摘要
分布式电源、储能设备及主动负荷等需求侧资源可通过广泛负荷聚集商的统一管理参与电力市场的运营,其在提升系统经济效益的同时,也给系统优化调度带来挑战。建立了广泛负荷聚集商管理多种需求侧资源参与电力主能量及辅助服务市场的日前、实时两阶段随机优化市场策略模型,同时采用条件风险价值(CVaR)方法对市场价格波动及需求侧资源不确定性风险进行管理。利用夏普利值(Shapley value)分配方法及风险贡献度理论,对各类需求侧资源在广泛负荷聚集商运营中的效益及风险贡献进行分析。算例说明了所提出模型的有效性,定量说明了广泛负荷聚集商在需求侧资源聚合管理中提升效益、平抑风险的两方面作用,以及各类需求侧资源在聚集商运营中的效益及风险贡献。
Demand-side resources(DSRs)including distributed generator,energy storage system and active loads can participate in the electricity market operation through the collaborate management of DSR aggregator,which brings in economic benefits to the system as well as challenges on the optimal operation.A two-stage stochastic optimization model of DSR aggregator market strategy is built by taking its participation in the day-ahead energy market,real-time energy market and reserve market into consideration.In order to study the influence of DSR and electricity price uncertainties on the market strategy of DSR aggregator,conditional value at risk(CVaR)is employed for the risk measurement and management.Moreover,Shapley value theory and risk contribution theory are used to study the profit contribution of DSRs and the risk contribution of different risk factors in the operation of DSR aggregator.Numerical studies show the validity and effectiveness of the model proposed.The effects that DSR aggregator conducts on the DSR management,which are economical benefit increase and risk alleviation are proved through the numerical studies quantitatively as well as the profit and risk contribution of each DSR in the DSR aggregator operation.
作者
李博嵩
王旭
蒋传文
赵岩
LI Bosong;WANG Xu;JIANG Chuanwen;ZHAO Yan(School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China;State Grid Shanghai Municipal Electric Power Company, Shanghai 200122, China)
出处
《电力系统自动化》
EI
CSCD
北大核心
2018年第16期119-126,共8页
Automation of Electric Power Systems
基金
国家自然科学基金资助项目(51577116)
中国博士后科学基金面上项目(2017M611562)~~
关键词
负荷聚集商
电力市场
夏普利值
风险贡献度
随机优化市场策略模型
load aggregator
electricity market
Shapley value
risk contribution
stochastic optimization model of market strategy