A Heuristic-Driven Charging Strategy of Electric Vehicle for Grids with High EV Penetration
The widespread adoption of electric vehicles (EVs) poses challenges associated with charging infrastructures and their impact on the electrical grid. To address these challenges, smart charging approaches have emerged as a key solution that optimizes charging processes and contributes to a smarter a...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/16/19/6959 |
| _version_ | 1797575896781029376 |
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| author | Bahman Ahmadi Elham Shirazi |
| author_facet | Bahman Ahmadi Elham Shirazi |
| author_sort | Bahman Ahmadi |
| collection | DOAJ |
| description | The widespread adoption of electric vehicles (EVs) poses challenges associated with charging infrastructures and their impact on the electrical grid. To address these challenges, smart charging approaches have emerged as a key solution that optimizes charging processes and contributes to a smarter and more efficient grid. This paper presents an innovative multi-objective optimization framework for EV smart charging (EVSC) using the Dynamic Hunting Leadership (DHL) method. The framework aims to improve the voltage profile of the system in addition to eliminating voltage violations and energy not supplied (ENS) to EVs within the network. The proposed approach considers both residential EV chargers and parking stations, incorporating realistic EV charger behaviors based on constant current charging and addressing the problem as a mixed integer non-linear programming (MINLP) problem. The performance of the optimization method is evaluated on a distribution network with varying levels of EV penetration connected to the chargers in the grid. The results demonstrate the effectiveness of the DHL algorithm in minimizing conflicting objectives and improving the grid’s voltage profile while considering operational constraints. This study provides a road map for EV aggregators and EV owners, guiding them on how to charge EVs based on preferences while minimizing adverse technical impacts on the grid. |
| first_indexed | 2024-03-10T21:45:49Z |
| format | Article |
| id | doaj.art-86cafa8699344e57b9270838c195ae8e |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T21:45:49Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-86cafa8699344e57b9270838c195ae8e2023-11-19T14:21:17ZengMDPI AGEnergies1996-10732023-10-011619695910.3390/en16196959A Heuristic-Driven Charging Strategy of Electric Vehicle for Grids with High EV PenetrationBahman Ahmadi0Elham Shirazi1Department of Electrical Engineering, Mathematics and Computer Science, University of Twente, 7522 NB Enschede, The NetherlandsDepartment of Engineering Technology, University of Twente, 7522 NB Enschede, The NetherlandsThe widespread adoption of electric vehicles (EVs) poses challenges associated with charging infrastructures and their impact on the electrical grid. To address these challenges, smart charging approaches have emerged as a key solution that optimizes charging processes and contributes to a smarter and more efficient grid. This paper presents an innovative multi-objective optimization framework for EV smart charging (EVSC) using the Dynamic Hunting Leadership (DHL) method. The framework aims to improve the voltage profile of the system in addition to eliminating voltage violations and energy not supplied (ENS) to EVs within the network. The proposed approach considers both residential EV chargers and parking stations, incorporating realistic EV charger behaviors based on constant current charging and addressing the problem as a mixed integer non-linear programming (MINLP) problem. The performance of the optimization method is evaluated on a distribution network with varying levels of EV penetration connected to the chargers in the grid. The results demonstrate the effectiveness of the DHL algorithm in minimizing conflicting objectives and improving the grid’s voltage profile while considering operational constraints. This study provides a road map for EV aggregators and EV owners, guiding them on how to charge EVs based on preferences while minimizing adverse technical impacts on the grid.https://www.mdpi.com/1996-1073/16/19/6959smart gridelectric vehiclecharging strategyoptimization algorithmdistribution network |
| spellingShingle | Bahman Ahmadi Elham Shirazi A Heuristic-Driven Charging Strategy of Electric Vehicle for Grids with High EV Penetration Energies smart grid electric vehicle charging strategy optimization algorithm distribution network |
| title | A Heuristic-Driven Charging Strategy of Electric Vehicle for Grids with High EV Penetration |
| title_full | A Heuristic-Driven Charging Strategy of Electric Vehicle for Grids with High EV Penetration |
| title_fullStr | A Heuristic-Driven Charging Strategy of Electric Vehicle for Grids with High EV Penetration |
| title_full_unstemmed | A Heuristic-Driven Charging Strategy of Electric Vehicle for Grids with High EV Penetration |
| title_short | A Heuristic-Driven Charging Strategy of Electric Vehicle for Grids with High EV Penetration |
| title_sort | heuristic driven charging strategy of electric vehicle for grids with high ev penetration |
| topic | smart grid electric vehicle charging strategy optimization algorithm distribution network |
| url | https://www.mdpi.com/1996-1073/16/19/6959 |
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