Impact of forecasting errors on microgrid optimal power management

The paper presents a DC microgrid model and its power management system based on metaheuristical optimization of a rolling horizon. The crucial components of such a system are forecasts of photovoltaic production and load power. The paper experimentally demonstrates how the forecasting error affects power management in terms of increased operational costs and increased probability of constraints violation. It is demonstrated that the benefits of the optimized power scheduling decrease linearly with increasing mean absolute percentage error (MAPE) of load and photovoltaic production forecast. In our scenario, the state-of-charge constraints for electric vehicle battery were not affected by inaccurate forecasts, which is very important for the electric vehicle user's acceptance of the power management system.