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Keywords:

  • wind energy;
  • wind power forecasting;
  • ramps’ characterization;
  • edge model;
  • edge detection

Abstract

Wind power forecasting is a recognized means of facilitating large-scale wind power integration into power systems. Recently, there has been focus on developing dedicated short-term forecasting approaches for large and sharp wind power variations, so-called ramps. Accurate forecasts of specific ramp characteristics (e.g., timing and probability of occurrence) are important, as related forecast errors may lead to potentially large power imbalances, with a high impact on the power system. Various works about ramps’ periodicity or predictability have led to the development of new characterization approaches. However, a thorough analysis of these approaches has not yet been carried out. Such an analysis is necessary to ensure the reliability of subsequent conclusions on ramps’ characteristics. In this paper, we propose a comprehensive framework for evaluating and comparing different characterization approaches of wind power ramps. As a first step, we introduce a theoretical model of a ramp inspired from edge-detection literature. The proposed model incorporates some important aspects of the wind power production process so as to reflect its non-stationary and bounded aspects, as well as the random nature of ramp occurrences. Then, we introduce adequate evaluation criteria from signal-processing and statistical literature, in order to assess the ability of an approach for reliably estimating ramp characteristics (i.e., timing and intensity). On the basis of simulations from this model and using the evaluation criteria, we study the performance of different ramp detection filters and multi-scale characterization approaches. Our results show that some practical choices in wind-energy literature are inappropriate, while others, namely, from signal-processing literature, are preferable. Copyright © 2014 John Wiley & Sons, Ltd.