考虑用户动态响应意愿的多主体嵌套博弈虚拟电厂优化策略

doi:10.19595/j.cnki.1000-6753.tces.251517

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Abstract

With the advancement of power system reform and the marketization of the retail electricity sector, diverse stakeholders are progressively entering the electricity market. As a key platform for aggregating distributed energy resources (DER), Virtual Power Plants (VPPs) must contend with game-theoretic situations arising from divergent interest demands among different stakeholders. However, in practical operation, inter-stakeholder conflicts of interest, the complex characteristics of collaborative planning, and the stochastic fluctuations of DER output severely limit the VPP's effectiveness in optimizing energy allocation and reducing operational costs. Conventional optimization approaches exhibit significant shortcomings in addressing multi-stakeholder gaming, managing differentiated risks, and enhancing user response flexibility. To this end, this paper proposes a multi-agent nested game optimization strategy for virtual power plants considering customers' dynamic response willingness.
Firstly, a two-level nested game architecture was constructed, consisting of a Distribution Network Operator (DNO) and multiple VPPs aggregating DERs. The upper level established a leader-follower game model between the DNO and the VPP aggregator. In this model, the DNO transmitted risks and adapted to market changes through a dynamic pricing mechanism, while the VPP alliance adjusted its generation and consumption plans based on electricity price signals. The lower layer formulated a Nash game among VPPs. This game optimized internal power transactions with the core objective of minimizing costs, achieving complementary and mutual support of resources. This structure constructed a dual system of 'overall regulation - local optimization', thereby further enhancing the system's dynamic adaptability.Secondly, the Conditional Value-at-Risk (CVaR) theory was introduced to construct a multi-level risk-sharing mechanism based on differentiated risk aversion coefficients. Risk types of entities were classified, and targeted transaction electricity prices were formulated. This approach balanced the risk tolerance of different entities, thereby enhancing the system's stability in coping with DER uncertainties.Thirdly, a satisfaction model based on customers' dynamic response willingness was established. Historical electricity consumption data were combined, and response enthusiasm factors were updated in real-time. This process formed an adjustable load dynamic response range with memory function. Simultaneously, customers' acceptance of load adjustments was quantified, and satisfaction was incorporated into the optimization objective. This step fully exploited the regulation potential of flexible loads and optimized the synergistic efficiency of VPP scheduling.
In the case study simulation, a DNO and three regional VPPs are considered as the research subjects. 1000 wind and solar power output scenarios are generated using Latin hypercube sampling. Following a screening process, 10 representative scenarios are selected. The validation is performed by setting the confidence level γ to 0.95 and the risk aversion coefficient interval to 0.01-1. The results indicate that an optimal equilibrium between risk and return can be achieved when the risk aversion coefficient α is approximately 0.1. At this point, the DNO's revenue is increased by 6.8% compared to the traditional leader-follower game model, while the profits of VPP1, VPP2, and VPP3 are improved by 18.7%, 13.8%, and 40.9%, respectively. It is also observed that the proportion of power sharing transaction volume among VPPs remains stable across all periods, and the electricity price resulting from the Nash game is lower than that of the leader-follower game, effectively facilitating local energy consumption. Furthermore, the responsiveness of flexible loads, such as air conditioners(AC), electric vehicles(EV), and commercial loads(CL), is dynamically adjusted based on shifts in user behavior and market conditions, leading to a significant improvement in the smoothness of the load curve and a pronounced peak shaving and valley filling effect.
The paper confirms that this strategy can effectively resolve conflicts of interest among multiple entities, achieve collaborative optimization of risk sharing and user-friendliness, and has achieved remarkable results in improving VPP economics and dispatch flexibility, providing a feasible path for the efficient operation of VPPs in the context of electricity market reform. Future research should focus on the precise real-time control of massive distributed resources by VPP. To address the computational bottlenecks arising from the rapidly expanding scale of equipment, artificial intelligence (AI) can be introduced to facilitate millisecond-level optimization calculations and precise power allocation, thereby substantially improving the overall control precision and operational efficiency of VPP.

Key words： Multiagent collaboration nested games risk aversion factor customers' dynamic response willingness power sharing

Received: 02 September 2025

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	Lei Yang
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	Tao Yuan
	Ding Shichuan
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Cite this article:

Lei Yang,Lei Wei,Tao Yuan等. A Multi-Agent Nested Game Optimization Strategy for Virtual Power Plants Considering Customers' Dynamic Response Willingness[J]. Transactions of China Electrotechnical Society, 0, (): 20251517-.

