Performance Enhancement of Charge Scheduling Algorithm using Greedy Method

The development of electric vehicles (EVs) has underlined the need of fast, reasonably priced, grid-stable charge scheduling schemes. This work maximizes EV charge scheduling using a Greedy Algorithm to solve dynamic electricity pricing, changing charging station availability, and changing grid conditions. The method achieves a compromise between charging cost, time, and grid stability using a multi-objective optimization framework. Time window constraints, grid load limits, minimum State of Charge (SoC) required to consider charging stations enable one to locate suitable charging options for every EV. Implementing regionally ideal decisions at every level helps the method schedule EV charging sessions to maintain grid stability and reduce costs and wait times. With the complexity and dynamic character of EV charge scheduling, this practical and computationally efficient solution benefits consumers and grid operators.