Skip to main content

Advertisement

SpringerLink
Log in

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

  • Original Paper
  • Published:
Archives of Computational Methods in Engineering Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

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

References

  1. Deb S, Tammi K, Kalita K, Mahanta P (2018) Impact of electric vehicle charging station load on distribution network. Energies 11(1):178

    Google Scholar 

  2. Clement N, Kristien EH, Johan D (2010) The impact of charging plug-in hybrid electric vehicles on a residential distribution grid. IEEE Trans Power Syst 25(1):371–380

    Google Scholar 

  3. Dubey A, Surya S (2015) Electric vehicle charging on residential distribution systems: impacts and mitigations. IEEE Access 3:1871–1893

    Google Scholar 

  4. Deb S, Kalita K, Mahanta P (2017) Review of impact of electric vehicle charging station on the power grid. In: 2017 international conference on technological advancements in power and energy (TAP Energy). IEEE

  5. Kongjeen Y, Bhumkittipich K (2018) Impact of plug-in electric vehicles integrated into power distribution system based on voltage-dependent power flow analysis. Energies 11(6):1–16

    Google Scholar 

  6. Deb S, Kalita K, Mahanta P (2017) Impact of electric vehicle charging stations on reliability of distribution network. In: 2017 international conference on technological advancements in power and energy (TAP Energy). IEEE

  7. Deb S, Tammi K, Kalita K, Mahanta P (2018) Review of recent trends in charging infrastructure planning for electric vehicles. In: Wiley Interdisciplinary Reviews: Energy and Environment, p e306

  8. Rahman I, Vasant PM, Singh BSM, Abdullah-Al-Wadud M, Adnan N (2016) Review of recent trends in optimization techniques for plug-in hybrid, and electric vehicle charging infrastructures. Renew Sustain Energy Rev 58:1039–1047

    Google Scholar 

  9. Zhang H, Hu Z, Xu Z, Song Y (2017) Optimal planning of PEV charging station with single output multiple cables charging spots. IEEE Trans Smart Grid 8(5):2119–2128

    Google Scholar 

  10. Zhang H, Moura SJ, Hu Z, Qi W, Song Y (2018) A second-order cone programming model for planning PEV fast-charging stations. IEEE Trans Power Syst 33(3):2763–2777

    Google Scholar 

  11. Martínez-Lao J, Montoya FG, Montoya MG, Manzano-Agugliaro F (2017) Electric vehicles in Spain: an overview of charging systems. Renew Sustain Energy Rev 77:970–983

    Google Scholar 

  12. Shareef H, Islam MM, Mohamed A (2016) A review of the stage-of-the-art charging technologies, placement methodologies, and impacts of electric vehicles. Renew Sustain Energy Rev 64:403–420

    Google Scholar 

  13. Islam MM, Shareef H, Mohamed A (2015) A review of techniques for optimal placement and sizing of electric vehicle charging stations. Przegląd Elektrotechniczny 8:122–126

    Google Scholar 

  14. Amjad M, Ahmad A, Rehmani MH, Umer T (2018) A review of EVs charging: from the perspective of energy optimization, optimization approaches, and charging techniques. Transp Res Part D Transp Environ 62:386–417

    Google Scholar 

  15. Fister Jr I, Yang XS, Fister I, Brest J, Fister D (2013) A brief review of nature-inspired algorithms for optimization. arXiv preprint arXiv:1307.4186

  16. Dhal KG, Ray S, Das A, Das S (2019) A survey on nature-inspired optimization algorithms and their application in image enhancement domain. Arch Comput Methods Eng 26:1–32

    MathSciNet  Google Scholar 

  17. Vikhar PA (2016) Evolutionary algorithms: a critical review and its future prospects. In: International conference on global trends in signal processing, information computing and communication (ICGTSPICC), 2016, pp 261–265. IEEE

  18. Bäck T, Schwefel HP (1993) An overview of evolutionary algorithms for parameter optimization. Evol Comput 1(1):1–23

    Google Scholar 

  19. Goldberg DE, Holland JH (1988) Genetic algorithms and machine learning. Mach Learn 3(2):95–99

    Google Scholar 

  20. Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359

    MathSciNet  MATH  Google Scholar 

  21. Rechenberg I (1978) Evolutionsstrategien. In: Simulationsmethoden in der Medizin und Biologie. Springer, Berlin, pp 83–114

  22. Beyer HG, Schwefel HP (2002) Evolution strategies–a comprehensive introduction. Nat Comput 1(1):3–52

    MathSciNet  MATH  Google Scholar 

  23. Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the sixth international symposium on micro machine and human science, 1995. MHS’95. IEEE, pp 39–43

  24. Meng XB, Liu Y, Gao X, Zhang H (2014) A new bio-inspired algorithm: chicken swarm optimization. In: International conference in swarm intelligence. Springer, Cham, pp 86–94

