A Review on Optimization Techniques for Electric Vehicles Planning
in Distribution Networks
Bindeshwar Singh
1
, Arvind Pratap
2
, and Prabhakar Tiwari
2
1Department of Electrical Engineering, Kamla Nehru Institute of Technology, Sultanpur, INDIA
2Dept. of Electrical Engineering, Madan Mohan Malaviya University, Gorakhpur, INDIA
Keywords: Coordinated Control, Distribution Systems, Electric Vehicles, Load Models, Optimization Techniques.
Abstract: This paper presents a study of various optimization techniques for electric vehicles (EVs) planning in
distribution systems with load models for the minimization of the system's actual and reactive power losses
to boost system performance. System performance such as available power transfer capability, system
versatility, system loadability, system power factors, system protection, system reliability, voltage profile,
system oscillations, system stability, power quality and greenhouse gas (GHG) environmental performance,
etc. are achieved by planning electric vehicles (EVs) in distribution systems with load model. The authors
strongly believe that this analysis paper would be very useful for researchers, designers, clinicians,
academics and scientists to find appropriate references in the field of planning EVs in distribution systems
with load models to improve system efficiency from various points of view of objective functions.
1 INTRODUCTION
In the present scenario of energy planning, the
different types of EVs are having important roles.
The power technology planning such as EVs
planning, are studied and analyzed for future
research. But in this article only considered the EVs
planning in distribution systems with load models
mainly focused.
The conventional optimization techniques such
as Value-Based Control Technique (VBCT), Index
Methods (IM), Adaptive Control Algorithm (ACA),
Frequency Variable (FV), Static Voltage Stability
Assessment Method (SVSAM), Sensitivity Based
Methods (SBM), Eigen-Value Analysis (EVA),
Optimal Power Flow (OPF), Power train Systems
Analysis Toolkit (PSAT), SBM, and OPF, EVs
planning have been studied in review.
The analytical optimization-based techniques
such as Mixed-Integers (MI), Non Linear
Programming (NLP), Analytical Approaches (AA),
Optimization Algorithm (OA), Robust Optimization
(RO), Linear Programming (LP), Dynamic
Programming (DP), Dual Programming, Mix Integer
Linear Programming (MILP), Stochastic Dynamic
Programming (SDP), Sequential Quadratic
Programming (SQL), and Ordinal Optimization
(OO) approach EVs planning have been also studies
in past literature.
The artificial intelligence computational
techniques, Monte Carlo (MC) Algorithms,
Emultion Based (EB) Method, Simulated Annealing
(SA) Based Approach, Genetic Algorithms (GA),
Particle Swarm Optimization (PSO) Techniques,
Fuzzy Logic (FL) Based Method, Artificial Neural
Network (ANN) Based Algorithms, Tabu Search
(TS) algorithms, Cluster-Wise Fuzzy Regression
(CWFR) Analysis, Artificial Bee Colony (ABC)
Algorithms, Honey Bee Mating Optimization
(HBMO), Fuzzy Logic, Particle Swarm
Optimization, Monte Carlo, Heuristic Planning
Algorithms (HPA), Genetic Alogithm, Artificial
Neural Network, Ant Bees Colony and Search
Algorithm EVs planning have been studied in past
study. Hybrid optimization techniques (HOTs) for
EVs planning have been also found in past research
literatures.
The optimization-based techniques such as Mix
Integer nonlinear Programming, Agglomerative
Hierarchical Clustering (AHC) Method, Whale
Optimization Algorithm, Optimal Power Flow ,
MINLP & NSGA-II, Generalized Nash Equilibrium
Problem (GNEP) & Relaxation Algorithm (RA),
Quadratic Roteted Conic Programming (QRCP) &
Multi Stage Optimization Coordination Method
Singh, B., Pratap, A. and Tiwari, P.
A Review on Optimization Techniques for Electric Vehicles Planning in Distribution Networks.
DOI: 10.5220/0010562900003161
In Proceedings of the 3rd International Conference on Advanced Computing and Software Engineering (ICACSE 2021), pages 81-94
ISBN: 978-989-758-544-9
Copyright
c
 2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
81

(MSOC), Greedy Randomized Adaptive Search
(GRAS) &Tabu Search, Karush-Kuhn-Tucker
(KKT) conditions and Stochastic Framework (SF)
for EVs planning are proposed for future research.
