计及海上风电制氢的海岛多能微网鲁棒优化调度

doi:10.11930/j.issn.1004-9649.202407136

中国电力 ›› 2025, Vol. 58 ›› Issue (7): 68-79.DOI: 10.11930/j.issn.1004-9649.202407136

• 海上风电制氢技术经济、规划运行及政策机制 • 上一篇下一篇

计及海上风电制氢的海岛多能微网鲁棒优化调度

高芳杰¹(), 孙玉杰², 李忆³, 乐鹰³, 张继广³, 许传博⁴(), 刘敦楠⁴

1. 中北大学经济与管理学院，山西太原　030051
2. 北京国氢中联氢能科技研究院有限公司，北京　100007
3. 华电电力科学研究院有限公司，浙江杭州　310030
4. 华北电力大学经济与管理学院，北京　102206

收稿日期:2024-07-31 发布日期:2025-07-30 出版日期:2025-07-28
作者简介:
高芳杰（1993），女，博士，讲师，从事海上风电制氢、能源系统优化运行等研究，E-mail：gfangj2024@163.com
许传博（1993），男，通信作者，博士，讲师，硕士生导师，从事氢能工程技术经济、电氢耦合规划管理、能源系统建模与优化等研究，E-mail：chuanbo_xu@ncepu.edu.cn
基金资助:
国家自然科学基金资助项目（面向新型能源体系的氢储能资源协同优化配置及激励机制研究，72303063）；中国氢能联盟2023政研项目（深远海风电制氢关键技术及发展趋势研究，CHA2023RP001）。

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

GAO Fangjie¹(), SUN Yujie², LI Yi³, LE Ying³, ZHANG Jiguang³, XU Chuanbo⁴(), LIU Dunnan⁴

1. School of Economics and Management, North University of China, Taiyuan 030051, China
2. China Hydrogen Alliance Research Institute Co., Ltd., Beijing 100007, China
3. Huadian Electric Power Research Institute Co., Ltd., Hangzhou 310030, China
4. School of Economics and Management, North China Electric Power University, Beijing 102206, China

Received:2024-07-31 Online:2025-07-30 Published:2025-07-28
Supported by:
This work is supported by National Natural Science Foundation of China (Research on Collaborative Optimal Allocation and Incentive Mechanism of Hydrogen Energy Storage Resources for New Energy System, No.72303063) and China Hydrogen Energy Alliance 2023 Policy Research Project (Research on Key Technologies and Development Trends of Hydrogen Production from Deep-sea Wind Power, No.CHA2023RP001).

摘要/Abstract

摘要：

海上风电制氢技术可为解决偏远海岛用户的用能需求提供可行途径。针对海上风电出力不确定性和海岛供能方式单一的问题，提出一种基于概率盒改进多场景置信间隙决策的计及海上风电制氢的海岛多能微网鲁棒优化调度模型。首先，根据海岛用能实际情况，构建含海上风电制氢系统的海岛多能微网运行框架。然后，在考虑风电出力不确定性和需求响应的基础上，基于概率盒理论和多场景置信间隙决策方法构建海岛多能微网鲁棒优化调度模型，并采用灰狼优化算法进行求解。最后，以广东某海岛为例进行仿真分析。算例结果表明，所提模型能够有效提高风电消纳水平，多能互补有效降低用能成本，更具经济性和环保性。

关键词: 海上风电制氢, 风电不确定性, 海岛多能微网, 需求响应, 鲁棒优化

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

高芳杰, 孙玉杰, 李忆, 乐鹰, 张继广, 许传博, 刘敦楠. 计及海上风电制氢的海岛多能微网鲁棒优化调度[J]. 中国电力, 2025, 58(7): 68-79.

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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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202407136

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

图/表 17

图 1 海岛多能微网运行框架

Fig.1 The island multi-energy microgrid operation framework

表 1 设备的相关参数

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

图 2 售能价格

Fig.2 The price of energy sold

图 3 负荷需求量

Fig.3 The load demand

图 4 拟合的风速累积分布函数范围

Fig.4 The fitted wind speed cumulative distribution function range

图 5 不同参数下的风速概率分布

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

图 6 风机出力预测值

Fig.6 The predicted output power of the wind turbine

图 7 算法适应度函数值与迭代次数的关系

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

图 8 不考虑风险和多能替代的设备出力

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

图 9 不考虑风险和多能替代的氢气出力

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

图 10 仅考虑多能替代的设备出力

Fig.10 Equipment output only considering multi-energy replacement

图 11 仅考虑多能替代的氢气出力

Fig.11 Hydrogen output only considering multi-energy replacement

图 12 不考虑风险的多能替代方式

Fig.12 Multi-energy replacement methods without considering risk

表 2 考虑风险后的结果

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

图 13 考虑风险和多能替代的设备出力

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

图 14 考虑风险和多能替代的氢气出力

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

图 15 考虑风险的多能替代方式

Fig.15 Multi-energy replacement methods considering risk

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计及海上风电制氢的海岛多能微网鲁棒优化调度

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

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计及海上风电制氢的海岛多能微网鲁棒优化调度

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

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