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针对购买、维护光伏发电和储能设备会产生大量经济性成本，同时在部分天气情况下不充足的光照条件给光伏制氢储能系统的运行带来挑战的问题，提出了一种基于改进的多目标灰狼优化（Improved Grey Wolf Optimization，IGWO）算法的光伏制氢储能系统配置优化方法。对系统进行建模，增设两种储能设备以保证系统的稳定运行。IGWO算法使用混沌理论进行种群的初始化，使种群更彻底地搜索解空间；对狼群位置的更新使用莱维轨迹进行扰动以扩大搜索范围，使算法不易陷入局部最优点；使用贪婪策略更新个体的位置。以降低光伏制氢储能系统的经济性成本、弃光惩罚成本和购电成本为优化目标，使用该优化算法求解系统各组件的配置容量。算例分析结果表明，IGWO算法相较于原始方法可更加有效地降低光伏制氢储能系统的经济性成本、弃光率和购电率。
Abstract：Due to the significant economic costs associated with purchasing and maintaining photovoltaic power generation and energy storage equipment, as well as the challenges posed by insufficient sunlight under certain weather for the operation of PV hydrogen storage systems, this paper proposes an optimization method for the configuration of PV hydrogen storage systems based on a multi -objective improved grey wolf optimization(IGWO) algorithm. The system is modeled, and two types of energy storage devices are added to ensure stable operation of the system. The proposed IGWO algorithm uses chaos theory for population initialization, allowing for a more thorough search of the solution space. Lévy trajectory is used to disturb the update of the wolf pack positions to expand the search range, making it less likely for the algorithm to get trapped in local optima. A greedy strategy is used to update the positions of individuals. Taking the economic cost, penalty cost for curtailment, and electricity cost for purchase of PV hydrogen storage system as optimization objectives, the proposed optimization algorithm is used to solve the configuration capacity of the system components, aiming to minimize these costs. The results indicate that the IGWO algorithm is more effective than the original method in reducing the economic costs, curtailment, and purchasing costs of the PV hydrogen storage system.
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基本信息:
DOI：10.19929/j.cnki.nmgdljs.2025.0007
中图分类号:
引用信息:
[1]许龙虎1,刘少鹏2,卢皓天1.基于改进多目标灰狼优化算法的光伏制氢储能系统配置优化[J],2025,43(01):39-48.DOI:10.19929/j.cnki.nmgdljs.2025.0007.
基金信息:
中国电力工程顾问集团有限公司科技项目“光伏水电解制氢技术研究与应用示范”（DG3-A03-2022）