BUSCADOR RECOLECTA

Wind has been the largest contributor to the growth of renewal energy during the early 21st century. However, the natural uncertainty that arises in assessing the wind resource implies the occurrence of wind power forecasting errors which perform a considerable role in the impacts and costs in the wind energy integration and its commercialization. The main goal of this paper is to provide a deeper insight in the analysis of timing errors which leads to the proposal of a new methodology for its control and measure. A new methodology, based on Dynamic TimeWarping, is proposed to be considered in the estimation of accuracy as attribute of forecast quality. A new dissimilarity measure, the Temporal Distortion Index, among time series is introduced to complement the traditional verication measures found in the literature. Furthermore we provide a bi-criteria perspective to the problem of comparing different forecasts. The methodology is illustrated with several examples including a real case., This paper has been supported under Grants MTM 2012-36025 and DPI 490 2013-47279-C2-1-R. The authors are grateful to the research staff of the National Renewable Energy Center of Spain (CENER) for their help in the development of this new methodology of analysis of errors contributing with their prediction model LocalPred.

The storage of energy facilitates the management of renewable energy systems by reducing the mismatch between the supplied energy and the forecasted production due to forecasting errors. The storage increases the reliability of the renewable energy system and enables participation in the electricity market by committing to the sale of electricity for the following day. Nevertheless, the inclusion of the energy storage capacity requires the development of new management policies. In this paper, we propose a management strategy for a renewable energy system with storage capacity that integrates tactical and operational decisions in a single mathematical model that makes use of an updated probabilistic wind speed forecast. Management policies are obtained by solving a sequence of rolling-horizon stochastic optimization problems whose formulation is inspired by the Stochastic Approximation Average technique. The management policies are illustrated by their application to wind-farms using hydrogen as the energy storage medium.