Coordinated integration of distributed energy resources in unit commitment

Distributed energy resources, such as electric vehicles and roof-top solar panels have become increasingly popular essential components of power distribution systems. However, their growth poses significant challenges to system operation, bidirectional flow, intermittent generation, increased peak demand, unexpected frequency/voltage fluctuation. To tackle these challenges, we developed a vehicle-to-grid model, which incorporated dynamic usages, driving times, distance, charging/discharging locations. The machine learning method principal component analysis XGBoost were used develop the prediction model. We also novel unit commitment model coordinate aggregate distributed resources in generation economic dispatch. Electric vehicle charging was elastic demand discharging sources. considered negative load. Simulation results showed that uncontrolled could negatively impact For example, load ramping rate by 384%. proposed models mitigate improve efficiency. average cost reduced 21%, 79%, 27%. • (EV) locations, are modeled. incorporates application prediction. can be integrate high-level penetration EV renewable into reduce costs integration.