Electric Vehicle Scheduling and Vehicle-to-Grid Integration in Microgrids
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912539551465472 |
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| author | Cho, Nathan Lodi, Andrea Scaglione, Anna |
| author_facet | Cho, Nathan Lodi, Andrea Scaglione, Anna |
| contents | The logistical challenges and high costs associated with procuring and transporting fuel to remote military bases underscore the need for sustainable and resilient energy solutions. Integrating renewable energy sources and electric vehicles into military microgrids offers a promising approach to enhance energy security and operational readiness. This paper explores the optimization of travel, charging, and discharging schedules for a fleet of military electric trucks, aiming to minimize reliance on fuel-generated electricity while ensuring that mission-critical transportation needs are met. We extend the classical Vehicle Scheduling Problem by incorporating Electric Vehicle Scheduling Problem dynamics and Vehicle-to-Grid capabilities, developing a comprehensive optimization model that addresses both logistical and energy demands within a military microgrid context. Utilizing a column generation approach, we efficiently solve large-scale instances and demonstrate significant improvements in fuel efficiency, renewable energy utilization, and overall operational cost. Computational experiments using realistic demand and solar generation data illustrate that integration of vehicle-to-grid enabled electric vehicles substantially reduces fuel consumption and can generate surplus energy returned to the grid. The results indicate that while battery constraints may require an increased fleet size, strategic scheduling of charging and discharging yields considerable economic and operational benefits. Our findings provide valuable insights for planners aiming to optimize energy use, reduce dependence on traditional fuel sources, and enhance operational resilience in remote environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_06752 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Electric Vehicle Scheduling and Vehicle-to-Grid Integration in Microgrids Cho, Nathan Lodi, Andrea Scaglione, Anna Optimization and Control The logistical challenges and high costs associated with procuring and transporting fuel to remote military bases underscore the need for sustainable and resilient energy solutions. Integrating renewable energy sources and electric vehicles into military microgrids offers a promising approach to enhance energy security and operational readiness. This paper explores the optimization of travel, charging, and discharging schedules for a fleet of military electric trucks, aiming to minimize reliance on fuel-generated electricity while ensuring that mission-critical transportation needs are met. We extend the classical Vehicle Scheduling Problem by incorporating Electric Vehicle Scheduling Problem dynamics and Vehicle-to-Grid capabilities, developing a comprehensive optimization model that addresses both logistical and energy demands within a military microgrid context. Utilizing a column generation approach, we efficiently solve large-scale instances and demonstrate significant improvements in fuel efficiency, renewable energy utilization, and overall operational cost. Computational experiments using realistic demand and solar generation data illustrate that integration of vehicle-to-grid enabled electric vehicles substantially reduces fuel consumption and can generate surplus energy returned to the grid. The results indicate that while battery constraints may require an increased fleet size, strategic scheduling of charging and discharging yields considerable economic and operational benefits. Our findings provide valuable insights for planners aiming to optimize energy use, reduce dependence on traditional fuel sources, and enhance operational resilience in remote environments. |
| title | Electric Vehicle Scheduling and Vehicle-to-Grid Integration in Microgrids |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2508.06752 |