Study on Typical Working Conditions of Hydrogen Production by Photovoltaic Electrolysis of Water and Performance Degradation of Proton Exchange Membrane Electrolytic Stacks
Xu Yanhui, Chen Haowei, Hu Junjie
School of Electrical and Electronic Engineering North China Electricity Power University Beijing 102206 China
Abstract The photovoltaic (PV) power generation is greatly influenced by weather factors and has great fluctuation. Hydrogen production equipment by electrolysis of water, especially proton exchange membrane (PEM) electrolytic stacks, has the characteristics of fast power response and wide power range, and is very suitable for absorbing PV output with strong fluctuation and preparing green hydrogen. However, at present, there is little research on the performance change of electrolytic stacks under fluctuation power input, and there is a lack of working modes to simulate and test the typical application scenario of electrolytic stacks, which can not provide typical input conditions for the performance attenuation test of electrolytic stacks. Therefore, an extraction method of typical working conditions of PV electrolytic water hydrogen production was put forward in this paper, which can obtain the typical working conditions curve reflecting the performance attenuation characteristics of electrolytic stacks under PV fluctuation input. Firstly, empirical wavelet transform (EWT) was used to analyze the initial PV output curves in time and frequency domain, and PV output components with different fluctuation frequencies were separated. Secondly, the fluctuation characteristics of different time periods weredefined and extracted to reduce the dimension of the output curves. Thirdly, the weighted fuzzy clustering method was used to reduce the scenes of many fluctuation feature sequences, and typical PV output scenes were obtained. Finally, according to the characteristic sequence, the working condition indexes are set and the simplified working condition curves are reconstructed, and the cycle working condition spectrum of the electrolytic stacks was obtained. A PV water electrolysis hydrogen production system in the "Three North" area of China was selected as an example, and the PV output data with 1 minute resolution in three years were analyzed. The frequency band number N of EWT was selected as 5, and the initial data curves were decomposed into five components. According to the characteristics of feature sequence data, the optimal clustering number was 4 for scene reduction. In order to compare the advantages of clustering results, the attenuation model of electrolytic stack was introduced to calculate the change values of membrane thickness and catalyst activity of electrolytic stack under different PV input scenarios. The results show that compared with the typical working conditions obtained by other clustering methods, this method has taken into account the degradation of electrolytic stacks, and the separation (SP) index values, containing thickness of PEM and normalized electrochemical active surface area of catalyst, are 0.066 7 and 0.244 3 respectively, which are better than the other three methods. Compared with four kinds of typical original curves, the typical operating curve obtained by the simplification method in this paper has a difference of 4.055 4%, 6.454 6%, 3.616 8% and 2.163 3% respectively in voltage degradation rate, which proves the feasibility of the simplification method. Through simulation analysis, the conclusions are following: (1) the typical working conditions have a good role in describing the performance attenuation of electrolytic stacks. (2) Compared with other methods, the typical working condition curves of PV electrolytic hydrogen production generated by this method can distinguish the performance attenuation of electrolytic stacks, which is more effective in the performance test experiment of electrolytic stacks. (3) For voltage degradation of electrolytic stacks, the difference between the typical working conditions and the actual curves is less than 10%, which is of testing significance.
Xu Yanhui,Chen Haowei,Hu Junjie. Study on Typical Working Conditions of Hydrogen Production by Photovoltaic Electrolysis of Water and Performance Degradation of Proton Exchange Membrane Electrolytic Stacks[J]. Transactions of China Electrotechnical Society, 2024, 39(19): 6228-6243.
