邱一苇, 孙清洁, 吴晨旭, 朱杰, 曾扬俊, 陈实, 周步祥. 计及风功率不确定性的离网风储制氢日内-实时双层有功平衡优化控制[J]. 电网技术, 2025, 49(1): 52-62. DOI: 10.13335/j.1000-3673.pst.2024.1177
引用本文: 邱一苇, 孙清洁, 吴晨旭, 朱杰, 曾扬俊, 陈实, 周步祥. 计及风功率不确定性的离网风储制氢日内-实时双层有功平衡优化控制[J]. 电网技术, 2025, 49(1): 52-62. DOI: 10.13335/j.1000-3673.pst.2024.1177
QIU Yiwei, SUN Qingjie, WU Chenxu, ZHU Jie, ZENG Yangjun, CHEN Shi, ZHOU Buxiang. Optimized Bi-level Control for Day-ahead and Real-time Active Power Balancing of an Off-grid Wind Power-to-hydrogen System With ESS Considering Wind Power Uncertainty[J]. Power System Technology, 2025, 49(1): 52-62. DOI: 10.13335/j.1000-3673.pst.2024.1177
Citation: QIU Yiwei, SUN Qingjie, WU Chenxu, ZHU Jie, ZENG Yangjun, CHEN Shi, ZHOU Buxiang. Optimized Bi-level Control for Day-ahead and Real-time Active Power Balancing of an Off-grid Wind Power-to-hydrogen System With ESS Considering Wind Power Uncertainty[J]. Power System Technology, 2025, 49(1): 52-62. DOI: 10.13335/j.1000-3673.pst.2024.1177

计及风功率不确定性的离网风储制氢日内-实时双层有功平衡优化控制

Optimized Bi-level Control for Day-ahead and Real-time Active Power Balancing of an Off-grid Wind Power-to-hydrogen System With ESS Considering Wind Power Uncertainty

  • 摘要: 离网型风电制氢系统因未与大电网连接,通常需由制氢装置与所配储能协同调节,消纳风功率波动、避免失稳。二者调节需兼顾制氢机组灵活性约束、氢产量及储能电池退化,以满足绿氢经济性目标。为此,提出双层有功平衡优化控制方法。首先,上层基于伊藤过程模型刻画风电出力波动,计及制氢多机集群实时功率调节的“灵活性备用”,制定日内15 min分辨率的调度计划。而后,下层控制依据上层调度给出的制氢机启停状态组合和功率基准,计及储能退化成本,实时优化调整制氢及储能功率,消纳秒级风功率波动。最后,基于内蒙某工程方案构造算例,验证所提方法的有效性。

     

    Abstract: Since off-grid wind power-to-hydrogen (Wp2H) systems are not connected to the utility grid, the hydrogen production load and the energy storage system (ESS) must be coordinated to absorb wind power fluctuations and maintain system stability. Such coordination must consider the flexibility constraints of hydrogen production units, the overall hydrogen yield, and the degradation of batteries in the ESS to meet the economic goals of green hydrogen production. Therefore, an optimized bi-level control method for the active power balance is proposed. Firstly, using the Itô process to characterize wind power fluctuations and considering the real-time flexibility reserve of multiple electrolyzers, the upper level is formulated as an intra-day scheduling problem with a 15-minute resolution. Secondly, the lower level optimizes the real-time power of the electrolyzers and the ESS based on their on-off states and baseline power commanded by the upper level, considering the degradation cost of the ESS. This helps to absorb second-level wind power fluctuations. Finally, based on a project plan in Inner Mongolia, China, a case study verifies the effectiveness of the proposed method.

     

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