Optimal allocation strategy of wind hydrogen and ammonia storage under multi-factor constraints of source-storage-load cooperation

doi:10.19799/j.cnki.2095-4239.2024.1250

Abstract

Abstract:

Hydrogen and ammonia production from wind power offers a practical solution for addressing the on-site consumption challenges of wind energy in China's "Three North" region. To enhance the economic performance and operational stability of wind-hydrogen-ammonia synthesis systems, this study proposes an optimal allocation strategy that considers multi-factor constraints in source-storage-load coordination. First, a system model for wind turbine-driven hydrogen and ammonia production is established, incorporating variables such as hydrogen production equipment power, energy storage configuration ratio, hydrogen storage tank capacity, grid electricity consumption, annual shutdown frequency, hydrogen and ammonia outputs, and liquid ammonia costs under different operational conditions. Second, an economic objective model is formulated as a boundary condition to guide a source-storage-load collaborative optimization strategy. This strategy identifies optimal output levels for grid electricity extraction, hydrogen storage, and battery energy storage to achieve cost-effective configurations across different scenarios. Finally, using typical annual wind speed data from Urad Middle Banner as a benchmark, an economic comparison is conducted across four working scenarios. Results demonstrate that the proposed control strategy effectively meets field demands, achieving the lowest unit ammonia cost, maximum system output, minimal downtime, and the shortest payback period. This study enhances the local consumption and self-balancing capabilities of wind power systems and offers engineering insights for improving the economic viability of hydrogen and ammonia production based on coordinated source-storage-load operation.

Key words: wind power, green electricity ammonia production, optimal economic performance, system configuration

CLC Number:

TK 513

Yanhu ZHANG, Hui WANG, Shaokun ZOU, An WEI, Dejun LUO, Youhua JIANG. Optimal allocation strategy of wind hydrogen and ammonia storage under multi-factor constraints of source-storage-load cooperation[J]. Energy Storage Science and Technology, 2025, 14(6): 2558-2566.

