Electric Vehicle Charging Pile Sharing Method Based on Multi-Subject Game and Win-Win
Huang Xiaoqing1, Li Longyi1, Xu Pengxin1, Wang Xiuru2, Han Shaohua2
1. College of Electrical and Information Engineering Hunan University Changsha 410082 China; 2. State Grid Suqian Power Supply Company Suqian 223800 China
Abstract:With the rapid growth of electric vehicles (EVs), it brings a series of problems to the configuration of charging facilities: (1) the configuration of charging piles is lagging behind the development speed of vehicles seriously; (2) unreasonable distribution of charging piles leads to a large number of idle piles; (3) the huge charging demand has impact on the operation of the power grid. For these problems, charging pile sharing is a feasible solution to alleviate the imbalance of vehicle-pile configuration. The four subjects of EV users, aggregators, charging piles and distribution network participate in shared charging activities. However, researches on this are limited to the single subject optimization, ignoring the master-slave game competition between EV users and distribution network. Towards the win-win goal of distribution network, EV users, charging piles and aggregators, a master-slave game model of charging pile sharing and matching is established. The model can be divided into three stages: in the first stage, the distribution network updates the access load of each distribution network node, takes the minimum operation cost of distribution network as the optimization objective, calculates the locational marginal price (LMP), and sends it to the aggregators and charging piles. In the second stage, the aggregators and the charging piles play a one-to-one pricing game based on the LMP, and the Bayesian Nash equilibrium solution of the game is taken as the final transaction price. In the third stage, the shared matching center aims at maximizing the number of successful matching pairs of vehicle-piles and optimizing the total charging cost of EV users, builds a one-to-many vehicle pile matching model based on the minimum cost and maximum flow model, solves the model to generate a vehicle-pile matching list, calculates the power vector of each distribution network node accessing EV load, and returns to the first stage. Simulation results the charge sharing data show that, the number of EV charges successfully matched by the experimental group using the proposed method is 5 195, which is 547, 191 and 0 more than other control groups respectively. In the control group, the standard deviation of the average frequency of charging piles used by aggregators is 2.24, 1.97, 1.17, respectively, compared with 1.09 in the experimental group. The result shows that the strategy can greatly increase the number of successful matching of shared charging piles, make the utilization ratio of each charging pile more balanced at different times, and avoid the situation that some charging piles are frequently used while some charging piles are idle. The study shows that the proposed charging pile sharing strategy can increase the revenue of charging piles and aggregators, reduce the cost of EV users. It is also helpful to reduce network loss and voltage deviation of distribution network, which is beneficial to all parties. Compared with the traditional method, the proposed bilevel iterative algorithm improves the solution efficiency. In large-scale EV matching problems, this method still has high efficiency.
黄小庆, 李隆意, 徐鹏鑫, 王秀茹, 韩少华. 多主体博弈共赢的电动汽车充电桩共享方法[J]. 电工技术学报, 2023, 38(11): 2945-2961.
Huang Xiaoqing, Li Longyi, Xu Pengxin, Wang Xiuru, Han Shaohua. Electric Vehicle Charging Pile Sharing Method Based on Multi-Subject Game and Win-Win. Transactions of China Electrotechnical Society, 2023, 38(11): 2945-2961.
