Abstract：As a new type of load, large-scale electric vehicles (EV) have great potential in participating in the reserve market and realizing load shaving and valley filling. However, due to the uncertainty of the user intention to participate in the regulation of electric vehicles aggregators (EVA), it is difficult to accurately quantify the reserve capacity of EV clusters, which affects the optimal decision-making of aggregators to participate in the market. Aiming at the uncertainty of user intention and the conflict of interests between EVA and EV users, this paper proposed an EVA Stackelberg game optimization dispatch strategy considering user intention. First of all, the anxiety of battery loss and the anxiety of time to flexibly use EV are portrayed respectively through the coefficient of exclusion psychology combined with Exponential function and decision factors combined with EV online time, and the irrational behavior of users in the decision-making process is reflected through the binomial distribution to determine whether a single user is willing to participate in EVA regulation, so as to quantify the intention of EV user clusters to participate in EVA regulation. Secondly, taking into account user intention, an EVA-EV Stackelberg game model is established with the goal of maximizing self-benefits. A probability distribution transformation and feedback loop iteration solution method of the intention model is proposed to achieve rapid convergence of the dual objective game problem, and the optimal reserve compensation price for EVA and the reserve capacity that the EV cluster can provide under its stimulation are obtained. Finally, the effectiveness of the intention representation method and the feasibility of the iterative solution method of the proposed Stackelberg game model are proved through a numerical example. It can be seen that the Stackelberg game model can significantly improve the economy of EVA and EV users, while achieving load peak shaving and valley filling, and promoting the Economic security operation of the grid. From the perspective of time cost and economic cost, the EV user intention model of the anxiety of battery loss and the anxiety of time to flexibly use EV is described, and the user intention evaluation is carried out based on the binomial distribution, which fully considers the uncertainty in the actual decision-making process of EV users. When the EV cluster reaches a certain size, the intention model is stable and feasible. Based on the above intention model, construct a Stackelberg game with EVA as the main body, achieve price incentives for EV users through cyclic iterative feedback, and optimize EV users' intention to make decisions based on price incentives. Balance the conflict of interest between EVA and EV users through real-time information exchange feedback mechanism, and develop the optimal reserve compensation price for EVA, achieving overall benefit balance between the two. The Stackelberg game model is established on the basis of an optimization model guided by the time-of-use electricity prices, which can achieve effective peak shaving and valley filling effects while achieving economic goals, reduce the peak valley difference of power grid load, and is of great significance for maintaining stable operation of the grid.
房宇轩, 胡俊杰, 马文帅. 计及用户意愿的电动汽车聚合商主从博弈优化调度策略[J]. 电工技术学报, 2024, 39(16): 5091-5103.
Fang Yuxuan, Hu Junjie, Ma Wenshuai. Optimal Dispatch Strategy for Electric Vehicle Aggregators Based on Stackelberg Game Theory Considering User Intention. Transactions of China Electrotechnical Society, 2024, 39(16): 5091-5103.
