摘要 考虑分布式光伏和负荷功率的不确定性,建立了二阶段鲁棒优化模型,用于评估主动配电网(ADN)的分布式光伏的最大准入容量。若干主动管理(AM)措施被应用于ADN中,包括带储能的多端智能软开关(E-SOP)调节、有载调压变压器(OLTC)调节、无功补偿、分布式光伏无功调节和网络重构。基于凸松弛技术,原问题可以分解为主问题和子问题。主问题优化典型日AM措施模拟运行策略,以计算待选节点上分布式光伏最大准入容量;子问题寻找不确定性的最恶劣场景。针对凸松弛引入的间隙,提出了一种将渐紧线切割算法和列与约束生成算法相结合的求解策略,以获得间隙足够小的原问题最优解。在改进的IEEE 33节点ADN上验证了模型和算法的有效性,分析了不确定性和AM措施对ADN中分布式光伏最大准入容量的影响。 A two-stage robust optimization model is established for the assessment of the maximum hosting capacity of distributed photovoltaic(PV)of an active distribution network(ADN)considering the uncertainties of distributed PV and load power.Several active management(AM)strategies are applied in the ADN,including the regulation of the multi-terminal soft open point with energy storage(E-SOP),the regulation of on-load tap changers(OLTC),the reactive power compensation,the reactive power control of distributed PV,and the network reconfiguration.based on the technology of convex relaxation,the original problem can be decomposed into a master problem and a subproblem.The master problem optimizes the simulated operation mode of AM strategies on a typical day to calculate the maximum hosting capacity of distributed PV at the candidate nodes.The subproblem searches for the worst scenario of uncertainties.In view of the gap introduced by the convex relaxation,a solving strategy combined the algorithm of increasingly tight linear cut(ITLC)with the column-and-constraint generation(C&CG)algorithm is proposed to obtain the optimal solution of the original problem with a sufficiently small gap.The validation of the model and algorithm is investigated on a modified IEEE 33-bus ADN.And the impact of the uncertainties and the AM strategies on the maximum hosting capacity of distributed PV in the ADN is analyzed.
出处 《电力系统自动化》 EI CSCD 北大核心 2021年第7期8-17,共10页 Automation of Electric Power Systems
基金 国家自然科学基金青年基金资助项目(51907123) 国家自然科学基金委员会-国家电网公司智能电网联合基金资助项目(U1966206) 上海市青年科技英才扬帆计划资助项目(18YF1411600)。
关键词 多端智能软开关 分布式光伏 最大准入容量 二阶段鲁棒优化模型 渐紧线切割算法 列与约束生成算法 multi-terminal soft open point distributed photovoltaic maximum hosting capacity two-stage robust optimization model algorithm of increasingly tight linear cut(ITLC) column-and-constraint generation(C&CG)algorithm
