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

doi:10.11930/j.issn.1004-9649.202301006

Abstract

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

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.

Add to citation manager EndNote|Ris|BibTeX

URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202301006

https://www.electricpower.com.cn/EN/Y2023/V56/I7/33

References

[1] YANG B, YU L, CHEN Y X, et al. Modelling, applications, and evaluations of optimal sizing and placement of distributed generations: a critical state-of-the-art survey[J]. International Journal of Energy Research, 2021, 45(3): 3615–3642.
[2] 乐健, 綦淦, 赵联港, 等. 主动配电网分布式经济调度系统的时延稳定性分析[J]. 电力系统保护与控制, 2022, 50(21): 75–87
LE Jian, QI Gan, ZHAO Liangang, et al. Time-delay stability analysis of an active distribution network adopting a distributed economic dispatch strategy[J]. Power System Protection and Control, 2022, 50(21): 75–87
[3] 李嘉乐, 杨博, 胡袁炜骥, 等. 考虑需求侧响应的电-氢混合储能系统选址定容[J]. 电网技术. DOI: 10.13335/j.1000-3673.pst.2022.2285.
LI Jiale, YANG Bo, HU Yuanweiji, et al. Location and capacity planning of electricity hydrogen hybrid energy storage system considering demand response[J]. Power System Technology. DOI: 10.13335/j.1000-3673.pst.2022.2285.
[4] LI H X, WANG S W. Two-time-scale coordinated optimal control of building energy systems for demand response considering forecast uncertainties[J]. Energy, 2022, 253: 124204.
[5] CHEN H, ZHANG B Y, GENG H P, et al. Demand response during the peak load period in China: potentials, benefits and implementation mechanism designs[J]. Computers & Industrial Engineering, 2022, 168: 108117.
[6] DAS C K, BASS O, KOTHAPALLI G, et al. Overview of energy storage systems in distribution networks: placement, sizing, operation, and power quality[J]. Renewable and Sustainable Energy Reviews, 2018, 91: 1205–1230.
[7] 安东, 杨德宇, 武文丽, 等. 基于改进多目标蜉蝣算法的配网电池储能系统最优选址定容[J]. 电力系统保护与控制, 2022, 50(10): 31–39
AN Dong, YANG Deyu, WU Wenli, et al. Optimal location and sizing of battery energy storage systems in a distribution network based on a modified multi-objective mayfly algorithm[J]. Power System Protection and Control, 2022, 50(10): 31–39
[8] YANG B, WANG J T, CHEN Y X, et al. Optimal sizing and placement of energy storage system in power grids: a state-of-the-art one-stop handbook[J]. Journal of Energy Storage, 2020, 32: 101814.
[9] 贠保记, 张恩硕, 张国, 等. 考虑综合需求响应与“双碳”机制的综合能源系统优化运行[J]. 电力系统保护与控制, 2022, 50(22): 11–19
YUN Baoji, ZHANG Enshuo, ZHANG Guo, et al. Optimal operation of an integrated energy system considering integrated demand response and a “dual carbon” mechanism[J]. Power System Protection and Control, 2022, 50(22): 11–19
[10] 党彬, 邹启群, 张滨, 等. 基于HSA-PSO的配电网源-储协同优化控制方法[J]. 中国电力, 2022, 55(4): 63–69
DANG Bin, ZOU Qiqun, ZHANG Bin, et al. Generation-storage cooperative optimization control method for distribution network based on HSA-PSO algorithm[J]. Electric Power, 2022, 55(4): 63–69
[11] 王波, 张占营, 张霄, 等. 直流微电网分布式储能系统精确电流分配策略[J]. 中国电力, 2022, 55(8): 96–103, 112
WANG Bo, ZHANG Zhanying, ZHANG Xiao, et al. Accurate current sharing strategy for distributed energy storage system in DC microgrids[J]. Electric Power, 2022, 55(8): 96–103, 112
[12] HE Y, GUO S, DONG P X, et al. Techno-economic comparison of different hybrid energy storage systems for off-grid renewable energy applications based on a novel probabilistic reliability index[J]. Applied Energy, 2022, 328: 120225.
[13] 黄雨涵, 丁涛, 李雨婷, 等. 碳中和背景下能源低碳化技术综述及对新型电力系统发展的启示[J]. 中国电机工程学报, 2021, 41(增刊1): 28–51
HUANG Yuhan, DING Tao, LI Yuting, et al. Decarbonization technologies and inspirations for the development of novel power systems in the context of carbon neutrality[J]. Proceedings of the CSEE, 2021, 41(S1): 28–51
