An Energy storage capacity allocation method for power system based on improved particle swarm optimization
Due to the limitations of configuration scenarios， energy storage capacity configuration takes long time and the net profit value of energy storage capacity is small. Research on energy storage capacity configuration method for power system based on an improved particle swarm optimization （PSO） is p...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | zho |
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zhejiang electric power
2022-11-01
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| Series: | Zhejiang dianli |
| Subjects: | |
| Online Access: | https://zjdl.cbpt.cnki.net/WKE3/WebPublication/paperDigest.aspx?paperID=b084256c-83f1-4012-993a-a5aab8801e65 |
| _version_ | 1797980616776482816 |
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| author | ZHAO Fei XUE Longjiang ZHUJingliang CHEN Ding FANG Jinghui WU Jun ZHU Yueren |
| author_facet | ZHAO Fei XUE Longjiang ZHUJingliang CHEN Ding FANG Jinghui WU Jun ZHU Yueren |
| author_sort | ZHAO Fei |
| collection | DOAJ |
| description | Due to the limitations of configuration scenarios， energy storage capacity configuration takes long time and the net profit value of energy storage capacity is small. Research on energy storage capacity configuration method for power system based on an improved particle swarm optimization （PSO） is proposed. First， the power system energy storage capacity is determined， and the storage capacity configuration constraints are analyzed in respect of the remaining power， charging and discharging efficiency， and reliability of the energy storage system； then， with the objective of maximum net revenue of storage capacity configuration， an economy model of the power system is constructed， and the improved particle swarm algorithm is introduced to solve the model and complete the configuration of the power system energy storage capacity. The experimental results show that the proposed method takes shorter configuration time while owning higher net energy storage capacity revenue， which verifies the application performance of the method. |
| first_indexed | 2024-04-11T05:56:57Z |
| format | Article |
| id | doaj.art-e09cd87b98e24a5aa654506421a0e2a3 |
| institution | Directory Open Access Journal |
| issn | 1007-1881 |
| language | zho |
| last_indexed | 2024-04-11T05:56:57Z |
| publishDate | 2022-11-01 |
| publisher | zhejiang electric power |
| record_format | Article |
| series | Zhejiang dianli |
| spelling | doaj.art-e09cd87b98e24a5aa654506421a0e2a32022-12-22T04:41:52Zzhozhejiang electric powerZhejiang dianli1007-18812022-11-014111172210.19585/j.zjdl.2022110031007-1881（2022）11-0017-06An Energy storage capacity allocation method for power system based on improved particle swarm optimizationZHAO Fei0XUE Longjiang1ZHUJingliang2CHEN Ding3FANG Jinghui4WU Jun5ZHU Yueren6State Grid Jiaxing Electric Power Supply Company, Jiaxing Zhejiang 314033, ChinaState Grid Pinghu Power Supply Company, Jiaxing Zhejiang 314000, ChinaState Grid Jiaxing Electric Power Supply Company, Jiaxing Zhejiang 314033, ChinaState Grid Jiaxing Electric Power Supply Company, Jiaxing Zhejiang 314033, ChinaState Grid Pinghu Power Supply Company, Jiaxing Zhejiang 314000, ChinaState Grid Pinghu Power Supply Company, Jiaxing Zhejiang 314000, ChinaState Grid Pinghu Power Supply Company, Jiaxing Zhejiang 314000, ChinaDue to the limitations of configuration scenarios， energy storage capacity configuration takes long time and the net profit value of energy storage capacity is small. Research on energy storage capacity configuration method for power system based on an improved particle swarm optimization （PSO） is proposed. First， the power system energy storage capacity is determined， and the storage capacity configuration constraints are analyzed in respect of the remaining power， charging and discharging efficiency， and reliability of the energy storage system； then， with the objective of maximum net revenue of storage capacity configuration， an economy model of the power system is constructed， and the improved particle swarm algorithm is introduced to solve the model and complete the configuration of the power system energy storage capacity. The experimental results show that the proposed method takes shorter configuration time while owning higher net energy storage capacity revenue， which verifies the application performance of the method.https://zjdl.cbpt.cnki.net/WKE3/WebPublication/paperDigest.aspx?paperID=b084256c-83f1-4012-993a-a5aab8801e65improved particle swarm optimizationenergy storage systemcapacity configurationtime of use tariffs |
| spellingShingle | ZHAO Fei XUE Longjiang ZHUJingliang CHEN Ding FANG Jinghui WU Jun ZHU Yueren An Energy storage capacity allocation method for power system based on improved particle swarm optimization Zhejiang dianli improved particle swarm optimization energy storage system capacity configuration time of use tariffs |
| title | An Energy storage capacity allocation method for power system based on improved particle swarm optimization |
| title_full | An Energy storage capacity allocation method for power system based on improved particle swarm optimization |
| title_fullStr | An Energy storage capacity allocation method for power system based on improved particle swarm optimization |
| title_full_unstemmed | An Energy storage capacity allocation method for power system based on improved particle swarm optimization |
| title_short | An Energy storage capacity allocation method for power system based on improved particle swarm optimization |
| title_sort | energy storage capacity allocation method for power system based on improved particle swarm optimization |
| topic | improved particle swarm optimization energy storage system capacity configuration time of use tariffs |
| url | https://zjdl.cbpt.cnki.net/WKE3/WebPublication/paperDigest.aspx?paperID=b084256c-83f1-4012-993a-a5aab8801e65 |
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