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Investments into wind power generation may be hampered by the uncertainty of future revenues caused by the natural variability of the wind resource, the impact of climate change on wind potential and future electricity prices, and the regulatory risks. We quantify the uncertainty of the economic value of wind farms in France, Germany, and Denmark, and evaluate the cost of support mechanisms needed to ensure the profitability of wind farms under present and future climates. To this end, we built a localised model for wind power output and a country-level model for electricity demand and prices. Our study reveals that support mechanisms are needed for current market conditions and the current climate, as well as under future climate conditions according to several scenarios for climate change and energy transition. The cost of support mechanisms during a 15-year period is evaluated to EUR 3.8 to EUR 11.5 billion per year in France, from EUR 15.5 to EUR 26.5 billion per year in Germany, and from EUR 1.2 to EUR 3.3 billion per year in Denmark, depending on the scenario considered and the level of penetration of wind energy. [ABSTRACT FROM AUTHOR]

Transmission system operator (TSOs) need to project the system state at the seasonal scale to evaluate the risk of supply-demand imbalance for the season to come. Seasonal planning of the electricity system is currently mainly adressed using climatological approach to handle variability of consumption and production. Our study addresses the need for quantitative measures of the risk of supply-demand imbalance, exploring the use of sub-seasonal to seasonal forecasts which have hitherto not been exploited for this purpose. In this study, the risk of supply-demand imbalance is defined using exclusively the wind energy production and the consumption peak at 7 pm. To forecast the risks of supply-demand imbalance at monthly to seasonal time horizons, a statistical model is developed to reconstruct the joint probability of consumption and production. It is based on a the conditional probability of production and consumption with respect to indexes obtained from a linear regression of principal components of large-scale atmospheric predictors. By integrating the joint probability of consumption and production over different areas, we define two kind of risk measures: one quantifies the probablity of deviating from the climatological means, while the other, which is the value at risk at 95% confidence level ( V a R 95 ) of the difference between consumption and production, quantifies extreme risks of imbalance. In the first case, the reconstructed risk accurately reproduces the actual risk with over 0.80 correlation in time, and a hit rate around 70–80%. In the second case, we find a mean absolute error (MAE) between the reconstructed and real extreme risk of 2.5 to 2.8 GW, a coefficient of variation of the root mean square error (CV-RMSE) of 3.8% to 4.2% of the mean actual V a R 95 and a correlation of 0.69 and 0.66 for winter and fall, respectively. By applying our model to ensemble forecasts performed with a numerical weather prediction model, we show that forecasted risk measures up to 1 month horizon can outperform the climatology often used as the reference forecast (time correlation with actual risk ranging between 0.54 and 0.82). At seasonal time horizon (3 months), our forecasts seem to tend to the climatology. [ABSTRACT FROM AUTHOR]

An avenue for modelling part of the long-term variability of the wind energy resource from knowledge of the large-scale state of the atmosphere is investigated. The timescales considered are monthly to seasonal, and the focus is on France and its vicinity. On such timescales, one may obtain information on likely surface winds from the large-scale state of the atmosphere, determining for instance the most likely paths for storms impinging on Europe. In a first part, we reconstruct surface wind distributions on monthly and seasonal timescales from the knowledge of the large-scale state of the atmosphere, which is summarized using a principal components analysis. We then apply a multi-polynomial regression to model surface wind speed distributions in the parametric context of the Weibull distribution. Several methods are tested for the reconstruction of the parameters of the Weibull distribution, and some of them show good performance. This proves that there is a significant potential for information in the relation between the synoptic circulation and the surface wind speed. In the second part of the paper, the knowledge obtained on the relationship between the large-scale situation of the atmosphere and surface wind speeds is used in an attempt to forecast wind speeds distributions on a monthly horizon. The forecast results are promising but they also indicate that the Numerical Weather Prediction seasonal forecasts on which they are based, are not yet mature enough to provide reliable information for timescales exceeding one month. [ABSTRACT FROM AUTHOR]