate EVs more securely, a cost-effective
separated DC converter is worth researching.
Examine the communications infrastructure for high
traffic loads and faulty communications networks.
On the distribution side, EV incorporated PS architecture,
i.e. peak shaving and load shifting, has been examined.
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