Using Not-Dominated Sorting Backtracking Search Algorithm for Optimal Power System Planning in the vicinity of the Electric Vehicle Charging Station and Scattered Generation Sources under Uncertainty Conditions

Document Type : Research Paper


1 Faculty of Electrical Engineering, Shahrood University of Technology, Shahrood, Iran

2 Department of Electrical Engineering, Rasht Branch, Islamic Azad University, Rasht, Iran


The cost of electricity generation depends on different parameters and characteristics. These parameters and characteristics are regional characteristics, fuel costs of fossil fuel power plants, government policies, and technological capabilities, which lead to fluctuations in the cost of electricity generation. The production cost has gradually decreased with the rapid progress of technology and gaining more experience in using wind and solar energy. Therefore, the cost of electricity generation per kilowatthour has declined significantly in recent years. On the other hand, the cost of fuel for gas and thermal power plants is increasing with the reduction of oil and gas reserves and the elimination of subsidies for petroleum products. Therefore, it is necessary to replace gas and thermal power plants with wind and photovoltaic power plants in the future. Moreover, using charging stations for electric vehicles as a source of energy exchange in the form of V2G and G2V greatly helps to manage the costs involved. Using electric vehicles reduces fuel costs, maintenance costs, and air pollution allows protecting the environment and reducing respiratory, heart, and lung diseases. In this paper, the optimal planning in the location of the photovoltaic power plant, wind, and charging station in the presence of load uncertainty and electricity price uncertainty are examined using the crowding-based distance index in the edited Not-Dominated Sorting Backtracking Search Algorithm. Examining the results in MATLAB software on the 33-bus IEEE network indicated the high accuracy, speed, and control of the algorithm.


[1] S. M. Alizadeh Masoumian, A. Alfi, A. RezaeeJordehi “Optimal placement of charging station and distributed
generation considering electricity price and load uncertainty using NSBSA algorithm” International Journal of
Nonlinear Analysis and Applications, ISI Listed & Scopus Vol: 11(special), 2020, p:211-230.
[2] V. Aravinthan and W. Jewell, ”Controlled Electric Vehicle Charging for Mitigating Impacts on Distribution
Assets,” in IEEE Transactions on Smart Grid, Vol. 6, No. 2, pp. 999-1009, March (2015).
[3] Annual Energy Outlook 2020. British Energy Information Administration; Independent statistics and analysis.London, England 2020.
[4] J. Branke, K. Deb, K. Miettinen and R. Slowinski, Multiobjective Optimization: Interactive and Evolutionary
Approaches. Theorical computer science and general issues, New York, USA: Springer press, (2008).
[5] P. Civicioglu, Backtracking Search Optimization Algorithm for numerical optimization problems. Applied Mathematics and Computation, 219 (2013): 8121–8144. (2013).
[6] M.cruz-Zambrano , et al , Optimal Location of Fast Charging Station in Barcelona : A Flow-Capturing International Conference on the European Approach , 10th Energy Market (EEM), (2013) .
[7] E. De Schepper, S. Van Passel and S. Lizin, Economic and environmental multi-objective optimisation to evaluate
the impact of Belgian policy on solar power and electric vehicles. J Environ Econ Policy 2016, 5: 1–27.
[8] U.S. Energy Information Administration. Primary Energy Consumption by Source and Sector, 2014. U.S. Energy
Information Administration; Washington, DC, USA: (2014).
[9] W. Gong, et all, Multiobjective adaptive surrogate modeling-based optimization for parameter estimation of large,
complex geophysical models, water resources research journal, (2015),
[10] International Energy Agency (IEA) Weo-2016 Special Report Energy and Air Pollution. International Energy
Agency; Paris, France: 2016 p. 266.
[11] L. Jia, et al, A Novel Approach for Urban Electric Vehicle Charging Facility Planning Considering Combination
of Slow and Fast Charging, International Conference on Power System Technology.[12] F.P. Perera, Multiple threats to child health from fossil fuel combustion: Impacts of air pollution and climate
change. Environ. Health Perspect. 125, (2017) 141–148.
[13] F.P. Perera, K. Wheelock, Y. Wang, D. Tang, A.E. Margolis, G. Badia, W. Cowell, R.L. Miller, V. Rauh and S.
Wang, et al. Combined effects of prenatal exposure to polycyclic aromatic hydrocarbons and material hardship
on child adhd behavior problems. Environ. Res. 160, (2018) 506–513.
[14] P. Phonrattanasak and N. Leeprechanan, Multiobjective Ant Colony Optimization for Fast Charging Stations
Planning in Residential Area, Innovation Smart Grid Technologies-Asia (ISGT Asia), ( 2014).
[15] P. Phonrattanasak and N. Leeprechanan, Optimal Placement of EV Fast Charging Station Considering the Impact
on Electrical Distribution and Traffic Condition, International Conference and Utility Exhibition on Green Energy
for Sustainable Development (ICUE), (2014).
[16] Y. Puranik and N.V. Sahinidis, Domain reduction techniques for global NLP and MINLP optimization. Constraints springer Verlag, 22 (2017): 338-376. 10.1007/s10601-016-9267-5.
[17] N. Rastegarfar, et al,”Optimal Placement of Fast th Charging Station in a Typical Microgrid in Iran, 10 International Conference on the European Energy Market (EEM).(2013).
[18] J. Vishnevetsky, D. Tang, H.W. Chang, E.L. Roen, Y. Wang, V. Rauh, S. Wang, R.L. Miller, J. Herbstman and
F.P. Perera, Combined effects of prenatal polycyclic aromatic hydrocarbons and material hardship on child IQ.
Neurotoxicol. Teratol. 49, (2015) 74–80.
[19] World Health Organization Ambient Air Pollution: A Global Assessment of Exposure and Burden of Disease.
(accessed on 7 September 2017); 2016 Available online:
[20] Sh. Ross and R. Weber, Multi-Objective Optimization using Evolutionary Algorithms. West Sussex, England:
Wiley press, (2001).
[21] Transport, European Environment Agency. Available online:
(accessed on 13 August 2020).
[22] University of Washington, Power Systems Test Case Archive. Available online:
edu/research/pstca/ (accessed on 12 June 2017).
[23] O. Veneri and et al , Performance Analysis on a Power Architecture for EV Ultra-Fast Charging Station ,
International Conference on Clean Electrical Power(ICCEP), (2013).
[24] L. Zhang and Y. Li, ”A Game-Theoretic Approach to Optimal Scheduling of Parking-Lot Electric Vehicle Charging,” in IEEE Transactions on Vehicular Technology, Vol. 65, No. 6, (2016) 4068-4078,
Volume 12, Special Issue
December 2021
Pages 161-172
  • Receive Date: 16 June 2020
  • Revise Date: 03 January 2021
  • Accept Date: 26 January 2021