赵立军, 张秀路, 韩丽维, 孙永辉, 王俊生, 刘自发, 于普洋. 基于多场景的配电网分布式光伏及储能规划[J]. 现代电力, 2022, 39(4): 460-468. DOI: 10.19725/j.cnki.1007-2322.2021.0257
引用本文: 赵立军, 张秀路, 韩丽维, 孙永辉, 王俊生, 刘自发, 于普洋. 基于多场景的配电网分布式光伏及储能规划[J]. 现代电力, 2022, 39(4): 460-468. DOI: 10.19725/j.cnki.1007-2322.2021.0257
ZHAO Lijun, ZHANG Xiulu, HAN Liwei, SUN Yonghui, WANG Junsheng, LIU Zifa, YU Puyang. Distributed Photovoltaic Generation and Energy Storage Planning of Distribution Network Based on Multi Scenarios[J]. Modern Electric Power, 2022, 39(4): 460-468. DOI: 10.19725/j.cnki.1007-2322.2021.0257
Citation: ZHAO Lijun, ZHANG Xiulu, HAN Liwei, SUN Yonghui, WANG Junsheng, LIU Zifa, YU Puyang. Distributed Photovoltaic Generation and Energy Storage Planning of Distribution Network Based on Multi Scenarios[J]. Modern Electric Power, 2022, 39(4): 460-468. DOI: 10.19725/j.cnki.1007-2322.2021.0257

基于多场景的配电网分布式光伏及储能规划

Distributed Photovoltaic Generation and Energy Storage Planning of Distribution Network Based on Multi Scenarios

  • 摘要: 针对基于系统最大运行断面的规划方法难以有效处理分布式光伏并网引起的不确定性问题,研究了分布式光伏与储能的联合规划特性;通过基于信息熵的场景提取方法,生成光伏、负荷的典型运行场景,综合考虑经济性、环保性、可靠性,建立基于多场景的光伏及储能选址定容规划模型,并利用重心反向学习结合粒子群算法(centroid opposition-based learning-particle swarm optimization,COBL-PSO)进行求解,采用雷达图对规划结果进行直观评估。通过算例对比分析,得到了满足配电网需求的光伏及储能电池的规划方案,验证了该模型的可行性及正确性。

     

    Abstract: In view of the fact that the planning method based on the maximum operation section of the system was difficult to effectively cope with the uncertainty caused by the grid-connection of distributed photovoltaic (abbr. PV) generation, the joint planning characteristics of distributed PV generation and energy storage was researched. By means of the information entropy-based scenario extraction method the typical operation scenarios of PV generation and loads were generated. Comprehensively considering the economy, environmental protection and reliability, a model for multi scenarios-based site-selection and capacity determination for PV generation and energy storage planning was established and this model was solved by centroid opposition-based learning-particle swarm optimization (abbr. COBL-PSO) algorithm, and the result of the planning was intuitively assessed by the radar chart. Through the contrastive analysis on the computing example, the planning scheme of PV generation and energy storage battery, which can meet the demand of distribution network, was obtained. Thus, both feasibility and correctness of the established model are verified.

     

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