考虑平抑风光波动的ALK-PEM电解制氢系统容量优化模型OACSTPCD
Capacity optimization model for an ALK-PEM electrolytic hydrogen production system considering the stabilization of wind and PV fluctuations
为提高风光资源的利用率,电解水制氢受到了广泛关注,但氢能在生产过程中易受到风光波动性的影响.由此,提出考虑平抑风光波动的碱性-质子交换膜(alkaline-proton exchange membrane,ALK-PEM)电解制氢系统容量优化模型.首先,为降低风光瞬时功率波动对电解制氢系统安全稳定运行的影响,采用 EMD 算法分析原始风电和光伏功率的瞬时波动特性,并经超级电容(supercapacitor,SC)进行平抑,构建了基于EMD的SC容量配置模型.其次,考虑不同电解槽运行特性因素,将质子交换膜(proton exchange membrane,PEM)电解槽和碱性(alkaline,ALK)电解槽相组合替代单类型电解槽的制氢系统,提出了ALK-PEM电解制氢系统容量优化模型,以提高制氢系统经济效益.最后,以某风电场和光伏电站数据为例,通过仿真计算得到系统的容量规划结果,验证所提模型在平抑波动和提高可再生能源利用率方面的有效性,并提高了制氢系统的经济性.
To enhance wind power and photovoltaic utilization,electrolytic hydrogen production has gained attention.However,hydrogen energy is vulnerable to the fluctuations of these resources during production.Therefore,this paper proposes an alkaline-proton exchange membrane(ALK-PEM)capacity optimization model for hydrogen electrolysis to mitigate these fluctuations.First,the EMD algorithm is used to analyze the transient fluctuation characteristics of raw wind and PV.Supercapacitors are employed to smooth these fluctuations,ensuring safe and stable operation of the hydrogen production system.An EMD-based SC capacity allocation model is constructed for this purpose.Second,a capacity optimization model is proposed for the ALK-PEM electrolytic hydrogen production system.By combining proton exchange membrane(PEM)and Alkaline(ALK)electrolyzers,it replaces the single-type electrolyzer system.This approach considers different electrolyzer operational characteristics,thereby improving economic efficiency.Finally,the system's capacity planning is simulated using data from a wind farm and a photovoltaic power plant.This validates the proposed model's effectiveness in smoothing fluctuations,enhancing renewable energy utilization,and improving the system's overall economy.
杨胜;樊艳芳;侯俊杰;白雪岩
新疆大学电气工程学院,新疆 乌鲁木齐 830047
氢能ALK-PEM电解经验模态分解风光波动性容量配置
hydrogenALK-PEM electrolysisEMDwind and PV fluctuationscapacity configuration
《电力系统保护与控制》 2024 (001)
85-96 / 12
新疆维吾尔自治区自然科学基金项目资助(2022D01C365,2022D01C662);2022 天山英才培养计划项目资助(2022TSYCLJ0019) This work is supported by the Natural Science Foundation of Xinjiang Uygur Autonomous Region(No.2022D01C365 and No.2022D01C662).
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