  25. Meng XB, Gao XZ, Lu L, Liu Y, Zhang H (2016) A new bio-inspired optimisation algorithm: bird Swarm Algorithm. J Exp Theor Artif Intell 28(4):673–687

    Google Scholar 

  26. Colorni A, Dorigo M, Maniezzo V (1992) Distributed optimization by ant colonies. In: Toward a practice of autonomous systems: proceedings of the first European conference on artificial life. MIT Press, p 134

  27. Wang GG, Deb S, Coelho LDS (2015) Elephant herding optimization. In: 3rd international symposium on computational and business intelligence (ISCBI), 2015, pp 1–5. IEEE

  28. Yang XS (2010) Firefly algorithm, stochastic test functions and design optimisation. Int J Bio Inspir Comput 2(2):78–84

    Google Scholar 

  29. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61

    Google Scholar 

  30. Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67

    Google Scholar 

  31. Yang XS, Deb S (2009) Cuckoo search via Le´vy flights. In: World Congress on Nature and Biologically Inspired Computing, 2009. NaBIC 2009. IEEE, pp 210–214

  32. Karaboga D, Basturk B (2008) On the performance of artificial bee colony (ABC) algorithm. Appl Soft Comput 8(1):687–697

    Google Scholar 

  33. Yang XS (2012) Flower pollination algorithm for global optimization. In: International conference on unconventional computing and natural computation. Springer, Berlin, pp 240–249

  34. Dhiman G, Kaur A (2017) Spotted hyena optimizer for solving engineering design problems. In: International conference on machine learning and data science (MLDS), 2017, pp 114–119. IEEE

  35. Cheng MY, Prayogo D (2014) Symbiotic organisms search: a new metaheuristic optimization algorithm. Comput Struc 139:98–112

    Google Scholar 

  36. Shareef H, Ibrahim AA, Mutlag AH (2015) Lightning search algorithm. Appl Soft Comput 36:315–333

    Google Scholar 

  37. Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232–2248

    MATH  Google Scholar 

  38. Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220(4598):671–680

    MathSciNet  MATH  Google Scholar 

  39. Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2):60–68

    Google Scholar 

  40. Biyanto TR, Syamsi MN, Fibrianto HY, Afdanny N, Rahman AH, Gunawan KS, Pratama JA, Malwindasari A, Abdillah AI, Bethiana TN, Putra YA (2017) Optimization of energy efficiency and conservation in green building design using duelist, killer-whale and rain-water algorithms. In: IOP conference series: materials science and engineering, vol 267, no. 1. IOP Publishing, p 012036

  41. Rao RV, Kalyankar VD (2011) Parameters optimization of advanced machining processes using TLBO algorithm, vol 20. EPPM, Singapore

  42. Rao R (2016) Jaya: a simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int J Ind Eng Comput 7(1):19–34

    Google Scholar 

  43. Punnathanam V, Kotecha P (2016) Yin-Yang-pair optimization: a novel lightweight optimization algorithm. Eng Appl Artif Intell 54:62–79

    Google Scholar 

  44. Abd-El-Wahed WF, Mousa AA, El-Shorbagy MA (2011) Integrating particle swarm optimization with genetic algorithms for solving nonlinear optimization problems. J Comput Appl Math 235(5):1446–1453

    MathSciNet  MATH  Google Scholar 

  45. Deb S, Kalita K, Gao XZ, Tammi K, Mahanta P (2017) Optimal placement of charging stations using CSO-TLBO algorithm. In: Third international conference on research in computational intelligence and communication networks (ICRCICN), 2017, pp 84–89. IEEE

  46. Tuo S, Yong L, Li Y, Lin Y, Lu Q (2017) HSTLBO: a hybrid algorithm based on harmony search and teaching-learning-based optimization for complex high-dimensional optimization problems. PLoS ONE 12(4):e0175114

    Google Scholar 

  47. Mirjalili S, Wang GG, Coelho LDS (2014) Binary optimization using hybrid particle swarm optimization and gravitational search algorithm. Neural Comput Appl 25(6):1423–1435

    Google Scholar 

  48. Yang XS (2017) Social algorithms. In: Meyers RA (ed) Encyclopedia of complexity and systems sci-enc. Springer, Berlin

    Google Scholar 

  49. Ge S, Feng L, Liu H (2011) The planning of electric vehicle charging station based on grid partition method. In: International conference on electrical and control engineering (ICECE), 2011, pp 2726–2730. IEEE

  50. Pazouki S, Mohsenzadeh A, Haghifam MR (2013) Optimal planning of PEVs charging stations and demand response programs considering distribution and traffic networks. In: Smart Grid Conference (SGC), 2013, pp 90–95. IEEE

  51. Mohsenzadeh A, Pang C, Pazouki S, Haghifam M (2015) Optimal siting and sizing of electric vehicle public charging stations considering smart distribution network reliability. In: North American Power Symposium (NAPS), 2015, pp 1–6. IEEE

