Coordinated Optimization Method for Distributed Energy Storage and Dynamic Reconfiguration to Enhance the Economy and Reliability of Distribution Network

Caihong Zhao, Qing Duan, Junda Lu (), Haoqing Wang, Guanglin Sha, Jiaoxin Jia () and Qi Zhou
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Caihong Zhao: Country Distribution Technology Center, China Electric Power Research Institute, Beijing 100192, China
Qing Duan: Country Distribution Technology Center, China Electric Power Research Institute, Beijing 100192, China
Junda Lu: Key Laboratory of Distributed Energy Storage and Micro-Grid of Hebei Province, North China Electric Power University, Baoding 071003, China
Haoqing Wang: Country Distribution Technology Center, China Electric Power Research Institute, Beijing 100192, China
Guanglin Sha: Country Distribution Technology Center, China Electric Power Research Institute, Beijing 100192, China
Jiaoxin Jia: Key Laboratory of Distributed Energy Storage and Micro-Grid of Hebei Province, North China Electric Power University, Baoding 071003, China
Qi Zhou: Electric Power Science Research Institute of State Grid Jiangsu Electric Power Company, Nanjing 211103, China

Energies, 2024, vol. 17, issue 23, 1-22

Abstract: To fully leverage the application potential of distributed energy storage systems (DESS) and network reconfiguration, a coordinated optimization method is proposed to enhance the economic efficiency of distribution networks under normal conditions and the reliability of a power supply during fault conditions. First, a scenario-generation method is developed based on Latin hypercube sampling and Kantorovich distance synchronous back-substitution reduction is used to obtain the typical scenario of wind and solar output. Next, a planning operation coordinated optimization framework and model are established, considering both normal and fault states of the distribution network. In the planning layer, the objective is to minimize the annual comprehensive capital expenditures for the distribution network to improve the economic efficiency of the distribution network. The operation layer includes both normal operation and fault operation states, with the optimization goal of minimizing the sum of normal operation costs and the fault costs associated with load shedding. Subsequently, a hybrid optimization algorithm combining an improved Aquila Optimizer-Second-Order Cone Programming (IAO-SOCP) is proposed to solve the coordinated optimization model. Finally, the proposed coordinated optimization method is validated using an enhanced IEEE 33-bus distribution network case study. The results demonstrate that the method effectively reduces network losses and minimizes load shedding costs during fault conditions, thereby ensuring a balance between the economic efficiency and reliability of the distribution network.

Keywords: active distribution network; distributed energy storage; dynamic reconstruction; coordinated optimization; improved aquila optimizer (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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