Efficient energy management and cost optimization using multi-objective grey wolf optimization for EV charging/discharging in microgrid
The depletion of conventional energy sources coupled with the rising demand for electric vehicles (EVs) has significantly underscored the necessity for electric vehicle supply equipment (EVSE) with advanced energy supply management within microgrids. Effective energy management of EVs and EVSEs is i...
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Elsevier
2024-12-01
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| Series: | e-Prime: Advances in Electrical Engineering, Electronics and Energy |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S277267112400384X |
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| author | Swati Sharma Ikbal Ali |
| author_facet | Swati Sharma Ikbal Ali |
| author_sort | Swati Sharma |
| collection | DOAJ |
| description | The depletion of conventional energy sources coupled with the rising demand for electric vehicles (EVs) has significantly underscored the necessity for electric vehicle supply equipment (EVSE) with advanced energy supply management within microgrids. Effective energy management of EVs and EVSEs is imperative to satisfy EV owners and stabilize microgrids. This paper introduces multi-objective grey wolf optimization (MOGWO) to optimize EVSE and EV charging costs. MOGWO achieves substantial cost savings through dynamic pricing based on time of day, state-of-charge and hour-based scheduling which promotes off-peak charging thereby enhancing system efficiency and reducing costs. The algorithm also provides flexibility for trip interruptions and outperforms other optimization algorithms in minimizing EV charging/discharging costs by achieving reductions of up to ₹488 and obtaining average grid efficiency up to 76.41 % during charging and 87.41 % during discharging. Integrating Vehicle-to-Grid and Grid-to-Vehicle systems enhances microgrid resilience and delivers economic benefits to EV owners. The cost optimization for EV charging/discharging has been executed using MATLAB 2023a software to determine the most cost-effective charging paths by considering energy availability and grid conditions. The evolving sustainable energy landscape combined with MOGWO paves the way for smarter and more resilient microgrids in the era of electrified transportation. |
| format | Article |
| id | doaj-art-1e88b9653b6a436d8c7e83f5fc15f9fb |
| institution | Kabale University |
| issn | 2772-6711 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | e-Prime: Advances in Electrical Engineering, Electronics and Energy |
| spelling | doaj-art-1e88b9653b6a436d8c7e83f5fc15f9fb2024-12-16T05:38:50ZengElseviere-Prime: Advances in Electrical Engineering, Electronics and Energy2772-67112024-12-0110100804Efficient energy management and cost optimization using multi-objective grey wolf optimization for EV charging/discharging in microgridSwati Sharma0Ikbal Ali1Corresponding author.; Department of Electrical Engineering, Jamia Millia Islamia, New Delhi, 110025, IndiaDepartment of Electrical Engineering, Jamia Millia Islamia, New Delhi, 110025, IndiaThe depletion of conventional energy sources coupled with the rising demand for electric vehicles (EVs) has significantly underscored the necessity for electric vehicle supply equipment (EVSE) with advanced energy supply management within microgrids. Effective energy management of EVs and EVSEs is imperative to satisfy EV owners and stabilize microgrids. This paper introduces multi-objective grey wolf optimization (MOGWO) to optimize EVSE and EV charging costs. MOGWO achieves substantial cost savings through dynamic pricing based on time of day, state-of-charge and hour-based scheduling which promotes off-peak charging thereby enhancing system efficiency and reducing costs. The algorithm also provides flexibility for trip interruptions and outperforms other optimization algorithms in minimizing EV charging/discharging costs by achieving reductions of up to ₹488 and obtaining average grid efficiency up to 76.41 % during charging and 87.41 % during discharging. Integrating Vehicle-to-Grid and Grid-to-Vehicle systems enhances microgrid resilience and delivers economic benefits to EV owners. The cost optimization for EV charging/discharging has been executed using MATLAB 2023a software to determine the most cost-effective charging paths by considering energy availability and grid conditions. The evolving sustainable energy landscape combined with MOGWO paves the way for smarter and more resilient microgrids in the era of electrified transportation.http://www.sciencedirect.com/science/article/pii/S277267112400384XElectric vehicleGrid-to-vehicleVehicle-to-gridMicrogridMulti-objective grey wolf optimizationMicrogrid centralized control system |
| spellingShingle | Swati Sharma Ikbal Ali Efficient energy management and cost optimization using multi-objective grey wolf optimization for EV charging/discharging in microgrid e-Prime: Advances in Electrical Engineering, Electronics and Energy Electric vehicle Grid-to-vehicle Vehicle-to-grid Microgrid Multi-objective grey wolf optimization Microgrid centralized control system |
| title | Efficient energy management and cost optimization using multi-objective grey wolf optimization for EV charging/discharging in microgrid |
| title_full | Efficient energy management and cost optimization using multi-objective grey wolf optimization for EV charging/discharging in microgrid |
| title_fullStr | Efficient energy management and cost optimization using multi-objective grey wolf optimization for EV charging/discharging in microgrid |
| title_full_unstemmed | Efficient energy management and cost optimization using multi-objective grey wolf optimization for EV charging/discharging in microgrid |
| title_short | Efficient energy management and cost optimization using multi-objective grey wolf optimization for EV charging/discharging in microgrid |
| title_sort | efficient energy management and cost optimization using multi objective grey wolf optimization for ev charging discharging in microgrid |
| topic | Electric vehicle Grid-to-vehicle Vehicle-to-grid Microgrid Multi-objective grey wolf optimization Microgrid centralized control system |
| url | http://www.sciencedirect.com/science/article/pii/S277267112400384X |
| work_keys_str_mv | AT swatisharma efficientenergymanagementandcostoptimizationusingmultiobjectivegreywolfoptimizationforevchargingdischarginginmicrogrid AT ikbalali efficientenergymanagementandcostoptimizationusingmultiobjectivegreywolfoptimizationforevchargingdischarginginmicrogrid |