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A methodology for evaluating the spatial variability of wind energy resources: Application to assess the potential contribution of wind energy to baseload power

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  • Santos-Alamillos, F.J.
  • Pozo-Vázquez, D.
  • Ruiz-Arias, J.A.
  • Lara-Fanego, V.
  • Tovar-Pescador, J.

Abstract

We propose a method for analyzing the potential contribution of wind energy resources to stable (baseload) power within a region. The method uses principal component analysis (PCA) to analyze spatiotemporal balancing of wind energy resources and then assesses the optimal wind farm location to reduce wind power fluctuations. The ability of different reference wind turbines, alone or interconnected, to provide stable power is ultimately evaluated at selected locations. The method was tested in the southern Iberian Peninsula, including offshore areas. We used hourly wind energy estimates from the WRF mesoscale model at 3-km spatial resolution for the period 2008–2010. First, results show a valuable spatial balancing pattern between the wind energy resources in the northeast study region and Strait of Gibraltar area. The pattern was found to result from the interaction of mesoscale zonal flow with the complex topography of the region. Second, the results indicate that by taking advantage of the spatial balancing pattern, the optimal allocation and interconnection of wind farms across the region, can substantially reduce wind power fluctuations. This optimal allocation can in some cases generate stable power, thereby contributing to baseload power.

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  • Santos-Alamillos, F.J. & Pozo-Vázquez, D. & Ruiz-Arias, J.A. & Lara-Fanego, V. & Tovar-Pescador, J., 2014. "A methodology for evaluating the spatial variability of wind energy resources: Application to assess the potential contribution of wind energy to baseload power," Renewable Energy, Elsevier, vol. 69(C), pages 147-156.
  • Handle: RePEc:eee:renene:v:69:y:2014:i:c:p:147-156
    DOI: 10.1016/j.renene.2014.03.006
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    15. Zhang, Chongyu & Lu, Xi & Ren, Guo & Chen, Shi & Hu, Chengyu & Kong, Zhaoyang & Zhang, Ning & Foley, Aoife M., 2021. "Optimal allocation of onshore wind power in China based on cluster analysis," Applied Energy, Elsevier, vol. 285(C).
    16. Artur Amsharuk & Grażyna Łaska, 2023. "The Approach to Finding Locations for Wind Farms Using GIS and MCDA: Case Study Based on Podlaskie Voivodeship, Poland," Energies, MDPI, vol. 16(20), pages 1-24, October.
    17. Gruber, Katharina & Klöckl, Claude & Regner, Peter & Baumgartner, Johann & Schmidt, Johannes, 2019. "Assessing the Global Wind Atlas and local measurements for bias correction of wind power generation simulated from MERRA-2 in Brazil," Energy, Elsevier, vol. 189(C).
    18. Santos-Alamillos, F.J. & Thomaidis, N.S. & Quesada-Ruiz, S. & Ruiz-Arias, J.A. & Pozo-Vázquez, D., 2016. "Do current wind farms in Spain take maximum advantage of spatiotemporal balancing of the wind resource?," Renewable Energy, Elsevier, vol. 96(PA), pages 574-582.
    19. Ingeborg Graabak & Magnus Korpås, 2016. "Variability Characteristics of European Wind and Solar Power Resources—A Review," Energies, MDPI, vol. 9(6), pages 1-31, June.
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    22. Brijs, Tom & De Vos, Kristof & De Jonghe, Cedric & Belmans, Ronnie, 2015. "Statistical analysis of negative prices in European balancing markets," Renewable Energy, Elsevier, vol. 80(C), pages 53-60.
    23. 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.

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