摘要
独立储能对微电网运营商来说是一个重大的经济负担,为此提出了使用相邻储能资源的多微电网共享储能新模式。首先,考虑自有储能的差异性,构建共享储能系统模型,给出多微电网协同调度模型。其次,提出新的贡献率指标,设计改进Shapley值的效益分配方法。然后,针对源-荷不确定性,构建两阶段鲁棒优化模型,引入不确定调节参数控制方案的保守程度。最后,基于西北地区3个相邻微电网的数据,从效益和风险角度分析模型的有效性。结果表明:系统运行风险降低31.1%,调度成本节约3.75%。改进Shapley值分配方法使联盟整体的满意度提升7.33%。
Independent energy storage is a major economic burden for microgrid operators.For this reason,the paper proposes a new mode of shared energy storage for multiple microgrids using adjacent energy storage resources.Firstly,the shared energy storage system model is established by considering the differences of the owned energy storage,and the multi-microgrid cooperative dispatching model is constructed.Secondly,based on the new contribution index,a benefit allocation method with improved Shapley value is designed.Then a two-stage robust optimization model is constructed for the source-load uncertainty,and an uncertainty adjustment coefficient is introduced to control the conservative degree of the scheme.Finally,the simulation analysis is carried out using data from three adjacent microgrids in northwest region to analyze the effectiveness of the sharing owned energy storage model from the perspective of operational benefits and risks.The results show that the system operation risk is reduced by 31.1%,and the scheduling cost is saved by 3.75%.After utilizing the improved Shapley value for the benefit allocation,the overall satisfaction of the alliance is increased by 7.33%.
作者
金文
方登洲
李鸿鹏
陈天佑
李金孟
JIN Wen;FANG Dengzhou;LI Hongpeng;CHEN Tianyou;LI Jinmeng(State Grid Anhui Electric Power Co.,Ltd.Economic and Technological Research Institute,Hefei 230000,China;State Grid Anhui Electric Power Co.,Ltd.,Hefei 230000,China;School of Economics and Management,North China Electric Power University,Beijing,102206,China)
出处
《智慧电力》
北大核心
2024年第11期56-63,97,共9页
Smart Power
基金
国家社会科学基金资助项目(20BGL186)。
关键词
多微电网
共享自有储能
合作博弈
效益分配
源-荷不确定性
改进Shapley值
鲁棒优化
multi-microgrid
shared owned energy storage
cooperative game
benefit allocation
source-load uncertainty
improved Shapley value
robust optimization
