• Energy Research
• OA Publications Mandate: No close
• 2014 close

For reasons of environment protection and energy security, the share of renewable resources in the global energy supply is now rising at an overwhelming rate. The European Commission has set the target to reach a 20% share of energy from renewable sources by 2020 and further increases of this already ambitious objective will follow. A large fraction of this growth is to come from wind power. The production of electricity from this resource is both spatially distributed and highly dependent on atmospheric conditions and thus intermittent in nature, leading to challenging planning and risk management problems for the stakeholders of the wind energy industry. These new challenges, in particular, those related to investment planning and grid integration under the conditions of large-scale wind generation, call for better understanding of the spatial and temporal distribution of the wind resource and wind power production via precise statistical and probabilistic models. Besides, recent advances in climatology show that it may be possible to develop medium and long-term (seasonal to decadal) probabilistic forecasts of the wind power output with a better performance than that of forecasts based on climatological averages, leading to improved risk management tools for wind power producers and grid operators. The project FOREWER aims to address these crucial issues through a synergy between the statistical and probabilistic methodology and the modern meteorological models. This multidisciplinary public-private partnership brings together mathematicians working on stochastic modeling and risk management, statisticians, and meteorologists from the academic community as well as engineers from the key players of the renewable energy industry. Our goal is first of all to develop reliable theoretical and numerical models and scenario generators for the wind resource distribution and power output at various spatial and temporal scales with a focus on medium to long term (seasonal to decadal). We shall then evaluate the potential of these tools for solving the forecasting and risk management problems relevant for the industrial partners of the project, such as the evaluation of the sensitivity of a proposed wind farm to climate variability and optimal placement of wind farms, determination of the required capacity of back-up generators and optimal operation of these assets, and integration of renewable power sources into the grid. On the one hand, state of the art statistical and probabilistic modeling tools (wavelets, stochastic processes) will be applied to the historical weather simulations performed at LMD (consortium partner), in order to understand the multiscale behavior of the wind resource, analyze its variability modes and identify the predictable components of the distribution. On the other hand, powerful statistical learning methods, developed by the statistics group at LPMA (coordinating partner) will be adapted to identify the salient predicting features as well as the connections between renewable power production and the meteorological variables. The statistical forecasting methodology successfully used by LPMA to predict the power consumption curve will be adapted to obtain seasonal and decadal projections of these relationships and produce reliable probabilistic forecasts of the renewable power production taking into account the climate non-stationarity. The major innovations of the proposed project are - Analysis of the medium and long-term probabilistic predictability of the wind resource using state-of-the-art statistical tools. - The end-to-end approach, which consists in considering the whole chain of wind power production from the modeling and prediction of the renewable resource to the management of the associated risks. The predictive power of our models will be analyzed in case studies with our industrial partners.