  52. Pazouki S, Mohsenzadeh A, Haghifam MR, Ardalan S (2015) Simultaneous allocation of charging stations and capacitors in distribution networks improving voltage and power loss. Can J Electr Comput Eng 38(2):100–105

    Google Scholar 

  53. Mohsenzadeh A, Pazouki S, Ardalan S, Haghifam MR (2018) Optimal placing and sizing of parking lots including different levels of charging stations in electric distribution networks. Int J Ambient Energy 39(7):743–750

    Google Scholar 

  54. Shojaabadi S, Abapour S, Abapour M, Nahavandi A (2016) Optimal planning of plug-in hybrid electric vehicle charging station in distribution network considering demand response programs and uncertainties. IET Gener Transm Distrib 10(13):3330–3340

    Google Scholar 

  55. Wang S, Xu Y, Dong ZY, Zhao J, Yao W, Luo F, Wang Y (2016) A stochastic collaborative planning approach for electric vehicle charging stations and power distribution system. In: Power and Energy Society General Meeting (PESGM), 2016, pp 1–5. IEEE

  56. Islam MM, Mohamed A, Shareef H (2015) Optimal allocation of rapid charging stations for electric vehicles. In: IEEE student conference on research and development (SCOReD), 2015, pp 378–383. IEEE

  57. Deb S, Gao XZ, Tammi K, Kalita K, Mahanta A (xxxx) Pareto dominance based multi-objective chicken swarm optimization and teaching learning based optimization algorithm for charging station placement problem. Swarm and Evolutionary Computation, Elsevier (under review)

  58. Phonrattanasak P, Leeprechanon N (2014) Optimal placement of EV fast charging stations considering the impact on electrical distribution and traffic condition. In: International conference on and utility exhibition on green energy for sustainable development (ICVE), Pattaya, pp 1–6

  59. Liu ZF, Zhang W, Ji X, Li K (2012) Optimal planning of charging station for electric vehicle based on particle swarm optimization. In: Innovative smart grid technologies-Asia (ISGT Asia), 2012 IEEE, pp 1–5

  60. Lin W, Hua G (2015) The flow capturing location model and algorithm of electric vehicle charging stations. In: International conference on logistics, informatics and service sciences (LISS), 2015, pp 1–6. IEEE

  61. Chen YW, Cheng CY, Li SF, Yu CH (2018) Location optimization for multiple types of charging stations for electric scooters. Appl Soft Comput 67:519–528

    Google Scholar 

  62. Islam MM, Shareef H, Mohamed A (2018) Optimal location and sizing of fast charging stations for electric vehicles by incorporating traffic and power networks. IET Intel Transport Syst 12(8):947–957

    Google Scholar 

  63. Islam MM, Shareef H, Mohamed A (2017) Improved approach for electric vehicle rapid charging station placement and sizing using Google maps and binary lightning search algorithm. PLoS ONE 12(12):e0189170

    Google Scholar 

  64. Aljanad A, Mohamed A, Shareef H, Khatib T (2018) A novel method for optimal placement of vehicle-to-grid charging stations in distribution power system using a quantum binary lightning search algorithm. Sustain Cities Soc 38:174–183

    Google Scholar 

  65. Islam M, Shareef H, Mohamed A (2016) Optimal siting and sizing of rapid charging station for electric vehicles considering Bangi city road network in Malaysia. Turk J Electr Eng Comput Sci 24:5

    Google Scholar 

  66. Awasthi A, Venkitusamy K, Padmanaban S, Selvamuthukumaran R, Blaabjerg F, Singh AK (2017) Optimal planning of electric vehicle charging station at the distribution system using hybrid optimization algorithm. Energy 133:70–78

    Google Scholar 

  67. Ho YC, Pepyne DL (2002) Simple explanation of the no-free-lunch theorem and its implications. Optim Theory Appl 115(3):549–570

    MathSciNet  MATH  Google Scholar 

Download references

Funding

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

Author information

Authors and Affiliations

  1. Centre of Energy, Indian Institute of Technology, Guwahati, India

    Sanchari Deb

  2. School of Computing, University of Eastern Finland, Kuopio, Finland

    Xiao-Zhi Gao

  3. Department of Mechanical Engineering, Aalto University, Espoo, Finland

    Kari Tammi

  4. Department of Mechanical Engineering, Indian Institute of Technology, Guwahati, India

    Karuna Kalita & Pinakeswar Mahanta

  5. Department of Mechanical Engineering, National Institute of Technology, Yupia, India

    Pinakeswar Mahanta

Authors
  1. Sanchari Deb
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiao-Zhi Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kari Tammi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Karuna Kalita
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Pinakeswar Mahanta
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sanchari Deb.

Ethics declarations

Conflict of interest

We have no conflict of interest with this research article.

Human and Animal Rights

We use no animal in this research.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11831-019-09374-4

Search

Navigation