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To reduce storage costs and improve coordination among source, load, and storage, this paper proposes a bi-level optimization strategy for distributed generation and energy storage planning. The upper level minimizes planning cost and determines siting and sizing using an SVM-based classification model. The lower level minimizes operational cost and improves voltage stability under system constraints. A quantum adaptive particle swarm optimization algorithm solves the model. Simulations on a 33-bus system verify its cost-effectiveness and stability improvement.

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Coordinated DG and Storage Optimization Under Source-Load Synergy

  • 会议论文

  • 发表日期：

    2026年

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  • Shengsuo Niu,
  • Xuejian Kang,
  • Huijuan Wang,
  • Xufeng Zhen,
  • Haifeng Su,
  • Haiping Liang

摘要

To reduce storage costs and improve coordination among source, load, and storage, this paper proposes a bi-level optimization strategy for distributed generation and energy storage planning. The upper level minimizes planning cost and determines siting and sizing using an SVM-based classification model. The lower level minimizes operational cost and improves voltage stability under system constraints. A quantum adaptive particle swarm optimization algorithm solves the model. Simulations on a 33-bus system verify its cost-effectiveness and stability improvement.