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考虑极限场景的并网型微电网分布鲁棒优化方法 被引量:16

Distributionally Robust Optimization Method for Grid-connected Microgrid Considering Extreme Scenarios
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摘要 负荷与分布式电源的不确定性是微电网运行优化的难点。文中提出一种考虑极限场景的并网型微电网日前分布鲁棒优化方法,以保证微电网能量管理的鲁棒性和高效性。首先,计及源荷不确定性,构造基于Wasserstein距离的概率分布模糊集,并利用极限场景法修正该模糊集,提高模糊集的鲁棒性。其次,考虑到并网模式下微电网与配电网是不同的利益主体,分别建立微电网与配电网的日前运行优化模型,采用改进目标级联分析法进行解耦与迭代求解。最后,通过仿真对比验证所提方法的有效性。 Uncertainties of loads and distributed generators are the difficulties in the microgrid operation optimization.This paper proposes a day-ahead distributionally robust optimization method for grid-connected microgrid considering extreme scenarios to ensure the robustness and efficiency of microgrid energy management.Firstly,considering the uncertainty of sources and loads,a fuzzy set of probability distribution based on Wasserstein distance is constructed,and the fuzzy set is modified by the extreme scenario method to improve the robustness of the fuzzy set.Secondly,considering that the microgrid and the distribution network are different stakeholders in the grid-connected mode,the day-ahead operation optimization models of the microgrid and the distribution network are established,respectively,and the improved analytical target cascading is used to carry out decoupling and iterative solution.Finally,the effectiveness of the proposed method is verified by the simulation comparison.
作者 曹金声 曾君 刘俊峰 薛峰 CAO Jinsheng;ZENG Jun;LIU Junfeng;XUE Feng(School of Electric Power,South China University of Technology,Guangzhou 510640,China;Guangdong Key Laboratory of Clean Energy Technology(South China University of Technology),Guangzhou 510640,China;School of Automation Science and Engineering,South China University of Technology,Guangzhou 510640,China;Dongguan Power Supply Bureau of Guangdong Power Grid Corporation,Dongguan 523009,China)
出处 《电力系统自动化》 EI CSCD 北大核心 2022年第7期50-59,共10页 Automation of Electric Power Systems
基金 国家自然科学基金资助项目(51877085,62173148) 国家重点研发计划资助项目(2017YFB0903205)。
关键词 微电网 概率分布模糊集 分布鲁棒优化 极限场景 改进目标级联分析法 microgrid fuzzy set of probability distribution distributionally robust optimization extreme scenario improved analytical target cascading
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