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The use of cost-generation curves for the analysis of wind electricity costs in Spain

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  • Fueyo, Norberto
  • Sanz, Yosune
  • Rodrigues, Marcos
  • Montañés, Carlos
  • Dopazo, César

Abstract

The cost of the electricity generated from onshore wind is assessed through a method based on an estimation of the geographical distribution of the technical potential and a cost structure for the estimation of the local unit cost. Generation-cost curves are then employed to portray the evolution of the specific generating cost with the increase of the generated energy, until the limit of the technical potential is reached. The study also relates the energy cost to the land occupancy, the installed power and the capacity factor, and includes an assessment of the interplay between land usage and the cost of wind electricity. An analysis is presented to determine the uncertainty in the costs of the several model parameters. The method is applied to Spain, and allows to establish that, for an electricity-generation level of 300Â TWÂ h/y (roughly equal to the overall demand in Spain in 2008), the specific marginal cost is around 8.5Â c[euro]/kWÂ h.

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  • Fueyo, Norberto & Sanz, Yosune & Rodrigues, Marcos & Montañés, Carlos & Dopazo, César, 2011. "The use of cost-generation curves for the analysis of wind electricity costs in Spain," Applied Energy, Elsevier, vol. 88(3), pages 733-740, March.
    • Handle: RePEc:eee:appene:v:88:y:2011:i:3:p:733-740
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    6. Joselin Herbert, G.M. & Iniyan, S. & Amutha, D., 2014. "A review of technical issues on the development of wind farms," Renewable and Sustainable Energy Reviews, Elsevier, vol. 32(C), pages 619-641.
    7. Russell McKenna & Stefan Pfenninger & Heidi Heinrichs & Johannes Schmidt & Iain Staffell & Katharina Gruber & Andrea N. Hahmann & Malte Jansen & Michael Klingler & Natascha Landwehr & Xiaoli Guo Lars', 2021. "Reviewing methods and assumptions for high-resolution large-scale onshore wind energy potential assessments," Papers 2103.09781, arXiv.org.
    8. Navid Salehi & Herminio Martínez-García & Guillermo Velasco-Quesada, 2022. "Networked Microgrid Energy Management Based on Supervised and Unsupervised Learning Clustering," Energies, MDPI, vol. 15(13), pages 1-15, July.
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    10. Watts, David & Oses, Nicolás & Pérez, Rodrigo, 2016. "Assessment of wind energy potential in Chile: A project-based regional wind supply function approach," Renewable Energy, Elsevier, vol. 96(PA), pages 738-755.
    11. Schallenberg-Rodriguez, Julieta, 2013. "A methodological review to estimate techno-economical wind energy production," Renewable and Sustainable Energy Reviews, Elsevier, vol. 21(C), pages 272-287.
    12. McKenna, Russell & Pfenninger, Stefan & Heinrichs, Heidi & Schmidt, Johannes & Staffell, Iain & Bauer, Christian & Gruber, Katharina & Hahmann, Andrea N. & Jansen, Malte & Klingler, Michael & Landwehr, 2022. "High-resolution large-scale onshore wind energy assessments: A review of potential definitions, methodologies and future research needs," Renewable Energy, Elsevier, vol. 182(C), pages 659-684.
    13. Yang, Jiawei & Paudel, Amrit & Gooi, Hoay Beng & Nguyen, Hung Dinh, 2021. "A Proof-of-Stake public blockchain based pricing scheme for peer-to-peer energy trading," Applied Energy, Elsevier, vol. 298(C).
    14. Elattar, Ehab E., 2018. "Modified harmony search algorithm for combined economic emission dispatch of microgrid incorporating renewable sources," Energy, Elsevier, vol. 159(C), pages 496-507.
    15. Ulazia, Alain & Sáenz, Jon & Ibarra-Berastegui, Gabriel & González-Rojí, Santos J. & Carreno-Madinabeitia, Sheila, 2017. "Using 3DVAR data assimilation to measure offshore wind energy potential at different turbine heights in the West Mediterranean," Applied Energy, Elsevier, vol. 208(C), pages 1232-1245.
    16. Sun, Yanwei & Wang, Run & Liu, Jian & Xiao, Lishan & Lin, Yanjie & Kao, William, 2013. "Spatial planning framework for biomass resources for power production at regional level: A case study for Fujian Province, China," Applied Energy, Elsevier, vol. 106(C), pages 391-406.
    17. Elattar, Ehab E., 2019. "Environmental economic dispatch with heat optimization in the presence of renewable energy based on modified shuffle frog leaping algorithm," Energy, Elsevier, vol. 171(C), pages 256-269.
    18. Jäger, Tobias & McKenna, Russell & Fichtner, Wolf, 2016. "The feasible onshore wind energy potential in Baden-Württemberg: A bottom-up methodology considering socio-economic constraints," Renewable Energy, Elsevier, vol. 96(PA), pages 662-675.
    19. Ulazia, Alain & Saenz, Jon & Ibarra-Berastegui, Gabriel, 2016. "Sensitivity to the use of 3DVAR data assimilation in a mesoscale model for estimating offshore wind energy potential. A case study of the Iberian northern coastline," Applied Energy, Elsevier, vol. 180(C), pages 617-627.
    20. 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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