车网互动技术激励机制演化分析

doi:10.16183/j.cnki.jsjtu.2023.603

摘要/Abstract

摘要：

电动汽车可以为电力系统的灵活运行提供重要支持,其中车网互动(V2G)模式是电动汽车参与电网调频调压的重要方式.然而,V2G市场化进程至今较为缓慢,缺乏有效的运行机制,使大规模电动汽车难以参与电网辅助服务.为此,创新性地提出一种电力监管部门、电力公司和电动汽车三方演化博弈模型,探究三方各利益主体的策略选择对V2G市场运行的影响,以期找到促进V2G模式发展的长期演化的政府补贴机制和电价机制.首先,为研究三方演化博弈中多个策略均衡点的稳定性,建立该博弈的复制者动态方程,并利用李雅普诺夫稳定性理论分析均衡点的稳定性,给出促进V2G发展的补贴额度.然后,对我国上海市实际电价进行仿真分析,界定了可以促使电动汽车参与V2G模式的政府补贴系数区间和电价区间,为电力监管部门与电力公司制定补贴、定价策略提供理论支撑.

关键词: 演化博弈, 电动汽车, 车网互动, 演化稳定策略

Abstract:

Electric vehicles (EVs) can provide significant support for the flexible operation of power systems, in which vehicle-to-grid (V2G) mode is an important way for EVs to participate in the frequency and voltage regulation of power grids. However, the commercialization of V2G has experienced slow progress to date, and the lack of an effective market operation mechanism makes it difficult for large-scale EVs to participate in the ancillary services of the grid. Therefore, a novel evolutionary game model is proposed with the participation of the electricity regulatory commission, power grid company, and EVs and the impact of the strategic choices of the three parties on the operation of the V2G market is explored to identify the subsidy and pricing mechanisms for the government to facilitate the long-term evolution of the V2G. First, replicator dynamic equations for the game are established to investigate the stability of multiple strategy equilibrium points in the three-party evolutionary game. Then, the Lyapunov stability theory is employed to analyze the stability of these equilibrium points and to determine the subsidy amount to promote V2G development. Next, a simulation analysis is conducted on the actual electricity price data from Shanghai in China, which quantitatively identified the government subsidy coefficient range and electricity price range to incentivize EV participation in the V2G model. The simulation results provide theoretical support for the electricity regulatory commission and power grid company in formulating subsidy and pricing strategies.

Key words: evolutionary game, electric vehicles (EVs), vehicle-to-grid (V2G), evolutionary stable strategy

中图分类号:

TM732

潘益, 王明深, 缪惠宇, 袁晓冬, 韩华春. 车网互动技术激励机制演化分析[J]. 上海交通大学学报, 2025, 59(11): 1637-1646.

PAN Yi, WANG Mingshen, MIAO Huiyu, YUAN Xiaodong, HAN Huachun. An Evolutionary Game Approach to Incentive Mechanism of Vehicle-to-Grid[J]. Journal of Shanghai Jiao Tong University, 2025, 59(11): 1637-1646.

