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<p>Offshore oceans host abundant wind energy with huge potential for development. However, the high uncertainty of offshore wind power and the slow regulation response of nuclear power units hinder their collaborative operation. To enhance dispatch efficiency, this study constructs a wind-nuclear-storage renewable energy system that accounts for offshore wind power uncertainty and introduces the sea wind power step consumption - carbon trading linkage (SPCL) strategy. First, the optimization scheduling model incorporating the renewable energy generation is established, and the SPCL strategy is proposed to improve offshore wind power absorption. Second, a multi-objective intelligent algorithm-based approach is proposed to solve the optimization scheduling model. The case results demonstrated that under transitional season, raising the penetration rate to 39.58% resulted in a 32.32% reduction in economic costs and a 54.72% reduction in environmental costs.</p>

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Optimization Scheduling of Wind-Nuclear-Storage Combined Power Systems Considering Offshore Wind Power Uncertainty: A Computational Intelligence Algorithm-Based Solution Method

  • 期刊论文

  • 发表日期：

    2025年8月19日

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  • Dawei Chen,
  • Guojun Bao,
  • Ye Tian,
  • Zhijie Zeng,
  • Zhicheng Li,
  • Yuan Wei,
  • Yatao Lin

摘要

Offshore oceans host abundant wind energy with huge potential for development. However, the high uncertainty of offshore wind power and the slow regulation response of nuclear power units hinder their collaborative operation. To enhance dispatch efficiency, this study constructs a wind-nuclear-storage renewable energy system that accounts for offshore wind power uncertainty and introduces the sea wind power step consumption - carbon trading linkage (SPCL) strategy. First, the optimization scheduling model incorporating the renewable energy generation is established, and the SPCL strategy is proposed to improve offshore wind power absorption. Second, a multi-objective intelligent algorithm-based approach is proposed to solve the optimization scheduling model. The case results demonstrated that under transitional season, raising the penetration rate to 39.58% resulted in a 32.32% reduction in economic costs and a 54.72% reduction in environmental costs.