[1] 杨镜司, 秦文萍, 史文龙, 等. 基于电动汽车参与调峰定价策略的区域电网两阶段优化调度[J]. 电工技术学报, 2022, 37(1): 58-71. Yang Jingsi, Qin Wenping, Shi Wenlong, et al.Two-stage optimal dispatching of regional power grid based on electric vehicles' participation in peak-shaving pricing strategy[J]. Transactions of China Electrotechnical Society, 2022, 37(1): 58-71. [2] Aiso K, Akatsu K.High speed SRM using vector control for electric vehicle[J]. CES Transactions on Electrical Machines and Systems, 2020, 4(1): 61-68. [3] Mobarak M H, Bauman J.Vehicle-directed smart charging strategies to mitigate the effect of long-range EV charging on distribution transformer aging[J]. IEEE Transactions on Transportation Electrification, 2019, 5(4): 1097-1111. [4] Chen Jie, Huang Xiaoqing, Cao Yijia, et al.Electric vehicle charging schedule considering shared charging pile based on Generalized Nash Game[J]. International Journal of Electrical Power & Energy Systems, 2022, 136: 107579. [5] 许刚, 张丙旭, 张广超. 电动汽车集群并网的分布式鲁棒优化调度模型[J]. 电工技术学报, 2021, 36(3): 565-578. Xu Gang, Zhang Bingxu, Zhang Guangchao.Distributed and robust optimal scheduling model for large-scale electric vehicles connected to grid[J]. Transactions of China Electrotechnical Society, 2021, 36(3): 565-578. [6] Li Ruoyang, Wu Qiuwei, Oren S S.Distribution locational marginal pricing for optimal electric vehicle charging management[J]. IEEE Transactions on Power Systems, 2014, 29(1): 203-211. [7] Tan Jun, Wang Lingfeng.Real-time charging navigation of electric vehicles to fast charging stations: a hierarchical game approach[J]. IEEE Transactions on Smart Grid, 2017, 8(2): 846-856. [8] Lu Zhigang, Shi Lina, Geng Lijun, et al.Non-cooperative game pricing strategy for maximizing social welfare in electrified transportation networks[J]. International Journal of Electrical Power & Energy Systems, 2021, 130: 106980. [9] Li Xuecheng, Xiang Yue, Lü Lin, et al.Price incentive-based charging navigation strategy for electric vehicles[J]. IEEE Transactions on Industry Applications, 2020, 56(5): 5762-5774. [10] Zhang Yongmin, You Pengcheng, Cai Lin.Optimal charging scheduling by pricing for EV charging station with dual charging modes[J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 20(9): 3386-3396. [11] Liu Zhaoxi, Wu Qiuwei, Huang Shaojun, et al.Optimal day-ahead charging scheduling of electric vehicles through an aggregative game model[J]. IEEE Transactions on Smart Grid, 2018, 9(5): 5173-5184. [12] 史一炜, 冯冬涵, Zhou E, 等. 基于主从博弈的充电服务商充电引导方法及其定价策略[J]. 电工技术学报, 2019, 34(增刊2): 742-751. Shi Yiwei, Feng Donghan, Zhou E, et al.Stackelberg game based on supervised charging method and pricing strategy of charging service providers[J]. Transactions of China Electrotechnical Society, 2019, 34(S2): 742-751. [13] Li Hepeng, Wan Zhiqiang, He Haibo.Constrained EV charging scheduling based on safe deep reinforcement learning[J]. IEEE Transactions on Smart Grid, 2020, 11(3): 2427-2439. [14] Zhao Zhenli, Zhang Lihui, Zhu Jinrong, et al.Pricing of private charge sharing service based on bilateral bargaining game[J]. Sustainable Cities and Society, 2020, 59: 102179. [15] Hu Zechun, Zhan Kaiqiao, Zhang Hongcai, et al.Pricing mechanisms design for guiding electric vehicle charging to fill load valley[J]. Applied Energy, 2016, 178: 155-163. [16] Bai Linquan, Wang Jianhui, Wang Chengshan, et al.Distribution locational marginal pricing (DLMP) for congestion management and voltage support[J]. IEEE Transactions on Power Systems, 2018, 33(4): 4061-4073. [17] 郑重, 苗世洪, 李超, 等. 面向微型能源互联网接入的交直流配电网协同优化调度策略[J]. 