Probabilistic Power Forecasting in a Microgrid for the Integration of Distributed Energy Resources

The integration of distributed energy resources in the power grid supposes a great challenge, considering the expected growing in the electric mobility and the renewable generation. Therefore, to support the successful deployment of these resources, energy management according to the expected behaviors of the elements of power grids is required. In this sense, the probabilistic forecasting methods are gaining acceptance, as they provide richer information than deterministic methods thanks to the consideration of expected probabilities. One of their problems is that the application of metrics for choosing the best models is not simple, depending on the objective of the type of probabilistic prediction that must be performed. In this paper, diverse types of forecasting are performed, and two possible approaches for selecting interval forecasting models are compared in a case study centered on the Smart Polygeneration Microgrid (SPM) located on the Savona Campus of University of Genova (Italy). The results show that, for the machine learning models that were tested, it is better to obtain a model for each of the different types of forecasting that are required (quantiles, and the prediction intervals) to have better-quality predictions. The average improvement on the quality of the tested prediction intervals for the three forecasted variables were a 27.40%, 7.39%, and 28.65% in their Winkler score respectively.