Optimal design and operation of a wind farm/battery energy storage considering demand side management
Abstract Balancing electricity demand and sustainable energy generation like wind energy presents challenges for the smart grid. To address this problem, the optimization of a wind farm (WF) along with the battery energy storage (BES) on the supply side, along with the demand side management (DSM) o...
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| Format: | Article |
| Language: | English |
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Wiley
2024-12-01
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| Series: | IET Renewable Power Generation |
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| Online Access: | https://doi.org/10.1049/rpg2.12951 |
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| author | Siyu Tao Chaohai Zhang Andrés E. Feijóo‐Lorenzo Victor Kim |
| author_facet | Siyu Tao Chaohai Zhang Andrés E. Feijóo‐Lorenzo Victor Kim |
| author_sort | Siyu Tao |
| collection | DOAJ |
| description | Abstract Balancing electricity demand and sustainable energy generation like wind energy presents challenges for the smart grid. To address this problem, the optimization of a wind farm (WF) along with the battery energy storage (BES) on the supply side, along with the demand side management (DSM) on the consumer side, should be considered during its planning and operation stages. An optimization framework with two levels to simultaneously decide the layout and operation of the WF/BES is put forward in this paper. The first‐level model consists of determining the WF/BES capacities, the WF configuration, and the connection buses. It is tackled by the mixed‐discrete particle swarm optimization algorithm. The multi‐objective optimization problem (MOOP) model in the second level determines the operation schedule of the WF/BES and other generators taking the DSM into consideration. The MOOP model in the second level is transformed to a single‐objective optimization problem via the maximum fuzzy satisfaction method, and is then solved by the genetic algorithm. The proposed model and the strategy are verified by the Barrow offshore WF test case, which is integrated into the IEEE‐118 system. Simulation results indicate that the wind and load patterns, the DSM and the BES price are the three key factors influencing the WF/BES design optimization. |
| format | Article |
| id | doaj-art-167544622b9d4b8eae8fa8b52ecbb720 |
| institution | Kabale University |
| issn | 1752-1416 1752-1424 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Renewable Power Generation |
| spelling | doaj-art-167544622b9d4b8eae8fa8b52ecbb7202025-01-30T12:15:53ZengWileyIET Renewable Power Generation1752-14161752-14242024-12-0118163563357310.1049/rpg2.12951Optimal design and operation of a wind farm/battery energy storage considering demand side managementSiyu Tao0Chaohai Zhang1Andrés E. Feijóo‐Lorenzo2Victor Kim3College of Automation Engineering, Center for More‐Electric‐Aircraft Power System Nanjing University of Aeronautics and Astronautics Nanjing ChinaCollege of Automation Engineering, Center for More‐Electric‐Aircraft Power System Nanjing University of Aeronautics and Astronautics Nanjing ChinaDepartment of Electrical Engineering University of Vigo Vigo SpainCollege of Electrical and Information Engineering University of Nairobi Nairobi KenyaAbstract Balancing electricity demand and sustainable energy generation like wind energy presents challenges for the smart grid. To address this problem, the optimization of a wind farm (WF) along with the battery energy storage (BES) on the supply side, along with the demand side management (DSM) on the consumer side, should be considered during its planning and operation stages. An optimization framework with two levels to simultaneously decide the layout and operation of the WF/BES is put forward in this paper. The first‐level model consists of determining the WF/BES capacities, the WF configuration, and the connection buses. It is tackled by the mixed‐discrete particle swarm optimization algorithm. The multi‐objective optimization problem (MOOP) model in the second level determines the operation schedule of the WF/BES and other generators taking the DSM into consideration. The MOOP model in the second level is transformed to a single‐objective optimization problem via the maximum fuzzy satisfaction method, and is then solved by the genetic algorithm. The proposed model and the strategy are verified by the Barrow offshore WF test case, which is integrated into the IEEE‐118 system. Simulation results indicate that the wind and load patterns, the DSM and the BES price are the three key factors influencing the WF/BES design optimization.https://doi.org/10.1049/rpg2.12951battery management systemswind farm design and operation |
| spellingShingle | Siyu Tao Chaohai Zhang Andrés E. Feijóo‐Lorenzo Victor Kim Optimal design and operation of a wind farm/battery energy storage considering demand side management IET Renewable Power Generation battery management systems wind farm design and operation |
| title | Optimal design and operation of a wind farm/battery energy storage considering demand side management |
| title_full | Optimal design and operation of a wind farm/battery energy storage considering demand side management |
| title_fullStr | Optimal design and operation of a wind farm/battery energy storage considering demand side management |
| title_full_unstemmed | Optimal design and operation of a wind farm/battery energy storage considering demand side management |
| title_short | Optimal design and operation of a wind farm/battery energy storage considering demand side management |
| title_sort | optimal design and operation of a wind farm battery energy storage considering demand side management |
| topic | battery management systems wind farm design and operation |
| url | https://doi.org/10.1049/rpg2.12951 |
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