Probabilistic energy storage sizing for reducing wind power forecast uncertainty

Abstract

A novel method is proposed for designing an energy storage system (ESS) which is dedicated to reducing the uncertainty of the short term wind power forecast. The investigation focuses on the statistical behaviour of the forecast error and the state of charge (SOC) of the ESS. This approach gives an insight into the influence of the forecast conditions on the distribution of SOC. With this knowledge, an optimised sizing of the ESS can be done with a well defined uncertainty limit. One-year power output data measurements and two types for forecast were used for this study. In addition, different forecast quality degrees are simulated based on the persistence approach. With the forecast data, empirical probability density functions (pdf's) of the SOC are generated which is the base of the proposed method. This approach can lead to a considerable reduction of the ESS and provides important information about the unserved energy. This unserved energy is the remaining forecast error or uncertainty. As a consequence, the proposed probabilistic method permits the sizing of the energy storage system as a function of the desired remaining forecast uncertainty.