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文章摘要
基于电解槽状态识别的风光制氢系统能量管理优化
Energy management optimization of wind-solar hydrogen production system based on electrolytic cell state recognition
Received:December 07, 2022  Revised:February 10, 2023
DOI:10.19753/j.issn1001-1390.2023.10.002
中文关键词: 碱性电解槽  状态识别  风光制氢系统  能量管理  GA算法  SPEA2算法
英文关键词: alkaline water electrolysis, state recognition, wind-solar hydrogen production system, energy management, GA algorithm, SPEA2 algorithm
基金项目:国网青海省电力公司科技项目资助(522807220002)、浙江省教育厅科研项目资助(Y202044527)、(Y202250621)
Author NameAffiliationE-mail
Nian Heng College of Electrical Engineering Zhejiang University nianheng@zju.edu.cn 
Chen Leilei College of Electrical Engineering Zhejiang University 519208846@qq.com 
Zhao Jianyong* College of Electrical Engineering Zhejiang University jyzhao@zju.edu.cn 
Ma Runsheng State Grid Qinghai Electric Power Company Electric Power Research Institute 513799139@qq.com 
Zhao Wenqiang State Grid Qinghai Electric Power Company Electric Power Research Institute 513799139@qq.com 
Wang Kerong State Grid Qinghai Electric Power Company Electric Power Research Institute 513799139@qq.com 
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中文摘要:
      针对碱性电解槽备用状态的选取问题,提出了一种基于碱性电解槽状态识别的风光制氢系统能量管理优化策略。文章确定制氢系统组成,建立系统模块模型,包括风电、光电、储能电池、碱性电解槽模块;确定系统风光消纳率、售氢收益比为目标,确定系统运行约束条件,采用SPEA2、GA结合算法求解多目标、多决策变量;之后结合天气预测数据、电网数据,以能量管理优化策略确定电解槽冷、热备用状态;最后利用实际算例论证优化策略可行性。算例结果显示能量管理优化策略能准确识别碱性电解槽冷、热备用状态,能够利用冷备用状态节约系统电能,合理分配系统内功率,提高系统收益。
英文摘要:
      Aiming at the problem of selecting the alkaline water electrolyzers standby state, an optimization strategy for energy management of wind-solar hydrogen generation system based on alkaline water electrolyzers state recognition was proposed. The composition of hydrogen production system was determined, and the system module model was established, including wind power, photoelectric, energy storage battery and electrolytic cell module. The absorption rate of wind-solar power generation and the profit ratio of hydrogen sales were determined as the objectives, and the constraints of system operation were determined. SPEA2 and GA combined algorithm were used to solve multi-objective and multi-decision variables at the same time. Then, combined with the weather forecast data and power grid data, the cold or hot standby state of the electrolyzer was determined by the energy management optimization strategy. Finally, a practical example was used to demonstrate the feasibility of the optimization strategy. The results of the example show that the energy management optimization strategy can accurately identify the cold or hot standby state of alkaline water electrolyzers, and can use the cold standby state to save the system energy, reasonably distribute the power in the system, and improve the system revenue.
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