基于HC-MOPSO的储能电站两阶段选址定容方法

doi:10.11930/j.issn.1004-9649.202407093

摘要/Abstract

摘要：

针对大规模储能规划难以兼顾电网有功功率与节点电压耦合影响的问题，提出一种基于层次聚类（hierarchical clustering，HC）-多目标粒子群（multi objective particle swarm optimization，MOPSO）算法的储能电站规划方法。首先，基于系统有功功率与节点电压间的耦合作用，建立其灵敏度模型，并采用HC算法得到电网区域划分结果，根据灵敏度指标排序选取各次区域内的电压主导节点作为储能电站接入点；其次，以系统静态电压稳定裕度最大、总投资与运行成本以及总有功网损最小为目标，建立储能电站容量配置模型，并设计嵌入潮流计算的MOPSO算法对模型进行求解。最后，以IEEE39节点电力系统网络为例，验证所提方法和模型的可行性与有效性。仿真结果表明，本文提出的规划方法相较于传统方法可以进一步降低系统有功线损，并提高静态电压稳定裕度。

关键词: 电网分区, 选址规划, 容量配置, 电压主导节点, 静态电压稳定裕度

Abstract:

A planning method for energy storage stations based on Hierarchical Clustering (HC) and Multi Objective Particle Swarm Optimization (MOPSO) is proposed to address the difficulty of balancing the coupling effects of active power and node voltage in large-scale energy storage planning. Firstly, based on the coupling effect between system active power and node voltage, a sensitivity model is established, and the HC algorithm is used to obtain the results of power grid regional division. Furthermore, based on sensitivity indicators, select the voltage dominant nodes within each sub region as the access points for energy storage power stations; Secondly, a capacity configuration model for energy storage power stations is established with the objectives of maximizing the system's static voltage stability margin, minimizing total investment and operating costs, and minimizing total active power losses. The MOPSO algorithm embedded in power flow calculation is designed to solve the model. Finally, taking the IEEE 39 node power system network as an example, the feasibility and effectiveness of the proposed method and model are verified.The simulation results show that the planning method proposed in this paper can further reduce the active line loss of the system and improve the static voltage stability margin compared to traditional methods.

Key words: power grid zoning, site selection planning, capacity configuration, voltage dominant node, static voltage stability margin

白望望, 杨德州, 李万伟, 王涛, 张耀忠. 基于HC-MOPSO的储能电站两阶段选址定容方法[J]. 中国电力, 2024, 57(12): 148-156.

Wangwang BAI, Dezhou YANG, Wanwei LI, Tao WANG, Yaozhong ZHANG. A Two-Stage Site Selection and Capacity Determination Method for Energy Storage Power Stations Based on HC-MOPSO[J]. Electric Power, 2024, 57(12): 148-156.

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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202407093

https://www.electricpower.com.cn/CN/Y2024/V57/I12/148

图/表 11

图 1 电网分区与储能电站选址流程

Fig.1 Grid zoning and site selection process for energy storage power stations

图 2 储能电站容量配置流程图

Fig.2 Flow chart of capacity configuration for energy storage power stations

图 3 IEEE 39节点系统拓扑结构

Fig.3 IEEE 39 node system topology

图 4 39节点系统负荷节点聚类谱系

Fig.4 Clustering spectrum of 39-node system load nodes

表 1 39节点系统负荷区域划分结果

Table 1 39 node system load area division results

次区域编号		各次区域负荷节点
1		4, 5, 6, 7, 10, 11, 12, 13, 14, 15, 31, 32
2		8, 9, 39
3		1, 2, 3, 25, 30, 37
4		26, 27, 28, 29, 38
5		16, 17, 18, 21, 22, 23, 24, 35, 36
6		19, 20, 33, 34

表 2 39节点系统各分区相对灵敏度指标

Table 2 Subregional relative integrated sensitivity metrics for the 39-node system

分区编号		各分区灵敏度指标
1		0.00, 1.00, 0.85, 0.00, 0.68, 0.74, 0.00, 0.46, 0.15, 0.00, 3.90
2		0.00, 0.46, 0.46
3		0.00, 0.06, 0.00, 0.03, 0.09
4		0.04, 0.04, 0.04, 0, 0.04
5		0.00, 0.00, 0.00, 0.00, 0.22325736, 0.01, 0.000111202, 0.24
6		0.22, 0.00, 0.22

图 5 39节点系统主导节点选取示意

Fig.5 Schematic diagram of the selection of the dominant node of the 39-node system

图 6 储能电站容量配置模型帕累托图

Fig.6 Pareto diagram of capacity configuration model for energy storage power stations

表 3 储能电站容量可行解

Table 3 Feasible solution for the optimal capacity of energy storage power stations

