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Academic Journal of Engineering and Technology Science, 2020, 3(1); doi: 10.25236/AJETS.2020.030105.

Optimization of Hybrid Energy Storage Capacity for Electric Vehicle Photovoltaic Charging Stations based on Multi-Objective Quantum Particle Swarm Optimization

Author(s)

Jicheng Liu1,3, Qiongjie Dai1,2,3,*

Corresponding Author:
Qiongjie Dai
Affiliation(s)

1. School of Economics and Management, North China Electric Power University, Changping, Beijing, China, 102206; ljch@ncepu.edu.cn
2. School of Mathematics and Computer Engineering, Ordos Institute of Technology, Ordos, Inner Mongolia, China, 017000;
3. Beijing Key Laboratory of New Energy and Low-Carbon Development (North China Electric Power University), Changping, Beijing, China, 102206
*Correspondence: daiqiongjie@yeah.net

Abstract

An optimized allocation method of hybrid energy storage capacity has been proposed aimed at the random and intermittent characteristics of photovoltaic power generation in photovoltaic charging stations of electric vehicle. The method aims at maximizing the annual net profit of photovoltaic charging stations of electric vehicles and the photovoltaic consumption rate, and then establishes an optimization allocation model of multi-objective hybrid energy storage capacity. In this paper, an improved MOPSO is used to solve the Pareto solution for optimal allocation of the capacity of a hybrid energy storage system, and an improved TOPSIS algorithm is used to select the optimal solution in the Pareto optimal solution set as well. Through examples, the simulation is performed in two scenarios of single energy storage and hybrid energy storage. The simulation result shows that hybrid energy storage can reduce costs and promote photovoltaic energy consumption. Finally, the results of the improved algorithm are compared with the basic MOPSO, which verifies that the proposed algorithm is more effective and reasonable.

Keywords

Multi-Objective Quantum Particle Swarm, Hybrid Energy Storage Capacity, Electric Vehicle Photovoltaic Charging Stations

Cite This Paper

Jicheng Liu, Qiongjie Dai. Optimization of Hybrid Energy Storage Capacity for Electric Vehicle Photovoltaic Charging Stations based on Multi-Objective Quantum Particle Swarm Optimization. Academic Journal of Engineering and Technology Science (2020) Vol. 3 Issue 1: 26-39. https://doi.org/10.25236/AJETS.2020.030105.

References

[1] Hassan H. Eldeeb, Samy Faddel, Osama A. Mohammed. Multi-Objective Optimization Technique for the Operation of Grid tied PV Powered EV Charging Station. Electric Power Systems Research 2018; 164:201-211. doi.org/10.1016/j.epsr.2018.08.004.
[2] Pinak J. Tulpule, Vincenzo Marano, Stephen Yurkovich, Giorgio Rizzoni. Economic and environmental impacts of a PV powered workplace parking garage charging station.Applied Energy 2013; 108:323-332. doi.org/10.1016/j.apenergy.2013.02.068.
[3] Chen Zheng, Xiao Xiangning, Lu Xinyi, Liu Nian, Zhang Jianhua. Multi-Objective Optimization for capacity configuration of PV-Based Electric Vehicle Charging Stations. Transaction of China Electrotechnical society 2013; 28:238-247.
[4] Lu jinling, Yang Yue, Wang yang, He tongxiang. Copula-based capacity configuration of energy storage system for a PV-assisted electric vehicles charging station. Acta Energiae Solaris Sinica 2016; 37:780-786.
[5] Vinit Kumar, Villuri Ravi Teja, Mukesh Singh, S. Mishra. Energetic, economic and environmental viability of off-grid PV-BESS for charging electric vehicles: Case study of Spain. Sustainable Cities and Society 2018; 37: 519-529. doi.org/10.1016/j.scs.2017.12.009.
[6] Zhixiong Luo, Fang He, Xi Lin, Jianjun Wu, Meng Li. Joint deployment of charging stations and photovoltaic power plants for electric vehicles, Transportation Research Part D: Transport and Environment 2020; 79:1361-9209.doi.org/10.1016/j.trd.2020.102247.
[7] Alan R. Kennedy, Ronald J. Cross, Kenneth W. Muir. Preparation and crystal structure of trans-[NiBr{C6H3-2,6-(CH2PCy2)2}]. Inorganica Chimica Acta 1995;231:195-200.doi.org/10.1016/0020-1693(94)04321-L.
[8] Sun J, Feng Bin, Xu wenbo. A global search strategy of quantum-behaved particle swarm optimization. IEEE Conference on cybernetics and Intelligent Systems. Piscataway, NJ: IEEE Press 2004:111-116.
[9] Li Xueqiang, Liu Hailin. New Evolutionary Algorithm for complicated Multiobjecetive Optimization. Journal of system Simulation 2011; 23:1860-1865.
[8] Li Yangyang, Xiang Rongrong, Jiao Lingcheng, et al. An improved cooperative quantum-based particle swarm optimization. Soft comput 2012; 16:1061-1069.