Electric Vehicle Scheduling and Vehicle-to-Grid Integration in Microgrids

Abstract

We jointly schedule electric trucks and stationary batteries in an isolated microgrid with solar and fossil generation. Truck routes determine when their batteries can charge or return energy through vehicle-to-grid (V2G). The model assigns every transport task, selects assets, and minimizes daily asset and fossil generation costs. For a fixed task sequence and charging opportunities, energy scheduling is solved by a continuous network flow model. We solve the fleet-level linear programming (LP) relaxation by column generation. A dynamic program jointly generates truck routes and energy profiles using fixed state-of-charge increments. We give conditions under which this discretization is exact. The method solves the LP relaxation for instances with up to 1,050 tasks. On the 10-450 task benchmark ladder, every reported integer schedule is within 1.4% of its LP lower bound. In matched comparisons, stationary storage provides most of the combined storage and V2G benefit and can determine feasibility under tight generation limits. Solar surplus relative to task traction energy helps explain investment tradeoffs; charger cost and truck availability affect when bidirectional charging becomes economical. These results support site-specific investment comparisons.