Optimal Placement and Capacity of Battery Energy Storage System in Distribution Networks Integrated With PV and EVs Using Metaheuristic Algorithms
In this research, the optimal placement and capacity of battery energy storage systems (BESS) in distribution networks integrated with photovoltaics (PV) and electric vehicles (EVs) have been proposed. The main objective function is to minimize the system costs including installation, replacement, a...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10171373/ |
| _version_ | 1827903738862370816 |
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| author | Natsawat Pompern Suttichai Premrudeepreechacharn Apirat Siritaratiwat Sirote Khunkitti |
| author_facet | Natsawat Pompern Suttichai Premrudeepreechacharn Apirat Siritaratiwat Sirote Khunkitti |
| author_sort | Natsawat Pompern |
| collection | DOAJ |
| description | In this research, the optimal placement and capacity of battery energy storage systems (BESS) in distribution networks integrated with photovoltaics (PV) and electric vehicles (EVs) have been proposed. The main objective function is to minimize the system costs including installation, replacement, and operation and maintenance costs of the BESS. The replacement cost has been considered over 20 years while the operation and maintenance costs are the costs incurred by transmission line loss, voltage regulation, and peak demand. To solve the problem, three metaheuristic algorithms, namely particle swarm optimization (PSO), african vultures optimization algorithm (AVOA), and salp swarm algorithm (SSA), are employed. The proposed approach is evaluated on the IEEE 33- and 69-bus distribution systems integrated with PV and EVs. The results provided by the considered algorithms are compared in terms of the objective function, system efficiency enhancement, payback period, and statistical analysis. The simulation results show that after the BESS installation, the voltage profile can be improved, transmission loss is reduced, and peak demand is decreased where PSO provides the best objective values and AVOA achieves the fastest payback periods in both systems. |
| first_indexed | 2024-03-13T00:17:52Z |
| format | Article |
| id | doaj.art-e2ea21dcaf354626aaddc81d1d0171df |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-13T00:17:52Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-e2ea21dcaf354626aaddc81d1d0171df2023-07-11T23:00:31ZengIEEEIEEE Access2169-35362023-01-0111683796839410.1109/ACCESS.2023.329159010171373Optimal Placement and Capacity of Battery Energy Storage System in Distribution Networks Integrated With PV and EVs Using Metaheuristic AlgorithmsNatsawat Pompern0https://orcid.org/0009-0009-4702-4812Suttichai Premrudeepreechacharn1Apirat Siritaratiwat2https://orcid.org/0000-0001-6568-4675Sirote Khunkitti3https://orcid.org/0000-0001-8870-7307Department of Electrical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, ThailandDepartment of Electrical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, ThailandDepartment of Electrical Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen, ThailandDepartment of Electrical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, ThailandIn this research, the optimal placement and capacity of battery energy storage systems (BESS) in distribution networks integrated with photovoltaics (PV) and electric vehicles (EVs) have been proposed. The main objective function is to minimize the system costs including installation, replacement, and operation and maintenance costs of the BESS. The replacement cost has been considered over 20 years while the operation and maintenance costs are the costs incurred by transmission line loss, voltage regulation, and peak demand. To solve the problem, three metaheuristic algorithms, namely particle swarm optimization (PSO), african vultures optimization algorithm (AVOA), and salp swarm algorithm (SSA), are employed. The proposed approach is evaluated on the IEEE 33- and 69-bus distribution systems integrated with PV and EVs. The results provided by the considered algorithms are compared in terms of the objective function, system efficiency enhancement, payback period, and statistical analysis. The simulation results show that after the BESS installation, the voltage profile can be improved, transmission loss is reduced, and peak demand is decreased where PSO provides the best objective values and AVOA achieves the fastest payback periods in both systems.https://ieeexplore.ieee.org/document/10171373/Battery energy storage systemsphotovoltaicselectric vehiclesmetaheuristic algorithmsparticle swarm optimizationAfrican vultures optimization algorithm |
| spellingShingle | Natsawat Pompern Suttichai Premrudeepreechacharn Apirat Siritaratiwat Sirote Khunkitti Optimal Placement and Capacity of Battery Energy Storage System in Distribution Networks Integrated With PV and EVs Using Metaheuristic Algorithms IEEE Access Battery energy storage systems photovoltaics electric vehicles metaheuristic algorithms particle swarm optimization African vultures optimization algorithm |
| title | Optimal Placement and Capacity of Battery Energy Storage System in Distribution Networks Integrated With PV and EVs Using Metaheuristic Algorithms |
| title_full | Optimal Placement and Capacity of Battery Energy Storage System in Distribution Networks Integrated With PV and EVs Using Metaheuristic Algorithms |
| title_fullStr | Optimal Placement and Capacity of Battery Energy Storage System in Distribution Networks Integrated With PV and EVs Using Metaheuristic Algorithms |
| title_full_unstemmed | Optimal Placement and Capacity of Battery Energy Storage System in Distribution Networks Integrated With PV and EVs Using Metaheuristic Algorithms |
| title_short | Optimal Placement and Capacity of Battery Energy Storage System in Distribution Networks Integrated With PV and EVs Using Metaheuristic Algorithms |
| title_sort | optimal placement and capacity of battery energy storage system in distribution networks integrated with pv and evs using metaheuristic algorithms |
| topic | Battery energy storage systems photovoltaics electric vehicles metaheuristic algorithms particle swarm optimization African vultures optimization algorithm |
| url | https://ieeexplore.ieee.org/document/10171373/ |
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