Research on the optimal configuration of grid-forming energy storage connected to power systems with high proportional renewable energy based on CMMOPSO

doi:10.19799/j.cnki.2095-4239.2024.0762

Abstract

Abstract:

Grid-forming energy storage plays an important role in new power systems; for instance, such storage participates in the frequency modulation and inertia control of power systems. However, whether such grid-forming energy storage can fully function in a regulatory role depends on the correct configuration of its capacity and location. To solve the problem of optimal allocation of grid-forming energy storage access to the power grid, this work examined the optimal allocation method of fixed volume location of grid-forming energy storage participating in the primary frequency modulation of a power system with a high proportion of new energy. First, the control strategy of the storage, including its active power frequency droop control strategy and the virtual synchronous machine control strategy, was examined. A comprehensive frequency regulation method combining the two control strategies was proposed. The primary frequency regulation effect index, power grid vulnerability index, and energy storage economy were considered as multiple objectives. Accordingly, a combination of the cross-mutation multi-objective particle swarm optimization algorithm with the TOPSIS algorithm was adopted. Based on the information entropy method weight from the Pareto solution set, an optimization plan was devised for determining the location and capacity of the grid-forming energy storage power stations. Finally, validation and optimization analyses were conducted for an actual calculation example of a regional power grid.

Key words: grid-forming energy storage, fixed capacity site selection, high proportional renewable energy

CLC Number:

TM 712

Mosi LIU, Zhiyuan SUN, Jinzhao LI, Kun ZHENG, Lichun CHEN. Research on the optimal configuration of grid-forming energy storage connected to power systems with high proportional renewable energy based on CMMOPSO[J]. Energy Storage Science and Technology, 2025, 14(1): 162-171.

