Two-Stage Stochastic Optimization Based Weekly Operation Strategy for Electric-Hydrogen Coupled Microgrid

doi:10.11930/j.issn.1004-9649.202408035

Abstract

Abstract:

To fully capitalize on the medium- and long-term storage benefits of hydrogen energy, a two-stage weekly optimization scheduling strategy for electric-hydrogen coupled microgrids is proposed, utilizing a scenario-based stochastic optimization model. Firstly, the mathematical models for the electric-hydrogen coupled equipment within the microgrid are developed. Secondly, aiming to minimize the weekly operation cost, the cycles of electric and hydrogen energy storage are set as weekly and daily, respectively, and the first-stage weekly scheduling model of the microgrid based on weekly prediction data is established. Then, to account for the uncertainty in wind power generation, typical scenarios of prediction errors are introduced. A day-ahead scheduling model considering uncertainty is constructed, aiming to minimize the sum of the daily expected operational cost and the deviation penalty for the two-stage hydrogen storage tank's state of charge. The final weekly operation scheme is determined through rolling optimization. Finally, case studies demonstrate that the proposed strategy effectively reduces the microgrid's operating costs and enhances energy utilization.

Key words: electric-hydrogen coupling, hydrogen energy storage, multi-time scale, stochastic optimization, scenario-based method

CHEN Minghongtian, GENG Jianghai, ZHAO Yuze, XU Peng, HAN Yushan, ZHANG Yuming, ZHANG Zimo. Two-Stage Stochastic Optimization Based Weekly Operation Strategy for Electric-Hydrogen Coupled Microgrid[J]. Electric Power, 2025, 58(5): 82-90.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202408035