[1] 国家发展改革委等部门关于进一步提升电动汽车充电基础设施服务保障能力的实施意见(发改能源规〔2022〕53号)[J]. 财会学习, 2022(7): I0001-I0002. [2] 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. [3] Yue Huanzhan, Zhang Qian, Zeng Xiaosong, et al.Optimal scheduling strategy of electric vehicle cluster based on index evaluation system[J]. IEEE Transactions on Industry Applications, 2023, 59(1): 1212-1221. [4] 吴巨爱, 薛禹胜, 谢东亮, 等. 电动汽车参与电量市场与备用市场的联合风险调度[J]. 电工技术学报, 2023, 38(23): 6407-6418. Wu Juai, Xue Yusheng, Xie Dongliang, et al.The joint risk dispatch of electric vehicle in day-ahead electricity energy market and reserve market[J]. Transactions of China Electrotechnical Society, 2023, 38(23): 6407-6418. [5] 蔡国伟, 姜雨晴, 黄南天, 等. 电力需求响应机制下基于多主体双层博弈的规模化电动汽车充放电优化调度[J]. 中国电机工程学报, 2023, 43(1): 85-99. Cai Guowei, Jiang Yuqing, Huang Nantian, et al.Large-scale electric vehicles charging and discharging optimization scheduling based on multi-agent two-level game under electricity demand response mechanism[J]. Proceedings of the CSEE, 2023, 43(1): 85-99. [6] 龚诚嘉锐, 林顺富, 边晓燕, 等. 基于多主体主从博弈的负荷聚合商经济优化模型[J]. 电力系统保护与控制, 2022, 50(2): 30-40. Gong Chengjiarui, Lin Shunfu, Bian Xiaoyan, et al.Economic optimization model of a load aggregator based on the multi-agent Stackelberg game[J]. Power System Protection and Control, 2022, 50(2): 30-40. [7] 董运昌, 刘世民, 曲朝阳, 等. 计及用户响应电价关联与多主体共赢的电动汽车充放电定价优化[J]. 电力自动化设备, 2022, 42(7): 134-142. Dong Yunchang, Liu Shimin, Qu Zhaoyang, et al.Charging and discharging pricing optimization of electric vehicles considering correlation of user response to electricity price and win-win results of multi-stakeholder[J]. Electric Power Automation Equipment, 2022, 42(7): 134-142. [8] 黄小庆, 李隆意, 徐鹏鑫, 等. 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, et al.Electric vehicle charging pile sharing method based on multi-subject game and win-win[J]. Transactions of China Electrotechnical Society, 2023, 38(11): 2945-2961. [9] 张潇, 栗然, 马涛, 等. 基于主从博弈和贪心策略的含电动汽车主动配电网优化调度[J]. 电力自动化设备, 2020, 40(4): 103-110. Zhang Xiao, Li Ran, Ma Tao, et al.Stackelberg game and greedy strategy based optimal dispatch of active distribution network with electric vehicles[J]. Electric Power Automation Equipment, 2020, 40(4): 103-110. [10] 章攀钊, 谢丽蓉, 马瑞真, 等. 考虑电动汽车集群可调度能力的多主体两阶段低碳优化运行策略[J]. 电网技术, 2022, 46(12): 4809-4825. Zhang Panzhao, Xie Lirong, Ma Ruizhen, et al.Multi-player two-stage low carbon optimal operation strategy considering electric vehicle cluster schedulability[J]. Power System Technology, 2022, 46(12): 4809-4825. [11] 潘振宁, 余涛, 王克英. 考虑多方主体利益的大规模电动汽车分布式实时协同优化[J]. 中国电机工程学报, 2019, 39(12): 3528-3541. Pan Zhenning, Yu Tao, Wang Keying.Decentralized coordinated dispatch for real-time optimization of massive electric vehicles considering various interests[J]. Proceedings of the CSEE, 2019, 39(12): 3528-3541. [12] 李怡然, 张姝, 肖先勇, 等. V2G模式下计及供需两侧需求的电动汽车充放电调度策略[J]. 电力自动化设备, 2021, 41(3): 129-135, 143. Li Yiran, Zhang Shu, Xiao Xianyong, et al.Charging and discharging scheduling strategy of EVs considering demands of supply side and demand side under V2G mode[J]. Electric Power Automation Equipment, 2021, 41(3): 129-135, 143. [13] 王俊杰, 贾雨龙, 米增强, 等. 基于双重激励机制的电动汽车备用服务策略[J]. 电力系统自动化, 2020, 44(10): 68-76. Wang Junjie, Jia Yulong, Mi Zengqiang, et al.Reserve service strategy of electric vehicles based on double-incentive mechanism[J]. Automation of Electric Power Systems, 2020, 44(10): 68-76. [14] 朱兰, 刘伸, 唐陇军, 等. 充放电不确定性响应建模与电动汽车代理商日前调度策略[J]. 电网技术, 2018, 42(10): 3305-3317. Zhu Lan, Liu Shen, Tang Longjun, et al.Modeling of charging and discharging uncertainty and research on day-ahead dispatching strategy of electric vehicle agents[J]. Power System Technology, 2018, 42(10): 3305-3317. [15] Yuan Haifeng, Lai Xinhui, Wang Yudong, et al.Reserve capacity prediction of electric vehicles for ancillary service market participation[C]//2021 IEEE 2nd China International Youth Conference on Electrical Engineering (CIYCEE), Chengdu, China, 2022: 1-7. [16] 吴赋章, 杨军, 林洋佳, 等. 考虑用户有限理性的电动汽车时空行为特性[J]. 电工技术学报, 2020, 35(7): 1563-1574. Wu Fuzhang, Yang Jun, Lin Yangjia, et al.Research on spatiotemporal behavior of electric vehicles considering the users’ bounded rationality[J]. Transac-tions of China Electrotechnical Society, 2020, 35(7): 1563-1574. [17] 黄瑞锦, 顾高峰. 基于混合Logit模型的电动汽车购买意愿影响因素研究[J]. 交通运输研究, 2021, 7(1): 95-103, 114. Huang Ruijin, Gu Gaofeng.Factors impacting purchasing intentions to electric vehicles based on mixed Logit model[J]. Transport Research, 2021, 7(1): 95-103, 114. [18] 马文帅, 胡俊杰, 房宇轩, 等.