LAUSR

The transition to electric vehicles (EVs) has prodigious plausibility in reducing green house gas (GHG). But EVs acceptance is, however, hindered by several challenges; among them is their avidity for quicker charging at lower price. This article considers a photovoltaic (PV)-powered station equipped with an energy storage system (ESS), which is assumed to be capable of assigning variable charging rates to different EVs to fulfill their demands inside their declared deadlines at minimum price. To ensure fairness, a charging rate-dependent pricing mechanism is proposed to assure a higher price for enjoying a higher charging rate. The PV generation profile and future load request are forecasted at each time slot, to handle the respective uncertainty. An integer linear programming (ILP)-based centralized system is first proposed to minimize the charging price per EV. Due to the larger computational time, we subsequently present two game theoretic algorithms, i.e., game 1 and game 2. In game 1, players are oblivious of upcoming charging requests, whereas in game 2, players consider the future anticipated load to select their charging strategies. The games are shown to converge to a Nash equilibrium. The average unit price of game 2 is shown to be the same as the one of the optimal solution and takes considerably less computation time than the centralized method.