Data Enrichment with Climate Reanalysis Data and Machine Learning for Analyzing Supply Adequacy in Renewable Power Systems

To overcome the limitations in using historical time series data for the supply adequacy analysis of renewable power systems, a data enhancement process is implemented allowing for a temporal enrichment of the available historical data. The methodology is based on the direct conversion of gridded climate reanalysis time series into aggregate output estimates where the simulated aggregate output is estimated by machine learning and historical power system data are used as training and testing data set. As a case study, the data enrichment methodology was accomplished with Hungarian power system data. From the enriched wind and solar electricity generation time series, availability statistics were derived that can be integrated into analytical probabilistic adequacy risk assessment models to describe the availability of wind and solar energy as aggregate, multi-state units.