
华北电力大学经济与管理学院,北京市 昌平区,102206
Published:2026
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XING Da, 1, QI Xin, et al. Robust Optimization Model and Multi-attribute Decision-making Method for Multi-type Collaborative Capacity Configuration of New Energy Bases Considering Electro-hydrogen Conversion[J]. 2026, 50(3): 1162-1174.
XING Da, 1, QI Xin, et al. Robust Optimization Model and Multi-attribute Decision-making Method for Multi-type Collaborative Capacity Configuration of New Energy Bases Considering Electro-hydrogen Conversion[J]. 2026, 50(3): 1162-1174. DOI: 10.13335/j.1000-3673.pst.2025.0837.
为破解高比例可再生能源接入下新能源基地面临的化石能源调峰依赖性与间歇性消纳能力弱双重结构性难题,该文提出考虑电氢转化的容量优化配置方法。首先,建立电氢双向耦合的广义储能系统建模框架,通过电解槽(electrolyzer,EL)、氢储(hydrogen storage,HS)、氢燃料电池(hydrogen fuel cell,HFC)的协同机制,实现丰季风光过剩电能→氢能→枯季电能缺口的跨季节能量转移,提升系统时空调节弹性,解析氢储能在源荷动态匹配中的时空协同机制;然后,综合考虑风光出力多态性与跨季节调节需求,提出基于多场景置信间隙理论(multi-scenario confidence gap decision theory,MCGDT)的鲁棒优化模型,以系统全生命周期成本最小化和碳减排为目标,融合电力平衡、备用容量及碳排放约束,生成高比例新能源与电氢储能的协同配置方案;再然后,通过设置电氢双向、单向及混合储能等对比方案,选用能源可用率(Energy Availability)、经济可行性(Economy)、环境相容性(Environment)、运行弹性(Elasticity)(4E)评价指标,提出G1(序关系分析法)-熵权多属性决策方法。最后,以甘肃省外送新能源基地实证表明:1)电氢双向模式下系统置信鲁棒度达0.91,可再生能源占比45%,氢燃料电池可有效填补最劣场景下12.7%的电力短缺;2)电氢单向模式可再生能源占比提升至67.1%,度电成本0.207元/(kW·h),售氢收益降低系统成本11.2%;3)2035年后"电氢单向+抽蓄"模式综合评价指数0.91,碳排放强度降低42.7g/(kW·h),电解槽成本降至2380元/kW时系统效益提升23.6%。该研究通过融合电氢转化与MCGDT理论,攻克配置方案经济性与鲁棒性难以协同的瓶颈,为新型电力系统下新能源基地规划提供量化决策工具。
To address the dual structural challenges faced by new energy bases under high renewable energy penetration—strong reliance on traditional fossil fuels for peak load regulation and weak intermittent power absorption capacity—electro-hydrogen conversion technology offers a novel capacity allocation pathway. This paper proposes a capacity optimisation model for new energy bases that incorporates electro-hydrogen conversion. First
a generalized energy storage system modeling framework with bidirectional coupling between electricity and hydrogen is established. Through the synergy mechanism of electrolyzers (EL)
hydrogen storage (HS)
and hydrogen fuel cells (HFC)
it achieves cross-seasonal energy transfer from surplus wind/solar power during wet seasons → hydrogen energy → electricity gap compensation during dry seasons. This enhances the system's spatiotemporal regulation flexibility and elucidates the spatial-temporal coordination mechanism of hydrogen storage in matching dynamic source-load profiles. Then
with the dual objectives of minimising system lifecycle costs and reducing carbon emissions
the model integrates constraints on power balance
reserve capacity
and carbon emissions to form a configuration scheme that coordinates high proportions of renewable energy with electro-hydrogen energy storage. Finally
a case study is conducted on a new energy power-exporting base in Gansu Province. The results indicate: 1) Under the bidirectional electro-hydrogen conversion mode
the system's confidence robustness reaches 0.91
with renewable energy accounting for 45% of electricity generation
an annual average comprehensive cost of 3.398 billion yuan
and hydrogen fuel cells effectively compensating for power shortages under the most adverse scenarios; 2) Under the unidirectional electro-hydrogen conversion mode
the proportion of renewable energy in electricity generation increases to 67.1%
hydrogen sales revenue reached 256 million yuan
and the cost per kilowatt-hour dropped to 0.161 yuan/(kW·h)
offering superior economic performance; 3) Compared to energy storage technologies
pumped storage demonstrated the best overall performance before 2030. After 2035
the ‘electricity-hydrogen unidirectional + pumped storage’ mode achieved a comprehensive evaluation index of 0.91
with carbon emissions intensity reduced by 42.7 g/(kW·h)
and the cost of electro-hydrogen conversion equipment decreases (electrolyser costs drop to 2
380 yuan/kW by 2040)
significantly enhancing system efficiency. Therefore
the model proposed in this paper
by integrating electro-hydrogen conversion with multi-scenario confidence interval theory
effectively enhances renewable energy absorption capacity
balances economic and environmental benefits
and provides a quantitative decision-making tool and practical reference for low-carbon planning of renewable energy bases under the new power system.
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