Open AccessErratum: A wavelet‐based approach for large wind power ramp characterization
Author(s) -
Gallego Cristóbal,
Costa Alexandre,
Cuerva Álvaro,
Landberg Lars,
Greaves Beatrice,
Collins Jonathan
Publication year - 2013
Publication title -
wind energy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.743
H-Index - 92
eISSN - 1099-1824
pISSN - 1095-4244
DOI - 10.1002/we.1626
Subject(s) - wind power , event (particle physics) , computer science , electric power system , wavelet , schedule , wind power forecasting , wind speed , function (biology) , operations research , reliability engineering , power (physics) , engineering , meteorology , electrical engineering , geography , artificial intelligence , physics , quantum mechanics , operating system , evolutionary biology , biology
The large and rapid variations (ramp events) of wind power output experienced in wind farms and portfolios represent one of the main challenges facing short‐term wind power forecasting. In countries with high wind power penetration, a ramp event forecasting tool is required by transmission system operators and energy traders to schedule ancillary services properly and minimize economic penalties in liberalized electricity markets, respectively. From the forecaster/modeller's point of view, locating ramp events within a wind power time series is important, because it allows them to regard meteorological processes and operational states of the wind farm in the proper time periods to analyse the ramp causes. This work introduces the ramp function as a means of characterizing the ramp performance of a wind power time series. The underlying idea is that a ramp event is characterized by high‐power output gradients evaluated under different time scales. The ramp function is based on the wavelet transform and provides a continuous index related to the ramp intensity at each time step, which permits to take into account the fuzzy limits of the ramp notion, as well as the development of new approaches to wind power ramp analysis that are not feasible from a binary classification standpoint. Several advantages of the ramp function for end‐users are outlined, and applications concerning different aspects of ramp forecasting are described for several wind farms located in Spain. Copyright © 2013 John Wiley & Sons, Ltd.