TWO-STAGE OPERATION OPTIMIZATION OF ELECTRO-HYDROGEN HYBRID ENERGY STORAGE SYSTEM WITH MULTI-TYPE ELECTROLYSIS SYNERGY UNDER MULTI-FREQUENCY FLUCTUATIONS OF WIND AND SOLAR POWER

Zhang Xinghao; Xu Chuanbo

doi:10.19912/j.0254-0096.tynxb.2024-1817

PDF(1727 KB)

Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (2) : 702-713. DOI: 10.19912/j.0254-0096.tynxb.2024-1817

TWO-STAGE OPERATION OPTIMIZATION OF ELECTRO-HYDROGEN HYBRID ENERGY STORAGE SYSTEM WITH MULTI-TYPE ELECTROLYSIS SYNERGY UNDER MULTI-FREQUENCY FLUCTUATIONS OF WIND AND SOLAR POWER

Zhang Xinghao¹, Xu Chuanbo^1,2

Author information +

History +

Abstract

This study addresses the variability of wind and photovoltaic power generation by proposing a two-stage optimization model for a hybrid energy storage system that includes supercapacitors and alkaline/proton exchange membrane hydrogen production. Initially, the raw power signals of wind and solar power are decomposed using empirical mode decomposition and reconstructed into multi-frequency scale fluctuations based on grid-connected power limits. Subsequently, in the first stage, a multi-type electrolysis power allocation strategy is proposed to construct a multi-objective capacity planning model that includes the levelized cost of hydrogen production and the rate of wind and solar power curtailment, which is solved using an improved non-dominated sorting genetic algorithm. Then, in the second stage, an operational optimization model is constructed to minimize the comprehensive cost, which is solved using Gurobi. Actual case results demonstrate that, compared to standalone alkaline electrolyzer and standalone proton exchange membrane electrolyzer systems, the system proposed in this study reduces the exceedance of grid-connected fluctuating power by 35.15% and 20.15%, respectively, and lowers the fluctuation penalty cost by 9.7% and 1.04%, respectively. Additionally, its levelized cost of hydrogen is 39.89% lower than that of the standalone proton exchange membrane system, and the curtailment rate of wind and solar power is reduced by 20.15%.

Key words

wind and photovoltaic power grid-connection / empirical mode decomposition / supercapacitor / electrolytic hydrogen production / levelized hydrogen production cost

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

Zhang Xinghao, Xu Chuanbo. TWO-STAGE OPERATION OPTIMIZATION OF ELECTRO-HYDROGEN HYBRID ENERGY STORAGE SYSTEM WITH MULTI-TYPE ELECTROLYSIS SYNERGY UNDER MULTI-FREQUENCY FLUCTUATIONS OF WIND AND SOLAR POWER[J]. Acta Energiae Solaris Sinica. 2026, 47(2): 702-713 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1817

