Energy storage sizing for wind power: impact of the autocorrelation of day‐ahead forecast errors. (3rd October 2013)

• Record Type: Journal Article
• Authors: Haessig, Pierre; Multon, Bernard; Ahmed, Hamid Ben; Lascaud, Stéphane; Bondon, Pascal
• Is Part Of: Wind energy. Volume 18:Number 1(2015)
• Page Start: 43
• Page End: 57
• Publication Date: 2013-10-03
• Subjects: Wind power -- Periodicals; 621.312136
• DOI: 10.1002/we.1680
• Languages: English
• ISSNs: 1095-4244

Abstract

<abstract abstract-type="main" id="we1680-abs-0001"> <title>ABSTRACT</title> <p>The availability of day‐ahead production forecast is an important step toward better dispatchability of wind power production. However, the stochastic nature of forecast errors prevents a wind farm operator from holding a <italic>firm production commitment</italic>. 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 <italic>correlated along several hours</italic>. We thus use a data‐fitted autoregressive model that captures this correlation to <italic>quantify the impact</italic> of correlation on storage sizing.</p> <p>With a Monte Carlo approach, we study the behavior and the performance of an energy storage system 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 underestimation 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. Copyright © 2013 John Wiley &amp; Sons, Ltd.</p> </abstract>