[1] Hamada H, Kusayanagi Y, Tatematsu M, et al.Challenges for a reduced inertia power system due to the large-scale integration of renewable energy[J]. Global Energy Interconnection, 2022, 5(3): 266-273. [2] 国家能源局. 2021年光伏发电建设运行情况[EB/OL]. (2022-03-09)[2023-09-27]. www.nea.gov.cn. [3] Badwal S P S, Giddey S S, Munnings C, et al. Emerging electrochemical energy conversion and storage technologies[J]. Frontiers in Chemistry, 2014, 2: 79. [4] 刘玮, 万燕鸣, 熊亚林, 等. 碳中和目标下电解水制氢关键技术及价格平准化分析[J]. 电工技术学报, 2022, 37(11): 2888-2896. Liu Wei, Wan Yanming, Xiong Yalin, et al.Key technology of water electrolysis and levelized cost of hydrogen analysis under carbon neutral vision[J]. Transactions of China Electrotechnical Society, 2022, 37(11): 2888-2896. [5] 黄嘉豪, 田志鹏, 雷励斌, 等. 氢储运行业现状及发展趋势[J]. 新能源进展, 2023, 11(2): 162-173. Huang Jiahao, Tian Zhipeng, Lei Libin, et al.Advances and development trends of hydrogen storage and refueling industry[J]. Advances in New and Renewable Energy, 2023, 11(2): 162-173. [6] 郜捷, 宋洁, 王剑晓, 等. 支撑中国能源安全的电氢耦合系统形态与关键技术[J]. 电力系统自动化, 2023, 47(19): 1-15. Gao Jie, Song Jie, Wang Jianxiao, et al.Form and key technologies of integrated electricity-hydrogen system supporting energy security in China[J]. Automation of Electric Power Systems, 2023, 47(19): 1-15. [7] 刘语忱, 闫群民, 郭阳, 等. 基于完备局部均值分解和相关分析的光伏发电侧电-氢混合储能优化配置[J]. 电气技术, 2022, 23(11): 21-29. Liu Yuchen, Yan Qunmin, Guo Yang, et al.Optimal configuration of electricity-hydrogen hybrid energy storage on photovoltaic generation side based on ensemble local mean decomposition and correlation analysis[J]. Electrical Engineering, 2022, 23(11): 21-29. [8] 李建林, 李光辉, 梁丹曦, 等. “双碳目标”下可再生能源制氢技术综述及前景展望[J]. 分布式能源, 2021, 6(5): 1-9. Li Jianlin, Li Guanghui, Liang Danxi, et al.Review and prospect of hydrogen production technology from renewable energy under targets of carbon peak and carbon neutrality[J]. Distributed Energy, 2021, 6(5): 1-9. [9] 郭秀盈, 李先明, 许壮, 等. 可再生能源电解制氢成本分析[J]. 储能科学与技术, 2020, 9(3): 688-695. Guo Xiuying, Li Xianming, Xu Zhuang, et al.Cost analysis of hydrogen production by electrolysis of renewable energy[J]. Energy Storage Science and Technology, 2020, 9(3): 688-695. [10] 袁铁江, 计力, 田雪沁,等. 考虑燃料电池汽车加氢负荷的电-氢系统协同优化运行[J]. 电力系统自动化, 2023, 47(5): 16-25. Yuan Tiejiang, Ji Li, Tian Xueqin, et al.Synergistic optimal operation of electricity-hydrogen systems considering hydrogen refueling loads for fuel cell vehicles[J]. Automation of Electric Power Systems, 2023, 47(5): 16-25. [11] 杨紫娟, 田雪沁, 吴伟丽, 等. 考虑电解槽组合运行的风电-氢能-HCNG耦合网络容量优化配置[J]. 电力系统自动化, 2023, 47(12): 76-85. Yang Zijuan, Tian Xueqin, Wu Weili, et al.Optimal capacity configuration of wind-hydrogen-HCNG coupled network considering combined electrolyzer operation[J]. Automation of Electric Power Systems, 2023, 47(12): 76-85. [12] Gandía L M, Oroz R, Ursúa A, et al.Renewable hydrogen production: performance of an alkaline water electrolyzer working under emulated wind conditions[J]. Energy & Fuels, 2007, 21(3): 1699-1706. [13] Bhogilla S S, Ito H, Kato A, et al.Experimental study on a laboratory scale Totalized Hydrogen Energy Utilization System for solar photovoltaic application[J]. Applied Energy, 2016, 177: 309-322. [14] Clarke R E, Giddey S, Ciacchi F T, et al.Direct coupling of an electrolyser to a solar PV system for generating hydrogen[J]. International Journal of Hydrogen Energy, 2009, 34(6): 2531-2542. [15] Kim H, Park M, Lee K S.One-dimensional dynamic modeling of a high-pressure water electrolysis system for hydrogen production[J]. International Journal of Hydrogen Energy, 2013, 38(6): 2596-2609. [16] 李军舟, 赵晋斌, 陈逸文, 等. 