Abstract：Distributed hydrogen production by offshore wind power and centralized delivery mode is an important route for efficiently utilizing offshore wind resources and achieving low-carbon and clean hydrogen production. The wake effect will cause a loss in hydrogen production by offshore wind power. Nevertheless, only taking the wake effect as the optimization objective to increase hydrogen production can lead to wind turbines’ unevenly distributed fatigues, adding maintenance costs to wind farms. Meanwhile, the accuracy of wind speed prediction is closely related to the offshore wind power’s hydrogen production optimization problem. Insufficient prediction accuracy increases wind speed uncertainty and further affects optimization. Firstly, to better characterize the wake effect caused by wind turbines, the Gauss wake model is established for wind speed deficit and wake deflection calculation. Then, the sum of square free stream superposition (SOSFS) method is applied to superpose the wake effect caused by multiple upstream wind turbines. Therefore, the output power of each wind turbine can be obtained. The fatigue coefficient is introduced to measure the fatigue due to the power generation of each wind turbine. Secondly, the hydrogen production power relationship of a single wind turbine unitis analyzed. The distributed hydrogen production by the offshore wind power model, including proton exchange membrane (PEM) electrolyzer modeling, is established to calculate the amount of hydrogen production with a given wind turbine output power. Based on the Gauss wake model, wind turbine fatigue coefficient, and distributed hydrogen production model, a multi-objective rolling optimal operation method that considers the wake effect and fatigue distribution is put forward. The hydrogen production during each time period is independent of the optimization of distributed hydrogen production by offshore wind power, and the fatigue of wind turbines is a cumulative quantity. Therefore, to balance the cumulative fatigue of each wind turbine while increasing the intra-day hydrogen production amount, the two objectives are set to maximize the total amount of hydrogen production and minimize the standard deviation of each wind turbine’s fatigue coefficient. With the purpose of solving multi-objective optimization problem as well as promoting optimization effectiveness, a Multi-objective PID-based search algorithm (MO-PSA) is consequently proposed. Finally, four case studies involving (1) greedy operation, (2) optimizing each time period in turn,(3) optimizing all time periods globally, and (4) rolling optimization are proposed. Simulation and verification are conducted in the 5×5 square layout offshore wind farm with25 wind turbines. The results show that the proposed rolling optimization method can improve hydrogen production amount by 5.12% and 19.16% compared to greedy operation and global optimization, respectively. Moreover, the rolling optimization method lowers the standard deviation of the cumulative fatigue coefficient of each wind turbine by 18.21% compared with case (2). As for the proposed multi-objective optimization algorithm, under a similar calculation time, MO-PSA improves the amount of hydrogen production by 0.915%~4.973% and decreases the standard deviation of wind turbine fatigue coefficients by 1.943%~47.153% compared to several existing optimization algorithms in solving the multi-objective optimization problem in this paper.
周帅, 艾欣, 高飞, 张智. 考虑尾流效应与疲劳分布的分布式海上风电制氢滚动优化运行方法[J]. 电工技术学报, 2025, 40(14): 4584-4600.
Zhou Shuai, Ai Xin, Gao Fei, Zhang Zhi. Rolling Optimal Operation Method of Distributed Hydrogen Production by Offshore Wind Power Considering Wake Effect and Fatigue Distribution. Transactions of China Electrotechnical Society, 2025, 40(14): 4584-4600.
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