International audienceAvailability of day-ahead production forecast is an important step towards better dispatchability of wind power production. However, the stochastic nature of forecast errors prevents a wind farm operator from holding a firm production commitment. In order to mitigate the deviation from the commitment, an energy storage system connected to the wind farm is considered. One statistical characteristic of day-ahead forecast errors has a major impact on storage performance: errors are significantly correlated along several hours. We thus use a data-fitted autoregressive model that captures this correlation to quantify the impact of correlation on storage sizing. With a Monte Carlo approach, we study the behavior and the performance of an energy storage system (ESS) using the autoregressive model as an input. The ability of the storage system to meet a production commitment is statistically assessed for a range of capacities, using a mean absolute deviation criterion. By parametrically varying the correlation level, we show that disregarding correlation can lead to an underes- timation of a storage capacity by an order of magnitude. Finally, we compare the results obtained from the model and from field data to validate the model

Dispatchability of wind power is significantly increased by the availability of day-ahead production forecast. However, forecast errors prevent a wind farm operator from holding a firm production commitment. An energy storage system (ESS) connected to the wind farm is thus considered to reduce deviations from the commitment. We statistically assess the performance of the storage in a stochastic framework where day-ahead forecast errors are modeled with an autoregressive model. This stochastic model, fitted on prediction/production data from an actual wind farm captures the significant correlation along time of forecast errors, which severely impacts the ESS performance. A thermoelectrical model for Sodium Sulfur (NaS) batteries reproduces key characteristics of this technology including charging/discharging losses, statedependent electrical model and internal temperature variations. With help of a cost analysis which includes calendar and cycling aging, we show trade-offs in storage capacity sizing between deviation from commitment and storage costs due to energy losses and aging.