The 3-hourly gridded ECMWF ERA-Interim climate reanalysis dataset, spanning 1979-2013, was used to investigate the spatial stationarity of the previously documented relationships between wind speeds and the North Atlantic Oscillation (NAO) state in Europe. Over much of western Europe, wind speeds were found to be affected strongly by the concomitant states of the secondary and tertiary atmospheric teleconnections, namely the East Atlantic (EA) and the Scandinavian (SCA) patterns. These modify the geographic positions of the NAO dipole and modulate the influence of the NAO on wind statistics on regional scales, producing non-stationarities in the NAO-wind speed relationships. The interactions of these teleconnections play an important role in modifying wind speeds within Europe. Finally, systematic north-south changes in the Weibull distribution scale and shape parameters are documented along the western margin of Europe, as a function of different states of the NAO, the EA and the SCA. These effects influence both monthly averaged wind speeds and the statistical distributions of 3-hourly wind data, implying strong impacts on wind energy resources and expected wind power production. The results have implications for regional to continent-scale long-term planning of wind-farm siting to minimise the impact of resource intermittency.

Forecasting for wind and solar renewable energy is becoming more important as the amount of energy generated from these sources increases. Forecast skill is improving, but so too is the way forecasts are being used. In this paper, we present a brief overview of the state‐of‐the‐art of forecasting wind and solar energy. We describe approaches in statistical and physical modeling for time scales from minutes to days ahead, for both deterministic and probabilistic forecasting. Our focus changes then to consider the future of forecasting for renewable energy. We discuss recent advances which show potential for great improvement in forecast skill. Beyond the forecast itself, we consider new products which will be required to aid decision making subject to risk constraints. Future forecast products will need to include probabilistic information, but deliver it in a way tailored to the end user and their specific decision making problems. Businesses operating in this sector may see a change in business models as more people compete in this space, with different combinations of skills, data and modeling being required for different products. The transaction of data itself may change with the adoption of blockchain technology, which could allow providers and end users to interact in a trusted, yet decentralized way. Finally, we discuss new industry requirements and challenges for scenarios with high amounts of renewable energy. New forecasting products have the potential to model the impact of renewables on the power system, and aid dispatch tools in guaranteeing system security. This article is categorized under: Energy Infrastructure > Systems and Infrastructure Wind Power > Systems and Infrastructure Photovoltaics > Systems and Infrastructure

Seven adaptive approaches to post-processing wind speed forecasts are discussed and compared. Forecasts of the wind speed over 48 h are run at horizontal resolutions of 7 and 3 km for a domain centred over Ireland. Forecast wind speeds over a 2 year period are compared to observed wind speeds at seven synoptic stations around Ireland and skill scores calculated. Two automatic methods for combining forecast streams are applied. The forecasts produced by the combined methods give bias and root mean squared errors that are better than the numerical weather prediction forecasts at all station locations. One of the combined forecast methods results in skill scores that are equal to or better than all of its component forecast streams. This method is straightforward to apply and should prove beneficial in operational wind forecasting.

To study climate-related aspects of power system operation with large volumes of wind generation, data with sufficiently wide temporal and spatial scope are required. The relative youth of the wind industry means that long-term data from real systems are not available. Here, a detailed aggregated wind power generation model is developed for the Republic of Ireland using MERRA reanalysis wind speed data and verified against measured wind production data for the period 2001-2014. The model is most successful in representing aggregate power output in the middle years of this period, after the total installed capacity had reached around 500MW. Variability on scales of greater than 6 hours is captured well by the model; one additional higher resolution wind dataset was found to improve the representation of higher frequency variability. Finally, the model is used to hindcast hypothetical aggregate wind production over the 34-year period 1980-2013, based on existing installed wind capacity. A relationship is found between several of the production characteristics, including capacity factor, ramping and persistence, and two large-scale atmospheric patterns-the North Atlantic Oscillation and the East Atlantic Pattern.

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