Multi-objective optimization of the grid-connected wind-solar-storage coupled hydrogen production system based on a genetic algorithm

doi:10.19799/j.cnki.2095-4239.2025.0404

Abstract

Abstract:

Under the "dual carbon" goals, renewable energy-based hydrogen production has become a key pathway to advance the transition toward a low-carbon, clean, and efficient energy system. However, grid-connected hydrogen production faces challenges such as limited renewable energy accommodation and mismatches between hydrogen production efficiency and operational economics. This study develops a multi-objective optimization model to minimize the levelized cost of hydrogen (LCOH) and maximize the grid-feed-in ratio of renewable energy, hydrogen yield, and equivalent full-load hours of the electrolyzer. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is employed to generate a Pareto solution set, and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method with entropy-based weights is applied to identify the optimal configuration. A comparative analysis is performed with and without an energy storage system. Results show that the grid-connected hydrogen production system with energy storage achieves a grid-feed-in ratio of 5.78%, a hydrogen yield of 2899.3 tons, and a curtailment ratio of 6.49% relative to total renewable generation. In contrast, the system without energy storage records a higher grid-feed-in ratio of 6.84%, a lower hydrogen yield of 2771.82 tons, and a curtailment ratio of 11.34%. These findings demonstrate that energy storage effectively reduces grid feed-in and curtailment, thereby enhancing hydrogen production. Furthermore, the operational characteristics of the optimized system on four representative days—vernal equinox, summer solstice, autumnal equinox, and winter solstice are examined. Results indicate that integrating energy storage not only mitigates renewable energy fluctuations but also reduces electrolyzer load variability, thereby improving intra-day operational stability and renewable energy utilization. This study provides theoretical support and engineering reference for the optimal configuration of grid-connected hydrogen production systems.

Key words: grid-connected hydrogen production, new energy, energy storage system, multi-objective optimization

CLC Number:

TK 91

Yanwen WANG, Bo WEI, Lifang ZHANG, Xiaojun PAN, Ruyi LIU, Miao GUO, Kang CHEN, Yanglong DUAN, Feng YE, Huaiwu PENG, Chao XU. Multi-objective optimization of the grid-connected wind-solar-storage coupled hydrogen production system based on a genetic algorithm[J]. Energy Storage Science and Technology, 2025, 14(10): 3814-3823.

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分时电价机制	峰电时段	平电时段	谷电时段
大风季月份	6：00—8：00、18：00—22：00	其余时间	0：00—4：00、11：00—16：00
小风季月份	6：00—8：00、18：00—22：00	其余时间	11：00—16：00
电价/(元/kWh)	0.585705	0.382725	0.227505
售电价格/(元/kWh)	0.2829	0.2829	0.2829

项目	投资建设成本	年运维成本	其他	单位
电解槽	1500	70.5	—	元/kW
光伏电站	3357.4	33.57	—	元/kW
风电站	3704.4	103.71	—	元/kW
电化学储能	850	21.25	—	元/kWh
储氢罐	1082.3	32.5	—	元/kW
项目运行年份	—	—	20	年
项目内部收益率	—	—	7	%

项目	GFR	LCOH	ELFH	H₂P
信息熵	0.99792	0.99937	0.99919	0.99946
熵权	0.51239	0.15579	0.19908	0.13274

项目	规模	其他值	单位
风电	20		MW
光伏	75		MW
电解槽	65		MW
储氢罐		3	个

项目	GFR	LCOH	ELFH	H₂P
信息熵	0.99756	0.99899	0.99886	0.9988
熵权	0.42136	0.17389	0.19695	0.2078