文章摘要
田壁源,常喜强,戚红燕,张新燕.基于混合博弈的园区虚拟电厂广义储能共享与协同优化调度[J].电力需求侧管理,2023,25(4):08-14
基于混合博弈的园区虚拟电厂广义储能共享与协同优化调度
Generalized energy storage sharing and collaborative optimal scheduling of parklevel vritual power plants based on mixed game
投稿时间:2023-01-19  修订日期:2023-04-26
DOI:10. 3969 / j. issn. 1009-1831. 2023. 04. 002
中文关键词: 广义储能  虚拟电厂  储能共享  Stackelberg 博弈  混合博弈
英文关键词: generalized energy storage  virtual power plant  energy storage sharing  Stackelberg game  mixed game
基金项目:国家自然科学基金项目(51667018)
作者单位
田壁源 国网新疆电力有限公司 乌鲁木齐供电公司乌鲁木齐 830011 
常喜强 国网新疆电力有限公司乌鲁木齐 830018 
戚红燕 国网新疆电力有限公司 乌鲁木齐供电公司乌鲁木齐 830011 
张新燕 新疆大学 电气工程学院乌鲁木齐 830047 
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中文摘要:
      通过分析含广义储能虚拟电厂以“虚拟”集成实体整体性参与调度优化和电力市场的流程,提出基于混合博弈的园区虚拟电厂广义储能两阶段共享策略及双层协调优化调度模型。第一阶段根据园区各类储能资源的响应特性,建立了包含实际储能与需求侧灵活性负荷、电动汽车构成的广义储能共享模型,进而构建了聚合多类储能资源的虚拟电厂主体架构。第二阶段建立以虚拟电厂运营商为上层领导者,园区储能服务商、负荷聚合商及能源供应商为下层跟随者的双层混合博弈模型。上层与下层之间采用基于Stackelberg随者之间采用合作博弈实现多主体实时协同优化。算例分析表明:所提模型不仅实现了虚拟电厂利润最大与广义储能资源效用最优,也有效权衡各运营商间的利益。
英文摘要:
      The flow of virtual power plant(VPP)with generalized energy storage(GES)participating in dispatching optimization and power market as a“virtual”integrated entity is analyzed.A two-stage sharing strategy and a two-level coordinated optimal dispatching model of park-level VPP-GES based on hybrid game are proposed. First stage:according to the response characteristics of various energy storage resources in the park, a GES sharing model composed of actual energy storage, demand side flexible load and EV is established, and then the main structure of VPP aggregating multiple energy storage resources is constructed. Second stage:a two-tier hybrid game model with VPP operators as the upper leaders and park energy storage service providers, load aggregators and energy suppliers as the lower followers is established. The stackelberg game is used between the upper and lower levels to share and interact the purchase and sale price and GES, so as to ensure the win-win interests of leaders and followers. Cooperative game is adopted among the followers of the lower level to realize multi-agent real-time collaborative optimization. The example analysis shows that the proposed not only maximizes the prs.
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