A Two-Stage Site Selection and Capacity Determination Method for Energy Storage Power Stations Based on HC-MOPSO
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...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
State Grid Energy Research Institute
2024-12-01
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| Series: | Zhongguo dianli |
| Subjects: | |
| Online Access: | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202407093 |
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| Summary: | 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. |
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| ISSN: | 1004-9649 |