Recent optimization techniques (ROTs) for EVs
planning have been also found in recent research
works of literature.
The literature survey, quoted in the review
article, deals with the preparation of EVs in power
systems with different load models, such as static
and dynamic, using various optimization strategies
to increase system performance from the perspective
of different target functions. The literature review
shows that in the open works of literature, the
investigation of various system performances with
EVs planning in power systems with different load
models using existing optimization techniques such
as Ant Lion optimization, Spider Monkey
optimization, Whale optimization algorithm, Grey
Wolf optimization, etc. has not been used.
A review article presents an impact assessment
EVs planning in systems with different load models
from different objective functions for enhancement
of system performances. This article mainly focused
on the impact assessment of EVs in distribution
system with ZIP load models from different
objective functions for enhancement of the system
performances such as real and reactive power losses,
system stability, system security, system reliability,
system loadability, bandwidth of operation, system
oscillation, greenhouse gases, etc. are not published
any journals.
The main contributions of this review paper are
as follows:
• System performance variations for EVs planning
for different load models such as static, realistic,
ZIP, composite, frequency-dependent load
models, etc.
• Robustness of the proposed algorithms for EVs
planning with load models.
• Practical system validity for EVs planning with
load models.
The rest structures of the paper are as follows:
Section 2 discusses EVs planning. The paper's
findings and future scope of research are discussed
in Section 3.
2 LITERATURE SURVEY FOR
DGS WITH EVS PLANNING
A literature survey for EVs planning are discussed in
sub-sections 2.1-2.3, subsequently. EVs Planning
The different optimization techniques are discussed
for EVs planning with load models are as follows:
2.1 Conventional Optimization
Techniques
The conventional optimization techniques for EVs
planning [1-10] are presented as follows: For
improved system efficiency, Zhang et al. proposed
charge-depleting control techniques and fuel
optimization of blended-mode PHEVs. Sanjaka et al.,
presented a source-to-wheel analysis of PHEVs.
Zhang et al., represented the impact of silicon carbide
devices on hybrid electric and PHEVs. Seshadri and
Alireza presented a possible factor for electrification:
energy-based value proposition study of PHEVs for
system efficiency improvement. Reza et al.,
presented an on the conversion of hybrid electric
vehicles to plug-in for system performance
enhancement. The architecture of a Bayesian network
model for optimum site selection of electric vehicle
charging stations was suggested by Seyed and
Sarder. Yongxiu et al. proposed a production pattern
design for Chinese EVs based on a life cycle cost
study of the essential cost. Simone et al., presented
socio-technical inertia: understanding the barriers to
EVs. Harun and Seddik, presented an optimal
minimization of PEVs charging cost with vehicle-to-
home and vehicle-to-grid concepts. Hua et al.,
presented an ADMM-Based multiperiod OPF
considering PEVs charging for system performance
enhancement. The conventional optimization
techniques for EVs planning are presented in Table 1.
2.2 Optimization Techniques
The EV planning optimization strategies are
presented as follows: Gong et al., for system
performance improvement, proposed trip-based
optimal power control of PHEVs. Kristien et al.,
addressed the effect of charging PHEVs for system
efficiency improvement on a residential delivery
grid. Saber et al. addressed the resource scheduling
of renewable and plug-in vehicles under instability
in a smart grid. Micro turbine-powered PHEVs
should be handled for energy management using the
telemetry equivalent consumption minimization
technique, according to Geng et al. Kum et al.
suggested optimizing PHEV energy and catalyst
temperature for low fuel consumption and pollution
at the tailpipe. A two-stage energy management
regulation of fuel cell PHEVs considering fuel cell
durability was proposed by Geng et al. A description
and analysis of control strategies for PHEVs for
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system performance improvement was proposed by
Sanjaka and Ali. Linni et al. suggested a regulated
charging of PHEVs to minimize load variance in the
household smart microgrid. Justin et al., presented
the optimum involvement of PHEVs in the power
market pooled by distribution feeders. Wisdom and
Chris, introduced hybrid electric vehicle modeling
and control (a comprehensive review). The
macroeconomic effects of fiscal subsidies for the
development of electric vehicles in Iceland has been
presented by Ehsan et al.: consequences for
government and market prices.
