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<p>The time-varying characteristics of distributed generation (DG) output upper limit and load demand bring new challenges to the safe and economic operation of active distribution network (ADN). Based on the dual uncertainty of DG output and load demand, a novel cooperative dynamic optimization method of network reconfiguration (NR) and DG scheduling is proposed considering the security and economic objectives of ADN. Firstly, the ADN operation is classified into normal and risky states according to the node voltage and branch load rate. Secondly, the mathematical models of cooperative dynamic optimization for NR and DG scheduling in different states are developed, respectively. Thirdly, a co-evolutionary algorithm of Improved non-dominated sorting genetic algorithm II (INSGA-II) and improved particle swarm optimization algorithm (IPSOA) is designed to solve the proposed model. Finally, the effectiveness of the proposed model and method is verified in the IEEE 33-bus distribution system.</p>

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A Novel Cooperative Dynamic Optimization Method of Network Reconfiguration and DG Scheduling in Active Distribution Network

  • 期刊论文

  • 发表日期：

    2025年8月5日

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  • Zhihong Liu,
  • Juan Su,
  • Songhuai Du,
  • Jinli Wang,
  • Wen Zhang

摘要

The time-varying characteristics of distributed generation (DG) output upper limit and load demand bring new challenges to the safe and economic operation of active distribution network (ADN). Based on the dual uncertainty of DG output and load demand, a novel cooperative dynamic optimization method of network reconfiguration (NR) and DG scheduling is proposed considering the security and economic objectives of ADN. Firstly, the ADN operation is classified into normal and risky states according to the node voltage and branch load rate. Secondly, the mathematical models of cooperative dynamic optimization for NR and DG scheduling in different states are developed, respectively. Thirdly, a co-evolutionary algorithm of Improved non-dominated sorting genetic algorithm II (INSGA-II) and improved particle swarm optimization algorithm (IPSOA) is designed to solve the proposed model. Finally, the effectiveness of the proposed model and method is verified in the IEEE 33-bus distribution system.