We propose a novel way to use Electric Vehicles (EVs) as dynamic mobile energy storage with the goal to support grid balancing during peak load times. EVs seeking parking in a busy expensive inner city area, can get free parking with a valet company in exchange for being utilized for grid support. The valet company would have an agreement with the local utility company to receive varying rewards for discharging EVs at designated times and locations of need (say, where power lines are congested). Given vehicle availabilities, the valet company would compute an optimal schedule of which vehicle to utilize where and when so as to maximize rewards collected. Our contributions are a detailed description of this new concept along with supporting theory to bring it to fruition. On the theory side, we provide new hardness results, as well as efficient algorithms with provable performance guarantees that we also test empirically.
Electric vehicles (EVs) still have relatively long and frequent charging cycles. Moreover, charging resources are typically limited and must therefore be used efficiently. The V-Charge project has the vision to provide a solution by combining autonomous valet parking with e-mobility, introducing improved parking and charging comfort. V-Charge proposes a solution for charging autonomous EVs in parking places and efficiently using scarce charging resources, thus simplifying the life of the customer and increasing the feasibility of EVs. For the management of parking lots and charging resources, V-Charge provides a server back end and a communication infrastructure. In this paper, we present our design of scheduling concepts for a coordinated charging strategy that is implemented by this back end. Through intensive simulations, we show that the V-Charge server is able to efficiently handle realistic parking volume and performs well in fulfilling customer requirements, e.g., energy demand for the next driving tasks. Moreover, we evaluate the suitability of various scheduling strategies in different usage scenarios. For the simulation setup, real-world parking statistics obtained from Hamburg Airport and the City of Braunschweig, Germany, are used.