多因素约束下源-储-荷协同的风储氢氨优化配置策略研究

doi:10.19799/j.cnki.2095-4239.2024.1250

摘要/Abstract

摘要：

风电制氢、制氨是我国“三北”地区解决风电就地消纳问题的重要手段，为提高风电制氢合成氨系统稳定运行情况下的经济性，本工作提出了一种多因素约束下源-储-荷协同的风储氢氨优化配置策略。首先通过分析制氢设备功率、储能配置比例、储氢罐配置容量、电网取电电量、系统年停机次数、年产氢量、产氨量以及液氨成本等多种制约因素，构建了风电制氢合成氨系统模型及其不同工况的经济性关系。其次，以经济性目标模型边界为切入点，采用源-储-荷协同的风储氢氨优化协同策略，优选出电网取电、储氢罐储能及蓄电池储能最佳出力点，以便适应不同工作场景的经济性最优配置目标。最后，以乌拉特中旗典型年风速数据及其现场实际为基准，构建了4种不同场景下不同优化策略的经济性对比。结果表明：本工作所提控制策略在各个场景均能很好适应现场需求，达到单位氨成本最低，系统产量最多，停机次数最少，投资回收期最短的效果。本研究有助于增强风电的就地消纳能力和系统自平衡能力，为提升源-储-荷协同的风储氢氨制备工艺及经济性提供工程借鉴和参考。

关键词: 风电, 绿电制氨, 经济性最优, 系统配置

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

中图分类号:

TK 513

张彦虎, 汪慧, 邹绍琨, 韦安, 罗德俊, 江友华. 多因素约束下源-储-荷协同的风储氢氨优化配置策略研究[J]. 储能科学与技术, 2025, 14(6): 2558-2566.

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.

图/表 10

图1

表1

表2

表3

表4

图2

图3

图4

图5

图6

参考文献 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]	王婷婷, 李斯胜, 于伟, 能锋田, 李星南, 杨佳琳, 熊亮. 基于BP神经网络结合ERA5数据的风电功率预测[J]. 储能科学与技术, 2025, 14(1): 183-189.
[2]	聂亚惠, 周学志, 郭丁彰, 徐玉杰, 陈海生. 铁轨重力储能系统关键影响因素及其与风电场的耦合研究[J]. 储能科学与技术, 2024, 13(6): 1900-1910.
[3]	金都, 刘广忱, 孙博文, 黄天园, 张建伟, 田桂珍, 荆丽丽. 计及风电场的飞轮储能一次调频控制策略[J]. 储能科学与技术, 2024, 13(6): 1911-1920.
[4]	吴新友. 面向风电系统的混合储能容量优化配置研究[J]. 储能科学与技术, 2024, 13(10): 3593-3595.
[5]	武鸿鑫, 李爱魁, 董存, 孙树敏, 李广磊, 王士柏. 计及调频备用的储能平抑风电功率波动控制策略[J]. 储能科学与技术, 2023, 12(4): 1194-1203.
[6]	黎福超, 陈明彪, 杜群, 陈永珍, 宋文吉, 林文野, 冯自平. 基于流态冰浆蓄冷的深远海风电就地消纳[J]. 储能科学与技术, 2023, 12(12): 3730-3739.
[7]	陈俊涛, 王亚军, 宋顺一, 曲文浩, 柳亦兵. 飞轮储能辅助风电一次调频仿真分析[J]. 储能科学与技术, 2023, 12(1): 172-179.
[8]	陈玉龙, 武鑫, 滕伟, 柳亦兵. 用于风电功率平抑的飞轮储能阵列功率协调控制策略[J]. 储能科学与技术, 2022, 11(2): 600-608.
[9]	张诗钽, 楚帅, 葛维春, 李音璇, 刘闯. 大规模弃风与储热协调调控评估方法[J]. 储能科学与技术, 2022, 11(1): 283-290.
[10]	蔡新雷, 崔艳林, 董锴, 孟子杰, 潘远, 喻振帆, 王吉兴, 孟乡占, 余洋. 基于改进K-means和MADDPG算法的风储联合系统日前优化调度方法[J]. 储能科学与技术, 2021, 10(6): 2200-2208.
[11]	段俊东, 李高尚, 李一石, 付子恒, 黄泓叶. 考虑风电消纳的电动汽车充电站有序充电控制[J]. 储能科学与技术, 2021, 10(2): 630-637.
[12]	曹锐鑫, 张　瑾, 朱嘉坤. 用户侧电化学储能装置最优系统配置与充放电策略研究[J]. 储能科学与技术, 2020, 9(6): 1890-1896.
[13]	张雪平, 齐凤升, 邢作霞, 单建标, 李宝宽. 基于风电消纳电热储能装置热流固耦合研究[J]. 储能科学与技术, 2019, 8(4): 689-695.
[14]	郭伟, 张建成, 李翀, 苏浩. 针对并网型风储微网的飞轮储能阵列系统控制方法[J]. 储能科学与技术, 2018, 7(5): 810-814.
[15]	李娜，白恺，柳玉，王开让，巩宇，董建明. 用于提升风电场短期功率预测准确率的储能系统出力控制策略[J]. 储能科学与技术, 2018, 7(1): 100-.