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Supply-cost curves for geographically distributed renewable-energy resources


  • Izquierdo, Salvador
  • Dopazo, César
  • Fueyo, Norberto


The supply-cost curves of renewable-energy sources are an essential tool to synthesize and analyze large-scale energy-policy scenarios, both in the short and long terms. Here, we suggest and test a parametrization of such curves that allows their representation for modeling purposes with a minimal set of information. In essence, an economic potential is defined based on the mode of the marginal supply-cost curves; and, using this definition, a normalized log-normal distribution function is used to model these curves. The feasibility of this proposal is assessed with data from a GIS-based analysis of solar, wind and biomass technologies in Spain. The best agreement is achieved for solar energy.

Suggested Citation

  • Izquierdo, Salvador & Dopazo, César & Fueyo, Norberto, 2010. "Supply-cost curves for geographically distributed renewable-energy resources," Energy Policy, Elsevier, vol. 38(1), pages 667-672, January.
  • Handle: RePEc:eee:enepol:v:38:y:2010:i:1:p:667-672

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    References listed on IDEAS

    1. Jebaraj, S. & Iniyan, S., 2006. "A review of energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 10(4), pages 281-311, August.
    2. Schneider, Daniel R. & Duić, Neven & Bogdan, Željko, 2007. "Mapping the potential for decentralized energy generation based on renewable energy sources in the Republic of Croatia," Energy, Elsevier, vol. 32(9), pages 1731-1744.
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    4. Gómez, Antonio & Zubizarreta, Javier & Rodrigues, Marcos & Dopazo, César & Fueyo, Norberto, 2010. "Potential and cost of electricity generation from human and animal waste in Spain," Renewable Energy, Elsevier, vol. 35(2), pages 498-505.
    5. de Vries, Bert J.M. & van Vuuren, Detlef P. & Hoogwijk, Monique M., 2007. "Renewable energy sources: Their global potential for the first-half of the 21st century at a global level: An integrated approach," Energy Policy, Elsevier, vol. 35(4), pages 2590-2610, April.
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    Cited by:

    1. Yuan, Xue-liang & Zuo, Jian, 2011. "Pricing and affordability of renewable energy in China – A case study of Shandong Province," Renewable Energy, Elsevier, vol. 36(3), pages 1111-1117.
    2. Calvert, K., 2011. "Geomatics and bioenergy feasibility assessments: Taking stock and looking forward," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(2), pages 1117-1124, February.
    3. Bidart, Christian & Fröhling, Magnus & Schultmann, Frank, 2014. "Electricity and substitute natural gas generation from the conversion of wastewater treatment plant sludge," Applied Energy, Elsevier, vol. 113(C), pages 404-413.
    4. Tapia-Ahumada, Karen & Octaviano, Claudia & Rausch, Sebastian & Pérez-Arriaga, Ignacio, 2015. "Modeling intermittent renewable electricity technologies in general equilibrium models," Economic Modelling, Elsevier, vol. 51(C), pages 242-262.
    5. Bidart, Christian & Fröhling, Magnus & Schultmann, Frank, 2014. "Livestock manure and crop residue for energy generation: Macro-assessment at a national scale," Renewable and Sustainable Energy Reviews, Elsevier, vol. 38(C), pages 537-550.
    6. Calvert, K. & Pearce, J.M. & Mabee, W.E., 2013. "Toward renewable energy geo-information infrastructures: Applications of GIScience and remote sensing that build institutional capacity," Renewable and Sustainable Energy Reviews, Elsevier, vol. 18(C), pages 416-429.
    7. Gómez, Antonio & Zubizarreta, Javier & Dopazo, César & Fueyo, Norberto, 2011. "Spanish energy roadmap to 2020: Socioeconomic implications of renewable targets," Energy, Elsevier, vol. 36(4), pages 1973-1985.


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