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[1] 郑冉,夏彦辉,赵学茂,等.基于云-边-端协同控制的综合型虚拟电厂[J].电气技术,2023,24(09):40-48.
Zheng Ran, Xia Yanhui, Zhao Xuemao, et al.Integrated virtual power plant based on cloud-edge-terminal collaborative control[J]. Electrical Engineering, 2023, 24(09): 40-48.
[2] 姜云鹏,任洲洋,李秋燕,等.考虑多灵活性资源协调调度的配电网新能源消纳策略[J].电工技术学报,2022,37(7):1820-1835.
Jiang Yunpeng, Ren Zhouyang, Li Qiuyan, et al.An accommodation strategy for renewable energy in distribution network considering coordinated dispatching of multi-flexible resources[J]. Transactions of China Electrotechnical Society, 2022, 37(7): 1820-1835.
[3] 董雷,李扬,陈盛,等.考虑多重不确定性与电碳耦合交易的多微网合作博弈优化调度[J].电工技术学报,2024,39(09):2635-2651.
Dong Lei, Li Yang, Chen Sheng, et al.Multi-microgrid cooperative game optimization scheduling considering multiple uncertainties and coupled electricity-carbontransactions[J]. Transactions of China Electrotechnical Society, 2024, 39(9): 2635-2651.
[4] 李翔宇,赵冬梅.分散架构下多虚拟电厂分布式协同优化调度[J].电工技术学报,2023,38(07):1852-1863.
Li Xiangyu, Zhao Dongmei.Distributed Coordinated Optimization Scheduling of Multiple Virtual Power Plants Based on Decentralized Control Structure[J]. Transactions of China Electrotechnical Society, 2023, 38(07): 1852-1863.
[5] BAGCHI A, GOEL L, WANG P.Adequacy Assessment of Generating Systems Incorporating Storage Integrated Virtual Power Plants[J]. IEEE Transactions on Smart Grid, 2019, 10(3): 3440-3451.
[6] 王波,王蔚,马恒瑞,等.基于Wasserstein两阶段分布鲁棒的多主体多能微网合作博弈优化调度[J/OL].电工技术学报,1-18[2025-09-03].
Wang Bo, Wang Wei, Ma Hengrui, et al.Multi-Agent Multi-Energy Microgrid Cooperative Game Scheduling Based on Wasserstein Two Stage Robust Optimization[J/OL]. Transactions of China Electrotechnical Society, 1-18[2025-09-03].
[7] GHAVIDEL S, GHADI M J, AZIZIVAHED A, et al.Risk-Constrained Bidding Strategy for a Joint Operation of Wind Power and CAES Aggregators[J]. IEEE Transactions on Sustainable Energy, 2020, 11(1): 457-466.
[8] YI Z, XU Y, WANG H, et al.Coordinated Operation Strategy for a Virtual Power Plant With Multiple DER Aggregators[J]. IEEE Transactions on Sustainable Energy, 2021, 12(4): 2445-2458.
[9] 宋铎洋,薛田良,李艺瀑,等.考虑风光不确定性的虚拟电厂合作博弈调度及收益分配策略[J].电力工程技术,2025,44(01):193-206.
Song Duoyang, Xue Tianliang, Li Yipu, et al.Cooperative game scheduling and revenue allocation strategy for virtual power plants considering scenery uncertainty[J]. Power Engineering Technology, 2025, 44(01): 193-206.
[10] WANG Y, AI X, TAN Z, et al.Interactive Dispatch Modes and Bidding Strategy of Multiple Virtual Power Plants Based on Demand Response and Game Theory[J]. IEEE Transactions on Smart Grid, 2016, 7(1): 510-519.
[11] BRUNINX K, PANDZIC H, LE Cadre H, et al.On the Interaction Between Aggregators, Electricity Markets and Residential Demand Response Providers[J]. IEEE Transactions on Power Systems, 2020, 35(2): 840-853.
[12] 程雪婷,暴悦爽,金玉龙,等.考虑新能源出力不确定性风险的虚拟电厂双层调度策略[J].现代电力,2023,40(06):967-975.
Cheng Xueting, Bao Yueshuang, Jin Yulong, et al.A two-tier scheduling strategy for virtual power plants considering the risk of new energy output uncertainty[J]. Modern Electric Power, 2023, 40(06): 967-975.
[13] 张涛,刘景,杨晓雷,等.计及主/被动需求响应与条件风险价值的微网经济调度[J].高电压技术,2021,47(09):3292-3304.
Zhang Tao, Liu Jing, Yang Xiaolei, et al.Economic scheduling of microgrids accounting for active/passive demand response and conditional value-at-risk[J]. High Voltage Technology, 2021, 47(09): 3292-3304.
[14] 王俊,徐箭,王晶晶,等.基于条件风险价值的虚拟电厂参与能量及备用市场的双层随机优化[J].电网技术,2024,48(06):2502-2510.
