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Caractérisation et prédiction probabiliste des variations brusques et importantes de la production éolienne

Abstract : Today, wind energy is the fastest growing renewable energy source. The variable and partially controllable nature of wind power production causes difficulties in the management of power systems. Forecasts of wind power production 2-3 days ahead can facilitate its integration. Though, particular situations result in unsatisfactory prediction accuracy. Errors in forecasting the timing of large and sharp variations of wind power can result in large energy imbalances, with a negative impact on the management of a power system. The objective of this thesis is to propose approaches to characterize such variations, to forecast their timing, and to estimate the associated uncertainty. First, we study different alternatives in the characterization of wind power variations. We propose an edge model to represent the random nature of edge occurrence, along with representing appropriately the bounded and non-stationary aspects of the wind power production process. From simulations, we make a parametric study to evaluate and compare the performances of different filters and multi-scale edge detection approaches. Then, we propose a probabilistic forecasting approach of edge occurrence and timing, based on numerical weather prediction ensembles. Their conversion into power provides an ensemble of wind power scenarios from which the different forecast timings of an edge are combined. The associated uncertainty is represented through temporal confidence intervals with conditionally estimated probabilities of occurrence. We evaluate the reliability and resolution of those estimations based on power measurements from various real world case studies.
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Submitted on : Thursday, March 21, 2013 - 2:23:17 PM
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  • HAL Id : pastel-00803234, version 1


Arthur Bossavy. Caractérisation et prédiction probabiliste des variations brusques et importantes de la production éolienne. Autre. Ecole Nationale Supérieure des Mines de Paris, 2012. Français. ⟨NNT : 2012ENMP0058⟩. ⟨pastel-00803234⟩



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