Grid-scale fluctuations and forecast error in wind power

Wind power fluctuations at the turbine and farm scales are generally not expected to be correlated over large distances. When power from distributed farms feeds the electrical grid, fluctuations from various farms are expected to smooth out. Using data from the Irish grid as a representative example, we analyze wind power fluctuations entering an electrical grid. We find that not only are grid-scale fluctuations temporally correlated up to a day, but they possess a self-similar structure—a signature of long-range correlations in atmospheric turbulence affecting wind power. Using the statistical structure of temporal correlations in fluctuations for generated and forecast power time series, we quantify two types of forecast error: a timescale error (${e}_{\tau }$) that quantifies deviations between the high frequency components of the forecast and generated time series, and a scaling error (${e}_{\zeta }$) that quantifies the degree to which the models fail to predict temporal correlations in the fluctuations for generated power. With no a priori knowledge of the forecast models, we suggest a simple memory kernel that reduces both the timescale error (${e}_{\tau }$) and the scaling error (${e}_{\zeta }$)