Optimal Operation Method of Energy Storage System in PV-integrated EV Charging Station Applying NSGA-III and Fuzzy Clustering

doi:10.12204/j.issn.1000-7229.2021.03.004

Abstract

Abstract:

To alleviate the problems of high operating cost of PV-integrated EV charging station and large load fluctuation on the grid side, an optimization algorithm combining NSGA-III (nondominated sorting genetic algorithm III) and fuzzy clustering is proposed for optimization operation of energy storage. Firstly, on the basis of analyzing the structure of the charging station, a multi-objective operation model of the energy storage is established to minimize the load variance of grid side, the operation and maintenance cost of the energy storage, and the purchase cost from the grid. Then, NSGA-III algorithm is applied to solve multi-objective model. For the Pareto sets containing a lot of information, it is difficult for the operators to make a decision. A method based on fuzzy clustering is proposed to select the optimal solution from Pareto sets. Finally, extensive experimental analyses demonstrate the efficiency of the proposed method, and show that, compared with particle swarm optimization, the economy of the system and the grid-side load level are improved on the basis of the load demand, so that the overall performance of the charging station is optimal comprehensively.

Key words: PV-integrated EV charging station, energy storage system, optimization operation, NSGA-III, fuzzy clustering

CLC Number:

TM732

FU Zhangjie, WANG Yufei, XUE Hua, ZHANG Yu, SHI Shanshan, FANG Chen. Optimal Operation Method of Energy Storage System in PV-integrated EV Charging Station Applying NSGA-III and Fuzzy Clustering[J]. ELECTRIC POWER CONSTRUCTION, 2021, 42(3): 27-34.

