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A bilateral secure economic dispatch model for wind-penetrated power systems using classification iteration optimization

Author

Listed:
  • Meng, Anbo
  • Tan, Zhenglin
  • Liu, Honghui
  • Huang, Yue
  • Liu, Yuchen
  • Luo, Gongpu
  • Luo, Jianqiang
  • Yin, Hao

Abstract

With the rapid integration of wind energy into power systems, oscillation stability challenges and limited schedulable resources complicate economic dispatch (ED), particularly under wind-grid interaction risks. This paper proposes a bilateral secure economic dispatch (BSED) model integrating demand-side responses (e.g., schedulable data centers) and supply-side coordination to enhance dispatch flexibility, system stability, and wind power utilization. The BSED framework incorporates stability constraints to mitigate oscillation issues caused by wind generation. To address high-dimensional, non-convex optimization challenges in scheduling, a classification iteration optimization (CIO) method is introduced to eliminate stagnant local optima in real-time, maintaining population diversity and algorithmic efficiency. Embedding CIO into existing intelligent algorithms improves optimization performance, enabling effective BSED solutions. Tests demonstrate that CIO-enhanced algorithms sustain dynamic population evolution throughout iterations, outperforming base versions in average fitness and optimal fitness. The BSED model is validated across scenarios with diverse wind turbine locations, demand response levels, and data center types, confirming its adaptability in balancing stability and cost-effectiveness. This work provides a systematic approach to co-optimize security and economy in wind-integrated grids through cross-domain resource coordination and advanced optimization, offering a reliable platform for large-scale renewable energy integration.

Suggested Citation

  • Meng, Anbo & Tan, Zhenglin & Liu, Honghui & Huang, Yue & Liu, Yuchen & Luo, Gongpu & Luo, Jianqiang & Yin, Hao, 2025. "A bilateral secure economic dispatch model for wind-penetrated power systems using classification iteration optimization," Renewable Energy, Elsevier, vol. 249(C).
  • Handle: RePEc:eee:renene:v:249:y:2025:i:c:s0960148125008638
    DOI: 10.1016/j.renene.2025.123201
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