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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摘要

电力体制改革和售电侧放开政策推动了多元化主体进入市场,形成了多决策主体博弈的格局。虚拟电厂(Virtual Power Plants,VPP)通过整合调控分布式能源资源(Distributed Energy Resources,DER)参与电力交易,但运营主体间的利益冲突、协同规划的复杂性以及DER的不确定性,进一步限制了VPP优化能源分配、减少成本的潜力。本文提出了一种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。首先,建立了配电网运营商(Distribution Network Operators,DNO)与多VPP的Stackelberg-Nash博弈嵌套模型,形成“总体—局部”的双重优化框架。其次,引入条件风险价值(Conditional Value at Risk,CVaR)理论,考虑不同主体风险承受度构建了多场景风险评估模型,DNO通过定价机制传导风险。然后,考虑不同用户的动态响应意愿,构建了用户满意度模型,通过实时调整响应域,最大限度释放调节潜力。最后,选取安徽宣城地区案例数据,通过仿真分析与示范工程验证了该策略在经济性和调度灵活性的有效性。结果表明,该策略充分发挥了各主体特性,有效推动了多主体协同优化运行。

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关键词 ：多主体协同, 嵌套博弈, 风险规避系数, 用户动态响应意愿, 电力共享

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

收稿日期: 2025-09-02

PACS:

TM73

基金资助:

国家自然科学基金项目(52407074)、安徽省自然科学基金青年项目(2308085QE177)、安徽省教育厅高校自然科学重点研究项目; (2023AH050092)资助

通讯作者: 宫璇女,1982年生,副教授,研究方向为电力系统大电网运行与控制和电动汽车与电网互动、电网区域间低频振荡、电驱动系统和单元储能系统与配电网的互动。E-mail：18816264309@163.com

作者简介: 雷杨男,1992年生,硕士生导师,研究方向为综合能源系统规划规划与运行、人工智能在能源系统中的应用。E-mail：yleimirror@ahu.edu.cn

引用本文:

雷杨, 雷威, 陶源, 丁石川, 宫璇. 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[J]. 电工技术学报, 0, (): 20251517-. Lei Yang, Lei Wei, Tao Yuan, Ding Shichuan, Gong Xuan. A Multi-Agent Nested Game Optimization Strategy for Virtual Power Plants Considering Customers' Dynamic Response Willingness. Transactions of China Electrotechnical Society, 0, (): 20251517-.

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