电工技术学报, 2022, 37(1): 192-207. Zheng Zhong, Miao Shihong, Li Chao, et al.Coordinated optimal dispatching strategy of AC/DC distribution network for the integration of micro energy Internet[J]. Transactions of China Electrotechnical Society, 2022, 37(1): 192-207. [18] Wei Wei, Wu Lei, Wang Jianhui, et al.Network equilibrium of coupled transportation and power distribution systems[J]. IEEE Transactions on Smart Grid, 2018, 9(6): 6764-6779. [19] Farivar M, Low S H.Branch flow model: relaxations and convexification—part I[J]. IEEE Transactions on Power Systems, 2013, 28(3): 2554-2564. [20] 谢仕炜, 林伟伟, 张亚超. 基于变分不等式理论的电力-交通耦合网络均衡状态研究[J]. 中国电机工程学报, 2022, 42(17): 6220-6239. Xie Shiwei, Lin Weiwei, Zhang Yachao.Research on coupled power-transportation network equilibrium state based on variational inequality theory[J]. Proceedings of the CSEE, 2022, 42(17): 6220-6239. [21] Shi Xiaoying, Xu Yinliang, Guo Qinglai, et al.A distributed EV navigation strategy considering the interaction between power system and traffic network[J]. IEEE Transactions on Smart Grid, 2020, 11(4): 3545-3557. [22] Tushar W, Chai Bo, Yuen C, et al.Energy storage sharing in smart grid: a modified auction-based approach[J]. IEEE Transactions on Smart Grid, 2016, 7(3): 1462-1475. [23] He Li, Zhang Jie.A community sharing market with PV and energy storage: an adaptive bidding-based double-side auction mechanism[J]. IEEE Transactions on Smart Grid, 2021, 12(3): 2450-2461. [24] 熊宇峰, 司杨, 郑天文, 等. 基于主从博弈的工业园区综合能源系统氢储能优化配置[J]. 电工技术学报, 2021, 36(3): 507-516. Xiong Yufeng, Si Yang, Zheng Tianwen, et al.Optimal configuration of hydrogen storage in industrial park integrated energy system based on stackelberg game[J]. Transactions of China Electrotechnical Society, 2021, 36(3): 507-516. [25] 王昀, 谢海鹏, 孙啸天, 等. 计及激励型综合需求响应的电-热综合能源系统日前经济调度[J]. 电工技术学报, 2021, 36(9): 1926-1934. Wang Yun, Xie Haipeng, Sun Xiaotian, et al.Day-ahead economic dispatch for electricity-heating integrated energy system considering incentive integrated demand response[J]. Transactions of China Electrotechnical Society, 2021, 36(9): 1926-1934. [26] 詹祥澎, 杨军, 吴赋章, 等. 基于去中心化交易模式的充电站日前购电策略[J]. 电力系统自动化, 2019, 43(24): 23-31, 49. Zhan Xiangpeng, Yang Jun, Wu Fuzhang, et al.Day-ahead electricity purchasing strategy of charging station based on decentralized trading mode[J]. Automation of Electric Power Systems, 2019, 43(24): 23-31, 49. [27] 谢仕炜, 胡志坚, 王珏莹. 考虑时-空耦合的城市电力-交通网络动态流量均衡[J]. 中国电机工程学报, 2021, 41(24): 8408-8424. Xie Shiwei, Hu Zhijian, Wang Jueying.Dynamic flow equilibrium of urban power and transportation networks considering the coupling in time and space[J]. Proceedings of the CSEE, 2021, 41(24): 8408-8424. [28] 文云峰, 林晓煌. 孤岛与并网模式下微电网最低惯量需求评估[J]. 中国电机工程学报, 2021, 41(6): 2040-2053. Wen Yunfeng, Lin Xiaohuang.Minimum inertia requirement assessment of microgrids in islanded and grid-connected modes[J]. Proceedings of the CSEE, 2021, 41(6): 2040-2053. [29] 李咸善, 方子健, 李飞, 等. 含多微电网租赁共享储能的配电网博弈优化调度[J]. 中国电机工程学报, 2022, 42(18): 6611-6625. Li Xianshan, Fang Zijian, Li Fei, et al.Game-based optimal dispatching strategy for distribution network with multiple microgrids leasing shared energy storage[J]. Proceedings of the CSEE, 2022, 42(18): 6611-6625. [30] 祁兵, 刘思放, 李彬, 等. 共享风险链路组与风险均衡的电力通信网路由优化策略[J]. 电力系统自动化, 2020, 44(8): 168-175. Qi Bing, Liu Sifang, Li Bin, et al.Routing optimization strategy for power communication network with shared risk link group and risk balance[J]. Automation of Electric Power Systems, 2020, 44(8): 168-175. [31] 曾博, 徐富强, 刘一贤, 等. 综合考虑经济-环境-社会因素的多能耦合系统高维多目标规划[J]. 电工技术学报, 2021, 36(7): 1434-1445. Zeng Bo, Xu Fuqiang, Liu Yixian, et al.High-dimensional multiobjective optimization for multi-energy coupled system planning with consideration of economic, environmental and social factors[J]. Transactions of China Electrotechnical Society, 2021, 36(7): 1434-1445. [32] 罗志刚, 韦钢, 朱兰, 等. 含分布式电源的城市配电网交直流改造方案综合决策[J]. 电力系统自动化, 2020, 44(11): 87-94. Luo Zhigang, Wei Gang, Zhu Lan, et al.Comprehensive decision on AC/DC transformation scheme of urban distribution network with distributed generator[J]. Automation of Electric Power Systems, 2020, 44(11): 87-94.