发电技术

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计及多时间尺度非预期性的海上风电演化调度方法

刘楠1,董骁翀1,冯春晖2,陈来军1,2,梅生伟1,2   

  1. 1. 清华大学电机工程与应用电子技术系,北京市 海淀区 100084; 2. 青海大学能源与电气工程学院,青海省 西宁市 810016

  • 出版日期:2026-04-28 发布日期:2026-04-28
  • 基金资助:
    国家自然科学基金资助项目(U22A20224);中国博士后科学基金资助项目(BX20250414)。

Offshore Wind Power Evolutionary Dispatch Considering Multi-Timescale Non-Anticipativity

LIU Nan1, DONG Xiaochong1, FENG Chunhui2, CHEN Laijun1,2, MEI Shengwei1,2   

  1. 1. Department of Electrical Engineering, Tsinghua University, Haidian District, Beijing 100084, China; 2. School of Energy and Electrical Engineering, Qinghai University, Xining 810016, Qinghai Province, China
  • Published:2026-04-28 Online:2026-04-28

摘要:

【目的】 海上风电与多类型储能相结合已成为提升其运行灵活性的重要途径,尤其在实现多时间尺度不确定性平抑方面展现出潜力。为充分挖掘历史数据信息并增强海上风电在多时间尺度上的调度能力,本文提出一种考虑多时间尺度非预期性的海上风电演化调度方法。【方法】首先,采用数据驱动方法构建风电出力与负荷需求的场景概率分布模型,以刻画季节性与日内尺度的不确定性特征;其次,建立氢储能与电化学储能的混合储能协同优化模型,兼顾跨季节能量转移与快速功率调节能力;最后,研究设计了“预调度生成-控制演化-实时调度”三层递进式调度框架,实现长期规划策略与短期运行决策的动态衔接。【结果】研究基于比利时Elia数据仿真验证,所提方法在满足非预期性约束的前提下,显著提升了海上风电系统的供电灵活性,有效降低了负荷损失风险。【结论】研究成果可为高比例可再生能源电力系统的长期运行模拟与调度优化提供理论支持与方法参考。[1]



基金项目:国家自然科学基金资助项目(U22A20224);中国博士后科学基金资助项目(BX20250414)。

Project supported by the National Natural Science Foundation of China under Grant (U22A20224) and by the China Postdoctoral Science Foundation under Grant (BX20250414).

关键词:

"> 海上风电;不确定性;多时间尺度;非预期性;演化调度

Abstract:

[Objectives] Combining offshore wind power with multiple energy storage systems has become a key approach to enhancing operational flexibility, particularly in mitigating uncertainties across multiple time scales. To fully leverage historical data and improve multi-scale dispatchability, this paper proposes an evolutionary dispatch method for offshore wind power that accounts for multi-timescale non-anticipativity. [Methods] First, a data-driven scenario probability distribution model is developed to characterize seasonal and intra-day uncertainties in wind generation and load demand. Second, a hybrid storage coordination model integrating hydrogen and electrochemical storage is established to enable both seasonal energy shifting and fast power regulation. Finally, a three-layer framework pre-scheduling generation, control evolution, and real-time scheduling is designed, to dynamically link long-term planning with short-term operations. [Results] Case studies using Belgian Elia system data demonstrate that the proposed method significantly enhances power supply flexibility and reduces load loss while satisfying non-anticipativity constraints. [Conclusions] The findings provide theoretical and methodological support for long-term operation simulation and scheduling optimization in high-renewable power systems.

Key words:

offshore wind power, uncertainty; multi-timescale, non-anticipativity, evolutionary dispatch