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Introducing a new wind speed complementarity model

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  • Jung, Christopher
  • Schindler, Dirk

Abstract

Minimizing the residual load is essential in countries with a high share of variable renewable energies (VRE). Optimally, new wind turbine sites provide high resource availability and the potential for smoothing the temporal course of the residual load. Current high spatial resolution wind atlases are limited to the ability to find high capacity factor (CF) sites but do not provide CF during periods of high residual load. Thus, we derive a new high-spatiotemporal resolution wind speed model for adjusting wind turbine site selection to residual load. The model (WiCoMo) is developed at six-hourly temporal resolution and 25 m × 25 spatial resolution up to 200 m height from 1991 to 2018 in the study area of Germany. Residual load-weighted and adjusted CF in 2015–2018 is mapped using the new model and considering the national residual load. Besides, complementarity between CF and VRE is evaluated. According to the standard CF, there is a northwest-southeast CF gradient. Relying on the residual load adjusted CF site suitability in southern Germany increases compared to the rest of the country. The introduced model may be a valuable tool for adapting wind resource assessment in Germany on the increasing role of VRE in the electricity mix.

Suggested Citation

  • Jung, Christopher & Schindler, Dirk, 2023. "Introducing a new wind speed complementarity model," Energy, Elsevier, vol. 265(C).
    • Handle: RePEc:eee:energy:v:265:y:2023:i:c:s036054422203170x
    DOI: 10.1016/j.energy.2022.126284
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    1. Jakub Jurasz & Jerzy Mikulik & Paweł B. Dąbek & Mohammed Guezgouz & Bartosz Kaźmierczak, 2021. "Complementarity and ‘Resource Droughts’ of Solar and Wind Energy in Poland: An ERA5-Based Analysis," Energies, MDPI, vol. 14(4), pages 1-24, February.
    2. Veronesi, F. & Grassi, S. & Raubal, M., 2016. "Statistical learning approach for wind resource assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 836-850.
    3. Garrido-Perez, Jose M. & Ordóñez, Carlos & Barriopedro, David & García-Herrera, Ricardo & Paredes, Daniel, 2020. "Impact of weather regimes on wind power variability in western Europe," Applied Energy, Elsevier, vol. 264(C).
    4. Christian M. Grams & Remo Beerli & Stefan Pfenninger & Iain Staffell & Heini Wernli, 2017. "Balancing Europe’s wind-power output through spatial deployment informed by weather regimes," Nature Climate Change, Nature, vol. 7(8), pages 557-562, August.
    5. Carvalho, D. & Rocha, A. & Santos, C. Silva & Pereira, R., 2013. "Wind resource modelling in complex terrain using different mesoscale–microscale coupling techniques," Applied Energy, Elsevier, vol. 108(C), pages 493-504.
    6. Giovanni Gualtieri, 2021. "Reliability of ERA5 Reanalysis Data for Wind Resource Assessment: A Comparison against Tall Towers," Energies, MDPI, vol. 14(14), pages 1-21, July.
    7. Dayal, Kunal K. & Cater, John E. & Kingan, Michael J. & Bellon, Gilles D. & Sharma, Rajnish N., 2021. "Wind resource assessment and energy potential of selected locations in Fiji," Renewable Energy, Elsevier, vol. 172(C), pages 219-237.
    8. Jung, Christopher & Schindler, Dirk, 2018. "On the inter-annual variability of wind energy generation – A case study from Germany," Applied Energy, Elsevier, vol. 230(C), pages 845-854.
    9. Steven Chu & Arun Majumdar, 2012. "Opportunities and challenges for a sustainable energy future," Nature, Nature, vol. 488(7411), pages 294-303, August.
    10. Daaou Nedjari, H. & Haddouche, S. Kheder & Balehouane, A. & Guerri, O., 2018. "Optimal windy sites in Algeria: Potential and perspectives," Energy, Elsevier, vol. 147(C), pages 1240-1255.
    11. Crippa, Paola & Alifa, Mariana & Bolster, Diogo & Genton, Marc G. & Castruccio, Stefano, 2021. "A temporal model for vertical extrapolation of wind speed and wind energy assessment," Applied Energy, Elsevier, vol. 301(C).
    12. Asquith, William H., 2007. "L-moments and TL-moments of the generalized lambda distribution," Computational Statistics & Data Analysis, Elsevier, vol. 51(9), pages 4484-4496, May.
    13. Jung, Christopher & Schindler, Dirk, 2019. "The role of air density in wind energy assessment – A case study from Germany," Energy, Elsevier, vol. 171(C), pages 385-392.
    14. Jung, Christopher & Schindler, Dirk, 2019. "Wind speed distribution selection – A review of recent development and progress," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    15. Monforti, F. & Gaetani, M. & Vignati, E., 2016. "How synchronous is wind energy production among European countries?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 1622-1638.
    16. Gualtieri, Giovanni, 2019. "A comprehensive review on wind resource extrapolation models applied in wind energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 102(C), pages 215-233.
    17. Murthy, K.S.R. & Rahi, O.P., 2017. "A comprehensive review of wind resource assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 1320-1342.
    18. Miao, Haozeyu & Dong, Danhong & Huang, Gang & Hu, Kaiming & Tian, Qun & Gong, Yuanfa, 2020. "Evaluation of Northern Hemisphere surface wind speed and wind power density in multiple reanalysis datasets," Energy, Elsevier, vol. 200(C).
    19. Raynaud, D. & Hingray, B. & François, B. & Creutin, J.D., 2018. "Energy droughts from variable renewable energy sources in European climates," Renewable Energy, Elsevier, vol. 125(C), pages 578-589.
    20. Katikas, Loukas & Dimitriadis, Panayiotis & Koutsoyiannis, Demetris & Kontos, Themistoklis & Kyriakidis, Phaedon, 2021. "A stochastic simulation scheme for the long-term persistence, heavy-tailed and double periodic behavior of observational and reanalysis wind time-series," Applied Energy, Elsevier, vol. 295(C).
    21. Radünz, William Corrêa & Mattuella, Jussara M. Leite & Petry, Adriane Prisco, 2020. "Wind resource mapping and energy estimation in complex terrain: A framework based on field observations and computational fluid dynamics," Renewable Energy, Elsevier, vol. 152(C), pages 494-515.
    22. Jung, Christopher & Schindler, Dirk, 2022. "On the influence of wind speed model resolution on the global technical wind energy potential," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
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