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参考文献 24

[1]	International Energy Agency. The global EV outlook[EB/OL]. (2022-05-01)[2023-12-20]. https://iea.blob.core.windows.net/assets/ad8fb04c-4f75-42fc-973a-6e54c8a4449a/GlobalElectricVehicleOutlook2022.pdf.
[2]	HAO J J, HOU H, TANG J Y, et al. Collaborative and optimal operation of V2G for electric vehicles to promote renewable energy consumption[C]// 2022 7th Asia Conference on Power and Electrical Engineering. Hangzhou, China: IEEE, 2010: 1972-1976.
[3]	李伟豪, 杨伟, 左逸凡, 等. V2G模式下基于SaDE-BBO算法的有源配电网优化[J]. 电力工程技术, 2023, 42(4): 41-49.
	LI Weihao, YANG Wei, ZUO Yifan, et al. Optimization of active distribution network based on SaDE-BBO algorithm in V2G mode[J]. Electric Power Engineering Technology, 2023, 42(4): 41-49.
[4]	YU R, ZHONG W F, XIE S L, et al. Balancing power demand through EV mobility in vehicle-to-grid mobile energy networks[J]. IEEE Transactions on Industrial Informatics, 2016, 12(1): 79-90. doi: 10.1109/TII.2015.2494884 URL
[5]	KISACIKOGLU M C, OZPINECI B, TOLBERT L M. Effects of V2G reactive power compensation on the component selection in an EV or PHEV bidirectional charger[C]// 2010 IEEE Energy Conversion Congress & Exposition. Atlanta, USA: IEEE, 2010: 870-876.
[6]	ALMEHIZIA A A, SNODGRASS J M. Investigation of V2G economical viability[C]// 2018 IEEE Texas Power & Energy Conference. College Station, USA: IEEE, 2018: 1-6.
[7]	王庆园, 崔莉, 王明深, 等. 考虑快慢充负荷特性的电动汽车调峰定价策略[J]. 电力工程技术, 2023, 42(4): 31-40.
	WANG Qingyuan, CUI Li, WANG Mingshen, et al. Peak load regulation pricing strategy of electric vehicle considering fast and slow charging characteristics[J]. Electric Power Engineering Technology, 2023, 42(4): 31-40.
[8]	SHI W B, WONG V W S. Real-time vehicle-to-grid control algorithm under price uncertainty[C]// 2011 IEEE International Conference on Smart Grid Communications. Brussels, Belgium: IEEE, 2011: 261-266.
[9]	RAHBARI-ASR N, CHOW M Y, CHEN J M, et al. Distributed real-time pricing control for large-scale unidirectional V2G with multiple energy suppliers[J]. IEEE Transactions on Industrial Informatics, 2016, 12(5): 1953-1962. doi: 10.1109/TII.2016.2569584 URL
[10]	REN H, ZHANG A W, LI W. Study on optimal V2G pricing strategy under multi-aggregator competition based on game theory [C]// 2019 IEEE Sustainable Power and Energy Conference (iSPEC). Beijing, China: IEEE, 2019: 1027-1032.
[11]	QIU D W, YE Y J, PAPADASKALOPOULOS D, et al. A deep reinforcement learning method for pricing electric vehicles with discrete charging levels[J]. IEEE Transactions on Industry Applications, 2020, 56(5): 5901-5912. doi: 10.1109/TIA.28 URL
[12]	王精, 邢海军, 王华昕, 等. 考虑电动汽车及负荷聚合商参与的综合能源系统优化调度[J]. 上海交通大学学报, 2023, 57(7): 814-823. doi: 10.16183/j.cnki.jsjtu.2022.029
	WANG Jing, XING Haijun, WANG Huaxin, et al. Optimal scheduling of integrated energy system considering integration of electric vehicles and load aggregators[J]. Journal of Shanghai Jiao Tong University, 2023, 57(7): 814-823.
[13]	李林晏, 韩爽, 乔延辉, 等. 面向高比例新能源并网场景的风光-电动车协同调度方法[J]. 上海交通大学学报, 2022, 56(5): 554-563. doi: 10.16183/j.cnki.jsjtu.2022.040
	LI Linyan, HAN Shuang, QIAO Yanhui, et al. A wind-solar-electric vehicles coordination scheduling method for high proportion new energy grid-connected scenarios[J]. Journal of Shanghai Jiao Tong University, 2022, 56(5): 554-563.
[14]	SOVACOOL B K, KESTER J, NOEL L, et al. Actors, business models, and innovation activity systems for vehicle-to-grid (V2G) technology: A comprehensive review[J]. Renewable & Sustainable Energy Reviews, 2020, 131: 109963.
[15]	ZAGRAJEK K, PASKA J, SOSNOWSKI Ł, et al. Framework for the introduction of vehicle-to-grid technology into the Polish electricity market[J]. Energies, 2021, 14(12): 3673. doi: 10.3390/en14123673 URL
[16]	BRANDT T, WAGNER S, NEUMANN D. Evaluating a business model for vehicle-grid integration: Evidence from Germany[J]. Transportation Research Part D: Transport & Environment, 2017, 50: 488-504.
[17]	LI Y T, SU H, CHEN X X, et al. A V2G scheduling strategy based on electric vehicle users’ willingness model[C]// 2021 IEEE 5th Conference on Energy Internet and Energy System Integration. Taiyuan, China: IEEE, 2021: 237-243.
[18]	MOON Y, AHN J, HUR W, et al. Economic valuation of vehicle-grid integration (VGI) in a demand response application from each stakeholder’s perspective[J]. Energies, 2021, 14(3): 761. doi: 10.3390/en14030761 URL
[19]	卢强, 陈来军, 梅生伟. 博弈论在电力系统中典型应用及若干展望[J]. 中国电机工程学报, 2014, 34(29): 5009-5017.
	LU Qiang, CHEN Laijun, MEI Shengwei. Typical applications and prospects of game theory in power system[J]. Proceedings of the CSEE, 2014, 34(29): 5009-5017.
[20]	秦博宇, 周星月, 丁涛, 等. 全球碳市场发展现状综述及中国碳市场建设展望[J]. 电力系统自动化, 2022, 46(21): 186-199.
	QIN Boyu, ZHOU Xingyue, DING Tao, et al. Review on development of global carbon market and prospect of China’s carbon market construction[J]. Automation of Electric Power Systems, 2022, 46(21): 186-199.
[21]	秦博宇, 李恒毅, 张哲, 等. 地下空间支撑下的电力能源系统:构想、挑战与展望[J]. 中国电机工程学报, 2022, 42(4): 1321-1332.
	QIN Boyu, LI Hengyi, ZHANG Zhe, et al. Underground space supported electric energy systems: Conceptions, challenges, and prospects[J]. Proceedings of the CSEE, 2022, 42(4): 1321-1332.
[22]	程乐峰, 余涛. 开放电力市场环境下多群体非对称演化博弈的均衡稳定性典型场景分析[J]. 中国电机工程学报, 2018, 38(19): 5687-5703.
	CHENG Lefeng, YU Tao. Typical scenario analysis of equilibrium stability of multi-group asymmetric evolutionary games in the open and ever-growing electricity market[J]. Proceedings of the CSEE, 2018, 38(19): 5687-5703.
[23]	LI X Z, TAN Y T, LIU X X, et al. A cost-benefit analysis of V2G electric vehicles supporting peak shaving in Shanghai[J]. Electric Power Systems Research, 2020, 179: 106058. doi: 10.1016/j.epsr.2019.106058 URL
[24]	WU X K, FREESE D, CABRERA A, et al. Electric vehicles’ energy consumption measurement and estimation[J]. Transportation Research Part D: Transport & Environment, 2015, 34: 52-67.