Amir et al., proposed a RO approach to
scheduling the transition to system performance
improvement PHEVs. For the grid implementation of
electric vehicles, Haddadian et al. suggested a safety-
constrained schedule of power generation with
thermal generating systems, variable electricity
sources, and storage of electric cars. Hamed et al.
addressed the long-term complex preparation of the
extension of generation capacity and power
transmission networks in multi-carrier energy
systems. Raji and Noboru proposed a more precise
dimensioning of RESs under the high degree of
incorporation of electric vehicles. A multi-objective
energy storage power delivery using PEVs in a
smart-micro grid was proposed by Vitor et al.
Stephanie et al., for real-world driving cycles,
proposed an energy-optimal regulation of PHEVs.
Menyang et al., proposed an AA for blended-mode
PHEV power management. Luting and Chen
suggested a consensus algorithm-based distributed
control system for large-scale PEV charging. Weihao
et al. suggested the best way to use PEVs in power
systems with high wind penetration. In a microgrid,
Peng et al. suggested a model predictive control
system for matching uncertain wind generation with
PEV charging demand. Maryam et al. suggested a
decentralised, robust model for organizing smart
delivery network and EV aggregator operation. Luis
and Raquel, proposed a rigorous stochastic
optimization approach for an EV aggregator bidding
technique. Xiangning et al., proposed a distribution
network scheduling integration of vehicle-to-grid EV
charging stations for system efficiency improvement.
Sheikhi et al., proposed a method of strategic
charging for smart grid PHEVs; a game-theoretical
approach. Junjie et al., presented smart grid EV fleet
management: a study of facets of facilities,
optimization, and regulation. Kumarsinh et al.,
proposed a coordinated EV charge with RESs for
system efficiency enhancement for the commercial
parking lot. Table 2 addresses the preparation of EVs
by optimization techniques.
2.3 Ai Computational Techniques
AI estimation methods for planning EVs are
discussed as follows: Li et al., introduced the power
and battery control of a hybrid EV plug-in series
using FL. A charging load profile on the grid
attributable to plug-in vehicles was proposed by
Soheil et al. A two-stage charging technique for
PEVs at the residential transformer level was
proposed by Genget al.. A smoothing of wind power
using the demand response of EVs was proposed by
Raoofat et al. A multi-objective optimal charging of
PEVs in unbalanced distribution networks was
introduced by Masoud and Ali. AI estimation
methods for planning EVs are discussed as follows:
Ning et al. suggested a fuzzy chance-constrained
unit interaction problem programme that took
demand response, electric vehicles, and wind power
into account. Naik et al. suggest a smart mass
transportation network expansion and its link to the
grid. Janjic suggested a two-step algorithm for
optimizing an energy delivery company's fleet of
vehicles. On the basis of FL, Qi et al. proposed an
energy storage approach for fuel
cell/battery/ultracapacitor hybrid vehicles. Saber et
al. proposed a new smart charging system for EVs
for smart grid frequency management. A fuzzy
algorithm for EV parking lot service was suggested
by Samy et al. Daya et al. addressed an investigation
and numerical improvement of the wavelet
controller for robustness in the electronic differential
of EVs. In a regenerative braking mode, Joy and
Ushakumari demonstrated the work of a three-phase
H-bridge inverter feeding permanent magnet
brushless direct current motor-generator drive in an
electric bike. A real-time energy regulation solution
was introduced by Suyang et al. for the smart home
energy management framework. Liyeet al. proposed
an estimation model for the economic operation of
the energy-internet-oriented active distribution
network. Reddy and Meikandasivam, using a water-
filling algorithm for load flattening and vehicle
prioritization using the adaptive neuro-fuzzy
inference method, proposed an optimal distribution
of PEV storage space. An automated failure analysis
of electrical machinery was proposed by Awadallah
& Morcos: a case study of permanent magnet
brushless direct current motors.
To improve the stability of the PEV power
system, Mitra suggested a wide-area control system.
Saberet al. suggested a resource scheduling
algorithm for a smart grid of renewables and plug-in
electric vehicles that is unstable. Charging
infrastructures, according to Huet al., should be
A Review on Optimization Techniques for Electric Vehicles Planning in Distribution Networks
83

strategically positioned to allow for large-scale
integration of pure EVs into the grid. Yachao et al.
suggested a multi-objective hydro-thermal-wind
synchronization scheduling combined with large-
scale EVs using improved multi-objective PSO.
