Differential Evolution and Fuzzy-Logic-Based Predictive Algorithm for V2G Charging Stations

This work proposes a differential evolution algorithm to control a vehicle-to-grid (V2G) system based on photovoltaic generation and energy cost curves, and constraints associated with the power converters' operation, battery charging strategy, and initial budgets. The algorithm is designed to trade off the batteries' state of charge and the profits gained from selling energy to the grid. To achieve this balance, a fuzzy controller is employed and acts based on forecasts of the photovoltaic generation and the cost of electricity, within prediction windows of 120 min, adapting the batteries' rate of charging or discharging. Simulation results show that for different curves and different initial budgets, the target state of charge is reached at the end of the time horizon. By evaluating the proposed scheme under different scenarios, the algorithm's performance is proven to be suitable for future practical deployment.