方案	储能电站容量/MW						投资与运行成本/亿元	总有功线损/ kW	静态电压稳定裕度
方案	Bus5	Bus9	Bus2	Bus26	Bus22	Bus19	投资与运行成本/亿元	总有功线损/ kW	静态电压稳定裕度
1	4	9	5	6	2.17	5	60.50	11750.66	18.17
2	10	10	10	6	3	3	81.52	6971.26	19.98
3	25.33	13.65	5.93	6.62	5	5	119.42	5397.22	23.08

图 7 不同方案系统静态电压稳定裕度及线损对比

Fig.7 Comparison diagram of static voltage stability margin and line loss for different schemes of systems

图 8 不同储能规划方案对比

Fig.8 Comparison of different energy storage planning schemes

参考文献 30

1	国务院发展研究中心国际技术经济研究所. 世界前沿技术发展报告[M]. 电子工业出版社, 2021.
2	李建林, 李雅欣, 周喜超. 电网侧储能技术研究综述[J]. 电力建设, 2020, 41 (6): 77- 84.
	LI Jianlin, LI Yaxin, ZHOU Xichao. Summary of research on grid-side energy storage technology[J]. Electric Power Construction, 2020, 41 (6): 77- 84.
3	陈涵, 徐玲铃, 杨柳, 等. 地区电网技术降损研究现状及展望[J/OL]. 电网技术, 1–13[2024-12-10].
	CHEN Han, XU Lingling, YANG Liu, etal. Research status and prospects on technical loss reduction of regional power grid. Power System Technology, 1–13[2024-12-10].
4	汤广福, 罗湘, 魏晓光. 多端直流输电与直流电网技术[J]. 中国电机工程学报, 2013, 33 (10): 8- 17, 24.
	TANG Guangfu, LUO Xiang, WEI Xiaoguang. Multi-terminal HVDC and DC-grid technology[J]. Proceedings of the CSEE, 2013, 33 (10): 8- 17, 24.
5	曹新慧, 车勇, 司政, 等. 广域储能电站定容-选址一体规划[J]. 中国电力, 2022, 55 (7): 110- 120.
	CAO Xinhui, CHE Yong, SI Zheng, et al. Integrated planning of optimal sizing and siting of energy storage plants across wide area[J]. Electric Power, 2022, 55 (7): 110- 120.
6	张智, 周明, 武昭原, 等. 考虑动态频率支撑的储能选址定容规划方法[J]. 中国电机工程学报, 2023, 43 (7): 2708- 2721.
	ZHANG Zhi, ZHOU Ming, WU Zhaoyuan, et al. Energy storage location and capacity planning method considering dynamic frequency support[J]. Proceedings of the CSEE, 2023, 43 (7): 2708- 2721.
7	高春雨, 王海云, 朱姝林. 基于电压稳定度的分布式储能选址定容规划[J]. 科学技术与工程, 2023, 23 (7): 2884- 2891.
	GAO Chunyu, WANG Haiyun, ZHU Shulin. Optimal siting and sizing of distributed energy storage based on voltage stability[J]. Science Technology and Engineering, 2023, 23 (7): 2884- 2891.
8	TANG X B, DENG K, WU Q W, et al. Optimal location and capacity of the distributed energy storage system in a distribution network[J]. IEEE Access, 2020, 8, 15576- 15585. DOI
9	KHALID M, SAVKIN A V. Optimization and control of a distributed battery energy storage system for wind power smoothing[C]//2011 19th Mediterranean Conference on Control & Automation (MED). Corfu, Greece. IEEE, 2011: 39–43.
10	徐吉智, 张新燕, 常喜强, 等. 基于PV曲线和改进遗传算法储能选址定容研究[J]. 太阳能学报, 2022, 43 (1): 263- 268.
	XU Jizhi, ZHANG Xinyan, CHANG Xiqiang, et al. Research on energy storage location and capacity determination based on PV curve and improved genetic algorithm[J]. Acta Energiae Solaris Sinica, 2022, 43 (1): 263- 268.
11	王子琪. 含风光区域电网的储能选址定容及能量管理研究[D]. 北京: 华北电力大学, 2021.
	WANG Ziqi. Research on the location and capacity of energy storage and erergy management of regional power grid with wind and solar energy[D]. Beijing: North China Electric Power University, 2022.
12	孙东磊, 赵龙, 孙凯祺, 等. 新能源接入下配电系统混合储能选址定容优化策略研究[J]. 太阳能学报, 2024, 45 (1): 423- 432.
	SUN Donglei, ZHAO Long, SUN Kaiqi, et al. Research on optimization strategy of Hess site and capacity selection in distribution system with renewable energy access[J]. Acta Energiae Solaris Sinica, 2024, 45 (1): 423- 432.
13	丁倩. 考虑可再生能源不确定性的储能选址定容规划方法研究[D]. 杭州: 杭州电子科技大学, 2020.
	DING Qian. A grid electrochemical energy storage siting and capacity planning considering renewable generation uncertainty[D]. Hangzhou: Hangzhou Dianzi University, 2020.
