针对光伏-燃料电池-电解池-储氢-锂电池耦合热电联供系统,建立电-氢转化技术路线与容量配置协同优化的混合整数线性规划模型。计及关键部件成本以及电-氢转化技术路线不确定性,可选择的技术有可逆固体氧化物电池、质子交换膜燃料电池、质子交换膜电解池。结果显示,技术路线的选择和部件成本变化对用能成本有重要影响,用能成本范围为[0.40,1.15]元/kWh;可逆固体氧化物电池由于可以双模式运行,装备利用率及经济性较好,相较质子交换膜电池和锂电池更具优势。然而,当可逆固体氧化物电池成本达到2 万元/kW且质子交换膜电解池成本低至0.25万元/kW时,质子交换膜电池储能技术路线经济性更优;当可逆固体氧化物电池和质子交换膜电解池成本分别高于1.6万元/kW、0.4万元/kW,且锂电池成本低至0.04万元/kWh时,仅适合采用锂电池作为储能部件,此时用能成本低于0.73元/kWh。
Abstract
With the lowest total cost as the objective, this paper establishes a collaborative optimization model of technology selection and capacity allocation for the photovoltaic-fuel cell-electrolyzer-hydrogen storage-lithium battery coupling system, accounting for component cost uncertainty. Proton exchange membrane fuel cells, proton exchange membrane electrolyzers, and reversible solid oxide cells are optional hydrogen-electricity conversion technologies. Results revealed that the technical path selected and variations in component prices have a significant effect on the system configuration. Reversible solid oxide cells are able to operate in dual modes, resulting in high equipment utilization and economy, offering greater advantages over lithium battery storage technology, and proton exchange membrane cells. The levelized cost of energy ranges in [0.40, 1.15] RMB/kWh. However, the electricity-hydrogen coupling technology route based on proton exchange membrane cells has greater advantages when the cost of reversible solid oxide cells is more than 20000 RMB/kW and the cost of proton exchange membrane electrolyzers is less than 2500 RMB/kW. When the cost of reversible solid oxide cells and proton exchange membrane electrolyzers is higher than 16000 RMB/kW and 4000 RMB/kW, respectively, and the cost of lithium batteries is as low as 400 RMB/kWh, it is only suitable to use lithium batteries as energy storage components. Under those scenarios, the levelized cost of energy is less than 0.73 RMB/kWh.
关键词
氢 /
不确定性分析 /
燃料电池 /
电解池 /
容量优化
Key words
hydrogen /
uncertainty analysis /
fuel cells /
electrolyzer /
capacity optimization
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基金
国家重点研发计划项目（2023YFB4204000; 2022YFE0129400）
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