References

[1] WANG F, HARINDINTWALI J D, YUAN Z Z, et al.Technologies and perspectives for achieving carbon neutrality[J]. The innovation, 2021, 2(4): 100180.
[2] OLABI A G, ALI ABDELKAREEM M.Renewable energy and climate change[J]. Renewable and sustainable energy reviews, 2022, 158: 112111.
[3] GIELEN D, BOSHELL F, SAYGIN D, et al.The role of renewable energy in the global energy transformation[J]. Energy strategy reviews, 2019, 24: 38-50.
[4] 赵雪莹, 李根蒂, 孙晓彤, 等. “双碳”目标下电解制氢关键技术及其应用进展[J]. 全球能源互联网, 2021(5): 436-446.
ZHAO X Y, LI G D, SUN X T, et al.Key technology and application progress of hydrogen production by electrolysis under peaking carbon dioxide emissions and carbon neutrality targets[J]. Journal of global energy interconnection, 2021(5): 436-446.
[5] 许传博, 刘建国. 氢储能在我国新型电力系统中的应用价值、挑战及展望[J]. 中国工程科学, 2022, 24(3): 89-99.
XU C B, LIU J G.Hydrogen energy storage in China’s new-type power system: application value, challenges, and prospects[J]. Strategic study of CAE, 2022, 24(3): 89-99.
[6] JACOB A S, BANERJEE R, GHOSH P C.Sizing of hybrid energy storage system for a PV based microgrid through design space approach[J]. Applied energy, 2018, 212: 640-653.
[7] 唐娴, 林茵茵, 陈永淑, 等. 基于储能融合的电网资本性项目规划研究[J]. 电测与仪表, 2025, 62(4): 44-52.
TANG X, LIN Y Y, CHEN Y S, et al.Research on power grid capital project planning based on energy storage integration[J]. Electrical measurement & instrumentation, 2025, 62(4): 44-52.
[8] 张利伟, 史泽辉, 周文婷, 等. 计及电池寿命的电/热/氢混合储能系统容量优化配置[J]. 电网技术, 2025, 49(3): 998-1006.
ZHANG L W, SHI Z H, ZHOU W T, et al.Capacity optimization configuration of battery, thermal and hydrogen hybrid energy storage system considering battery life[J]. Power system technology, 2025, 49(3): 998-1006.
[9] 王嘉乐, 郭苏, 何意, 等. 基于灵活储氢和蓄电池联合储能的混合可再生能源系统规划运行协同优化[J]. 太阳能学报, 2024, 45(7): 41-49.
WANG J L, GUO S, HE Y, et al.Multi stage planning and operation optimization of hybrid renewable energy systems with flexible hydrogen-battery stroage[J]. Acta energiae solaris sinica, 2024, 45(7): 41-49.
[10] 曾振松, 郑洁云, 魏鑫, 等. 微电网的氢-氨混合储能系统容量优化配置研究[J]. 广东电力, 2024, 37(7): 42-49.
ZENG Z S, ZHENG J Y, WEI X, et al.Research on capacity optimization configuration of hydrogen-ammonia hybrid storage system in microgrid[J]. Guangdong electric power, 2024, 37(7): 42-49.
[11] 葛磊蛟, 崔庆雪, 李明玮, 等. 风光波动性电源电解水制氢技术综述[J]. 综合智慧能源, 2022, 44(5): 1-14.
GE L J, CUI Q X, LI M W, et al.Review on water electrolysis for hydrogen production powered by fluctuating wind power and PV[J]. Integrated intelligent energy, 2022, 44(5): 1-14.
[12] 丁明, 吴杰, 张晶晶. 面向风电平抑的混合储能系统容量配置方法[J]. 太阳能学报, 2019, 40(3): 593-599.
DING M, WU J, ZHANG J J.Capacity optimization method of hybrid energy storage system for wind power smoothing[J]. Acta energiae solaris sinica, 2019, 40(3): 593-599.
[13] 侯力枫. 提高风储发电经济性的双储能系统模型预测控制方法[J]. 电网与清洁能源, 2020, 36(5): 97-106.
HOU L F.A model predictive control method of the dual energy storage system for improving wind storage economy[J]. Power system and clean energy, 2020, 36(5): 97-106.
[14] HAN X J, CHEN F, CUI X W, et al.A power smoothing control strategy and optimized allocation of battery capacity based on hybrid storage energy technology[J]. Energies, 2012, 5(5): 1593-1612.
[15] 冯磊, 杨淑连, 徐达, 等. 考虑风电输出功率波动性的混合储能容量多级优化配置[J]. 热力发电, 2019, 48(10): 44-50.
FENG L, YANG S L, XU D, et al.Multistage optimal capacity configuration of hybrid energy storage considering wind power fluctuation[J]. Thermal power generation, 2019, 48(10): 44-50.
[16] 张琪. 基于可再生能源水电解制氢技术发展概述[J]. 当代化工研究, 2023(2): 14-16.
ZHANG Q.Overview of the development of hydrogen production technology by water electrolysis based on renewable energy[J]. Modern chemical research, 2023(2): 14-16.
[17] 郑博, 白章, 袁宇, 等. 多类型电解协同的风光互补制氢系统与容量优化[J]. 中国电机工程学报, 2022, 42(23): 8486-8496.
ZHENG B, BAI Z, YUAN Y, et al.Hydrogen production system and capacity optimization based on synergistic operation with multi-type electrolyzers under wind-solar power[J]. Proceedings of the CSEE, 2022, 42(23): 8486-8496.
[18] 黄启帆, 陈洁, 曹喜民, 等. 基于碱性电解槽和质子交换膜电解槽协同制氢的风光互补制氢系统优化[J]. 电力自动化设备, 2023, 43(12): 168-174.
