极地研究 ›› 2025, Vol. 37 ›› Issue (4): 830-841.DOI: 10.13679/j.jdyj.20240063

• 研究论文 • 上一篇    下一篇

基于改进灰狼算法的南极中山站微电网风光氢储优化配置研究

周晓杰1孟润泉1王彬2窦银科2韩肖清1   

  1. 1太原理工大学电力系统运行与控制山西省重点实验室, 山西 太原 030024; 

    2山西省能源互联网研究院, 山西 太原 030000

  • 收稿日期:2024-07-08 修回日期:2024-10-24 出版日期:2025-12-30 发布日期:2026-01-12
  • 通讯作者: 孟润泉
  • 基金资助:
    山西省能源互联网研究院重大科研支撑项目、国家自然科学基金和山西省重点研发计划资助

Optimization of wind, solar, and hydrogen energy in the microgrid of Zhongshan Station, Antarctica, based on improved Gray Wolf Algorithm

ZHOU Xiaojie1, MENG Runquan1WANG Bin2DOU Yinke2HAN Xiaoqing1   

  1. 1Shanxi Key Laboratory of Power System Operation and Control Taiyuan University of Technology, Taiyuan 030024, China;
    2Shanxi Energy Internet Research Institute, Taiyuan 030000, China
  • Received:2024-07-08 Revised:2024-10-24 Online:2025-12-30 Published:2026-01-12

摘要: 针对南极中山科考站特殊的地理环境, 本文充分考虑低温对各发电单元影响的基础上, 基于富余风光制氢、配有蓄电池和超级电容等储能装置的风光氢储等, 建立了一套独立微电网优化配置模型该模型具有以下特点: (1)在考虑极地低温环境对风机和太阳能电池板出力、蓄电池容量、超级电容容量以及用电负荷实时影响的基础上, 分析整个微电网的系统结构并对各设备模型进行低温建模; (2)以系统的持续可靠运行为首要目标, 年度平均成本最小为次要目标, 各设备的运行条件及全年的弃风弃光率、负荷缺失率为约束, 采用改进灰狼算法对极地环境下的微电网进行优化配置; (3)通过算例对所提出的方法进行验证实验结果表明, 文提出的算法不仅具有更广遍历范围, 而收敛性与精确性方面均表现出更优的性能, 具有一定的参考价值。

关键词: 可再生能源, 微电网, 优化算法, 储能, 极地环境

Abstract: This paper proposes an independent microgrid optimization configuration model comprising surplus solar hydrogen production and solar hydrogen storage in devices such as batteries and supercapacitors for Zhongshan Station. The special environment of Antarctica and the influence of low temperature on the power generation units are taken into consideration. The model has the following characteristics: (1) the system structure of the entire microgrid is analyzed and each device is simulated taking into consideration the real-time effects of the low temperature environment on the output of fans and solar panels, the capacity of batteries, the capacity of supercapacitors, and the power load. (2) The improved gray wolf algorithm was adopted to optimize the configuration of the microgrid for the polar environment. Constraints included the operating conditions of each equipment, the annual wind and light abandonment rate, and load loss rate. Continuous and reliable operation of the system and minimum annual average cost were the primary and secondary goals, respectively. (3) The proposed method was verified with a numerical experiment. Experimental results show that the proposed algorithm can be used to design a microgrid with a wide range and superior convergence and accuracy. The results of this study can serve as a reference for future developments.

Key words: renewable energy, microgrids, optimization algorithms, energy storage, polar environment