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High-resolution working layouts and time series for renewable energy generation in Europe

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  • Grothe, Oliver
  • Kächele, Fabian
  • Wälde, Mira

Abstract

The stability and manageability of power systems with a growing share of renewable energies depend on accurate forecasts and feed-in information. This study provides synthetic wind and solar power generation time series for approximately 1,500 European transmission nodes in hourly resolution from 2019 to 2022, along with data-driven layouts of renewable generator allocations. To create these time series and layouts, we develop weather-to-energy conversions using high-resolution weather data. Based on the conversions and elastic-net optimisation, the layouts, which we refer to as working layouts, represent a theoretical allocation of generators within each country that produces the current (or alternatively any historical) observed energy output characteristics based on the weather data. This work provides the necessary code to update and adapt layouts and time series for use in custom applications.

Suggested Citation

  • Grothe, Oliver & Kächele, Fabian & Wälde, Mira, 2025. "High-resolution working layouts and time series for renewable energy generation in Europe," Renewable Energy, Elsevier, vol. 239(C).
    • Handle: RePEc:eee:renene:v:239:y:2025:i:c:s0960148124020354
    DOI: 10.1016/j.renene.2024.121967
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    1. Sunderland, Keith M. & Narayana, Mahinsasa & Putrus, Ghanim & Conlon, Michael F. & McDonald, Steve, 2016. "The cost of energy associated with micro wind generation: International case studies of rural and urban installations," Energy, Elsevier, vol. 109(C), pages 818-829.
    2. Ahmed, R. & Sreeram, V. & Mishra, Y. & Arif, M.D., 2020. "A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    3. Voyant, Cyril & Notton, Gilles & Kalogirou, Soteris & Nivet, Marie-Laure & Paoli, Christophe & Motte, Fabrice & Fouilloy, Alexis, 2017. "Machine learning methods for solar radiation forecasting: A review," Renewable Energy, Elsevier, vol. 105(C), pages 569-582.
    4. Pierro, Marco & Gentili, Damiano & Liolli, Fabio Romano & Cornaro, Cristina & Moser, David & Betti, Alessandro & Moschella, Michela & Collino, Elena & Ronzio, Dario & van der Meer, Dennis, 2022. "Progress in regional PV power forecasting: A sensitivity analysis on the Italian case study," Renewable Energy, Elsevier, vol. 189(C), pages 983-996.
    5. Andresen, Gorm B. & Rodriguez, Rolando A. & Becker, Sarah & Greiner, Martin, 2014. "The potential for arbitrage of wind and solar surplus power in Denmark," Energy, Elsevier, vol. 76(C), pages 49-58.
    6. Murcia, Juan Pablo & Koivisto, Matti Juhani & Luzia, Graziela & Olsen, Bjarke T. & Hahmann, Andrea N. & Sørensen, Poul Ejnar & Als, Magnus, 2022. "Validation of European-scale simulated wind speed and wind generation time series," Applied Energy, Elsevier, vol. 305(C).
    7. Kubik, M.L. & Brayshaw, D.J. & Coker, P.J. & Barlow, J.F., 2013. "Exploring the role of reanalysis data in simulating regional wind generation variability over Northern Ireland," Renewable Energy, Elsevier, vol. 57(C), pages 558-561.
    8. Maciejowska, Katarzyna & Nitka, Weronika & Weron, Tomasz, 2021. "Enhancing load, wind and solar generation for day-ahead forecasting of electricity prices," Energy Economics, Elsevier, vol. 99(C).
    9. Wassila Tercha & Sid Ahmed Tadjer & Fathia Chekired & Laurent Canale, 2024. "Machine Learning-Based Forecasting of Temperature and Solar Irradiance for Photovoltaic Systems," Energies, MDPI, vol. 17(5), pages 1-20, February.
    10. Andresen, Gorm B. & Søndergaard, Anders A. & Greiner, Martin, 2015. "Validation of Danish wind time series from a new global renewable energy atlas for energy system analysis," Energy, Elsevier, vol. 93(P1), pages 1074-1088.
    11. Nie, Yuhao & Paletta, Quentin & Scott, Andea & Pomares, Luis Martin & Arbod, Guillaume & Sgouridis, Sgouris & Lasenby, Joan & Brandt, Adam, 2024. "Sky image-based solar forecasting using deep learning with heterogeneous multi-location data: Dataset fusion versus transfer learning," Applied Energy, Elsevier, vol. 369(C).