考虑动态功率区间和制氢效率的电转氢(P2H)设备容量配置优化[J]. 电工技术学报, 2023, 38(18): 4864-4874, 4920. Li Junzhou, Zhao Jinbin, Chen Yiwen, et al.Optimal capacity configuration of P2H equipment considering dynamic power range and hydrogen production efficiency[J]. Transactions of China Electrotechnical Society, 2023, 38(18): 4864-4874, 4920. [17] 孔令国, 陈钥含, 万燕鸣, 等. 计及调峰辅助服务的风电场/群经济制氢容量计算[J]. 电工技术学报, 2023, 38(16): 4406-4420. Kong Lingguo, Chen Yuehan, Wan Yanming, et al.Calculation of economics of power-to-gas capacity for wind farms/clusters with peak regulation auxiliary service response[J]. Transactions of China Electro- technical Society, 2023, 38(16): 4406-4420. [18] 赵礼辉, 王震, 冯金芝, 等. 基于用户大数据的电动汽车驱动系统可靠性试验循环工况构建方法[J]. 机械工程学报, 2021, 57(14): 129-140. Zhao Lihui, Wang Zhen, Feng Jinzhi, et al.Construction method for reliability test driving cycle of electric vehicle drive system based on users' big data[J]. Journal of Mechanical Engineering, 2021, 57(14): 129-140. [19] 杜旭浩, 李秉宇, 苗俊杰, 等. 分布式储能电池运行工况及性能检测分析[J]. 中国电力, 2021, 54(9): 119-124. Du Xuhao, Li Bingyu, Miao Junjie, et al.Operation condition and performance test analysis of distributed energy storage battery[J]. Electric Power, 2021, 54(9): 119-124. [20] 赵安新, 张智晟. 考虑电-气综合需求响应的综合能源系统低碳经济调度[J]. 电气工程学报, 2022, 17(4): 226-232. Zhao Anxin, Zhang Zhisheng.Low-carbon economic dispatch of integrated energy system considering integrated power demand response[J]. Journal of Electrical Engineering, 2022, 17(4): 226-232. [21] Xu Yanhui, Xu Yijia, Huang Yan.Generation of typical operation curves for hydrogen storage applied to the wind power fluctuation smoothing mode[J]. Global Energy Interconnection, 2022, 5(4): 353-361. [22] 姚宏民, 杜欣慧, 秦文萍. 基于密度峰值聚类及GRNN神经网络的光伏发电功率预测方法[J]. 太阳能学报, 2020, 41(9): 184-190. Yao Hongmin, Du Xinhui, Qin Wenping.PV power forecasting approach based on density peaks clustering and general regression neural network[J]. ActaEnergiae Solaris Sinica, 2020, 41(9): 184-190. [23] 董雪, 赵宏伟, 赵生校, 等. 基于SOM聚类和二次分解的BiGRU超短期光伏功率预测[J]. 太阳能学报, 2022, 43(11): 85-93. Dong Xue, Zhao Hongwei, Zhao Shengxiao, et al.Ultra-short-term forecasting method of photovoltaic power based on SOM clustering, secondary decomposition and BiGRU[J]. Acta Energiae Solaris Sinica, 2022, 43(11): 85-93. [24] Zheng Lingwei, Su Ran, Sun Xinyu, et al.Historical PV-output characteristic extraction based weather-type classification strategy and its forecasting method for the day-ahead prediction of PV output[J]. Energy, 2023, 271: 127009. [25] Kojima H, Nagasawa K, Todoroki N, et al.Influence of renewable energy power fluctuations on water electrolysis for green hydrogen production[J]. International Journal of Hydrogen Energy, 2023, 48(12): 4572-4593. [26] Koponen J, Ruuskanen V, Hehemann M, et al.Effect of power quality on the design of proton exchange membrane water electrolysis systems[J]. Applied Energy, 2020, 279: 115791. [27] Khatib F N, Wilberforce T, Ijaodola O, et al.Material degradation of components in polymer electrolyte membrane (PEM) electrolytic cell and mitigation mechanisms: a review[J]. Renewable and Sustainable Energy Reviews, 2019, 111: 1-14. [28] Gilles J.Empirical wavelet transform[J]. IEEE Transactions on Signal Processing, 2013, 61(16): 3999-4010. [29] 吕伟杰, 方一帆, 程泽. 