Multi-objective Dynamic Reconfiguration for Urban Distribution Network Considering Multi-level Switching Modes

作     者：Hongjun Gao Wang Ma Yingmeng Xiang Zao Tang Xiandong Xu Hongjin Pan Fan Zhang Junyong Liu Hongjun Gao;Wang Ma;Yingmeng Xiang;Zao Tang;Xiandong Xu;Hongjin Pan;Fan Zhang;Junyong Liu

作者机构：College of Electrical EngineeringSichuan UniversityChengdu 610065China Iowa State UniversityAmeslowa50010USA Key Laboratory of Smart Gridof Ministry of EducationTianjin UniversityTianjin 300072China 

出 版 物：《Journal of Modern Power Systems and Clean Energy》 (现代电力系统与清洁能源学报（英文）)

年 卷 期：2022年第10卷第5期

页      面：1241-1255页

核心收录：

学科分类：080802[工学-电力系统及其自动化] 0808[工学-电气工程] 08[工学] 

基　　金：supported by the National Key R&D Program of China (No.2019YFE0123600) National Natural Science Foundation of China (No.52077146) Young Elite Scientists Sponsorship Program by CSEE (No.CESS-YESS-2019027) 

主　　题：Binary particle swarm optimization(BPSO) dynamic reconfiguration multi-level switching mixed-integer second-order cone programming(MISOCP) urban distribution network(UDN) 

摘      要：The increasing integration of photovoltaic generators(PVGs) and the uneven economic development in different regions may cause the unbalanced spatial-temporal distribution of load demands in an urban distribution network(UDN). This may lead to undesired consequences, including PVG curtailment, load shedding, and equipment inefficiency, etc. Global dynamic reconfiguration provides a promising method to solve those challenges. However, the power flow transfer capabilities for different kinds of switches are diverse, and the willingness of distribution system operators(DSOs) to select them is also different. In this paper, we formulate a multi-objective dynamic reconfiguration optimization model suitable for multi-level switching modes to minimize the operation cost, load imbalance, and the PVG curtailment. The multi-level switching includes feeder-level switching, transformer-level switching, and substation-level switching. A novel load balancing index is devised to quantify the global load balancing degree at different levels. Then, a stochastic programming model based on selected scenarios is established to address the uncertainties of PVGs and loads. Afterward, the fuzzy c-means(FCMs) clustering is applied to divide the time periods of reconfiguration. Furthermore, the modified binary particle swarm optimization(BPSO)and Cplex solver are combined to solve the proposed mixed-integer second-order cone programming(MISOCP) model. Numerical results based on the 148-node and 297-node systems are obtained to validate the effectiveness of the proposed method.