混合电解槽阵列协同的风光耦合制氢系统双层多目标优化

马帅; 李国兴; 吕友军

doi:10.7652/xjtuxb202607011

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混合电解槽阵列协同的风光耦合制氢系统双层多目标优化

专题高纯气体制备 | 更新时间：2026-07-09

- 混合电解槽阵列协同的风光耦合制氢系统双层多目标优化
- Bi-Level Multi-Obj ective Optimization of a Wind-Solar Coupled Hydrogen Production System Based on the Synergy of Hybrid Electrolyzer Arrays
- 西安交通大学学报 2026年60卷第7期页码：109-119
- 作者机构：
  
  长安大学能源与电气工程学院，710064，西安
  西安交通大学绿色氢电全国重点实验室，710049，西安
- 作者简介：
  
  作者简介：马帅（2001—），男，硕士生；
  李国兴（通信作者），男，副教授，硕士生导师。
- 基金信息：
  
  国家自然科学基金资助项目(52336009;52406177)
- DOI：10.7652/xjtuxb202607011
  中图分类号： TM73
- 收稿：2025-12-11，
  
  纸质出版：2026-07-10
- 稿件说明：
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马帅, 李国兴, 吕友军. 混合电解槽阵列协同的风光耦合制氢系统双层多目标优化[J]. 西安交通大学学报, 2026,60(7):109-119.

MA Shuai, LI Guoxing, LÜYouj un. Bi-Level Multi-Obj ective Optimization of a Wind-Solar Coupled Hydrogen Production System Based on the Synergy of Hybrid Electrolyzer Arrays[J]. Journal of Xi'an Jiaotong University, 2026, 60(7): 109-119.
马帅, 李国兴, 吕友军. 混合电解槽阵列协同的风光耦合制氢系统双层多目标优化[J]. 西安交通大学学报, 2026,60(7):109-119. DOI： 10.7652/xjtuxb202607011.

MA Shuai, LI Guoxing, LÜYouj un. Bi-Level Multi-Obj ective Optimization of a Wind-Solar Coupled Hydrogen Production System Based on the Synergy of Hybrid Electrolyzer Arrays[J]. Journal of Xi'an Jiaotong University, 2026, 60(7): 109-119. DOI： 10.7652/xjtuxb202607011.

摘要

为解决单一类型电解槽阵列难以兼顾经济性与宽功率波动下的运行灵活性这一问题，提出一种基于混合电解槽阵列协同的风光耦合制氢系统，利用不同电解槽的特性差异实现优势互补，进而建立了系统的双层多目标优化策略。上层利用非支配排序遗传算法进行宏观容量配置多目标寻优；下层建立双层混合整数线性规划模型，其中聚合模型用于快速评估上层配置方案，精细化模型则准确刻画每个电解槽单元的动态运行特性。结果表明：相较于传统的顺序启停与定期轮换策略，所提策略使单位制氢成本分别降低了14.9%和22.0%，显著提升了系统的经济效益；此外，系统表现出良好的长周期运行适应性，在面临季节交替与波动氢气需求时，单位制氢成本的最高季节性增幅控制在11.16%以内；且相较于平稳需求场景，所提策略通过灵活调控进一步使制氢成本下降了3.93%～7.98%，有效克服了长周期波动影响。研究成果可为实现兼顾高效与灵活的可再生能源制氢系统提供可行方案。

Abstract

To address the challenge that single-type electrolyzer arrays struggle to balance economic viability with operational flexibility under wide power fluctuations，a wind-solar coupled hydrogen production system based on the synergy of hybrid electrolyzer arrays is proposed.By leveraging the complementary characteristics of different electrolyzers to obtain complementary benefits，a bi-level multi-obj ective optimization strategy for the system is established.In the upper level，a non-dominated sorting genetic algorithm is employed to perform multi-obj ective optimization for macro-level capacity allocation.In the lower level，a bi-level mixed-integer linear programming（MILP）model is formulated，consisting of an aggregation model for the rapid evaluation of upper-level configurations and a refined model to accurately characterize the operational dynamics of each electrolyzer unit.The results show that，the proposed optimization scheme reduces the unit hydrogen production cost by 14.9% relative to conventional sequential start-stop strategies and by 22.0% relative to periodic rotation strategies，significantly enhancing the economic efficiency of the system.Moreover，the system demonstrates excellent long-term operational adaptability.Under seasonal transitions and fluctuating hydrogen demands，the maximum seasonal increase in unit hydrogen production cost is controlled within 11.16%.Furthermore，compared with the steady demand scenario，the proposed strategy further reduces the hydrogen production cost by 3.93%—7.98% through flexible regulation，effectively overcoming the impacts of long-term fluctuations.These findings are intended to provide a feasible solution for achieving both high efficiency and flexibility in renewable energy-based hydrogen production.

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