Figures/Tables 10

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References 20

1	李建林, 崔宜琳, 熊俊杰, 等. "两个一体化"战略下储能应用前景分析[J]. 热力发电, 2021, 50(8): 1-8. DOI: 10.19666/j.rlfd.202103039.
	LI J L, CUI Y L, XIONG J J, et al. Analysis on application prospect of energy storage under the strategy of "two integrations"[J]. Thermal Power Generation, 2021, 50(8): 1-8. DOI: 10.19666/j.rlfd.2021 03039.
2	WANG C, GONG Z H, LIANG Y L, et al. Data-driven wind generation admissibility assessment of integrated electric-heat systems: A dynamic convex hull-based approach[J]. IEEE Transactions on Smart Grid, 2020, 11(5): 4531-4543. DOI: 10.1109/TSG.2020.2993023.
3	范高锋, 张楠, 梁志锋, 等. 我国"三北"地区弃风弃光原因分析[J]. 华北电力技术, 2016(12): 55-59. DOI: 10.16308/j.cnki.issn1003-9171.2016.12.008.
	FAN G F, ZHANG N, LIANG Z F, et al. Analysis on the "Three Norths" region wind and PV power limitation[J]. North China Electric Power, 2016(12): 55-59. DOI: 10.16308/j.cnki.issn1003-9171.2016.12.008.
4	陈健, 卜令兵. "碳中和"目标下分布式制氢技术优选[J]. 天然气工业, 2022, 42(4): 180-186. DOI: 10.3787/j.issn.1000-0976.2022. 04.016.
	CHEN J, BU L B. Selection of distributed hydrogen generation technologies under the goal of carbon neutrality[J]. Natural Gas Industry, 2022, 42(4): 180-186. DOI: 10.3787/j.issn.1000-0976. 2022.04.016.
5	曹蕃, 郭婷婷, 陈坤洋, 等. 风电耦合制氢技术进展与发展前景[J]. 中国电机工程学报, 2021, 41(6): 2187-2201. DOI: 10.13334/j.0258-8013.pcsee.200452.
	CAO F, GUO T T, CHEN K Y, et al. Progress and development prospect of coupled wind and hydrogen systems[J]. Proceedings of the CSEE, 2021, 41(6): 2187-2201. DOI: 10.13334/j.0258-8013.pcsee.200452.
6	凌文, 刘玮, 李育磊, 等. 中国氢能基础设施产业发展战略研究[J]. 中国工程科学, 2019, 21(3): 76-83.
	LING W, LIU W, LI Y L, et al. Development strategy of hydrogen infrastructure industry in China[J]. Strategic Study of CAE, 2019, 21(3): 76-83.
7	刘玮, 万燕鸣, 熊亚林, 等. "双碳"目标下我国低碳清洁氢能进展与展望[J]. 储能科学与技术, 2022, 11(2): 635-642. DOI: 10.19799/j.cnki.2095-4239.2021.0385.
	LIU W, WAN Y M, XIONG Y L, et al. Outlook of low carbon and clean hydrogen in China under the goal of "carbon peak and neutrality"[J]. Energy Storage Science and Technology, 2022, 11(2): 635-642. DOI: 10.19799/j.cnki.2095-4239.2021.0385.
8	ZHAO H X. Green ammonia supply chain and associated market structure[J]. Fuel, 2024, 366: 131216. DOI: 10.1016/j.fuel.2024. 131216.
9	Hydrogen insights report 2021[R]. Belgium: Hydrogen Council, McKinsey & Company, 2021.
10	SIDDIQUI O, DINCER I. Design and analysis of a novel solar-wind based integrated energy system utilizing ammonia for energy storage[J]. Energy Conversion and Management, 2019, 195: 866-884. DOI: 10.1016/j.enconman.2019.05.001.
11	吉旭, 周步祥, 贺革, 等. 大规模可再生能源电解水制氢合成氨关键技术与应用研究进展[J]. 工程科学与技术, 2022, 54(5): 1-11. DOI: 10.15961/j.jsuese.202200660.
	JI X, ZHOU B X, HE G, et al. Research review of the key technology and application of large-scale water electrolysis powered by renewable energy to hydrogen and ammonia production[J]. Advanced Engineering Sciences, 2022, 54(5): 1-11. DOI: 10.15961/j.jsuese.202200660.
12	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. DOI: 10.3969/j.issn.2097-0706.2022.05.001.
	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. DOI: 10.3969/j.issn.2097-0706.2022.05.001.
13	SALMON N, BAÑARES-ALCÁNTARA R. Green ammonia as a spatial energy vector: A review[J]. Sustainable Energy & Fuels, 2021, 5(11): 2814-2839. DOI: 10.1039/D1SE00345C.
14	李育磊, 刘玮, 董斌琦, 等. 双碳目标下中国绿氢合成氨发展基础与路线[J]. 储能科学与技术, 2022, 11(9): 2891-2899. DOI: 10.19799/j.cnki.2095-4239.2022.0324.
	LI Y L, LIU W, DONG B Q, et al. Green hydrogen ammonia synthesis in China under double carbon target: Research on development basis and route[J]. Energy Storage Science and Technology, 2022, 11(9): 2891-2899. DOI: 10.19799/j.cnki.2095-4239.2022.0324.
15	PALYS M J, DAOUTIDIS P. Using hydrogen and ammonia for renewable energy storage: A geographically comprehensive techno-economic study[J]. Computers & Chemical Engineering, 2020, 136: 106785. DOI: 10.1016/j.compchemeng.2020.106785.
16	ARMIJO J, PHILIBERT C. Flexible production of green hydrogen and ammonia from variable solar and wind energy: Case study of Chile and Argentina[J]. International Journal of Hydrogen Energy, 2020, 45(3): 1541-1558. DOI: 10.1016/j.ijhydene.2019.11.028.
17	王靖, 蒋迎花, 康丽霞, 等. 可再生能源制氢与波动氢气负荷耦合系统的调控策略[J]. 高校化学工程学报, 2021, 35(2): 336-347. DOI: 10.3969/j.issn.1003-9015.2021.02.019.
	WANG J, JIANG Y H, KANG L X, et al. Regulating strategies for coupling systems to match hydrogen production using renewable energy with fluctuating hydrogen demands[J]. Journal of Chemical Engineering of Chinese Universities, 2021, 35(2): 336-347. DOI: 10.3969/j.issn.1003-9015.2021.02.019.
18	林今, 余志鹏, 张信真, 等. 可再生能源电制氢合成氨系统的并/离网运行方式与经济性分析[J]. 中国电机工程学报, 2024, 44(1): 117-127. DOI: 10.13334/j.0258-8013.pcsee.230278.
	LIN J, YU Z P, ZHANG X Z, et al. On-grid/off-grid operation mode and economic analysis of renewable power to ammonia system[J]. Proceedings of the CSEE, 2024, 44(1): 117-127. DOI: 10. 13334/j.0258-8013.pcsee.230278.
19	帅挽澜, 朱自伟, 李雪萌, 等. 考虑风电消纳的综合能源系统"源-网-荷-储"协同优化运行[J]. 电力系统保护与控制, 2021, 49(19): 18-26. DOI: 10.19783/j.cnki.pspc.210037.
	SHUAI W L, ZHU Z W, LI X M, et al. "Source-network-load-storage" coordinated optimization operation for an integrated energy system considering wind power consumption[J]. Power System Protection and Control, 2021, 49(19): 18-26. DOI: 10. 19783/j.cnki.pspc.210037.
20	帅逸轩, 赵培轩, 刘慧敏, 等. 基于多功率耦合的风光互补制氢系统容量配置优化方法[J]. 太阳能学报, 2022, 43(11): 474-481. DOI: 10.19912/j.0254-0096.tynxb.2021-0491.
	SHUAI Y X, ZHAO P X, LIU H M, et al. Optimization of battery capacity for wind-solar complementary hydrogen production system under multi-power conditions[J]. Acta Energiae Solaris Sinica, 2022, 43(11): 474-481. DOI: 10.19912/j.0254-0096.tynxb. 2021-0491.

