Data-Driven Energy Management System With Gaussian Process Forecasting and MPC for Interconnected Microgrids

Interest in predicting and optimising microgrid operation with a high proportion of variable renewable energy generation is growing. In this paper, we study and experimentally analyse the performance of a Gaussian-process regression forecasting and model predictive control algorithm in the context of interconnected microgrids. The scheme, which operated at six hours time horizon, achieved superior results with only a small deviation from the optimal operation calculated offline assuming perfect foresight. We also demonstrate that whilst a longer horizon provides a better solution in terms of lower cost of electricity, the battery cycling rate is also higher. Finally, we demonstrate improvements in renewable and load forecasts by sharing information between the microgrids.