Nature-Inspired Optimization Algorithms Applied for Solving Charging Station Placement Problem: Overview and Comparison

Abstract

The escalated energy demand in conjunction with the global warming and environmental degradation has paved the path of transportation electrification. Electric Vehicles (EVs) need to recharge their batteries after travelling certain distance. Thus, large scale deployment of EVs calls for development of sustainable charging infrastructure. The placement of charging stations is a complex optimization problem involving a number of decision variables, objective functions, and constraints. Placement of charging station mimics a non-convex and non- combinatorial problem involving both transport and distribution network. The complex and non-linear nature of the charging station placement problem has compelled researchers to apply Nature Inspired Optimization (NIO) algorithms for solving the problem. This study aims to review the NIO algorithms applied for solving the charging station placement problem. This work will endow the research community with a systematic review of NIO algorithms for solving charging station placement problem thereby revealing the key features, advantages, and disadvantages of each of these algorithms. Thus, this work will help the researchers in selecting suitable algorithm for solving the charging station placement problem and will serve as a guide for developing efficient algorithms to solve the charging station placement problem.

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Abbreviations

EV:

Electric vehicle

NIO:

Natire inspired optimization

GA:

Genetic algorithm

DE:

Differential evolution

ES:

Evolutionary strategy

PSO:

Particle swarm optimization

CSO:

Chicken swarm optimization

BSA:

Bird swarm algorithm

ACO:

Ant Colony optimization

EHO:

Elephant herding optimization

FA:

Firefly algorithm

GWA:

Grey wolf algorithm

WOA:

Whale optimization algorithm

CS:

Cuckoo search

FPA:

Flower pollination algorithm

SOS:

Symbiotic organisms search

SA:

Simulated annealing

LSA:

Lightning search algorithm

GSA:

Gravitational search algorithm

RWA:

Rain water algorithm

HS:

Harmony search

TLBO:

Teaching learning based optimization

YYPA:

Ying Yang pair algorithm

SHO:

Spotted Hyena optimizer

NFL:

No free lunch theorem

THD:

Total harmonic distortion

b :

Bus number where charging station is to be placed

N Fb :

Number of fast charging station at bus b

N Sb :

Number of slow charging station at bus b

n fast CS :

Maximum number of fast charging stations that can be placed at a particular bus

n slow CS :

Maximum number of fast charging stations that can be placed at a particular bus

S min :

Lower bound of reactive power limit of each bus

S max :

Upper bound of reactive power limit of each bus

L max :

Loading margin of the network

V base :

Base voltage

n :

Total number of buses of the distribution network

w 1 :

Weight assigned to V

w 2 :

Weight assigned to R

w 21 :

Weight assigned to SAIFI

w 22 :

Weight assigned to SAIDI

w 23 :

Weight assigned to CAIDI

w 3 :

Weight assigned to Power loss

\(VSI_{base}\) :

Base value of Voltage Stability Index

\(SAIFI_{base}\) :

Base value of SAIFI

\(SAIDI_{base}\) :

Base value of SAIDI

\(CAIDI_{base}\) :

Base value of CAIDI

\(P_{loss}^{base}\) :

Base value of power loss

C installation :

Installation cost of charging station

C operation :

Operating cost of charging station

C penalty :

Penalty paid by utility

C travel :

Travelling distance cost from point of charging station to point of placement of charging station

VD :

Voltage Deviation

CIR :

Composite Reliability Index

V i base :

Voltage of ith bus for base case

V i :

Voltage of ith bus after placement of charging station

VD i :

Voltage Deviation of ith bus

L i :

Load at ith bus

P gi :

Active power generation of ith bus

P di :

Active power demand of ith bus

Q gi :

Reactive power generation of ith bus

Q di :

Reactive power demand of ith bus

V j :

Voltage of jth bus

Y ij :

Magnitude of (i,j)th term of bus admittance matrix

\(\theta_{ij}\) :

Angle of Yij

\(\delta_{i}\) :

Voltage angle of ith bus

\(\delta_{j}\) :

Voltage angle of jth bus

VSI l :

VSI after after the placement of EV charging stations

\(P_{loss}^{l}\) :

Power loss after the placement of EV charging stations

SAIFI l :

SAIFI after the placement of charging stations in the distribution network

SAIDI l :

SAIDI after the placement of charging stations in the distribution network

CAIDI l :

CAIDI after the placement of charging stations in the distribution network

pbest :

Particle’s best position

gbest :

Swarm’s best position

PN :

Total population

RN :

Set of roosters

HN :

Set of hens

CN :

Population of chicks

MN :

Set of mother hens

T k :

Teacher

m k :

mean value of decision variable

R t :

Random number between 0 and 2

gen :

Maximum generation

INV :

positive constant to introduce the frequency of CSO

t :

Current iteration count

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Funding

The fund was provided by National Natural Science Foundation of China (Grand No. 51875113).

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Correspondence to Sanchari Deb.

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Deb, S., Gao, XZ., Tammi, K. et al. Nature-Inspired Optimization Algorithms Applied for Solving Charging Station Placement Problem: Overview and Comparison. Arch Computat Methods Eng 28, 91–106 (2021). https://doi.org/10.1007/s11831-019-09374-4

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