Robust Optimization Scheduling of Island Multi-energy Microgrid Considering Offshore Wind Power to Hydrogen

doi:10.11930/j.issn.1004-9649.202407136

Abstract

Abstract:

The offshore wind power to hydrogen technology provides a feasible way to solve the energy consumption needs of remote island users. To address the issues of offshore wind power output uncertainty and single island energy supply mode, this paper proposes a robust optimal scheduling model for island multi-energy microgrid considering offshore wind power to hydrogen using probabilistic box-improved multi-scenario confidence gap decision-making. Firstly, an operation framework for island multi-energy microgrid containing offshore wind power to hydrogen system is designed based on the existing state of island energy use. Secondly, considering the uncertainty of wind power generation and the demand response, a robust optimal scheduling model for island multi-energy microgrid is developed using the probabilistic box theory and the multi-scenario confidence gap decision-making theory, and the grey wolf optimization algorithm is employed to solve the model. Finally, using an island in Guangdong for case study, the simulation analysis demonstrates that the proposed model can significantly improve the wind power consumption, effectively lower the energy-use cost of multi-energy complementarity, and be more economical and environmentally friendly.

Key words: offshore wind power to hydrogen, wind power uncertainty, island multi-energy microgrid, demand response, robust optimization

GAO Fangjie, SUN Yujie, LI Yi, LE Ying, ZHANG Jiguang, XU Chuanbo, LIU Dunnan. Robust Optimization Scheduling of Island Multi-energy Microgrid Considering Offshore Wind Power to Hydrogen[J]. Electric Power, 2025, 58(7): 68-79.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202407136

https://www.electricpower.com.cn/EN/Y2025/V58/I7/68

Figures/Tables 17

Fig.1 The island multi-energy microgrid operation framework

Table 1 The related parameters of the equipment

设备名称	转换效率	容量	运维成本/ (元·(kW·h)^–1)
电解氢设备	0.9	4000 kW	0.022
氢气压缩机	0.9	4000 kW	0.040
储氢罐	充/放气：0.95	200 m³	0.028
氢燃料电池	电转换：0.3；热转换：0.55	500 kW	0.040
甲烷反应器	0.7	500 m³	0.040
掺氢燃气轮机	电转换：0.4；热转换：0.35	1500 kW	0.011
燃气锅炉	0.9	1000 kW	0.040
碳捕集	0.9	1000 kW	0.050
电锅炉	0.9	1000 kW	0.040
电制冷机	3	1000 kW	0.020
吸收式制冷机	1.33	1000 kW	0.010
电储能	充/放电：0.9	600 kW	0.050

Fig.2 The price of energy sold

Fig.3 The load demand

Fig.4 The fitted wind speed cumulative distribution function range

Fig.5 The probability distribution of wind speed under different parameters

Fig.6 The predicted output power of the wind turbine

Fig.7 The relationship between the fitness function value of the algorithm and the number of iterations

Fig.8 The output of equipment without considering risk and multi-energy replacement

Fig.9 The hydrogen output without considering risk and multi-energy replacement

Fig.10 Equipment output only considering multi-energy replacement

Fig.11 Hydrogen output only considering multi-energy replacement

Fig.12 Multi-energy replacement methods without considering risk

Table 2 The result after considering risk

目标显著性水平	场景3			场景4
目标显著性水平	不确定性的置信水平	用户成本/元	系统利润/元	不确定性的置信水平	用户成本/元	系统利润/元
—	—	22680.19	11990.05	—	21651.43	13384.07
0.05	0.1126	23017.67	11764.34	0.1112	21942.09	13170.72
0.10	0.1374	23329.21	11540.48	0.1365	22211.75	12978.41
0.15	0.1647	23694.64	11284.17	0.1621	22497.28	12781.39

Fig.13 Equipment output considering risk and multi-energy replacement

Fig.14 Hydrogen output considering risk and multi-energy replacement

Fig.15 Multi-energy replacement methods considering risk

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