基于模糊C均值聚类和样本加权卷积神经网络的日前光伏出力预测研究[J]. 电网技术, 2022, 46(1): 231-238. Lü Weijie, Fang Yifan, Cheng Ze.Prediction of day-ahead photovoltaic output based on FCM- WS-CNN[J]. Power System Technology, 2022, 46(1): 231-238. [30] 张立军, 张潇. 基于改进CRITIC法的加权聚类方法[J]. 统计与决策, 2015, 31(22): 65-68. Zhang Lijun, Zhang Xiao.Weighted clustering method based on improved CRITIC method[J]. Statistics & Decision, 2015, 31(22): 65-68. [31] 朴尚哲, 超木日力格, 于剑. 模糊C均值算法的聚类有效性评价[J]. 模式识别与人工智能, 2015, 28(5): 452-461. Piao Shangzhe, Chao Murilige, Yu Jian.Cluster validity indexes for FCM clustering algorithm[J]. Pattern Recognition and Artificial Intelligence, 2015, 28(5): 452-461. [32] Chandesris M, Médeau V, Guillet N, et al.Membrane degradation in PEM water electrolyzer: Numerical modeling and experimental evidence of the influence of temperature and current density[J]. International Journal of Hydrogen Energy, 2015, 40(3): 1353-1366. [33] Frensch S H, Serre G, Fouda-Onana F, et al.Impact of iron and hydrogen peroxide on membrane degradation for polymer electrolyte membrane water electrolysis: computational and experimental investigation on fluoride emission[J]. Journal of Power Sources, 2019, 420: 54-62. [34] Siracusano S, Trocino S, Briguglio N, et al.Analysis of performance degradation during steady-state and load-thermal cycles of proton exchange membrane water electrolysis cells[J]. Journal of Power Sources, 2020, 468: 228390. [35] Siracusano S, Hodnik N, Jovanovic P, et al.New insights into the stability of a high performance nanostructured catalyst for sustainable water electrolysis[J]. Nano Energy, 2017, 40: 618-632. [36] Alia S M, Rasimick B, Ngo C, et al.Activity and durability of iridium nanoparticles in the oxygen evolution reaction[J]. Journal of the Electrochemical Society, 2016, 163(11): F3105-F3112. [37] Zheng Yao, Jiao Yan, Jaroniec M, et al.Advancing the electrochemistry of the hydrogen-evolution reaction through combining experiment and theory[J]. Angewandte Chemie International Edition, 2015, 54(1): 52-65. [38] Guo Mingming, Ji Mingjuan, Cui Wei.Theoretical investigation of HER/OER/ORR catalytic activity of single atom-decorated graphyne by DFT and comparative DOS analyses[J]. Applied Surface Science, 2022, 592: 153237. [39] Bystron T, Vesely M, Paidar M, et al.Enhancing PEM water electrolysis efficiency by reducing the extent of Ti gas diffusion layer passivation[J]. Journal of Applied Electrochemistry, 2018, 48(6): 713-723. [40] Bernhard D, Kadyk T, Kirsch S, et al.Model-assisted analysis and prediction of activity degradation in PEM-fuel cell cathodes[J]. Journal of Power Sources, 2023, 562: 232771. [41] Zhang Liqiang, Lin Junyu, Li Ming.Research on the typical working condition of energy storage batteries for a wave energy converter[C]//2018 IEEE Inter- national Power Electronics and Application Conference and Exposition (PEAC), Shenzhen, China, 2018: 1-6. [42] 国家市场监督管理总局, 国家标准化管理委员会. 车用质子交换膜燃料电池堆使用寿命测试评价方法: GB/T 38914—2020[S]. 北京: 中国标准出版社, 2020.
2024, Vol. 39 