Figures/Tables 11

Fig.1 Structure diagram of a charging station

Fig.2 Flow chart of the multi-objective model solving process

Fig.3 Power demand of EVs in a day

Fig.4 Photovoltaic power data in typical day

Fig.5 Pareto solution set

Table 1 Energy storage operation capacity under two algorithms

Table 2 Index opimization results of two algorithms

Fig.6 Power curve of grid under two algorithms

Fig.7 Power curve of energy storage under two algorithms

Table 3 Basic data of different deviation levels

Fig.8 Impact of different deviation levels on operation result

References 19

[1]	中华人民共和国国务院. “十二五”国家战略性新兴产业产业发展规划[EB/OL]. (2012-07-09)[2020-06-12]. http://www.gov.cn/zwgk/2012-07/20/content_2187770.htm.
[2]	徐岩, 何宸, 付超, 等. 多种调度模式下的光储电站经济性最优储能容量配置分析[J]. 太阳能学报, 2019,40(6):1632-1640.
	XU Yan, HE Chen, FU Chao, et al. Analysis of energy storage capacity allocation considering optimal economy of PV & battery cogeneration system under different scheduling modes[J]. Acta Energiae Solaris Sinica, 2019,40(6):1632-1640.
[3]	郭一飞, 高厚磊, 田佳. 引入聚类分析的光伏出力建模及其在可靠性评估中的应用[J]. 电力系统自动化, 2016,40(23):93-100.
	GUO Yifei, GAO Houlei, TIAN Jia. Photovoltaic output modeling by introducing clustering analysis and its application in reliability evaluation[J]. Automation of Electric Power Systems, 2016,40(23):93-100.
[4]	陈中, 陈妍希, 车松阳. 新能源汽车一体充能站框架及能量优化调度方法[J]. 电力系统自动化, 2019,43(24):41-51.
	CHEN Zhong, CHEN Yanxi, CHE Songyang. Framework of integrated charging station for renewable energy vehicle and energy optimal dispatching method[J]. Automation of Electric Power Systems, 2019,43(24):41-51.
[5]	陈健, 王成山, 赵波, 等. 考虑储能系统特性的独立微电网系统经济运行优化[J]. 电力系统自动化, 2012,36(20):25-31.
	CHEN Jian, WANG Chengshan, ZHAO Bo, et al. Economic operation optimization of a stand-alone microgrid system considering characteristics of energy storage system[J]. Automation of Electric Power Systems, 2012,36(20):25-31.
[6]	王守相, 张善涛, 王凯, 等. 计及分时电价下用户需求响应的分布式储能多目标优化运行[J]. 电力自动化设备, 2020,40(1):125-132.
	WANG Shouxiang, ZHANG Shantao, WANG Kai, et al. Multi-objective optimal operation of distributed energy storage considering user demand response under time-of-use price[J]. Electric Power Automation Equipment, 2020,40(1):125-132.
[7]	章美丹, 宋晓喆, 辛焕海, 等. 计及网损的配电网电池储能站优化运行策略[J]. 电网技术, 2013,37(8):2123-2128.
	ZHANG Meidan, SONG Xiaozhe, XIN Huanhai, et al. Optimal operation strategy of battery energy storage system in distribution networks with consideration of power losses[J]. Power System Technology, 2013,37(8):2123-2128.
[8]	邱晓燕, 沙熠, 宁雪姣, 等. 计及智能电网多不确定性的多目标储能优化[J]. 高电压技术, 2016,42(9):2715-2722.
	QIU Xiaoyan, SHA Yi, NING Xuejiao, et al. Multi-objective optimization of stored energy considering multi-uncertainty in smart grid[J]. High Voltage Engineering, 2016,42(9):2715-2722.
[9]	杨晓萍, 刘浩杰, 黄强. 考虑分时电价的风光储联合“削峰”优化调度模型[J]. 太阳能学报, 2018,39(6):1752-1760.
	YANG Xiaoping, LIU Haojie, HUANG Qiang. Optimal dispatching model of wind-sunlight-storage combining with“peak shaving”considering time-of-use electricity price[J]. Acta Energiae Solaris Sinica, 2018,39(6):1752-1760.
[10]	尤毅, 刘东, 钟清, 等. 主动配电网储能系统的多目标优化配置[J]. 电力系统自动化, 2014,38(18):46-52. doi: <a href='http://dx.doi.org/10.7500/AEPS20130722009'>10.7500/AEPS20130722009</a> URL
	YOU Yi, LIU Dong, ZHONG Qing, et al. Multi-objective optimal placement of energy storage systems in an active distribution network[J]. Automation of Electric Power Systems, 2014,38(18):46-52. doi: <a href='http://dx.doi.org/10.7500/AEPS20130722009'>10.7500/AEPS20130722009</a> URL
[11]	GUPTA R, NANDA S J. Many-objective B/NSGA-Ⅲ for band selection in cloud contaminated hyper-spectral images[C]// 2019 International Conference on Information Technology (ICIT). Bhubaneswar,India, 2019:348-352.
[12]	ZHANG Z Z, LEE C Y. Multiobjective approaches for the ship stowage planning problem considering ship stability and container rehandles[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2016,46(10):1374-1389. doi: 10.1109/TSMC.2015.2504104 URL
[13]	刘嘉蔚, 李奇, 陈维荣, 等. 基于多分类相关向量机和模糊C均值聚类的有轨电车用燃料电池系统故障诊断方法[J]. 中国电机工程学报, 2018,38(20):6045-6052.
	LIU Jiawei, LI Qi, CHEN Weirong, et al. A fault diagnosis method of fuel cell systems for tramways based on the multi-class relevance vector machine and fuzzy C means clustering[J]. Proceedings of the CSEE, 2018,38(20):6045-6052.
[14]	杨冰, 王丽芳, 廖承林. 大规模电动汽车充电需求及影响因素[J]. 电工技术学报, 2013,28(2):22-27, 35.
	YANG Bing, WANG Lifang, LIAO Chenglin. Research on power-charging demand of large-scale electric vehicles and its impacting factors[J]. Transactions of China Electrotechnical Society, 2013,28(2):22-27, 35.
[15]	陈丽丹, 聂涌泉, 钟庆. 基于出行链的电动汽车充电负荷预测模型[J]. 电工技术学报, 2015,30(4):216-225.
	CHEN Lidan, NIE Yongquan, ZHONG Qing. A model for electric vehicle charging load forecasting based on trip chains[J]. Transactions of China Electrotechnical Society, 2015,30(4):216-225.
[16]	WIRASINGHA S G, SCHOFIELD N, EMADI A. Plug-in hybrid electric vehicle developments in the US: Trends, barriers, and economic feasibility[C]// 2008 IEEE Vehicle Power and Propulsion Conference. Harbin:IEEE, 2008.
[17]	谭洋洋, 杨洪耕, 徐方维, 等. 基于投资收益与用户效用耦合决策的电动汽车充电站优化配置[J]. 中国电机工程学报, 2017,37(20):5951-5960.
	TAN Yangyang, YANG Honggeng, XU Fangwei, et al. Optimal allocation of charging station for electric vehicle based on coupled decision of investment benefit and user utility[J]. Proceedings of the CSEE, 2017,37(20):5951-5960.
[18]	罗毅, 李昱龙. 基于熵权法和灰色关联分析法的输电网规划方案综合决策[J]. 电网技术, 2013,37(1):77-81.
	LUO Yi, LI Yulong. Comprehensive decision-making of transmission network planning based on entropy weight and grey relational analysis[J]. Power System Technology, 2013,37(1):77-81.
[19]	上海市电力公司. 上海市电网销售电价[EB/OL].(2017-05-15)[2020-06-12]. http://sh.bendibao.com/cyfw/2015526/132966.shtm.