基于多目标人工蜂鸟算法的电-氢混合储能系统最优配置

doi:10.11930/j.issn.1004-9649.202301006

中国电力 ›› 2023, Vol. 56 ›› Issue (7): 33-42.DOI: 10.11930/j.issn.1004-9649.202301006

• 面向新型电力系统的氢能及其系统集成控制关键技术 • 上一篇下一篇

基于多目标人工蜂鸟算法的电-氢混合储能系统最优配置

卢子敬¹, 李子寿¹, 郭相国², 杨博³

1. 国网湖北省电力有限公司，湖北武汉 430077;
2. 中国电力工程顾问集团中南电力设计院有限公司，湖北武汉 430074;
3. 昆明理工大学电力工程学院，云南昆明 650500

收稿日期:2023-01-06 修回日期:2023-06-01 发布日期:2023-07-28
作者简介:卢子敬(1979-),男,硕士,高级工程师,从事储能规划研究,E-mail:13701161@qq.com;郭相国(1980-),男,硕士,高级工程师,从事电力系统规划研究,E-mail:guoxiangguo@csepdi.com;杨博(1988-),男,通信作者,博士,教授,博士生导师,从事新能源系统优化与控制等研究,E-mail:yangbo_ac@outlook.com
基金资助:
国家自然科学基金资助项目（61963020，62263014）。

Optimal Configuration of Electricity-Hydrogen Hybrid Energy Storage System Based on Multi-objective Artificial Hummingbird Algorithm

LU Zijing¹, LI Zishou¹, GUO Xiangguo², YANG Bo³

1. State Grid Hubei Electric Power Co., Ltd., Wuhan 430077, China;
2. Central Southern China Electric Power Design Institute Co., Ltd. of China Power Engineering Consulting Group, Wuhan 430074, China;
3. Faculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming 650500, China

Received:2023-01-06 Revised:2023-06-01 Published:2023-07-28
Supported by:
This work is supported by National Natural Science Foundation of China (No.61963020, No.62263014).

摘要/Abstract

摘要： 将氢气储能系统与电池储能系统相互结合，可实现长期且快速的能量吞吐并减小分布式电源渗透率提高对配电网稳定性的影响。综合考虑电-氢混和储能系统接入配电网的经济性和技术性要求，以最小化系统全生命周期成本、配电网的电压波动和净负荷波动为目标，建立了电-氢混和储能系统多目标优化配置模型。采用基于Pareto的多目标人工蜂鸟算法求解其规划方案，并与多目标粒子群算法以及多目标原子轨道搜索进行了对比。最后，基于扩展的IEEE-69节点标准测试系统进行求解。仿真结果表明：多目标人工蜂鸟算法能够获得解集质量更加优异的Pareto前沿。所得混合储能系统配置方案在兼顾经济效益的同时，改善了配电网的电压质量和负荷水平。与仅配置电池储能系统相比，电-氢混合储能系统对配电网净负荷波动与电压波动的改善分别提高了21.02%与16.66%，证明了本文所提优化配置方法的有效性和卓越性。

关键词: 电池储能系统, 氢储能系统, 多目标人工蜂鸟算法, 全生命周期成本

Abstract: Through the combination of hydrogen energy storage system and battery energy storage system, the long-term and fast energy conversion can be achieved and the impact of increasing penetration of distributed generation on the stability of distribution network can also be reduced. Considering the economic and technical requirements of hybrid electric-hydrogen energy storage system connected to distribution network, a multi-objective optimal configuration model of hybrid electric-hydrogen energy storage system is established to minimize the life cycle cost, voltage fluctuation and net load fluctuation of distribution network. The multi-objective artificial hummingbird algorithm based on Pareto is used to solve the planning scheme and then compared with the multi-objective particle swarm optimization and multi-objective atomic orbital search algorithm. Finally, the model based on the extended IEEE-69 bus standard test system is solved. The simulation results show that with the proposed multi-objective artificial hummingbird algorithm Pareto frontier can be obtained with better solution set quality. The resulting hybrid energy storage system configuration scheme can improve the voltage quality and load level of distribution network while taking into account the economic benefits. Compared with the energy storage system of exclusive battery configuration, the application of electric-hydrogen hybrid energy storage system can improve the distribution network net load fluctuation and voltage fluctuation by 21.02% and 16.66% respectively, which has verified the effectiveness and outstanding performance of the optimal configuration method proposed in this paper.

Key words: battery energy storage system, hydrogen energy storage system, multi-objective artificial hummingbird algorithm, life cycle cost

卢子敬, 李子寿, 郭相国, 杨博. 基于多目标人工蜂鸟算法的电-氢混合储能系统最优配置[J]. 中国电力, 2023, 56(7): 33-42.

LU Zijing, LI Zishou, GUO Xiangguo, YANG Bo. Optimal Configuration of Electricity-Hydrogen Hybrid Energy Storage System Based on Multi-objective Artificial Hummingbird Algorithm[J]. Electric Power, 2023, 56(7): 33-42.

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基于多目标人工蜂鸟算法的电-氢混合储能系统最优配置

Optimal Configuration of Electricity-Hydrogen Hybrid Energy Storage System Based on Multi-objective Artificial Hummingbird Algorithm

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基于多目标人工蜂鸟算法的电-氢混合储能系统最优配置

Optimal Configuration of Electricity-Hydrogen Hybrid Energy Storage System Based on Multi-objective Artificial Hummingbird Algorithm

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