Abstract
Space-time scenarios of renewable power generation are increasingly used as input to decision-making in operational problems. They may also be used in planning studies to account for the inherent uncertainty in operations. Similarly using scenarios to derive chance-constraints or robust optimization sets for corresponding optimization problems is useful in a power system context. Generating and evaluating such spacetime scenarios is difficult. While quite a number of proposals have appeared in the literature, a gap between methodological proposals and actual usage in operational and planning studies remains. Consequently, our aim here is to propose an open-source platform for space-time probabilistic forecasting of renewable energy generation (wind and solar power). This document covers both methodological and implementation aspects, to be seen as a companion document for the open-source scenario generation platform. It can generate predictive densities, trajectories and space-time interdependencies for renewable energy generation. The underlying model works as a post-processing of point forecasts. For illustration, two setups are considered: the case of day-ahead forecasts to be issued once a day, and for rolling windows with regular updates, with application to the western part of the United States, with both wind and solar power generation.
Info
Conference Paper, 2016
UN SDG Classification
DK Main Research Area
Science/Technology