[14] 蔡涛, 张钊诚, 袁奥特, 等. 锂离子电池储能安全管理中的机器学习方法综述[J]. 电力系统保护与控制, 2022, 50(24): 178–187
CAI Tao, ZHANG Zhaocheng, YUAN Aote, et al. Review of machine learning for safety management of li-ion battery energy storage[J]. Power System Protection and Control, 2022, 50(24): 178–187
[15] 柴秀慧, 张纯江, 柴建国, 等. 蓄电池-超级电容混合储能系统性能优化[J]. 电工电能新技术, 2019, 38(9): 33–41
CHAI Xiuhui, ZHANG Chunjiang, CHAI Jianguo, et al. Performance optimization of battery-ultracapacitor hybrid energy storage system[J]. Advanced Technology of Electrical Engineering and Energy, 2019, 38(9): 33–41
[16] 许传博, 刘建国. 氢储能在我国新型电力系统中的应用价值、挑战及展望[J]. 中国工程科学, 2022, 24(3): 89–99
XU Chuanbo, LIU Jianguo. Hydrogen energy storage in China’s new-type power system: application value, challenges, and prospects[J]. Strategic Study of CAE, 2022, 24(3): 89–99
[17] 方彤, 蒋东方, 杨洋, 等. 基于NSGA-II和熵权法的氢综合能源系统商业运营模式[J]. 中国电力, 2022, 55(1): 110–118
FANG Tong, JIANG Dongfang, YANG Yang, et al. Research on business operation mode of hydrogen integrated energy system based on NSGA-II and entropy weight method[J]. Electric Power, 2022, 55(1): 110–118
[18] 张驰, 周骏, 赵镔, 等. 零碳园区电-氢混合储能系统多目标优化配置[J]. 电力建设, 2022, 43(8): 1–12
ZHANG Chi, ZHOU Jun, ZHAO Bin, et al. Multi-objective optimal configuration of electricity-hydrogen hybrid energy storage system in zero-carbon park[J]. Electric Power Construction, 2022, 43(8): 1–12
[19] 闫庆友, 史超凡, 秦光宇, 等. 基于近端策略优化算法的电化学/氢混合储能系统双层配置及运行优化[J]. 电力建设, 2022, 43(8): 22–32
YAN Qingyou, SHI Chaofan, QIN Guangyu, et al. Research on two-layer configuration and operation optimization based on proximal policy optimization for electrochemical/hydrogen hybrid energy storage system[J]. Electric Power Construction, 2022, 43(8): 22–32
[20] 陈颖, 石永富, 钟鸿鸣, 等. 含高比例风光接入的输电网氢-电混合储能系统配置方法[J]. 电力建设, 2022, 43(11): 85–98
CHEN Ying, SHI Yongfu, ZHONG Hongming, et al. Configuration method for hydrogen-electricity hybrid energy storage system in transmission grid with high proportion of PV and wind power connection[J]. Electric Power Construction, 2022, 43(11): 85–98
[21] 冯飞波, 闫兴德, 郑宝强, 等. 蓄电-氢储混合储能系统的配电网双层优化[J]. 系统仿真学报, 2022, 34(7): 1405–1416
FENG Feibo, YAN Xingde, ZHENG Baoqiang, et al. Bi-level optimization of distribution network for hybrid energy storage system of storage battery and hydrogen storage[J]. Journal of System Simulation, 2022, 34(7): 1405–1416
[22] 王璟, 蒋小亮, 杨卓, 等. 光伏集中并网电压约束下的准入容量与电压波动的评估方法[J]. 电网技术, 2015, 39(9): 2450–2457
WANG Jing, JIANG Xiaoliang, YANG Zhuo, et al. Penetration capacity under voltage constraint and evaluation methodology of voltage fluctuation caused by centralized grid connection of photovoltaic power[J]. Power System Technology, 2015, 39(9): 2450–2457
[23] LIU J, CAO S L, CHEN X, et al. Energy planning of renewable applications in high-rise residential buildings integrating battery and hydrogen vehicle storage[J]. Applied Energy, 2021, 281: 116038.
[24] MAROCCO P, FERRERO D, LANZINI A, et al. Optimal design of stand-alone solutions based on RES+hydrogen storage feeding off-grid communities[J]. Energy Conversion and Management, 2021, 238: 114147.
[25] ZHAO W G, ZHANG Z X, MIRJALILI S, et al. An effective multi-objective artificial hummingbird algorithm with dynamic elimination-based crowding distance for solving engineering design problems[J]. Computer Methods in Applied Mechanics and Engineering, 2022, 398: 115223.
[26] SUN S N, NIE X T. Assessment of agent system project risk based on entropy method[C]//2010 International Conference on Management and Service Science. Wuhan, China. IEEE, 2010: 1–4.
[27] 杨耀红, 谭攀静. 基于多目标加权灰靶决策的供应链供应商选择[J]. 人民长江, 2012, 43(5): 102–105
YANG Yaohong, TAN Panjing. Selection of supplier in supply chains based on multi-objective weighted grey target decision[J]. Yangtze River, 2012, 43(5): 102–105