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

  • Sanchari DebEmail author
  • Xiao-Zhi Gao
  • Kari Tammi
  • Karuna Kalita
  • Pinakeswar Mahanta
Original Paper
  • 53 Downloads

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.

Nomenclature

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

Decision Variables

b

Bus number where charging station is to be placed

NFb

Number of fast charging station at bus b

NSb

Number of slow charging station at bus b

Constant Parameters

nfast CS

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

nslow CS

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

Smin

Lower bound of reactive power limit of each bus

Smax

Upper bound of reactive power limit of each bus

Lmax

Loading margin of the network

Vbase

Base voltage

n

Total number of buses of the distribution network

w1

Weight assigned to V

w2

Weight assigned to R

w21

Weight assigned to SAIFI

w22

Weight assigned to SAIDI

w23

Weight assigned to CAIDI

w3

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

Functions

Cinstallation

Installation cost of charging station

Coperation

Operating cost of charging station

Cpenalty

Penalty paid by utility

Ctravel

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

VD

Voltage Deviation

CIR

Composite Reliability Index

Variables

Vibase

Voltage of ith bus for base case

Vi

Voltage of ith bus after placement of charging station

VDi

Voltage Deviation of ith bus

Li

Load at ith bus

Pgi

Active power generation of ith bus

Pdi

Active power demand of ith bus

Qgi

Reactive power generation of ith bus

Qdi

Reactive power demand of ith bus

Vj

Voltage of jth bus

Yij

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

VSIl

VSI after after the placement of EV charging stations

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

Power loss after the placement of EV charging stations

SAIFIl

SAIFI after the placement of charging stations in the distribution network

SAIDIl

SAIDI after the placement of charging stations in the distribution network

CAIDIl

CAIDI after the placement of charging stations in the distribution network

NIO Parameters

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

Tk

Teacher

mk

mean value of decision variable

Rt

Random number between 0 and 2

gen

Maximum generation

INV

positive constant to introduce the frequency of CSO

t

Current iteration count

Notes

Funding

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

Compliance with Ethical Standards

Conflict of interest

We have no conflict of interest with this research article.

Human and Animal Rights

We use no animal in this research.

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Copyright information

© CIMNE, Barcelona, Spain 2019

Authors and Affiliations

  • Sanchari Deb
    • 1
    Email author
  • Xiao-Zhi Gao
    • 2
  • Kari Tammi
    • 3
  • Karuna Kalita
    • 4
  • Pinakeswar Mahanta
    • 4
    • 5
  1. 1.Centre of EnergyIndian Institute of TechnologyGuwahatiIndia
  2. 2.School of ComputingUniversity of Eastern FinlandKuopioFinland
  3. 3.Department of Mechanical EngineeringAalto UniversityEspooFinland
  4. 4.Department of Mechanical EngineeringIndian Institute of TechnologyGuwahatiIndia
  5. 5.Department of Mechanical EngineeringNational Institute of TechnologyYupiaIndia

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