设备功率	电解槽	制氨设备	制氮设备	蓄电池
最小功率	0.2×P_ele额定	0.7×P_制氨额定	0.7×P_制氮额定	0.7×P_bat额定
最大功率	1.1×P_ele额定	1.1×P_制氨额定	1.1×P_制氮额定	P_bat额定

设备配置场景	电解槽/MW	蓄电池/(MW/h)	储氢罐/Nm³
储能不从电网取电	(76~118)×5	(122~125)×3.44 MW 4~24 h	0
储氢不从电网取电	(118~121)×5	0	(2~34)×6×10⁴
储能从电网取电	(76~118)×5	(44~125)×3.44 MW 4~24 h	0
储氢从电网取电	(118~121)×5	0	(2~34)×6×10⁴

工作场景	上网电量/(10⁸ kW/h)	购电量 /(10⁸ kW/h)	产氢量/10⁸Nm³	产氨量/万吨	停机次数 /次	停机时长 /h	单位氨成本 /(元/kg)	年化成本 /亿元	投资回收期 /年
储能不取电	2.3179/6.44%	0	5.9142	28.163	29	2536	3.2590	9.1783	11.267
储氢不取电	4.8975/13.6%	0	5.9935	31.1	80	—	2.5463	7.9191	7.2725
储能取电	7.1635/19.9%	7.207	5.9142	33.169	36	—	2.9691	9.848	9.01
储氢取电	2.5034/6.95%	5.9527	7.5946	39.296	12	228	2.4457	9.6107	6.0953

配置场景	年化成本/亿元	氨成本/(元/kg)
储能不取电	6.7726	3.2590
储氢不取电	5.6288	2.5463
储能取电	7.5679	2.9691
储氢取电	6.5714	2.4457

