Abstract: This disclosure relates generally to a bidirectional charging at an electric vehicle (EV) charging station by an energy model that uses electricity bought from the day-ahead market for charging the fleet of electric vehicles (EVs) and uses the intra-day market for arbitrage. The competitive pricing of wholesale electricity markets and distributed energy resource capability of EV fleets (in addition) provide a revenue channel through energy arbitrage. To effectively handle electricity price variations and the energy demand of the EV fleet, the present disclosure utilizes a graph representation-based learning agent (LA3_D) with two-stage encoding for day-ahead charge planning; and a priority order based greedy heuristic (GH_I) for intra-day arbitrage planning. Because the agent learns the planning policy of mapping EVs to charging operations over several problem instances, it is able to solve a given instance with limited sub-optimality when put to test at different levels of scale.

Abstract: Disadvantage of state-of-the-art scheduling mechanisms for Electric Vehicle (EV) charging is that they fail to accommodate dynamic requirements in terms of charging needs. The disclosure herein generally relates to EV fleet charging, and, more particularly, to a method and system for Electric Vehicle (EV) fleet charging by accommodating one or more dynamic requirements. The system initially generates a base charging plan for a fleet of EVs. Further, the system checks if the base charging plan is to be modified to accommodate one or more dynamic charging requirements obtained. Upon determining that the base charging plan is to be modified, the system modifies the base charging plan till a) no more vehicles are left to charge, or b) all of a plurality of chargers have an assignment.