Research on the Control Strategy of Micro Wind-Hydrogen Coupled System Based on Wind Power Prediction and Hydrogen Storage System Charging/Discharging Regulation

Yuanjun Dai, Haonan Li, Baohua Li^*
College of Machine, Shanghai DianJi University, Shanghai, 201306, China
* Corresponding Author: Baohua Li. Email: email
(This article belongs to the Special Issue: Wind Energy Development and Utilization)

Energy Engineering https://doi.org/10.32604/ee.2024.047255

Received 31 October 2023; Accepted 28 December 2023; Published online 05 March 2024

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Abstract

This paper addresses the micro wind-hydrogen coupled system, aiming to improve the power tracking capability of micro wind farms, the regulation capability of hydrogen storage systems, and to mitigate the volatility of wind power generation. A predictive control strategy for the micro wind-hydrogen coupled system is proposed based on the ultra-short-term wind power prediction, the hydrogen storage state division interval, and the daily scheduled output of wind power generation. The control strategy maximizes the power tracking capability, the regulation capability of the hydrogen storage system, and the fluctuation of the joint output of the wind-hydrogen coupled system as the objective functions, and adaptively optimizes the control coefficients of the hydrogen storage interval and the output parameters of the system by the combined sigmoid function and particle swarm algorithm (sigmoid-PSO). Compared with the real-time control strategy, the proposed predictive control strategy can significantly improve the output tracking capability of the wind-hydrogen coupling system, minimize the gap between the actual output and the predicted output, significantly enhance the regulation capability of the hydrogen storage system, and mitigate the power output fluctuation of the wind-hydrogen integrated system, which has a broad practical application prospect.

Keywords

Micro wind-hydrogen coupling system; ultra-short-term wind power prediction; sigmoid-PSO algorithm; adaptive roll optimization; predictive control strategy