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.
A combination of an autonomous parking system with EV charging is studied by Timpner and Wolf @cite_15 , with the goal of scheduling the charging times of autonomous vehicles on a limited number of charging stations in a parking lot. The difference with our valet model is that they consider homogeneous charging stations, unidirectional flow of energy (no V2G), and charging station's utilization as the objective, while in our paper heterogeneous stations in different locations, EV discharging (V2G) and reward collection are critical. Furthermore, the authors propose 5 different scheduling algorithms, but no theoretical guarantee is given with respect to their stated objective, whereas we provide theoretical guarantees.
@cite_15
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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. This paper proposes an intelligent method for scheduling usage of available energy storage capacity from plug-in hybrid electric vehicles (PHEV) and electric vehicles (EV). The batteries on these vehicles can either provide power to the grid when parked, known as vehicle-to-grid (V2G) concept or take power from the grid to charge the batteries on the vehicles. A scalable parking lot model is developed with different parameters assigned to fleets of vehicles. The size of the parking lot is assumed to be large enough to accommodate the number of vehicles performing grid transactions. In order to figure out the appropriate charge and discharge times throughout the day, binary particle swarm optimization is applied. Price curves from the California ISO database are used in this study to have realistic price fluctuations. Finding optimal solutions that maximize profits to vehicle owners while satisfying system and vehicle owners constraints is the objective of this study. Different fleets of vehicles are used to approximate varying customer base and demonstrate the scalability of parking lots for V2G. The results are compared for consistency and scalability. Discussions on how this technique can be applied to other grid issues such as peaking power are included at the end. Vehicle-to-grid (V2G) units are gaining prominence and may dominate the auto-market in the near future. The V2G batteries require corporate parking lots for charging and discharging operations. The electric power capacity of an existing parking lot can be increased by the installation of photovoltaic (PV) rooftops. This paper describes mathematical models for estimating the electric power capacity of a V2G parking lot (VPL) system with PV canopy. The electric vehicle (EV) demand supply model was formulated as a queuing theory problem, exhibiting stochastic characteristics. New formulae were developed to address the impacts of battery charger efficiency on the amount of power demand during battery charging, and also how the latter is effected by inverter efficiency during discharging. Mathematical models for grid gain factor were developed. The proposed models were tested using Tesla Roadster EV and Nissan leaf EV. Promising simulation results are gained leading to a conclusion that vehicle parking lots with PV facilities can potentially relieve and enhance the grid capacity. Results show that 60 gain factor is possible. The effect of weather uncertainties and energy market price were studied. The study could be useful in battery-charger control studies. This paper proposes a novel cooperative charging strategy for a smart charging station in the dynamic electricity pricing environment, which helps electric vehicles (EVs) to economically accomplish the charging task by the given deadlines. This strategy allows EVs to share their battery-stored energy with each other under the coordination of an aggregator, so that more flexibility is given to the aggregator for better scheduling. Mathematically, the scheduling problem is formulated as a constrained mixed-integer linear program (MILP) to capture the discrete nature of the battery states, i.e., charging, idle and discharging. Then, an efficient algorithm is proposed to solve the MILP by means of dual decomposition and Benders decomposition. At last, the algorithm can be implemented in a distributed fashion, which makes it scalable and thus suitable for large-scale scheduling problems. Numerical results validate our theoretical analysis.