Valuing sub-seasonal to seasonal predictions for the wind energy sector

Both renewable energy supply and electricity demand are strongly influenced by meteorological conditions and their evolution over time in terms of climate variability and climate change. This works as a major barrier to wind energy integration in electricity networks as knowledge of power output and demand forecasting beyond a few days remains poor. Current methodologies assume that long-term resource availability is constant, ignoring the fact that future wind resources could be significantly different from the past wind energy conditions. Such uncertainties create risks that affect investment in wind energy projects at the operational stage where energy yields affect cash flow and the balance of the grid. Here we assess whether sub-seasonal to seasonal climate predictions (S2S) can skilfully predict wind speed in Europe. To illustrate S2S potential applications, two periods with an unusual climate behaviour affecting the energy market will be presented. We find that wind speed forecasted using S2S exhibit predictability some weeks and months in advance in important regions for the energy sector such as the North Sea. If S2S are incorporated into planning activities for energy traders, energy producers, plant operators, plant investors, they could help improve management climate variability related risks.

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Source https://zenodo.org/records/2643283
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Author Albert Soret,Sergio Lozano,Verónica Torralba,Llorenç Lledó,Andrea Manrique-Suñén,Nicola Cortesi,Nube González-Reviriego,Pierre-Antoine Bretonnière,Francisco J. Doblas-Reyes
Maintainer Albert Soret,Sergio Lozano,Verónica Torralba,Llorenç Lledó,Andrea Manrique-Suñén,Nicola Cortesi,Nube González-Reviriego,Pierre-Antoine Bretonnière,Francisco J. Doblas-Reyes
Maintainer Email Albert Soret,Sergio Lozano,Verónica Torralba,Llorenç Lledó,Andrea Manrique-Suñén,Nicola Cortesi,Nube González-Reviriego,Pierre-Antoine Bretonnière,Francisco J. Doblas-Reyes
Dataset subject Wind energy, climate services, S2S, cilmate predictions
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