Modeling the temporal correlation of hourly day-ahead short-term wind power forecast error for optimal sizing energy storage system

Abstract

Due to the inherent stochastic and intermittent nature of wind generation, it is very difficult to sharply improve the wind power forecasting accuracy. The sizing of energy storage system (ESS) for wind farm can effectively reduce the uncertainty caused by the inevitable forecast error of wind power. However, optimal sizing of ESS is a multi-period decision-making problem and it is the key point that exactly captures the variation magnitude and speed of forecast error. This paper proposes a method to establish the multivariate joint cumulative distribution function (JCDF) of hourly day-ahead short-term multi-period forecast errors using Normal/t copula. Based on the proposed JCDF and multiple scenarios technique, a model of optimal sizing of ESS is proposed in which the temporal correlation relationship between different time period forecast errors and probability distribution of each time period forecast error are both considered. The simulation results verify the effectiveness of the proposed methods and show that if the temporal correlation of wind power short-term forecast error is ignored, the rated energy and power capacity of sizing of ESS will be significantly misestimated.