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Globally, there is a strong push towards developing renewable energy sources such as wind, solar, and hydropower to address energy transition and climate change challenges. This includes optimizing power systems and smart grid scheduling to achieve clean, low-carbon energy transformation and ensure secure power supply. In this context, this paper aims to maximize renewable energy generation and minimize output fluctuations by constructing a joint dispatch model incorporating cascade hydropower. Firstly, uncertainty in renewable energy is characterized using multi-scenario generation based on Latin hypercube sampling and rapid reduction techniques based on Kantorovich distance. Then, the minimum sample size is computed, and an optimization sampling method based on Latin hypercube is used to seek elite solutions that meet constraints. Pareto optimal solution sets are identified through rapid non-dominated sorting, and a performance ranking method is applied to find satisfactory compromise solutions. Finally, the efficiency and quality of solutions obtained using this algorithm are compared and analyzed against multiple algorithms. Simulation results demonstrate that this model achieves good objective function performance while significantly improving solution efficiency.

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Integrated Scheduling Strategy of Hydropower-Wind-Solar Complementary Power Generation System Based on the Elite Optimization Algorithm

  • 会议论文

  • 发表日期：

    2025年

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  • Xu Zhang,
  • Jun Xie,
  • Yuanyu Ge,
  • Denghui Fu,
  • Kewei Cao,
  • Zhangwei Wang

摘要

Globally, there is a strong push towards developing renewable energy sources such as wind, solar, and hydropower to address energy transition and climate change challenges. This includes optimizing power systems and smart grid scheduling to achieve clean, low-carbon energy transformation and ensure secure power supply. In this context, this paper aims to maximize renewable energy generation and minimize output fluctuations by constructing a joint dispatch model incorporating cascade hydropower. Firstly, uncertainty in renewable energy is characterized using multi-scenario generation based on Latin hypercube sampling and rapid reduction techniques based on Kantorovich distance. Then, the minimum sample size is computed, and an optimization sampling method based on Latin hypercube is used to seek elite solutions that meet constraints. Pareto optimal solution sets are identified through rapid non-dominated sorting, and a performance ranking method is applied to find satisfactory compromise solutions. Finally, the efficiency and quality of solutions obtained using this algorithm are compared and analyzed against multiple algorithms. Simulation results demonstrate that this model achieves good objective function performance while significantly improving solution efficiency.