Optimal Configuration of Soft Open Point and Energy Storage Based on Snowflake-Shaped Grid Characteristics and Sensitivity Analysis
With the continuous penetration of flexible resources, the distribution network is gradually forming a two-way interactive supply and demand relationship with the transmission network and users. The deployment of soft open point (SOP) and energy storage represents a crucial strategy for voltage regu...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/22/10503 |
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| author | Zhe Wang Zhang Zhang Fengzhang Luo Xiaoyu Qiu Xuefei Zhang Jiali Duan |
| author_facet | Zhe Wang Zhang Zhang Fengzhang Luo Xiaoyu Qiu Xuefei Zhang Jiali Duan |
| author_sort | Zhe Wang |
| collection | DOAJ |
| description | With the continuous penetration of flexible resources, the distribution network is gradually forming a two-way interactive supply and demand relationship with the transmission network and users. The deployment of soft open point (SOP) and energy storage represents a crucial strategy for voltage regulation and power flow control in distribution networks. This article puts forth a methodology for optimizing the configuration of SOP and energy storage based on the characteristics of the snowflake-shaped grid and sensitivity analysis. Firstly, the location of the SOP is determined based on the characteristics of the interconnection nodes between snowflake websites. Secondly, the voltage sensitivity analysis is employed to identify nodes that have a significant impact on the system voltage distribution, thereby enabling the selection of an optimal energy storage site. Subsequently, a multi-objective optimization configuration model for SOP and energy storage is established, taking into account the economic efficiency and load balancing of the power grid. Finally, the method is verified using a snowflake-shaped grid in Tianjin. In comparison with the plan that solely considers the economic aspects of the power grid, the method proposed in this article can reduce the degree of load balancing by 50.93% while simultaneously increasing the annual comprehensive cost by only 24.35%. |
| format | Article |
| id | doaj-art-7ab98d74bf544edfacd95bcaa8018b9d |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-7ab98d74bf544edfacd95bcaa8018b9d2024-11-26T17:49:00ZengMDPI AGApplied Sciences2076-34172024-11-0114221050310.3390/app142210503Optimal Configuration of Soft Open Point and Energy Storage Based on Snowflake-Shaped Grid Characteristics and Sensitivity AnalysisZhe Wang0Zhang Zhang1Fengzhang Luo2Xiaoyu Qiu3Xuefei Zhang4Jiali Duan5State Grid Tianjin Electric Power Co., Ltd., Tianjin 300010, ChinaState Grid Tianjin Electric Power Co., Ltd., Tianjin 300010, ChinaKey Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, ChinaKey Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, ChinaState Grid Tianjin Electric Power Co., Ltd., Tianjin 300010, ChinaState Grid Tianjin Electric Power Co., Ltd., Tianjin 300010, ChinaWith the continuous penetration of flexible resources, the distribution network is gradually forming a two-way interactive supply and demand relationship with the transmission network and users. The deployment of soft open point (SOP) and energy storage represents a crucial strategy for voltage regulation and power flow control in distribution networks. This article puts forth a methodology for optimizing the configuration of SOP and energy storage based on the characteristics of the snowflake-shaped grid and sensitivity analysis. Firstly, the location of the SOP is determined based on the characteristics of the interconnection nodes between snowflake websites. Secondly, the voltage sensitivity analysis is employed to identify nodes that have a significant impact on the system voltage distribution, thereby enabling the selection of an optimal energy storage site. Subsequently, a multi-objective optimization configuration model for SOP and energy storage is established, taking into account the economic efficiency and load balancing of the power grid. Finally, the method is verified using a snowflake-shaped grid in Tianjin. In comparison with the plan that solely considers the economic aspects of the power grid, the method proposed in this article can reduce the degree of load balancing by 50.93% while simultaneously increasing the annual comprehensive cost by only 24.35%.https://www.mdpi.com/2076-3417/14/22/10503snowflake-shaped gridsoft open pointenergy storagesiting and sizingmulti-objective optimization |
| spellingShingle | Zhe Wang Zhang Zhang Fengzhang Luo Xiaoyu Qiu Xuefei Zhang Jiali Duan Optimal Configuration of Soft Open Point and Energy Storage Based on Snowflake-Shaped Grid Characteristics and Sensitivity Analysis Applied Sciences snowflake-shaped grid soft open point energy storage siting and sizing multi-objective optimization |
| title | Optimal Configuration of Soft Open Point and Energy Storage Based on Snowflake-Shaped Grid Characteristics and Sensitivity Analysis |
| title_full | Optimal Configuration of Soft Open Point and Energy Storage Based on Snowflake-Shaped Grid Characteristics and Sensitivity Analysis |
| title_fullStr | Optimal Configuration of Soft Open Point and Energy Storage Based on Snowflake-Shaped Grid Characteristics and Sensitivity Analysis |
| title_full_unstemmed | Optimal Configuration of Soft Open Point and Energy Storage Based on Snowflake-Shaped Grid Characteristics and Sensitivity Analysis |
| title_short | Optimal Configuration of Soft Open Point and Energy Storage Based on Snowflake-Shaped Grid Characteristics and Sensitivity Analysis |
| title_sort | optimal configuration of soft open point and energy storage based on snowflake shaped grid characteristics and sensitivity analysis |
| topic | snowflake-shaped grid soft open point energy storage siting and sizing multi-objective optimization |
| url | https://www.mdpi.com/2076-3417/14/22/10503 |
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