Abstract

This paper presents the ALAMO vision, which is a research project that aims at developing technologies to facilitate the planning and operation of power grids with very high penetration of Distributed Energy Resources, while at the same time assuring the privacy of the main actors on the demand side. The ALAMO project will address outstanding research challenges related to the development of accurate forecasting algorithms based on Federated Learning (e.g., forecasting in- front and behind-the-meter PV production); as well as challenges related to producing sharped and well-calibrated quantifications of epistemic and aleatoric uncertainty for such forecasting models. Finally, use-cases will be carefully crafted to understand how FL forecasts and uncertainty estimates can be incorporated into operational planning and operation tools. The use-cases will be demonstrated in virtual and physical testbeds in Portugal, USA, and Brazil.