https://www.electricpower.com.cn/EN/Y2025/V58/I5/82

Figures/Tables 13

References 24

1	张智刚, 康重庆. 碳中和目标下构建新型电力系统的挑战与展望[J]. 中国电机工程学报, 2022, 42 (8): 2806- 2819.
	ZHANG Zhigang, KANG Chongqing. Challenges and prospects for constructing the new-type power system towards a carbon neutrality future[J]. Proceedings of the CSEE, 2022, 42 (8): 2806- 2819.
2	刘映尚, 马骞, 王子强, 等. 新型电力系统电力电量平衡调度问题的思考[J]. 中国电机工程学报, 2023, 43 (5): 1694- 1705.
	LIU Yingshang, MA Qian, WANG Ziqiang, et al. Cogitation on power and electricity balance dispatching in new power system[J]. Proceedings of the CSEE, 2023, 43 (5): 1694- 1705.
3	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 Sheng, FAN Yanfang, HOU Junjie, 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.
4	李建林, 梁策, 曾飞, 等. 基于级联式模糊控制的电氢耦合直流微网能量管理策略研究[J]. 电力系统保护与控制, 2024, 52 (18): 87- 100.
	LI Jianlin, LIANG Ce, ZENG Fei, et al. An energy management strategy for an electricity-hydrogen coupled DC microgridbased on cascade fuzzy control[J]. Power System Protection and Control, 2024, 52 (18): 87- 100.
5	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, 2021, 58 (2): 2728- 2739.
6	李志伟, 赵雨泽, 吴培, 等. 基于制氢设备精细建模的综合能源系统绿氢蓝氢协调低碳优化策略[J]. 电网技术, 2024, 48 (6): 2317- 2326.
	LI Zhiwei, ZHAO Yuze, WU Pei, et al. Coordinated low-carbon optimization strategy for green and blue hydrogen in integrated energy systems based on detailed modeling of hydrogen production equipment[J]. Power System Technology, 2024, 48 (6): 2317- 2326.
7	ZHONG Z Y, FANG J K, HU K W, et al. Power-to-hydrogen by electrolysis in carbon neutrality: technology overview and future development[J]. CSEE Journal of Power and Energy Systems, 2023, 9 (4): 1266- 1283.
8	李江南, 程韧俐, 周保荣, 等. 含碳捕集及电转氢设备的低碳园区综合能源系统随机优化调度[J]. 中国电力, 2024, 57 (5): 149- 156.
	LI Jiangnan, CHENG Renli, ZHOU Baorong, et al. Stochastic optimization scheduling of low-carbon park integrated energy systems with carbon capture and power-to-hydrogen equipment[J]. Electric Power, 2024, 57 (5): 149- 156.
9	杨国山, 朱杰, 宋汶秦, 等. 基于伊藤过程的电制氢合成氨负荷随机最优控制[J]. 中国电力, 2023, 56 (7): 66- 77.
	YANG Guoshan, ZHU Jie, SONG Wenqin, et al. Flexible load stochastic optimal control of wind power-based hydrogen production and ammonia synthesis systems based on the it? process[J]. Electric Power, 2023, 56 (7): 66- 77.
10	王西胜, 刘辉, 刘迪, 等. 电网友好型风电场多功能电——氢混合储能系统容量配置[J]. 电力科学与技术学报, 2024, 39 (5): 141- 150.
	WANG Xisheng, LIU Hui, LIU Di, et al. Power System Protection and Control[J]. Journal of Electric Power Science and Technology, 2024, 39 (5): 141- 150.
11	刘继春, 周春燕, 高红均, 等. 考虑氢能-天然气混合储能的电-气综合能源微网日前经济调度优化[J]. 电网技术, 2018, 42 (1): 170- 179.
	LIU Jichun, ZHOU Chunyan, GAO Hongjun, et al. A day-ahead economic dispatch optimization model of integrated electricity-natural gas system considering hydrogen-gas energy storage system in microgrid[J]. Power System Technology, 2018, 42 (1): 170- 179.
12	熊宇峰, 陈来军, 郑天文, 等. 考虑电热气耦合特性的低碳园区综合能源系统氢储能优化配置[J]. 电力自动化设备, 2021, 41 (9): 31- 38.
	XIONG Yufeng, CHEN Laijun, ZHENG Tianwen, et al. Optimal configuration of hydrogen energy storage in low-carbon park integrated energy system considering electricity-heat-gas coupling characteristics[J]. Electric Power Automation Equipment, 2021, 41 (9): 31- 38.
13	师瑞峰, 宁津, 高毓钦, 等. 含氢储能的公路交通风、光自洽微网系统优化调度策略研究[J]. 太阳能学报, 2023, 44 (11): 513- 521.
	SHI Ruifeng, NING Jin, GAO Yuqin, et al. Research on optimal dispatch strategy of wind and solar self-consistent microgrid in road transportation system with hydrogen energy storage[J]. Acta Energiae Solaris Sinica, 2023, 44 (11): 513- 521.
14	LI Z C, XIA Y H, BO Y L, et al. Optimal planning for electricity-hydrogen integrated energy system considering multiple timescale operations and representative time-period selection[J]. Applied Energy, 2024, 362, 122965. DOI
15	WANG H F, YUAN L L, WANG W J, et al. Distributionally robust optimization for pumped storage power station capacity expanding based on underwater hydrogen storage introduction[J]. Energy, 2024, 310, 133254. DOI
16	赵波, 王文博, 陈哲, 等. 计及长短周期混合储能的多能微网能量-功率分布鲁棒优化[J]. 电力系统自动化, 2023, 47 (16): 22- 33. DOI
	ZHAO Bo, WANG Wenbo, CHEN Zhe, et al. Energy-power distributionally robust optimization for multi-energy microgrid considering long short-term hybrid energy storage[J]. Automation of Electric Power Systems, 2023, 47 (16): 22- 33. DOI
17	赵书强, 赵蓬飞, 韦子瑜, 等. 数据驱动下考虑多预测误差带信息的多场景随机优化调度[J]. 电力自动化设备, 2024, 44 (11): 52- 59.
	ZHAO Shuqiang, ZHAO Pengfei, WEI Ziyu, et al. Multi-scenario stochastic optimal scheduling considering multi-prediction error band information under data-driven[J]. Electric Power Automation Equipment, 2024, 44 (11): 52- 59.
18	涂青宇, 苗世洪, 杨志豪, 等. 一种考虑峰荷-电量分摊机制和风电关键场景的输-配电网协同调度策略[J]. 中国电机工程学报, 2024, 44 (2): 597- 610.
	TU Qingyu, MIAO Shihong, YANG Zhihao, et al. A collaborative scheduling strategy for transmission-distribution systems considering peak load-electricity quantity allocation mechanism and key wind power scenarios[J]. Proceedings of the CSEE, 2024, 44 (2): 597- 610.
19	鲍海波, 郭小璇. 求解含风电相关性区间潮流的仿射变换最优场景法[J]. 电力系统保护与控制, 2020, 48 (18): 114- 122.
	BAO Haibo, GUO Xiaoxuan. Optimal scenario algorithm based on affine transformation applied to interval power flow considering correlated wind power[J]. Power System Protection and Control, 2020, 48 (18): 114- 122.
20	谢敏, 罗文豪, 吉祥, 等. 随机风电接入的电力系统动态经济调度多场景协同优化[J]. 电力自动化设备, 2019, 39 (11): 27- 33.
	XIE Min, LUO Wenhao, JI Xiang, et al. Multi-scenario collaborative optimization for dynamic economic dispatch of power system with stochastic wind power integration[J]. Electric Power Automation Equipment, 2019, 39 (11): 27- 33.
21	彭春华, 熊志盛, 张艺, 等. 基于多场景置信间隙决策的风光储联合鲁棒规划[J]. 电力系统自动化, 2022, 46 (16): 178- 187. DOI
	PENG Chunhua, XIONG Zhisheng, ZHANG Yi, et al. Joint robust planning of wind-photovoltaic-energy storage system based on multi-scenario confidence gap decision[J]. Automation of Electric Power Systems, 2022, 46 (16): 178- 187. DOI
22	赵波, 林达, 陈哲, 等. 面向新能源消纳的长短周期混合储能关键技术及展望[J]. 新型电力系统, 2024, (2): 162- 178.
	ZHAO Bo, LIN Da, CHEN Zhe, et al. Key technologies and prospects of long-short-cycle hybrid energy storage for renewable energy consumption[J]. New Type Power Systems, 2024, (2): 162- 178.
23	张强, 王禹霖, 张茜. 基于剩余容量匹配的储能装置负荷分配控制技术[J]. 电力系统自动化, 2020, 44 (24): 126- 133. DOI
	ZHANG Qiang, WANG Yulin, ZHANG Qiang. Load distribution control technology for energy storage device based on residual capacity matching[J]. Automation of Electric Power Systems, 2020, 44 (24): 126- 133. DOI
24	付文杰, 王喻玺, 申洪涛, 等. 基于拉丁超立方抽样和场景消减的居民用户基线负荷估计方法[J]. 电网技术, 2022, 46 (6): 2298- 2307.
	FU Wenjie, WANG Yuxi, SHEN Hongtao, et al. Residential customer baseline load estimation based on Latin hypercube sampling and scenario subtraction[J]. Power System Technology, 2022, 46 (6): 2298- 2307.