Chunyan et al. suggested optimal spatio-temporal
scheduling for EVs and load aggregators, taking
response efficiency into account. For robust
monitoring of renewable energy, Saeid and Hosam
proposed transport-based load modelling and
sliding-mode PEV regulation. Casey et al. suggested
an evaluation of state-of-charge constraints and drive
signal energy quality on PHEV, vehicle-to-grid
reliability, and economics. Yue and David
suggested a Markov chain MC simulation of EV
consumption for network integration studies.
Pashajavid and Golkar suggested non-Gaussian
multivariate modelling of PEV load production.
Akashet al. suggested a stepwise power tariff model
with a game theory focused on MC simulation and
its implementations for household, agricultural,
commercial, and industrial customers. Alireza et al
proposed a stochastic characterization of the energy
markets for electricity, including PEVs for
optimizing device efficiency. Jun et al. addressed the
modeling of large-scale charging market for EVs: a
case study from New Zealand. Gray and Morsi
addressed the effect of single-phase charging of
PEVs and solar photovoltaic rooftops on the ageing
of distribution transformers. Gray and Morsi
addressed the role of prosumers (power producers
and consumers) owning solar photovoltaic rooftops
in reducing the effect of PEV charging on the ageing
of the transformer. Leonardo et al, proposed EV
models to determine supply security. Gray and
Morsi presented an economic evaluation of the
reconfiguration of phases to mitigate the disparity
due to the charging of PEVs. An efficient secondary
distribution system layout considering PEVs was
proposed by Abdelsamad et al.. Gray and Morsi
proposed a probabilistic quantification in secondary
distribution systems of voltage difference and
neutral current due to the charging of plug-in battery
EVs. Nima and Peng have proposed a probabilistic
approximation of the charging load profile of PEVs.
In the face of load and generation instability, Wang
et al. suggested an affine arithmetic-based direct
current power flow for automatic contingency
selection. On the distribution network, Zhou et
al.suggested a probability model and EV charging
load simulation approach. Jaber et al. proposed a
modern charging demand model based on the
accumulation of PHEVs. Nan et al. proposed a smart
residential group optimal scheduling solution that
took into account residential load uncertainties.
An HPA method evaluation of the effect of PEVs
on distribution networks for system performance
improvement was explored by Luis et al. Xiaohu et
al., provided high-frequency resonance reduction
with a broad variety of grid requirements for PHEVs
incorporation. According to Navarro et al., an EV
fast-charging station can be installed using clean
energy and storage technology. Moein et al.
suggested a novel Volt-VAR optimization engine for
smart delivery networks using the vehicle for grid
dispatch. Chen et al. presented a cost-benefit
analysis of an energy storage system based on
recycled EV batteries. Susana et al. proposed
electrical and parallel-hybrid EV modeling using the
Matlab/Simulink setting and charging station
planning through a geographic information system
and GA. The commercial EV fleet scheduling for
secondary frequency management was proposed by
Aleksandar et al.. Saeed et al. suggested
simultaneous planning for PHEV charging stations
and wind power generation in distribution networks,
taking into account uncertainties. Online modelling
and recognition of PEVs sharing a residential station
was proposed by Abdoul et al. Guohai et al.
suggested a neural network-based internal model
decoupling control of the three-motor drive system.
Panchal et al suggested a thermal and electrical
performance assessment of lithium-ion battery
modules for an EV under actual drive cycles.
Rathore and Roy discussed the effect on
transmission network extension planning of wind
instability, PEVs and the demand response program.
Tiago et al. proposed shared control of EVs in
simulated annealing to cope with energy and
ancillary services. The artificial intelligence
computational techniques for EVs planning are
presented in Table 3.
2.4 Hybrid Optimization Techniques
Hybrid optimization techniques for EVs planning are
discussed as follows: A description and analysis of
control methods for PHEVs was proposed by
Wirasingha and Emadi et al. Nojavan and Zare
proposed that an electricity retailer with BEVs could
have an acceptable energy price for customers.
Mehdi et al., proposed a risk-averse power planning
look-ahead of heterogeneous BEV aggregations that
allow vehicle-to-grid and grid-to-vehicle systems
based on the theory of information gap decision.
