计及电解槽特性的可再生能源电解制氢系统容量优化研究

王超; 陈志鑫; 朱建功; 徐建强; 张浩宇; 袁浩; 戴海峰; 魏学哲; 余卓平

doi:10.19799/j.cnki.2095-4239.2026.0695

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计及电解槽特性的可再生能源电解制氢系统容量优化研究

储能系统与工程 | 更新时间：2026-10-22

- 计及电解槽特性的可再生能源电解制氢系统容量优化研究
- Research on capacity optimization of renewable energy electrolytic hydrogen production system considering electrolyzer characteristics
- 储能科学与技术 2026年15卷第10期页码：3992-4003
- 作者机构：
  
  1.同济大学汽车与能源学院
  2.同济大学新能源汽车工程中心，上海 201804
- 作者简介：
  
  王超（1987—），男，博士研究生，工程师，研究方向为燃料电池及电解池，E-mail：wangchao_4@tongji.edu.cn；
  魏学哲，教授，研究方向为电化学电源管控，E-mail：weixzh@tongji.edu.cn。
- 基金信息：
  
  青海省重大科技专项(2023-GX-A2)
- DOI：10.19799/j.cnki.2095-4239.2026.0695
  中图分类号： TK 91
- 收稿：2026-08-10，
  
  修回：2026-09-08，
  
  纸质出版：2026-10-28
- 稿件说明：
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王超, 陈志鑫, 朱建功, 等. 计及电解槽特性的可再生能源电解制氢系统容量优化研究[J]. 储能科学与技术, 2026, 15(10): 3992-4003.

WANG Chao, CHEN Zhixin, ZHU Jiangong, et al. Research on capacity optimization of renewable energy electrolytic hydrogen production system considering electrolyzer characteristics[J]. Energy Storage Science and Technology, 2026, 15(10): 3992-4003.
王超, 陈志鑫, 朱建功, 等. 计及电解槽特性的可再生能源电解制氢系统容量优化研究[J]. 储能科学与技术, 2026, 15(10): 3992-4003. DOI： 10.19799/j.cnki.2095-4239.2026.0695.

WANG Chao, CHEN Zhixin, ZHU Jiangong, et al. Research on capacity optimization of renewable energy electrolytic hydrogen production system considering electrolyzer characteristics[J]. Energy Storage Science and Technology, 2026, 15(10): 3992-4003. DOI： 10.19799/j.cnki.2095-4239.2026.0695.

摘要

针对可再生能源电解制氢系统容量配置及能量分配问题，本研究提出一种考虑电解槽温度及氧中氢特性的可再生能源电解制氢系统双层容量优化方法。模型上层以最低平准化制氢成本为目标函数，以电解槽容量和电池容量为决策变量，利用自适应粒子群算法进行求解；下层以日均制氢量最大为目标函数，以各时段电解槽/电池功率及运行状态为决策变量，以功率平衡、弃电率、设备运行合理性作为约束条件，调用CPLEX求解混合整数规划问题。通过双层容量规划，实现容量配置与能量调度的协同优化。针对离网型可再生能源电解制氢系统进行案例分析，仿真结果表明，相较于传统的恒定设备负荷边界的双层容量优化方法，使用本研究建立的考虑电解槽温度与氧中氢特性的变负荷边界工况双层容量优化模型时，电解槽容量由17.954 kW减小至15.745 kW，电池容量由10.730 kWh增大至55.496 kWh，证明传统的恒定工况双层容量优化模型由于忽略电解槽温度和系统氧中氢特性，进而低估了为维持离网系统稳定运行所需的电池容量与氢气制取成本。

Abstract

To address the capacity configuration problem of renewable energy-powered water electrolysis hydrogen production systems

this paper proposes a bi-level capacity optimization method that accounts for electrolyzer temperature and hydrogen-in-oxygen (HTO) characteristics. In the upper-level model

the levelized cost of hydrogen is minimized

with the capacities of the electrolyzer and the battery as decision variables and the equipment capacity limits as constraints. An adaptive particle swarm optimization algorithm is employed to solve this optimization problem. The lower-level model maximizes the average daily hydrogen production

with the power levels and operating states of equipment in each time period as decision variables

subject to constraints including power balance

equipment operational rationality

and grid operational rationality. The resulting mixed-integer programming problem is solved using CPLEX. Through this bi-level capacity planning scheme

coordinated optimization between capacity sizing and energy scheduling is achieved. A case study is conducted on an off-grid renewable energy-powered water electrolysis hydrogen production system. Simulation results demonstrate that

compared with the conventional bi-level capacity optimization method employing fixed equipment loading boundaries

the proposed bi-level optimization model

which incorporates the temperature-dependent and HTO characteristics of the electrolyzer under variable loading conditions

yields a reduction in electrolyzer capacity from 17.954 kW to 15.745 kW

while the battery capacity increases from 10.730 kWh to 55.496 kWh. These results confirm that the conventional bi-level optimization model with constant operating conditions underestimates both the battery capacity required to maintain system stability and the corresponding hydrogen production cost

owing to its neglect of electrolyzer temperature dynamics and HTO constraints.

关键词

Keywords

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