Smart ride and delivery services with electric vehicles: Leveraging bidirectional charging for profit optimisation

With the rising popularity of electric vehicles (EVs), modern service systems, such as ride-hailing delivery services, are increasingly integrating EVs into their operations. Unlike conventional vehicles, EVs often have a shorter driving range, necessitating careful consideration of charging when fulfilling requests. With recent advances in Vehicle-to-Grid (V2G) technology—allowing EVs to also discharge energy back to the grid—new opportunities and complexities emerge. We introduce the Electric Vehicle Orienteering Problem with V2G (EVOP-V2G): a profit-maximisation problem where EV drivers must select a subset of customer requests while managing when and where to charge or discharge. This involves navigating dynamic electricity prices, charging station selection, and route constraints. We formulate the problem as a Mixed Integer Programming (MIP) model and propose two near-optimal metaheuristic algorithms: one evolutionary (EA) and the other based on large neighbourhood search (LNS). We compare these three algorithms with a greedy baseline on real-world data, showing that the proposed methods achieve up to twice the profit. V2G contributes about 20 % of the total profit in the default settings. MIP finds optimal solutions for small cases (30 orders, 3 stations) but does not scale well. EA and LNS give near-optimal results for small cases and handle large ones (900 orders, 70 stations) efficiently. Our work highlights a promising path toward smarter, more profitable EV-based mobility systems that actively support the energy grid.