Online updating of a Markovian solar forecast representation

Abstract

Risk management is an essential task in power systems operations, and temporal variability of renewable energy resources requires new techniques to capture the effects of such variability on the system. We utilize use a Markovian representation of probabilistic solar power forecasts to enhance the temporal variability assessment of balancing area-wide solar power production. Using this representation, we determine the optimal amount of historical forecast and observational data to use when updating the Markov transition matrix – which captures the past variability of the observations with respect to the forecasts – for use in stochastic optimization problems. We propose two novel multivariate scoring metrics based upon the observation vector’s band depth and discuss the performance with respect to these two metrics, as well as the variogram-based score, for multiple transition matrices. The optimal amount of data to use for these online transition matrices depends upon the season and likely upon the prevailing weather regime. • Transition matrices updated by ranking recent solar observations among forecasts. • Introduced two novel transition matrix scoring metrics based on band depth. • Fewer transitions in the spring and summer months improve transition matrices. • Compared changes in reserves and Area Control Error with respect to a fixed matrix. • Using an online matrix improved the Area Control Error often substantially.