Figures/Tables 15

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Fig. 8

Fig. 9

Fig. 10

Fig. 11

Fig. 12

Fig. 13

Table 1

Table 2

References 21

1	国务院. 2030年前碳达峰行动方案. https://www.gov.cn/zhengce/content/ 2021-10/26/content_5644984.htm.
2	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. DOI: 10.7500/AEPS20200726004.
	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. DOI: 10.7500/AEPS20200726004.
3	方正, 黄云辉, 严文博, 等. 构网型变流器功率同步控制稳定性机理分析[J]. 电力系统自动化, 2024, 48(19): 101-108.
	FANG Z, HUANG Y H, YAN W B, et al. Stability mechanism analysis of power synchronization control for grid converter[J]. Automation of Electric Power Systems, 2024, 2024, 48(19): 101-108.
4	严文博, 黄云辉, 方正, 等. 低阻抗下构网型变流器稳定性机理分析[J/OL]. 高电压技术, 1-12 [2025-02-20]. https://doi.org/10.13336/j.1003-65 20.hve.20231950.
5	严文博, 黄云辉, 王栋, 等. 基于虚拟母线电压控制的构网型储能变流器稳定性优化研究[J]. 储能科学与技术, 2024, 13(5): 1532-1541. DOI: 10.19799/j.cnki.2095-4239.2023.0818
	YAN W B, HUANG Y H, WANG D, et al. Investigation of stability optimization of grid-forming energy storage converters based on virtual bus voltage control[J]. Energy Storage Science and Technology, 2024, 13(5): 1532-1541. DOI: 10.19799/j.cnki.2095-4239.2023.0818
6	中国电建. 世界首座百兆瓦级分散控制构网型独立储能电站正式投运[EB/OL]. [2024-08-10]. https://www.powerchina.cn/art/2023/7/4/art_7444_1704552. html.
7	中国储能网. 构网型储能示范样本: 揭秘中国海拔最高储能电站是如何的?[EB/OL]. [2024-08-10]. https://www.escn.com.cn/20240105/4c0c930a344 94e66b929594be8c 1bda4/c.html.
8	中国储能网. 五大构网型储能项目之最 [EB/OL]. [2024-08-10]. https://www.escn.com. cn/20240202/558d4ca265ea4083913792c6ca743f21/c.html
9	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. DOI:10.1109/SAUPEC57889. 2023.10057659.
10	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. DOI: 10.19799/j.cnki.2095-4239.2023.0267.
	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. DOI: 10.19799/j.cnki.2095-4239.2023.0267.
11	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): 4403-4412. DOI: 10.13336/j.1003-6520.hve.20220403.
	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): 4403-4412. DOI: 10.13336/j.1003-6520.hve.20220403.
12	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. DOI: 10.19799/j.cnki.2095-4239.2022.0331.
	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. DOI: 10.19799/j.cnki.2095-4239.2022.0331.
13	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. DOI:10.1109/AEEES56888. 2023.10114236.
14	张鸿宇, 王宇. 国外电网侧储能电站参与调频辅助服务市场的机制经验及对我国的启示[J]. 储能科学与技术, 2021, 10(2): 766-773. DOI: 10.19799/j.cnki.2095-4239.2020.0370.
	ZHANG H Y, WANG Y. Mechanism experience of foreign grid-side storage participating in frequency regulation auxiliary service market and its enlightenment to China[J]. Energy Storage Science and Technology, 2021, 10(2): 766-773. DOI: 10.19799/j.cnki.2095-4239.2020.0370.
15	严干贵, 王铭岐, 段双明, 等. 考虑荷电状态恢复的储能一次调频控制策略[J]. 电力系统自动化, 2022, 46(21): 52-61. DOI: 10.7500/AEPS20210705003.
	YAN G G, WANG M Q, DUAN S M, et al. Primary frequency regulation control strategy of energy storage considering state of charge recovery[J]. Automation of Electric Power Systems, 2022, 46(21): 52-61. DOI: 10.7500/AEPS20210705003.
16	李军徽, 高卓, 应鸿, 等. 基于动态下垂系数与SOC基点的储能一次调频控制策略[J]. 电力系统保护与控制, 2021, 49(5): 1-10.
	LI J H, GAO Z, YING H, et al. Primary frequency regulation control strategy of energy storage based on dynamic droop coefficient and SOC reference[J]. Power System Protection and Control, 2021, 49(5): 1-10.
17	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. DOI:10.1109/ACCESS.2021.3081676.
18	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. DOI: 10.19783/j.cnki.pspc.200923.
	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. DOI: 10.19783/j.cnki.pspc.200923.
19	王子欣, 苗世洪, 郭舒毓, 等. 考虑分布式电源出力随机特性的配电网节点脆弱性评估[J]. 电力自动化设备, 2021, 41(8): 33-40. DOI: 10.16081/j.epae.202106013.
	WANG Z X, MIAO S H, GUO S Y, et al. Node vulnerability evaluation of distribution network considering randomness characteristic of distributed generation output[J]. Electric Power Automation Equipment, 2021, 41(8): 33-40. DOI: 10.16081/j.epae.202106013.
20	南璐, 何川, 刘天琪. 考虑风光出力不确定性的电–气互联系统脆弱线路辨识[J]. 中国电机工程学报, 2022, 42(2): 524-537. DOI: 10.13334/j.0258-8013.pcsee.210236.
	NAN L, HE C, LIU T Q. Vulnerable lines identification of integrated electricity and natural gas systems considering wind and photovoltaic generation uncertainties[J]. Proceedings of the CSEE, 2022, 42(2): 524-537. DOI: 10.13334/j.0258-8013.pcsee.210236.
21	谷卫星, 王婷婷, 张鹏, 等. 基于组合赋权和TOPSIS的配电网CPS系统脆弱性评估[J]. 华北电力大学学报(自然科学版), 2023, 50(1): 56-66. DOI: 10.3969/j.ISSN.1007-2691.2023.01.07.
	GU W X, WANG T T, ZHANG P, et al. Vulnerability assessment of distribution network CPS system based on combination weighting and TOPSIS[J]. Journal of North China Electric Power University (Natural Science Edition), 2023, 50(1): 56-66. DOI: 10.3969/j.ISSN.1007-2691.2023.01.07.

待求量	最优解	边界方案1	边界方案2	边界方案3
储能1位置	39	41	40	11
储能2位置	8	16	36	33
储能1容量	1.589	2.228	2.938	1.112
储能2容量	1.470	2.310	2.787	1.127
SOC较低值	0.480	0.364	0.492	0.224
SOC较高值	0.819	0.833	0.750	0.763

指标	最优解	边界方案1	边界方案2	边界方案3
电网脆弱度	0.24082	0.23852	0.24156	0.23932
频率偏差方均根	0.0393	0.0400	0.0383	0.0412
储能成本/元	16639	18446	20476	14507