Optimal allocation of energy storage power station based on improved multi-objective particle swarm optimization

doi:10.19799/j.cnki.2095-4239.2023.0689

Abstract

Abstract:

The electrochemical energy storage power station has been gradually applied on a large scale in a high proportion of the new energy power grid, and its optimal configuration strategy largely determines the effectiveness of frequency and voltage regulation in its auxiliary power grid. To realize the optimal configuration of the electrochemical energy storage power station, this study first examines the control strategy of energy storage participating in the frequency and voltage regulation of the power system under a high proportion of new energy. It proposes a control method based on the variable coefficient frequency-voltage sag. Then, the multi-objective optimal configuration model for the energy storage system participating in the frequency and voltage regulation of the power grid under a high proportion of the new energy grid is established. The model considers the comprehensive indices of frequency modulation, voltage regulation, and system cost as optimization objectives. The improved multi-objective particle swarm optimization algorithm is employed to optimize the configuration of control parameters and the location and capacity of the energy storage system. To address subjectivity in the data, the entropy weight method is used to select the optimal scheme from the obtained multi-objective solution set. Finally, a practical example of a regional power grid of a city is used to optimize the allocation of energy storage. The comprehensive indices of regional power grid voltage regulation and frequency modulation were reduced by 9.2% and 25.1%, respectively. The voltage and frequency deviation of regional power grid nodes was considerably reduced, improving the effect of voltage regulation and frequency modulation. This verifies the effectiveness and superiority of the proposed energy storage fixed capacity location allocation method.

Key words: energy storage, new energy with high proportion, multi-objective, optimized configuration

CLC Number:

TM 712

Xiaying XIAO, Chuanguang FAN, Feng GUO, Tianxin YANG, Dong WANG, Yunhui HUANG. Optimal allocation of energy storage power station based on improved multi-objective particle swarm optimization[J]. Energy Storage Science and Technology, 2024, 13(2): 503-514.

