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

• Published in: Renewable energy Vol. 249; p. 123201
• Main Authors: Meng, Anbo, Tan, Zhenglin, Liu, Honghui, Huang, Yue, Liu, Yuchen, Luo, Gongpu, Luo, Jianqiang, Yin, Hao
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.08.2025
• Subjects: Bilateral secure economic dispatch (BSED); Classification iteration optimization (CIO); cost effectiveness; Demand-side response; economic dispatch; evolution; Supply-side response; wind; Wind power; wind turbines; Bilateral secure economic dispatch (BSED); Demand-side response; Wind power; Classification iteration optimization (CIO); Supply-side response
• ISSN: 0960-1481
• DOI: 10.1016/j.renene.2025.123201

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.