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.