Forecast-Based V2G Aggregation Model for Day-Ahead and Real-Time Operations

The expected increase of Electric Vehicles (EVs) will have a huge impact on the electrical grid operations. EVscan be considered as stochastic loads from the electric grid point of view. To avoid the creation of grid unbalances and favor the possibility of performing grid services, EVs scheduling can be optimized by aggregators, new entities which consider the coordination of more EVs at the same time. This work investigates the feasibility of a fully automated aggregator logic. The strategy considers the day-ahead optimization of the EVs fleet charge, considering a forecast on the arrival and departure times, and the energy required for the next trip. Forecasts are built from a real travel database. Moreover, the logic assures that in real-time operation, all vehicles can be recharged, and the overall power profile respects the day-ahead optimization without causing unbalances in the grid. The algorithm employs the forecasts and applies the logic to 3 EVs. The aggregator is able to recharge the vehicles fulfilling the electrical grid balance.