电动汽车用户参与调控意愿的多代理表征与可信容量量化[J]. 电力系统自动化, 2023, 47(18): 122-131. Ma Wenshuai, Hu Junjie, Fang Yuxuan, et al.Multi-agent representation for willingness of electric vehicle users to participate in regulation and quantification of reliable reserve capacity[J]. Automation of Electric Power Systems, 2023, 47(18): 122-131. [19] 徐湘楚, 米增强, 詹泽伟, 等. 考虑多重不确定性的电动汽车聚合商参与能量-调频市场的鲁棒优化模型[J]. 电工技术学报, 2023, 38(3): 793-805. Xu Xiangchu, Mi Zengqiang, Zhan Zewei, et al.A robust optimization model for electric vehicle aggregator participation in energy and frequency regulation markets considering multiple uncertainties[J]. Transactions of China Electrotechnical Society, 2023, 38(3): 793-805. [20] 王明深, 穆云飞, 贾宏杰, 等. 考虑用户出行时间和补偿价格的电动汽车集群响应能力评估模型[J]. 华北电力技术, 2017(3): 23-31. Wang Mingshen, Mu Yunfei, Jia Hongjie, et al.Response capacity evaluation model of EV aggregator considering the travelling time and compensation price[J]. North China Electric Power, 2017(3): 23-31. [21] 李瑶虹, 陈良亮, 刘卫东, 等. 基于用户出行链和调控意愿的城市级私家电动汽车调控能力评估[J]. 电力建设, 2021, 42(5): 100-112. Li Yaohong, Chen Liangliang, Liu Weidong, et al.Regulation ability estimation of private EVs at city level considering users’ trip chain and regulation willingness[J]. Electric Power Construction, 2021, 42(5): 100-112. [22] 张谦, 邓小松, 岳焕展, 等. 计及电池寿命损耗的电动汽车参与能量-调频市场协同优化策略[J]. 电工技术学报, 2022, 37(1): 72-81. Zhang Qian, Deng Xiaosong, Yue Huanzhan, et al.Coordinated optimization strategy of electric vehicle cluster participating in energy and frequency regulation markets considering battery lifetime degradation[J]. Transactions of China Electrotechnical Society, 2022, 37(1): 72-81. [23] Yan Linfang, Chen Xia, Zhou Jianyu, et al.Deep reinforcement learning for continuous electric vehicles charging control with dynamic user behaviors[J]. IEEE Transactions on Smart Grid, 2021, 12(6): 5124-5134. [24] 周玮, 徐从明, 杨丹霞, 等. P2P能源共享下考虑意愿动态调整的电动汽车群需求响应策略研究[J]. 中国电机工程学报, 2023, 43(21): 8217-8230. Zhou Wei, Xu Congming, Yang Danxia, et al.Research on demand response strategy of electric vehicles considering dynamic adjustment of willingness under P2P energy sharing[J]. Proceedings of the CSEE, 2023, 43(21): 8217-8230. [25] 周华嫣然, 周羿宏, 胡俊杰, 等. 人工智能技术支撑的集群电动汽车实时优化调度策略[J]. 电网技术, 2021, 45(4): 1446-1459. Zhou Huayanran, Zhou Yihong, Hu Junjie, et al.Real-time optimization scheduling strategy for aggregated electric vehicles supported by artificial intelligence technology[J]. Power System Technology, 2021, 45(4): 1446-1459. [26] 雷敏, 华一飞, 赵洪山, 等. 计及电池寿命的电动汽车参与电网调峰策略[J]. 现代电力, 2020, 37(5): 510-517. Lei Min, Hua Yifei, Zhao Hongshan, et al.Strategy of electric vehicles participating peak load regulation of power grid considering battery life[J]. Modern Electric Power, 2020, 37(5): 510-517. [27] 胡俊杰, 马文帅, 薛禹胜, 等. 基于CPSSE框架的电动汽车聚合商备用容量量化[J]. 电力系统自动化, 2022, 46(18): 46-54. Hu Junjie, Ma Wenshuai, Xue Yusheng, et al.Quantification of reserve capacity provided by electric vehicle aggregator based on framework of cyber-physical-social system in energy[J]. Automation of Electric Power Systems, 2022, 46(18): 46-54. [28] Zheng Yanchong, Yu Hang, Shao Ziyun, et al.Day-ahead bidding strategy for electric vehicle aggregator enabling multiple agent modes in uncertain electricity markets[J]. Applied Energy, 2020, 280: 115977. [29] 张利彪, 周春光, 马铭, 等. 基于粒子群算法求解多目标优化问题[J]. 计算机研究与发展, 2004, 41(7): 1286-1291. Zhang Libiao, Zhou Chunguang, Ma Ming, et al.Solutions of multi-objective optimization problems based on particle swarm optimization[J]. Journal of Computer Research and Development, 2004, 41(7): 1286-1291. [30] Maharjan S, Zhu Quanyan, Zhang Yan, et al.Dependable demand response management in the smart grid: a stackelberg game approach[J]. IEEE Transactions on Smart Grid, 2013, 4(1): 120-132. [31] 刘畅, 刘文霞, 高雪倩, 等. 基于主从博弈的配电网-多综合能源系统协调规划[J]. 电力自动化设备, 2022, 42(6): 45-52. Liu Chang, Liu Wenxia, Gao Xueqian, et al.Coordinative planning of distribution network and multiple integrated energy systems based on Stackelberg game[J]. Electric Power Automation Equipment, 2022, 42(6): 45-52.
2024, Vol. 39 