Reddy et al., proposed a novel approach for
optimizing the use of PHEV storage for grid service
with consumer flexibility in mind. John et al.,
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discussed the coordination of localized charging
PHEVs utilizing only local voltage magnitude
measurements. Farahani et al., including PHEV,
proposed a multi-objective clearing of the demand
for reactive capacity. An estimate of voltage
mismatch impacts of PHEV penetration in residential
low-voltage delivery networks was proposed by
Farhad et al. Arman et al, proposed an automatic
regulation of generation that integrates BEVs. The
effect of observability and multi-objective
optimization on the efficiency of the extended
Kalman filter for direct torque control of alternating
current machines with PHEV was proposed by
Ibrahim et al. Reza et al., discussed the conversion of
hybrid EVs to plug-in for system performance
enhancement. The hybrid optimization techniques for
EVs planning are presented in Table 4.
2.5 Other Optimization Techniques
Other optimization techniques for EVs planning are
presented as follows: Tara et al.proposed battery
storage sizing for system efficiency enhancement in
retrofitted PHEVs. Sara et al. proposed scheduling
PHEV charging in smart grids in real-time to
minimize power losses and increase the profile of
voltage. Jose et al., addressed a rational
configuration of the vehicle-to-grid control PHEVs
aggregator. A network security-aware charging of
PHEVs was proposed by Tian et al. Xu et al.,
introduced the EX-PHEV aggregator's decentralized
charging control technique focused on the enhanced
lagrangian process. Wolf et al., presented the use of
PHEV capabilities with a new software platform for
demand response optimization: Okeanos. In the
imperfect energy markets, Schill addressed BEVs:
The case of Germany. A QRTM of PHEVs charging
for system efficiency improvement was proposed by
Soares et al. Table 5 displays the other optimization
methods for planning EVs.
Table 1: Conventional optimization techniques for EVs planning with load models
Ref.
No.
System
p
erformances
Control
p
arameters
Proposed
methods
Load models Future
scopes
[
1
]
System power factor Location & types PSAT STATLM MOO
[2]
Environmental GHG Size & types PSAT STATLM RLMs
[3]
System oscillations Location & coordination PSAT STATLM RLMs
[4]
System flexibility Location & types PSAT STATLM HOTs
[5]
System security Size & types PSAT STATLM HOTs
[6]
System reliability Size & location SBM RLMs MOO
[7]
Frequency stability Location & types SBM STATLM HOTs
[8]
System loadability Size & types SBM STATLM HOTs
[9]
Voltage stability Size & location OPF STATLM RLMs
[10]
Frequency stability Location & types OPF STATLM RLMs
Table 2: Optimization Techniques for EVs Planning with Load Models
Ref.
No.
Authors Pub.
y
ea
r
System
p
erformances
Control
p
arameters
Proposed
methods
Load models Future
sco
p
es
[11]
Li et al. 2011 Rotor angle
stabilit
y
Location & types FL STATLM RLMs
[12]
Soheil et al. 2012 Frequency
stabilit
y
Size & types FL STATLM RLMs
[13]
Geng et al. 2013 System flexibility Location &
coordination
FL STATLM HOTs
[
14
]
Raoofat et al. 2018 System security Location & types FL STATLM RLMs
[
15
]
Masoudet al. 2015 System reliability Size & types FL STATLM RLMs
[16]
Ning et al. 2015 Frequency
stabilit
y
Size & location FL STATLM HOTs
[17]
Naik et al. 2019 System
loadabilit
y
Location & types FL STATLM RLMs
[
18
]
Janjic 2015 Voltage stability Size & types FL STATLM RLMs
A Review on Optimization Techniques for Electric Vehicles Planning in Distribution Networks
85

[19]
Qi et al. 2012 Frequency
stabilit
y
Size & location FL STATLM RLMs
[
20
]
Saber et al. 2016 Real power loss Location & types FL STATLM ROTs
[21]
Samy et al. 2017 System power
facto
r
Size & types FL STATLM ROTs
[22]
Daya et al. 2016 Rotor angle
stabilit
y
Size & location FL STATLM ROTs
[23]
Joy
&Ushakumari
2018 Environmental
GHG
Location &
coordination
FL STATLM ROTs
[24]
Suyang Z et al. 2014 System
oscillations
Location &
coordination
FL STATLM RLMs
[
25
]
Liye et al. 2019 System flexibility Size & types FL STATLM ROTs
[
26
]
Reddy et al. 2018 Real power loss Size & location FL STATLM ROTs
[27]
Awadallah et
al.
