中国电力2025，Vol.58Issue(8)：69-83,15.DOI:10.11930/j.issn.1004-9649.202411078
基于多主体三层博弈的区域综合能源系统低碳运行策略
Research on Low-carbon Operation Strategies for Regional Integrated Energy Systems Based on Multi-agent Three-level Game
摘要
Abstract
To address the conflicts of interests among multiple stakeholders in regional integrated energy systems,as well as the issues such as high investment costs,uneven capacity utilization,and significant carbon emissions associated with user-side distributed energy storage,we proposed a low-carbon operation strategy for regional integrated energy systems based on three-level game among cloud energy storage service providers,integrated energy system operator(IESO),and load aggregators(LA).Firstly,an energy trading framework was established between the IESO and LA for leasing cloud energy storage.Secondly,considering the profit maximization demands of multiple rational stakeholders,a three-layer game model for the integrated energy system was established.The first layer is a principal-agent game with IESO as the leader and LA alliance as the follower;the second layer is a master-slave game with cloud energy storage service provider as the supplier and IESO as the receiver;the third layer is a cooperative game among LA alliance members,and the revenue is distributed using the asymmetric Nash bargaining method.Finally,the model was solved using the bisection method,KKT conditions,and the alternating direction multiplier method(ADMM).The simulation results show that the proposed strategy not only promotes the system's low-carbon operation,but also satisfies the economic needs of all stakeholders.关键词
非对称纳什议价/三层博弈模型/云储能/负荷聚合商Key words
asymmetric Nash bargaining/three-level game model/cloud energy storage/load aggregator引用本文复制引用
王辉,夏玉琦,李欣,董宇成,周子澜..基于多主体三层博弈的区域综合能源系统低碳运行策略[J].中国电力,2025,58(8):69-83,15.基金项目
国家自然科学基金资助项目(基于集成-深度学习的高比例新能源大规模电力系统动态安全评估研究,52107107). This work is supported by National Natural Science Foundation of China(Research on Dynamic Security Assessment of High Proportion New Energy Large Scale Power Systems Based on Integration Deep Learning,No.52107107). （基于集成-深度学习的高比例新能源大规模电力系统动态安全评估研究,52107107）
