A Two-Stage Site Selection and Capacity Determination Method for Energy Storage Power Stations Based on HC-MOPSO

doi:10.11930/j.issn.1004-9649.202407093

Abstract

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

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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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202407093

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

Figures/Tables 11

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

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

Fig.3 IEEE 39 node system topology

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

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

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

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

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

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

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

Fig.8 Comparison of different energy storage planning schemes

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