
1.电网安全全国重点实验室,北京 100192
2.中国电力科学研究院有限公司,北京 100192
3.国网山西省电力公司电力科学研究院,太原 030001
Received:29 October 2024,
Revised:2024-12-23,
Published Online:19 February 2025,
Published:25 October 2025
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刘铠诚,王松岑,何桂雄等.家庭用户燃料电池热电联供系统能量管理策略及配置优化[J].综合智慧能源,2025,47(10):77-87.
LIU Kaicheng,WANG Songcen,HE Guixiong,et al.Energy management strategy and configuration optimization of fuel cell combined heat and power system for household consumers[J].BLASTING,2025,47(10):77-87.
刘铠诚,王松岑,何桂雄等.家庭用户燃料电池热电联供系统能量管理策略及配置优化[J].综合智慧能源,2025,47(10):77-87. DOI: 10.3969/j.issn.2097-0706.2025.10.009.
LIU Kaicheng,WANG Songcen,HE Guixiong,et al.Energy management strategy and configuration optimization of fuel cell combined heat and power system for household consumers[J].BLASTING,2025,47(10):77-87. DOI: 10.3969/j.issn.2097-0706.2025.10.009.
氢能是清洁零碳、可长期存储、灵活高效的二次能源。基于燃料电池的氢-电相互转化是氢能助力能源电力行业低碳转型的重要应用思路。针对家庭用户用能场景,建立包含燃料电池、电解池、蓄电池、储氢罐、储热水箱等模块的氢燃料电池热电联供系统多单元数学模型,分析典型地区用电、用热负荷需求,提出家庭用户典型日负荷曲线。分别针对峰谷电利用和清洁能源消纳两种典型场景提出系统能量管理策略,并通过多参数联合调试获得各场景下系统初步参数配置方案。采用粒子群优化算法,以经济性为目标函数,开展系统配置优化研究。优化后峰谷电利用和清洁能源消纳场景下系统年均成本分别下降了7.14%和6.15%。
Hydrogen energy is a clean, zero-carbon, long-term storable, flexible, and efficient secondary energy source. The hydrogen-electricity conversion using fuel cells is a crucial application for hydrogen energy to promote the low-carbon transition in the energy and power industry. For household energy use scenarios, a multi-unit mathematical model of a hydrogen fuel cell combined heat and power system was developed, incorporating modules such as fuel cells, electrolytic cells, batteries, hydrogen storage tanks, and heat storage tanks. The electricity and heat load demands of typical regions were analyzed, and a typical daily load curve for household consumers was proposed. Energy management strategies for the system were proposed based on two typical scenarios: peak-valley electricity utilization and clean energy consumption. Preliminary system parameter configurations for each scenario were obtained through multi-parameter joint debugging. The particle swarm optimization algorithm was employed to optimize the system configuration, with economic cost as the objective function. After optimization, the annual average system costs in the scenarios of peak-valley electricity utilization and clean energy consumption were reduced by 7.14% and 6.15%, respectively.
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