14	ZHAI L M, XIAO C Q, LI X H, et al. Research on location determination and capacity optimization method for large-scale energy storage station in regional power grid[M]//Lecture Notes in Electrical Engineering. Singapore: Springer Nature Singapore, 2024: 359–369.
15	YOU W B, DING Y H. W-MOPSO in adaptive circuits for blast wave measurements[J]. IEEE Sensors Journal, 2021, 21 (7): 9323- 9332. DOI
16	黄蓉. 基于牛顿-拉夫逊法和P-Q分解法的配网潮流计算联合迭策略[J]. 自动化应用, 2023, (22): 55- 57.
	HUANG Rong. Joint iteration strategy for distribution network power flow calculation based on Newton-raphson method and P-Q decomposition method[J]. Automation Application, 2023, (22): 55- 57.
17	CAPÓ M, PÉREZ A, LOZANO J A. An efficient split-merge re-start for the K-means algorithm[J]. IEEE Transactions on Knowledge and Data Engineering, 2020, 34 (4): 1618- 1627.
18	GHOSH A, CHUNG J, YIN D, et al. An efficient framework for clustered federated learning[J]. IEEE Transactions on Information Theory, 2022, 68 (12): 8076- 8091. DOI
19	邓雅文, 侯鹏, 蒋卫国, 等. 基于多特征指标和层次聚类分析的河源区范围自动划分方法研究[J]. 地球信息科学学报, 2022, 24 (3): 469- 482.
	DENG Yawen, HOU Peng, JIANG Weiguo, et al. Automatic extraction of river source region boundary based on multi-characteristic indexes and hierarchical cluster analysis[J]. Journal of Geo-Information Science, 2022, 24 (3): 469- 482.
20	李建林, 康靖悦, 崔宜琳. 储能电站优化选址定容研究综述[J]. 电气时代, 2022, (6): 34- 37.
21	KHODADADI N, ABUALIGAH L, MIRJALILI S. Multi-objective stochastic paint optimizer (MOSPO)[J]. Neural Computing and Applications, 2022, 34 (20): 18035- 18058. DOI
22	刘君华. 电力系统无功源配置与分级电压控制的研究[D]. 杭州: 浙江大学, 2006.
	LIU Junhua. Research on reactive power source configuration and graded voltage control in power systems[D]. Hangzhou: Zhejiang University, 2006.
23	武亦文. 提升电网可靠性的储能电站规划技术研究[D]. 北京: 北方工业大学, 2023.
	WU Yiwen. Research on planning technology of energy storage power station to improve the reliability of power grid[D]. Beijing: North China University of Technology, 2023.
24	宋思锦. 含高比例新能源的配电网储能规划研究[D]. 济南: 山东大学, 2023.
	SONG Sijin. Research on distribution network energy storage planning with high proportion of new energy[D]. Jinan: Shandong University, 2023.
25	杨子昊, 娄柯, 蒋卓伟. 考虑需求响应的微电网优化调度[J]. 昆明理工大学学报(自然科学版), 2023, 48 (4): 74- 86.
	YANG Zihao, LOU Ke, JIANG Zhuowei. Optimal Scheduling of Microgrid Considering Demand Response[J]. Journal of Kunming University of Science and Technology(Natural Science), 2023, 48 (4): 74- 86.
26	刘子祺, 苏婷婷, 何佳阳, 等. 基于多目标粒子群算法的配电网储能优化配置研究[J]. 综合智慧能源, 2023, 45 (6): 9- 16.
	LIU Ziqi, SU Tingting, HE Jiayang, et al. Research on the optimal allocation of energy storage in distribution network based on multi-objective particle swarm optimization algorithm[J]. Huadian Technology, 2023, 45 (6): 9- 16.
27	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.
28	LIU J W, LI Q, CHEN W R, et al. Remaining useful life prediction of PEMFC based on long short-term memory recurrent neural networks[J]. International Journal of Hydrogen Energy, 2019, 44 (11): 5470- 5480.
29	PENG Y L, CHEN T, XIAO F, et al. Remaining useful lifetime prediction methods of proton exchange membrane fuel cell based on convolutional neural network-long short-term memory and convolutional neural network-bidirectional long short-term memory[J]. Fuel Cells, 2023, 23 (1): 75- 87.
30	WANG F K, MAMO T, CHENG X B. Bi-directional long short-term memory recurrent neural network with attention for stack voltage degradation from proton exchange membrane fuel cells[J]. Journal of Power Sources, 2020, 461, 228170.

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