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当前,高比例可再生能源(VRE)参与下的电力系统规划已引起全世界的广泛关注. 由于可再生能源的出力依赖于天气状况,所以其具有波动性,不确定性和低可信容量等特征,给电力系统规划提出了很大的挑战.当前,为解决电力系统中灵活性不足的问题,提出了包括热电灵活性改造,区域间互济,电能存储和需求响应(DR)等多种方案.显然,各种解决方案的性能和成本是不同的.因此,需要探索出一种考虑不同灵活性调节能力下的最优组合方案以满足高比例可再生能源系统的灵活性要求.本文研究重点考虑多种类型需求响应的灵活性性能,并提出考虑需求响应作用的电力系统优化规划随机模型.考虑了两种类型的需求响应模型,即可中断需求响应和可转移需求响应.同时,为加速模型优化求解过程,全年8760小时的时序负荷和可再生能源发电数据缩减为4周,并将灵活性机组进行聚类考虑到规划模型中.最后,基于IEEE RTS-96系统的算例仿真,证明了该方法的有效性以及需求响应在避免储能投资成本方面的潜力.“,”Electric system planning with high variable renewable energy (VRE) penetration levels has attracted great attention world-wide. Electricity production of VRE highly depends on the weather conditions and thus involves large variability, uncertainty, and low-capacity credit. This gives rise to significant challenges for power system planning. Currently, many solutions are proposed to address the issue of operational flexibility inadequacy, including flexibility retrofit of thermal units, inter-regional transmission, electricity energy storage, and demand response (DR). Evidently, the performance and the cost of various solutions are different. It is relevant to explore the optimal portfolio to satisfy the flexibility requirement for a renewable dominated system and the role of each flexibility source. In this study, the value of diverse DR flexibilities was examined and a stochastic investment planning model considering DR is proposed. Two types of DRs, namely interrupted DR and transferred DR, were modeled. Chronological load and renewable generation curves with 8760 hours within a whole year were reduced to 4 weekly scenarios to accelerate the optimization. Clustered unit commitment constraints for accommodating variability of renewables were incorporated. Case studies based on IEEE RTS-96 system are reported to demonstrate the effectiveness of the proposed method and the DR potential to avoid energy storage investment.