Heuristic Optimization of EV Charging Schedule Considering Battery Degradation Cost
It is expected that electric vehicles (EVs) will be important part of smart gird, not only in form of load but also as distributed energy source in Vehicle to Grid (V2G) system. As increase of EVs integration, V2G contributes to improve flexibility, reliability and stability of grid by providing anc...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Kaunas University of Technology
2018-12-01
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| Series: | Elektronika ir Elektrotechnika |
| Subjects: | |
| Online Access: | http://eejournal.ktu.lt/index.php/elt/article/view/22283 |
| _version_ | 1819183154554994688 |
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| author | Ahmet Dogan Mustafa Alci |
| author_facet | Ahmet Dogan Mustafa Alci |
| author_sort | Ahmet Dogan |
| collection | DOAJ |
| description | It is expected that electric vehicles (EVs) will be important part of smart gird, not only in form of load but also as distributed energy source in Vehicle to Grid (V2G) system. As increase of EVs integration, V2G contributes to improve flexibility, reliability and stability of grid by providing ancillary services. These services, however, could accelerate degradation of battery whose price is almost half of EV. Thus, battery degradation cost must be considered while scheduling of EV charging. In this paper, a battery degradation cost model of EV lithium-ion batteries was incorporated in the optimal charging schedule of 400 EVs. EVs are located to 33 bus system in order to consider network losses in calculations. Heuristic algorithms such as Genetic Algorithm (GA), Differential Evolution (DE), Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) are used for solving the associated optimization problem. The objective function aims to maximize user profit under dynamic pricing. Also, distribution system and EVs constraints are considered during optimization. The numerical results illustrate that each of the used heuristic algorithms able to mitigate peak loads and improve voltage levels. GA presents the most profitable charging scheduling in terms of EV owners.
DOI: http://dx.doi.org/10.5755/j01.eie.24.6.22283 |
| first_indexed | 2024-12-22T22:57:31Z |
| format | Article |
| id | doaj.art-e577ed1dbfc04fe39fbfa6988875b9a5 |
| institution | Directory Open Access Journal |
| issn | 1392-1215 2029-5731 |
| language | English |
| last_indexed | 2024-12-22T22:57:31Z |
| publishDate | 2018-12-01 |
| publisher | Kaunas University of Technology |
| record_format | Article |
| series | Elektronika ir Elektrotechnika |
| spelling | doaj.art-e577ed1dbfc04fe39fbfa6988875b9a52022-12-21T18:09:46ZengKaunas University of TechnologyElektronika ir Elektrotechnika1392-12152029-57312018-12-01246152010.5755/j01.eie.24.6.2228322283Heuristic Optimization of EV Charging Schedule Considering Battery Degradation CostAhmet DoganMustafa AlciIt is expected that electric vehicles (EVs) will be important part of smart gird, not only in form of load but also as distributed energy source in Vehicle to Grid (V2G) system. As increase of EVs integration, V2G contributes to improve flexibility, reliability and stability of grid by providing ancillary services. These services, however, could accelerate degradation of battery whose price is almost half of EV. Thus, battery degradation cost must be considered while scheduling of EV charging. In this paper, a battery degradation cost model of EV lithium-ion batteries was incorporated in the optimal charging schedule of 400 EVs. EVs are located to 33 bus system in order to consider network losses in calculations. Heuristic algorithms such as Genetic Algorithm (GA), Differential Evolution (DE), Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) are used for solving the associated optimization problem. The objective function aims to maximize user profit under dynamic pricing. Also, distribution system and EVs constraints are considered during optimization. The numerical results illustrate that each of the used heuristic algorithms able to mitigate peak loads and improve voltage levels. GA presents the most profitable charging scheduling in terms of EV owners. DOI: http://dx.doi.org/10.5755/j01.eie.24.6.22283http://eejournal.ktu.lt/index.php/elt/article/view/22283electric vehiclesoptimizationheuristic algorithmsev charging schedulevehicle to grid. |
| spellingShingle | Ahmet Dogan Mustafa Alci Heuristic Optimization of EV Charging Schedule Considering Battery Degradation Cost Elektronika ir Elektrotechnika electric vehicles optimization heuristic algorithms ev charging schedule vehicle to grid. |
| title | Heuristic Optimization of EV Charging Schedule Considering Battery Degradation Cost |
| title_full | Heuristic Optimization of EV Charging Schedule Considering Battery Degradation Cost |
| title_fullStr | Heuristic Optimization of EV Charging Schedule Considering Battery Degradation Cost |
| title_full_unstemmed | Heuristic Optimization of EV Charging Schedule Considering Battery Degradation Cost |
| title_short | Heuristic Optimization of EV Charging Schedule Considering Battery Degradation Cost |
| title_sort | heuristic optimization of ev charging schedule considering battery degradation cost |
| topic | electric vehicles optimization heuristic algorithms ev charging schedule vehicle to grid. |
| url | http://eejournal.ktu.lt/index.php/elt/article/view/22283 |
| work_keys_str_mv | AT ahmetdogan heuristicoptimizationofevchargingscheduleconsideringbatterydegradationcost AT mustafaalci heuristicoptimizationofevchargingscheduleconsideringbatterydegradationcost |