Multi-objective optimization of wind-hydrogen integrated energy system with aging factor

• DOI: 10.1016/j.ijhydene.2023.03.194
• Publication Date: 2023-04-02T12:46:18Z
• Authors: Xinghua Liu; Yubo Wang; Jiaqiang Tian; Gaoxi Xiao; Peng Wang
• Keywords: Capacity optimization; Power Balance; Operating cost

Large-scale hydrogen production with wind power generation has been gaining increasing attention and applications. Achieving a good balance between the capacity and cost of wind power generation however remains as a critical challenge restricting the development of wind-hydrogen integrated energy systems (WHIES). In addition, the aging factor may come in over time, making negative impacts on the efficiency and cost of WHIES. In this work, a method is proposed to seek a good balance between the capacity and cost of WHIES. Specifically, by comparing operational data and equipment condition, we evaluate the aging status of the wind power generation system and the hydrogen production system, then the aging economic model of WHIES is proposed. By taking into account the actual operating conditions in constructing the WHIES objective function with the aging factor, the proposed model allows striving to maximize the production capacity with the minimum cost. An improved multi-objective gray wolf optimizer algorithm is developed to solve the WHIES cost optimization problem. Finally, case studies are carried out via MATLAB based on the configuration and experimental data for a specific wind farm located in Ningxia, China. Our results help achieve a balance between maximizing capacity and minimizing cost under various conditions. National Research Foundation (NRF) This work was supported in part by the National Natural Science Foundation of China (Grant No. 62203352, U2003110), and in part by the Future Resilient Systems Programme (FRS-II) at the Singapore-ETH Centre (SEC), funded by National Research Foundation (NRF), Singapore, and in part by the Key Laboratory Project of Shaanxi Provincial Department of Education (No. 20JS110), and in part by the High Level Talents Plan of Shaanxi Province for Young Professionals.