    12. Gil, Hugo A. & Gomez-Quiles, Catalina & Riquelme, Jesus, 2012. "Large-scale wind power integration and wholesale electricity trading benefits: Estimation via an ex post approach," Energy Policy, Elsevier, vol. 41(C), pages 849-859.
    13. Oliver Grothe & Fabian Kächele & Mira Watermeyer, 2022. "Analyzing Europe’s Biggest Offshore Wind Farms: A Data Set with 40 Years of Hourly Wind Speeds and Electricity Production," Energies, MDPI, vol. 15(5), pages 1-24, February.
    14. Staffell, Iain & Pfenninger, Stefan, 2016. "Using bias-corrected reanalysis to simulate current and future wind power output," Energy, Elsevier, vol. 114(C), pages 1224-1239.
    15. Antweiler, Werner & Muesgens, Felix, 2021. "On the long-term merit order effect of renewable energies," Energy Economics, Elsevier, vol. 99(C).
    16. Rial A. Rajagukguk & Raden A. A. Ramadhan & Hyun-Jin Lee, 2020. "A Review on Deep Learning Models for Forecasting Time Series Data of Solar Irradiance and Photovoltaic Power," Energies, MDPI, vol. 13(24), pages 1-23, December.
    17. Grothe, Oliver & Müsgens, Felix, 2013. "The influence of spatial effects on wind power revenues under direct marketing rules," Energy Policy, Elsevier, vol. 58(C), pages 237-247.
    18. Katzenstein, Warren & Fertig, Emily & Apt, Jay, 2010. "The variability of interconnected wind plants," Energy Policy, Elsevier, vol. 38(8), pages 4400-4410, August.
    19. Tawn, R. & Browell, J., 2022. "A review of very short-term wind and solar power forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    20. Engelhorn, Thorsten & Müsgens, Felix, 2018. "How to estimate wind-turbine infeed with incomplete stock data: A general framework with an application to turbine-specific market values in Germany," Energy Economics, Elsevier, vol. 72(C), pages 542-557.
    21. Thomas Huld & Ana M. Gracia Amillo, 2015. "Estimating PV Module Performance over Large Geographical Regions: The Role of Irradiance, Air Temperature, Wind Speed and Solar Spectrum," Energies, MDPI, vol. 8(6), pages 1-23, June.
    22. Rose, Stephen & Apt, Jay, 2015. "What can reanalysis data tell us about wind power?," Renewable Energy, Elsevier, vol. 83(C), pages 963-969.
    23. González-Aparicio, I. & Monforti, F. & Volker, P. & Zucker, A. & Careri, F. & Huld, T. & Badger, J., 2017. "Simulating European wind power generation applying statistical downscaling to reanalysis data," Applied Energy, Elsevier, vol. 199(C), pages 155-168.
    24. Pfenninger, Stefan & Staffell, Iain, 2016. "Long-term patterns of European PV output using 30 years of validated hourly reanalysis and satellite data," Energy, Elsevier, vol. 114(C), pages 1251-1265.
    25. Koivisto, Matti & Jónsdóttir, Guðrún Margrét & Sørensen, Poul & Plakas, Konstantinos & Cutululis, Nicolaos, 2020. "Combination of meteorological reanalysis data and stochastic simulation for modelling wind generation variability," Renewable Energy, Elsevier, vol. 159(C), pages 991-999.
    26. Durisch, Wilhelm & Tille, Dierk & Wörz, A. & Plapp, Waltraud, 2000. "Characterisation of photovoltaic generators," Applied Energy, Elsevier, vol. 65(1-4), pages 273-284, April.
    27. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    28. Olauson, Jon & Bergkvist, Mikael, 2015. "Modelling the Swedish wind power production using MERRA reanalysis data," Renewable Energy, Elsevier, vol. 76(C), pages 717-725.
    29. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
    30. Staffell, Iain & Green, Richard, 2014. "How does wind farm performance decline with age?," Renewable Energy, Elsevier, vol. 66(C), pages 775-786.
    31. Pape, Christian & Hagemann, Simon & Weber, Christoph, 2016. "Are fundamentals enough? Explaining price variations in the German day-ahead and intraday power market," Energy Economics, Elsevier, vol. 54(C), pages 376-387.
    32. Frank, Christopher & Fiedler, Stephanie & Crewell, Susanne, 2021. "Balancing potential of natural variability and extremes in photovoltaic and wind energy production for European countries," Renewable Energy, Elsevier, vol. 163(C), pages 674-684.
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