参数	取值	参数	取值
电解槽容量/kW	600	储氢罐SOC初始值	0.2
电解槽效率/%	70	储氢罐SOC上、下限	0.9、0.1
燃料电池容量/kW	500	蓄电池容量/(kW·h)	300
燃料电池效率/%	60	蓄电池充、放效率/%	95、95
储氢罐容量/kg	250	蓄电池SOC初始值	0.3
储氢罐充、放效率/%	97、97	蓄电池SOC上、下限	0.9、0.1

参数	取值	参数	取值
电解槽容量/kW	600	储氢罐SOC初始值	0.2
电解槽效率/%	70	储氢罐SOC上、下限	0.9、0.1
燃料电池容量/kW	500	蓄电池容量/(kW·h)	300
燃料电池效率/%	60	蓄电池充、放效率/%	95、95
储氢罐容量/kg	250	蓄电池SOC初始值	0.3
储氢罐充、放效率/%	97、97	蓄电池SOC上、下限	0.9、0.1

时段	电价/ (元·(kW·h)^–1)	弃风惩罚系数/ (元·(kW·h)^–1)
01:00—07:00、22:00—24:00	0.38	0.4
07:00—11:00、14:00—18:00	0.68	0.4
11:00—14:00、18:00—22:00	1.20	0.4

时段	电价/ (元·(kW·h)^–1)	弃风惩罚系数/ (元·(kW·h)^–1)
01:00—07:00、22:00—24:00	0.38	0.4
07:00—11:00、14:00—18:00	0.68	0.4
11:00—14:00、18:00—22:00	1.20	0.4

场景		概率/%
1		35.7
2		31.6
3		32.7