策略配置方式	ERC	PG	EV
(SP, IM, PM)	γ_g(δ_gE_c+E_d$\sum _{i=1}^{n}$δ_iη_i)+φ_pE_c-φ_cE_d	μ_cE_c+E_dE_G-μ_dE_d-φ_pE_c	μ_dE_d-μ_cE_c+φ_cE_d
(SP, IM, NPM)	γ_gδ_gE_c+φ_pE_c	μ_cE_c-φ_pE_c	-μ_cE_c
(SP, NIM, PM)	γ_gδ_gE_c+φ_pE_c	μ_cE_c-φ_pE_c	-μ_cE_c
(SP, NIM, NPM)	γ_gδ_gE_c+φ_pE_c	μ_cE_c-φ_pE_c	-μ_cE_c
(MP, IM, NPM)	γ_g$\left({\delta }_{g}{E}_{c}+{E}_{d}\sum _{i=1}^{n}{\delta }_{i}{\eta }_{i}\right)$	μ_cE_c+E_dE_G-μ_dE_d	μ_dE_d-μ_cE_c
(MP, IM, NPM)	γ_gδ_gE_c	μ_cE_c	-μ_cE_c
(MP, NIM, PM)	γ_gδ_gE_c	μ_cE_c	-μ_cE_c
(MP, NIM, NPM)	γ_gδ_gE_c	μ_cE_c	-μ_cE_c

均衡点	λ₁, λ₂, λ₃	实部范围	稳定性结论
(0, 0, 0)	φ_pE_c, 0, 0	(+, 0, 0)	不稳定
(0, 0, 1)	φ_pE_c, E_dE_G-μ_dE_d, 0	(+, ×, 0)	不稳定
(0, 1, 0)	φ_pE_c, 0, μ_dE_d	(+, 0, +)	不稳定
(0, 1, 1)	φ_pE_c-φ_cE_d, μ_dE_d-E_dE_G, -μ_dE_d	(×, ×, ×)	不确定
(1, 0, 0)	-φ_pE_c, 0, 0	(-, 0, 0)	不确定
(1, 1, 0)	-φ_pE_c, 0, φ_cE_d+μ_dE_d	(-, 0, +)	不稳定
(1, 0, 1)	-φ_pE_c, μ_dE_d-E_dE_G, 0	(-, ×, 0)	不确定
(1, 1, 1)	φ_cE_d-φ_pE_c, μ_dE_d-E_dE_G, -μ_dE_d-φ_cE_d	(×, ×, ×)	不确定