HUANG Q F, CHEN J, CAO X M, et al.Optimization of wind-photovoltaic complementation hydrogen production system based on synergistic hydrogen production by alkaline electrolyzer and proton exchange membrane electrolyzer[J]. Electric power automation equipment, 2023, 43(12): 168-174.
[19] 白章, 韩运滨, 王智, 等. 基于功率分配策略的风光互补复合制氢系统与容量优化[J]. 油气储运, 2023, 42(8): 910-921, 943.
BAI Z, HAN Y B, WANG Z, et al.Wind-solar hybrid hydrogen production system and capacity optimization based on power allocation strategy[J]. Oil & gas storage and transportation, 2023, 42(8): 910-921, 943.
[20] 杨胜, 樊艳芳, 侯俊杰, 等. 考虑平抑风光波动的ALK-PEM电解制氢系统容量优化模型[J]. 电力系统保护与控制, 2024, 52(1): 85-96.
YANG S, FAN Y F, HOU J J, et al.Capacity optimization model for an ALK-PEM electrolytic hydrogen production system considering the stabilization of wind and PV fluctuations[J]. Power system protection and control, 2024, 52(1): 85-96.
[21] 冯云, 曹田田, 宋海涛, 等. 分布式制氢技术进展及成本分析[J]. 石油炼制与化工, 2022, 53(11): 11-16.
FENG Y, CAO T T, SONG H T, et al.Progress and cost analysis of distributed hydrogen production technology[J]. Petroleum processing and petrochemicals, 2022, 53(11): 11-16.
[22] 马晓锋, 张舒涵, 何勇, 等. PEM电解水制氢技术的研究现状与应用展望[J]. 太阳能学报, 2022, 43(6): 420-427.
MA X F, ZHANG S H, HE Y, et al.Research status and application prospect of PEM electrolysis water technology for hydrogen production[J]. Acta energiae solaris sinica, 2022, 43(6): 420-427.
[23] IVERSON Z, ACHUTHAN A, MARZOCCA P, et al.Optimal design of hybrid renewable energy systems (HRES) using hydrogen storage technology for data center applications[J]. Renewable energy, 2013, 52: 79-87.
[24] 苏建徽, 余世杰, 赵为, 等. 硅太阳电池工程用数学模型[J]. 太阳能学报, 2001, 22(4): 409-412.
SU J H, YU S J, ZHAO W, et al.Investigation on engineering analytical model of silicon solar cells[J]. Acta energiae solaris sinica, 2001, 22(4): 409-412.
[25] ZHANG K, ZHOU B, OR S W, et al.Optimal coordinated control of multi-renewable-to-hydrogen production system for hydrogen fueling stations[J]. IEEE transactions on industry applications, 2022, 58(2): 2728-2739.
[26] 施正荣, 翁楚, 蔡靖雍, 等. 基于PV/T的质子交换膜电解制氢系统动态性能研究[J]. 太阳能学报, 2023, 44(8): 164-170.
SHI Z R, WENG C, CAI J Y, et al.Study on dynamic performance of PV/T based on proton exchange membrane water electrolysis system[J]. Acta energiae solaris sinica, 2023, 44(8): 164-170.
[27] 袁铁江, 郭建华, 杨紫娟, 等. 平抑风电波动的电-氢混合储能容量优化配置[J]. 中国电机工程学报, 2024, 44(4): 1397-1405.
YUAN T J, GUO J H, YANG Z J, et al.Optimal allocation of power electric-hydrogen hybrid energy storage of stabilizing wind power fluctuation[J]. Proceedings of the CSEE, 2024, 44(4): 1397-1405.
[28] 闫群民, 刘语忱, 董新洲, 等. 基于CEEMDAN-HT的平抑光伏出力混合储能容量优化配置[J]. 电力系统保护与控制, 2022, 50(21): 43-53.
YAN Q M, LIU Y C, DONG X Z, et al.Hybrid energy storage capacity optimization configuration for smoothing PV output based on CEEMDAN-HT[J]. Power system protection and control, 2022, 50(21): 43-53.
[29] 林旗力, 戚宏勋, 黄晶晶, 等. 碱性-质子交换膜水电解复合制氢平准化成本分析[J]. 储能科学与技术, 2023, 12(11): 3572-3580.
LIN Q L, QI H X, HUANG J J, et al.Levelized cost of combined hydrogen production by water electrolysis with alkaline-proton exchange membrane[J]. Energy storage science and technology, 2023, 12(11): 3572-3580.
[30] 王茜, 张粒子. 采用NSGA-Ⅱ混合智能算法的风电场多目标电网规划[J]. 中国电机工程学报, 2011, 31(19): 17-24.
WANG Q, ZHANG L Z.Multi-objective transmission planning associated with wind farms applying NSGA-Ⅱ hybrid intelligent algorithm[J]. Proceedings of the CSEE, 2011, 31(19): 17-24.
[31] 朱新凯, 刘雅斌, 王景霞, 等. 基于非支配排序遗传算法与神经网络的20 MW双定子超导磁场调制电机优化设计[J]. 中国电机工程学报, 2025, 45(15): 6103-6116.
ZHU X K, LIU Y B, WANG J X, et al.Optimal design of 20 MW double-stator superconducting magnetic field modulation electrical machine based on non-dominated sorting genetic algorithm and neural network[J]. Proceedings of the CSEE, 2025, 45(15): 6103-6116.
[32] DEB K, AGRAWAL S, PRATAP A, et al.A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II[M]//Berlin, Heidelberg: Springer, 2000: 849-858.
[33] 左逢源, 张玉琼, 赵强, 等. 计及源荷不确定性的综合能源生产单元运行调度与容量配置两阶段随机优化[J]. 中国电机工程学报, 2022, 42(22): 8205-8215.
ZUO F Y, ZHANG Y Q, ZHAO Q, et al.Two-stage stochastic optimization for operation scheduling and capacity allocation of integrated energy production unit considering supply and demand uncertainty[J]. Proceedings of the CSEE, 2022, 42(22): 8205-8215.