SIMULATION AND OPTIMIZATION OF V2G ENERGY EXCHANGE IN AN ENERGY COMMUNITY

Abstract

This thesis shows how to improve energy exchange between electric vehicles and the grid in a small community using AI-based control. The community is made up of 50 EVs, a 150 kW PV system and real 2019 German load and solar data, simulated over a 24-hour period with 15-minute resolution. The thesis compares the most common optimization methods, NSGA-II, multi-objective PSO and a Deep Q, to demonstrate their ability to lower energy cost, reduce peak demand and limit battery degradation. A physics-based battery degradation model is included to ensure that the results reflect realistic battery behaviour. It was clearly shown in the thesis that the NSGA-II, among all other algorithms, gave the best and strongest overall performance, reaching to a 30% energy cost reduction while showing the best Pareto front hypervolume. The work shows that V2G becomes profitable when the time-of-use price spread is about 0.28 $/kWh or higher. Real OPSD profiles produced more variability but also more economic potential compared to synthetic ones.