Multi-time Scale Optimal Scheduling Model for Electric Vehicles Charging Considering Forecast Error in Micro-grid

Aiming at the problems caused by electric vehicles charging and discharging and wind power, photovoltaic and load forecasting errors in the microgrid, a multi-time scale optimal scheduling model for electric vehicles charging considering day-ahead prediction errors is proposed. The model consists of day-ahead scheduling plan and day-in short term rolling optimal scheduling. The day-ahead scheduling model takes into account the interests of both the microgrid operator and the electric vehicle owners, with the lowest microgrid operating cost and the lowest total charging cost for all the electric vehicle owners. The rolling scheduling model aims at maximizing the matching degree between the actual equivalent load and the predicted one in the day-ahead dispatching plan with the prerequisite that the actual total charging cost of vehicle owners is not greater than the planned cost.Finally, the gray wolf optimization algorithm is applied to solve the model. The optimization results show that the microgrid operating cost and the prediction error‘s impact on scheduling can be effectively reduced through the proposed model, which also benefits electric vehicle owners economically.