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References 16

1	陈亦平, 卓映君, 刘映尚, 等. 高比例可再生能源电力系统的快速频率响应市场发展与建议[J]. 电力系统自动化, 2021, 45(10): 174-183.
	CHEN Y P, ZHUO Y J, LIU Y S, et al. Development and recommendation of fast frequency response market for power system with high proportion of renewable energy[J]. Automation of Electric Power Systems, 2021, 45(10): 174-183.
2	ZHANG S Q, WANG Y L. Dynamic operation strategy of battery energy storage system considering energy arbitrage and frequency regulation[C]//2023 5th Asia Energy and Electrical Engineering Symposium (AEEES). March 23-26, 2023, Chengdu, China. IEEE, 2023: 910-915.
3	OKAFOR C E, FOLLY K A. Optimal placement of a battery energy storage system (BESS) in a distribution network[C]//2023 31st Southern African Universities Power Engineering Conference (SAUPEC). January 24-26, 2023, Johannesburg, South Africa. IEEE, 2023: 1-6.
4	SHI Y Y, XU B L, WANG D, et al. Using battery storage for peak shaving and frequency regulation: Joint optimization for superlinear gains[J]. IEEE Transactions on Power Systems, 2018, 33(3): 2882-2894.
5	郭雨涵, 郁丹, 杨鹏, 等. 基于贪婪算法的分布式储能系统容量优化配置方法[J]. 储能科学与技术, 2022, 11(7): 2295-2304.
	GUO Y H, YU D, YANG P, et al. Optimal capacity allocation method of a distributed energy storage system based on greedy algorithm[J]. Energy Storage Science and Technology, 2022, 11(7): 2295-2304.
6	肖小龙, 史明明, 周琦, 等. 基于改进海洋捕食者算法的配电网储能多目标优化配置[J]. 储能科学与技术, 2023, 12(8): 2565-2574.
	XIAO X L, SHI M M, ZHOU Q, et al. Multiobjective optimization configuration of energy storage in distribution networks based on improved marine predator algorithm[J]. Energy Storage Science and Technology, 2023, 12(8): 2565-2574.
7	谢雨龙, 罗逸飏, 李智威, 等. 考虑微网新能源经济消纳的共享储能优化配置[J]. 高电压技术, 2022, 48(11): 4403-4413.
	XIE Y L, LUO Y Y, LI Z W, et al. Optimal allocation of shared energy storage considering the economic consumption of microgrid new energy[J]. High Voltage Engineering, 2022, 48(11): 4403-4413.
8	李秀慧, 崔炎. 考虑调峰调频需求的新能源电网储能优化配置[J]. 储能科学与技术, 2022, 11(11): 3594-3602.
	LI X H, CUI Y. Optimal allocation of energy storage in renewable energy grid considering the demand of peak and frequency regulation[J]. Energy Storage Science and Technology, 2022, 11(11): 3594-3602.
9	AL AHMAD A, SIRJANI R, DANESHVAR S. New hybrid probabilistic optimisation algorithm for optimal allocation of energy storage systems considering correlated wind farms[J]. Journal of Energy Storage, 2020, 29: 101335.
10	RAHIMI T, DING L, KHESHTI M, et al. Inertia response coordination strategy of wind generators and hybrid energy storage and operation cost-based multi-objective optimizing of frequency control parameters[J]. IEEE Access, 2021, 9: 74684-74702.
11	陈景文, 肖妍, 莫瑞瑞, 等. 考虑光伏校正的微电网储能容量优化配置[J]. 电力系统保护与控制, 2021, 49(10): 59-66.
	CHEN J W, XIAO Y, MO R R, et al. Optimized allocation of microgrid energy storage capacity considering photovoltaic correction[J]. Power System Protection and Control, 2021, 49(10): 59-66.
12	刘冰倩, 吴涵, 黄建业, 等. 考虑有功-无功协同优化的风场、光伏电站储能系统容量配置研究[J]. 电力系统及其自动化学报, 2021, 33(11): 82-89.
	LIU B Q, WU H, HUANG J Y, et al. Research on energy storage system capacity configuration of wind farm and photovoltaic plant considering active and reactive power coordinated optimization[J]. Proceedings of the CSU-EPSA, 2021, 33(11): 82-89.
13	陆立民, 褚国伟, 张涛, 等. 基于改进多目标粒子群算法的微电网储能优化配置[J]. 电力系统保护与控制, 2020, 48(15): 116-124.
	LU L M, CHU G W, ZHANG T, et al. Optimal configuration of energy storage in a microgrid based on improved multi-objective particle swarm optimization[J]. Power System Protection and Control, 2020, 48(15): 116-124.
14	JANI V, ABDI H. Optimal allocation of energy storage systems considering wind power uncertainty[J]. Journal of Energy Storage, 2018, 20: 244-253.
15	GANESH S, ELANGOVAN P, RICHARD JIMREEVES J S, et al. Modified emperor penguin optimizer for optimal allocation of energy storage system and phasor measurement units[J]. Materials Today: Proceedings, 2021, 45: 7871-7875.
16	毕亮. 储能辅助风电参与电网一次调频的优化控制策略[D]. 吉林: 东北电力大学, 2020.
	BI L. Optimal control strategy of energy stotage assisting wind for power grid in the primary frequency regulation[D]. Jilin: Northeast Dianli University, 2020.

参数类型	取值	参数类型	取值
S_min	0.1	S₀	0.5
S_max	0.9	C_P /(元/MW)	150×10³
K_G /pu	12	C_E /(元/MWh)	250×10³
K_E /(MW/Hz)	10	K_M /(元/MWh)	70
∆f_{db_u} /Hz	-0.033	β	0.03
∆f_{db_h} /Hz	0.033	α_c /(元/MWh)	0.72
η_bc /%	90	α_f /(元/MWh)	0.314

典型解	储能1			储能2
典型解	位置	容量/MWh	额定功率/MW	位置	容量/MWh	额定功率/MW
最优解	4	0.81	2.18	39	1.6	2.03
边界解1	19	0.49	1.98	26	0.71	1.76
边界解2	4	2.9	2.51	31	0.79	1.96
边界解3	31	0.32	1.83	4	0.36	1.63

典型解	调压综合指标	频率偏差均方根值/Hz	储能净成本/元
最优解	0.240	0.0389	1256645
边界解1	0.238	0.0391	853679
边界解2	0.257	0.0382	1678087
边界解3	0.247	0.0394	778966

最优解编号	S_min	S_max	r	n
1	0.293	0.437	1656	18
2	0.318	0.639	2103	21
3	0.437	0.746	1663	11
平均值	0.349	0.607	1807	17

控制策略	调压综合指标	调频综合指标/Hz	储能经济指标/元
线性变系数控制策略	0.255	0.0407	1517846
本文所提控制策略	0.236	0.0383	1346561