2005 System power
facto
r
Size & types FL STATLM ROTs
[28]
Mitra 2010 Environmental
GHG
Size & location PSO STATLM ROTs
[29]
Saber et al. 2012 System
oscillations
Size & types PSO STATLM ROTs
[30]
Xu et al. 2015 System flexibility Location &
coordination
PSO STATLM RLMs
[31]
Yachaoet al. 2018 Power system
securit
y
Location & types PSO STATLM RLMs
[32]
Chunyan L et
al.
2018 Reactive power
losses
Location & types PSO STATLM RLMs
[33]
Saeid et al. 2012 System power
facto
r
Size & location MC RLMs
[34]
Casey et al. 2012 System
oscillations
Location & types MC STATLM RLMs
[
35
]
Yue et al. 2018 System stability Size & types MC STATLM RLMs
[36]
Pashajavid et
al.
2014 System security Size & location MC STATLM RLMs
[
37
]
Akashet al/ 2017 System reliability Location & types MC STATLM RLMs
[38]
Alirezaet al. 2019 Environmental
GHG
Size & types MC STATLM ROTs
[
39
]
Jun et al. 2017 System flexibility Size & location MC STATLM RLMs
[40]
Gray et al. 2019 System
loadabilit
y
Location & types MC STATLM RLMs
[
41
]
Gray et al. 2017 Voltage stability Size & types MC STATLM RLMs
[42]
Leonardo et al. 2014 Rotor angle
stabilit
y
Location & types MC STATLM RLMs
[43]
Gray et al. 2016 Frequency
stabilit
y
Size & types MC STATLM RLMs
[44]
Abdelsamad et
al.
2016 System flexibility Location &
coordination
MC STATLM MOO
[45]
Gray et al. 2016 Environmental
GHG
Location & types MC STATLM RLMs
[46]
Nimaet al. 2015 System
oscillations
Size & types MC STATLM RLMs
[47]
Wang et al. 2014 Frequency
stabilit
y
Size & location MC STATLM HOTs
[48]
Zhou et al. 2014 System
loadabilit
y
Location & types MC STATLM HOTs
[
49
]
Jaber et al. 2017 Voltage stability Size & types MC STATLM HOTs
[50]
Nan et al. 2019 Frequency
stabilit
y
Size & location MC STATLM ROTs
[
51
]
Luis et al. 2011 Real power loss Location & types HPA STATLM RLMs
[
52
]
Xiaohu et al. 2012 System power Size & types GA STATLM HOTs
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facto
r
[53]
Navarro et al. 2019 Rotor angle
stabilit
y
Location &
coordination
GA STATLM RLMs
[54]
Moeinet al. 2016 Environmental
GHG
Location & types GA STATLM RLMs
[55]
Chen et al. 2013 System
oscillations
Location & types GA STATLM HOTs
[
56
]
Susana et al. 2016 System flexibility Size & location GA STATLM RLMs
[57]
Aleksandaret
al.
2017 Real power loss Location & types GA STATLM RLMs
[58]
Saeed et al. 2016 System power
facto
r
Size & types GA STATLM HOTs
[59]
Abdoulet al. 2019 Environmental
GHG
Size & location ANN RLMs ROTs
[60]
Guohai et al. 2012 System
oscillations
Location &
coordination
ANN STATLM RLMs
[
61
]
Panchal et al. 2018 System flexibility Location & types ANN STATLM HOTs
[
62
]
Rathore et al. 2016 System security Size & types ABC STATLM RLMs
[
63
]
Tiago et al. 2016 System reliability Size & location SA STATLM RLMs
Table 3: AI computational techniques for EVs planning with load models
Ref.
No.
Authors Pub.
yea
r
System
p
erformances
Control
p
arameters
Proposed
methods
Load
models
Future
scopes
[64]
Gong et al. 2008 Rotor angle
stability
Size , types &
location
DP STATLM RLMs
[65]
Kristien et al. 2010 Frequency
stability
Size , types &
location
DP STATLM ROTs
[66]
Saber et al. 2012 System
flexibility
Size , types &
location
DP STATLM ROTs
[
67
]
Geng et al. 2011 System security Size & location DP STATLM ROTs
[68]
Kum et al. 2013 System
reliabilit
y
Location &
t
yp
es
DP STATLM ROTs
[69]
Geng et al. 2012 Frequency
stabilit
y
Size & types DP STATLM ROTs
[70]
Sanjaka et al. 2011 System
loadabilit
y
Location &
coordination
DP STATLM ROTs
[71]
Linni et al. 2013 Voltage
stabilit
y
Location &
t
yp
es
DP STATLM ROTs
[72]
Justin et al. 2013 Frequency
stability
Size & types DP STATLM ROTs
[73]
Wisdom et al. 2017 Real power loss Size & location DP STATLM ROTs
[74]
Ehsan et al. 2018 System power
facto
r
Location &
t
yp
es
DP STATLM ROTs
[75]
Amir et al. 2011 Rotor angle
stabilit
y
Size & types MILP STATLM RLMs
[76]
Haddadian et
al.