[1]	Tingting WANG, Sisheng LI, Wei YU, Fengtian NENG, Xingnan LI, Jialin YANG, Liang XIONG. Wind power prediction based on BP neural network combined with ERA5 data [J]. Energy Storage Science and Technology, 2025, 14(1): 183-189.
[2]	Xinyou WU. Research on optimal configuration of hybrid energy storage capacity of wind power system [J]. Energy Storage Science and Technology, 2024, 13(10): 3593-3595.
[3]	Hongxin WU, Aikui LI, Cun DONG, Shumin SUN, Guanglei LI, Shibo WANG. Control strategy for wind power fluctuation stabilization with energy storage and frequency modulation reserve [J]. Energy Storage Science and Technology, 2023, 12(4): 1194-1203.
[4]	Fuchao LI, Mingbiao CHEN, Qun DU, Yongzhen CHEN, Wenji SONG, Wenye LIN, Ziping FENG. Research on in-situ remote offshore wind-power consumption based on ice-slurry cold storage [J]. Energy Storage Science and Technology, 2023, 12(12): 3730-3739.
[5]	Juntao CHEN, Yajun WANG, Shunyi SONG, Wenhao QU, Yibing LIU. Simulation of the primary frequency modulation process of wind power with an auxiliary flywheel energy storage [J]. Energy Storage Science and Technology, 2023, 12(1): 172-179.
[6]	Yulong CHEN, Xin WU, Wei TENG, Yibing LIU. Power coordinated control strategy of flywheel energy storage array for wind power smoothing [J]. Energy Storage Science and Technology, 2022, 11(2): 600-608.
[7]	Shitan ZHANG, Shuai CHU, Weichun GE, Yinxuan LI, Chuang LIU. Evaluation method for the coordinated regulation of large-scale abandoned wind power and heat storage [J]. Energy Storage Science and Technology, 2022, 11(1): 283-290.
[8]	Xinlei CAI, Yanlin Cui, Kai DONG, Zijie MENG, Yuan PAN, Zhenfan YU, Jixing WANG, Xiangzhan MENG, Yang YU. Day-ahead optimal scheduling approach of wind-storage joint system based on improved K-means and MADDPG algorithm [J]. Energy Storage Science and Technology, 2021, 10(6): 2200-2208.
[9]	Ruixin CAO, Jin ZHANG, Jiakun ZHU. Study of optimal system configuration and charge-discharge strategy of user-side battery energy storage [J]. Energy Storage Science and Technology, 2020, 9(6): 1890-1896.
[10]	FAN Yongsheng, ZHAO Lulu, LIU Qinghua, LEMMON John, MIAO Ping. Economic analysis of flow battery energy storage for wind farm application [J]. Energy Storage Science and Technology, 2020, 9(3): 725-729.
[11]	ZHANG Xueping, QI Fengsheng, XING Zuoxia, SHAN Jianbiao, Li Baokuan. Thermal-fluid-solid coupling of electric heat storage device based on wind power absorption [J]. Energy Storage Science and Technology, 2019, 8(4): 689-695.
[12]	GUO Wei, ZHANG Jiancheng, LI Chong, SU Hao. Control method of flywheel energy storage array for grid-connected wind-storage microgrid [J]. Energy Storage Science and Technology, 2018, 7(5): 810-814.
[13]	DAI Xingjian, WEI Kunpeng, ZHANG Xiaozhang, JIANG Xinjian, ZHANG Kai. A review on flywheel energy storage technology in fifty years [J]. Energy Storage Science and Technology, 2018, 7(5): 765-782.
[14]	YANG Junfeng, ZHENG Xiaoyu, HUI Dong, YANG Shuili, LUO Weihua, LI Xiaofei. Capacity demand analysis of energy storage in the sending-side of a power grid for accommodating large-scale renewables [J]. Energy Storage Science and Technology, 2018, 7(4): 698-704.
[15]	LI Na, BAI Kai, LIU Yu, WANG Kairang, GONG Yu, DONG Jianming. The energy storage system output control strategy to improve the short term wind power forecasting accuracy rate [J]. Energy Storage Science and Technology, 2018, 7(1): 100-.