Wang Jun, Xu Jian, Wang Jingjing, et al.Conditional value-at-risk-based two-tier stochastic optimization of virtual power plant participation in energy and standby markets[J]. Grid Technology, 2024, 48(06): 2502-2510.
[15] 钟荣豪,张亚超,朱蜀,等.基于电-碳点对点交易的虚拟电厂联盟与配电网协同低碳运行策略研究[J].电网技术,2024,48(09):3554-3563.
Zhong Ronghao, Zhang Yachao, Zhu Shu, et al.Research on low-carbon operation strategy of virtual power plant alliance and distribution network based on electricity-carbon peer-to-peer trading[J]. Grid Technology, 2024, 48(09): 3554-3563.
[16] 孙毅,李飞,胡亚杰,等.计及条件风险价值和综合需求响应的产消者能量共享激励策略[J].电工技术学报,2023,38(09):2448-2463.
Sun Yi, Li Fei, Hu Yajie, et al.Energy Sharing Incentive Strategy of Prosumers Considering Conditional Value at Risk and Integrated Demand Response[J]. Transactions of China Electrotechnical Society, 2023, 38(09): 2448-2463.
[17] 刘昊,张恒旭.考虑风光储碳协同和用户满意度的分层优化调度策略[J/OL].电力系统自动化,1-14[2025-04-09].
Liu Hao, Zhang Hengxu.Hierarchical optimal scheduling strategy considering wind solar storage carbon synergy and user satisfaction[J/OL]. Power System Automation, 1-14[2025-04-09].
[18] ALTHAHER S, MANCARELLA P, MUTALE J.Automated Demand Response From Home Energy Management System Under Dynamic Pricing and Power and Comfort Constraints[J]. IEEE Transactions on Smart Grid, 2015, 6(4): 1874-1883.
[19] SHENG H, WANG C, LI B, et al.Multi-timescale Active Distribution Network Scheduling Considering Demand Response and User Comprehensive Satisfaction[J]. IEEE Transactions on Industry Applications, 2021, 57(3): 1995-2005.
[20] 曾君,徐冬冬,刘俊峰,等.考虑负荷满意度的微电网运行多目标优化方法研究[J].中国电机工程学报,2016,36(12):3325-3334.
Zeng Jun, Xu Dongdong, Liu Junfeng, et al.Multi-objective Optimal Operation of Microgrid Considering Dynamic Loads[J]. Proceedings of the CSEE, 2016, 36(12): 3325-3334.
[21] Tian S, Zhang G, Hu Y, et al.Cost-benefit Analysis of Virtual Power Plant for Demand Response Scenario[C]. 2022 9th International Forum on Electrical Engineering and Automation (IFEEA), 2022: 11-14.
[22] 成雨阳,高红均,王仁浚,等.虚拟电厂两阶段准线型需求响应优化及收益共享-风险共担决策方法[J].电网技术,2024,48(02):799-809.
Cheng Yuyang, Gao Hongjun, Wang Renjun,et al.Two-stage collinear demand response optimization and benefit-sharing-risk-sharing decision-making method for virtual power plant[J]. Grid Technology, 2024, 48(02): 799-809.
[23] 马云聪,武传涛,林湘宁,等.计及碳排放权交易的光热电站市场竞价策略研究[J].电力系统保护与控制,2023,51(4):82-92.
Ma Yuncong, Wu Chuantao, Lin Xiangning, et al.Bidding strategy for a concentrated solar power plant participating in the electricity market with the background of carbon trading[J]. Power System Protection and Control, 2023, 51(4): 82-92.
[24] 董雷,涂淑琴,李烨,等.基于元模型优化算法的主从博弈多虚拟电厂动态定价和能量管理[J].电网技术,2020,44(03):973-983.
Dong Lei, Tu Shuqin, Li Ye, et al.Dynamic pricing and energy management of multi-virtual power plant based on Master-slave Game optimization algorithm[J]. Power Grid Technology, 2020, 44(03): 973-983.
[25] 车兵,李轩,郑建勇,等.基于LHS与BR的风电出力场景分析研究[J].电力工程技术,2020,39(06):213-219.
Che Bing, Li Xuan, Zheng Jianyong, et al.Wind power output scenario analysis based on LHS and BR[J]. Electric Power Engineering Technology, 2020, 39(06): 213-219.
[26] 彭超逸,徐苏越,顾慧杰,等.基于主从博弈的虚拟电厂参与多元竞争市场投标策略研究[J].电力系统保护与控制,2024,52(07):125-137.
Peng Chaoyi, Xu Suyue, Gu Huijie, et al.Bidding strategy for a virtual power plant participating in a multiple competitive market based on the Stackelberg game[J]. Power System Protection and Control, 2024, 52(07): 125-137.
[27] Shmalo Y.Combinatorial Proof of Kakutani's Fixed Point Theorem[Z]. arXiv, 2018(2018).
[28] Durlauf S N, Blume L E. Non-Cooperative Games (Equilibrium Existence)[M]//Game Theory. Palgrave Macmillan UK, 2010: 263-271.