2015 Environmental
GHG
Size & location MILP STATLM RLMs
[77]
Hamedet al. 2018 System
oscillations
Location &
t
yp
es
MILP STATLM RLMs
[78]
Rajiet al. 2015 Frequency
stabilit
y
Size & types MILP STATLM RLMs
[79]
Vitoret al. 2016 System
flexibilit
y
Size & location MILP STATLM RLMs
[80]
Stephanie et
al.
2011 System security Location &
coordination
SDP STATLM RLMs
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Table 4: Hybrid Optimization Techniques for EVs Planning with Load Models
Ref.
No.
Authors EVs System
p
erformances
Control
p
arameters
Proposed
methods
Load
models
Future scopes
[91]
Wirasingha
& Emadi
PHEV System security Location &
types
FL + ANN STATLM MOO
[92]
Nojavan et
al.
BEV System
reliabilit
y
Size & types FL + MILP STATLM RLMs
[93]
Mehdi et
al.
BEV Frequency
stabilit
y
Location &
t
yp
es
PSO +GWO STATLM RLMs
[94]
Reddy et
al.
PHEV System
loadabilit
y
Location &
types
GA + FL STATLM RLMs
[95]
John et al. PHEV Voltage stability Size & types MC + MILP STATLM ROTs
[96]
Farahani et
al.
PHEV Real power loss Size & location PSO + FL STATLM MOO
[97]
Farhad et
al.
PHEV System power
facto
r
Location &
t
yp
es
MC + SBM STATLM ROTs
[98]
Arman et
al.
BEV Environmental
GHG
Size & types PSO + GA RLMs ROTs
[99]
Ibrahim et
al.
PHEV System
oscillations
Size & location KFM +
NSGA-II
STATLM MOO
[100]
Reza et al. PHEV System
flexibilit
y
Location &
t
yp
es
FL +PSAT STATLM ROTs
Table 5: Other Optimization Techniques for EVs Planning with Load Models
Ref.
No.
Authors
Pub.
y
ea
r
EVs
System
p
erformances
Control
p
arameters
Proposed
methods
Load models
[101]
Tara et al. 2010 PHEV Frequency stability Location &
types
SBF STATLM
[102]
Sara et al. 2011 PHEV Real power loss Size & types RTSLM STATLM
[103]
Jose et al. 2012 PHEV System power factor Size &
location
LM STATLM
[104]
Tian et al. 2018 PHEV Rotor angle stability Location &
types
LM STATLM
[105]
Xu et al. 2019 EX-PHEV Environmental GHG Size & types LM STATLM
[81]
Menyang et al. 2012 System
reliabilit
y
location &
coordination
SDP STATLM RLMs
[82]
Luting et al. 2019 Frequency
stabilit
y
Size & Types SDP STATLM RLMs
[83]
Weihao et al. 2013 System
loadabilit
y
Size & location SQP STATLM RLMs
[84]
Peng et al. 2019 Voltage
stabilit
y
Size & types SQP STATLM RLMs
[85]
Maryam et al. 2019 Frequency
stability
Size & location RO STATLM RLMs
[
86
]
Luis et al. 2017 Real power loss Size & types RO STATLM RLMs
[87]
Xiangninget
al.
2014 Voltage
stabilit
y
Location &
coordination
OO STATLM RLMs
[88]
Sheikhi et al. 2013 Frequency
stability
Location &
types
OA STATLM RLMs
[89]
Junjieet al. 2016 Reactive power
loss
Location &
types
OA STATLM RLMs
[90]
Kumarsinh et
al.
2017 System power
facto
r
Size & location LP STATLM MOO
ICACSE 2021 - International Conference on Advanced Computing and Software Engineering
88

[106]
Wolf et al. 2016 PHEV System oscillations Size &
location
GTSF STATLM
[107]
Schill 2011 BEV System flexibility Location &
t
yp
es
GTSF STATLM
[108]
Soares et al. 2014 PHEV Voltage stability Size & types QRTM STATLM
3 SUMMARY OF THE PAPER
The advantages and disadvantages of different
optimization techniques for EVs planning in
distributions systems with load models are given in
Table 6.
Table 6: Advantages and disadvantages of different optimization techniques for EVs planning in distributions systems with
load models
Methods Advantages Disadvantages Applications
GA It just needs a rough idea of the
objective function and does not
impose any constraints on it, such
as differentiabilit
y
or convexit
y
.
tremendously high time DGs, EVs, FACTs, Capacitor,
voltage/reactive power planning
PSO Simple implementation Slow convergence in refined
search stage
Sensor network planning
ACO Can be used in dynamic
a
pp
lication
Convergence is guaranteed, but
time to conver
g
ence is uncertain
Machine scheduling
GWO Higher precision and more consist
result
low solving precision, slow
convergence, and bad local
searching abilit
y
DG and FACTS controllers planning
ABC Few control parameters are
re
q
uire
d
Search space limited by initial
solution
Power system DG and EVs planning
FL It is simpler and more flexible It requires a lot of data Traffic control, improving the
efficienc
y
of automatic transmission
OPF Able to run a parallel computation Can be difficult to define initial
p
arameters
Power system stability analysis
MC Bypass the complexity of solving
the
p
roblem b
y
anal
y
tical metho
d
High precision comes at a high
com
p
utational cost
Power system DG, FACTS
controllers and EVs
p
lannin
g
LP Linear programming is adaptive
and more flexibility to analyze the
p
roblem
Linear programming is work
only with the linear variables
Power system operation and control
DP They required much less
com
p
utin
g
resources
They do not always reach the
g
lobal o
p
timum solution
Bank of capacitor and FACTS
controllers
p
lannin
g
KFM Computationally efficient Able to represent only Gaussian
distributions
Bank of capacitor, DGs, EVs and
FACTS controllers
p
lannin
g
GSA Ability to solve highly nonlinear
optimization problems
The difficulty for the
appropriate selection of
g
ravitational constant
p
aramete
r
Power system DGs, EVs and
FACTS controllers planning
SAA Strong global search capacity Convergence speed is slow and
p
arallel computing is difficult
Power system DGs, EVs and
FACTS controllers planning
TS It is a meta-heuristic search to
solve global optimization
p
roblems
It is relatively slow Transmission planning, optimal
capacitor placement, hydrothermal
schedulin
g
, reactive
p
ower
p
lannin
g
ALO Ant Lion Optimization (ALO) is
used to solved complicated
optimization problems in
engineering design particularly in
electrical en
g
ineerin
g
It is got a long run time due to
the random walking process
Hyperspectral imaging, agricultural
credit classification
A Review on Optimization Techniques for Electric Vehicles Planning in Distribution Networks
89

This survey paper presented the analysis of
literature reviewed for different EVs planning by
using conventional, optimization, AI, hybrid, other,
and recent optimization techniques for
enhancement of system performances like available
power transfer capacity, system flexibility, system
loadability, system power factors, system security,
system reliability, voltage profile, system
oscillations, system stability, power quality, and
environmental greenhouse gases (GHG), etc. from
different objective functions viewpoints. This
survey article is useful for researchers who are
working in the field of EVs planning in the
distribution system with load models.
4 CONCLUSIONS AND FUTURE
SCOPE OF SURVEY ARTICLE
An exhaustive literature survey plays an important
role in future system planning. This survey article
represents optimization techniques used for the
optimal setting of the system performance
parameter of the system.
System parameters, such as actual and reactive
power losses, etc., are often reduced by the
optimum positioning, dimensioning and properly
organized regulation of the various types of EVs,
such as BEVs, FCEVs, PHEVs and Ex-PHEVs, in
separate load models of delivery systems.
In the survey, the following potential scope of
research study is also assumed.
Hybrid optimization strategies for the optimum
positioning, dimensioning and properly organized
regulation of the various forms of EVs in
distribution systems with different load models can
be implemented in the future.
Dynamic load modes, as well as static load modes,
can be used by optimal alignment, dimensioning
and properly organized control.
In the future, the environmental effect of the
various forms of EVs in distribution networks with
different load models which be accomplished by
optimum positioning, dimensioning and properly
organized regulation.
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