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Large scale technical and economical assessment of wind energy potential with a GIS tool: Case study Iowa


  • Grassi, Stefano
  • Chokani, Ndaona
  • Abhari, Reza S.


The development of new wind energy projects requires a thorough analysis of land use issues and constraints. At ETH Zurich, an analytical approach has been developed using a Geographic Information System (GIS) to define the location of suitable sites for wind projects and to predict their economic exploitable energy production. The purpose is to estimate the average Annual Energy Production (AEP), with a GIS customized tool, based on physical factors (environmental and anthropological constraints), the wind resource distribution and the technical specifications of the large-scale wind turbines currently present in the US market. Economics data and regulatory parameters are also included. The wind energy potential of the state of Iowa has been estimated: the resulting average AEP of Iowa is 914TWh and the potential total installed capacity is 302GW. A sensitivity analysis of the influence of the Power Purchase Agreement (PPA) shows that a PPA of 6.5c$/kWh would enable to exploit the 85% of the buildable land with an IRR greater than 15%. This approach is applicable to both larger and more limited regions in order to support energy planners and wind farm developers to set energy strategies and to scout new profitable lands.

Suggested Citation

  • Grassi, Stefano & Chokani, Ndaona & Abhari, Reza S., 2012. "Large scale technical and economical assessment of wind energy potential with a GIS tool: Case study Iowa," Energy Policy, Elsevier, vol. 45(C), pages 73-85.
  • Handle: RePEc:eee:enepol:v:45:y:2012:i:c:p:73-85
    DOI: 10.1016/j.enpol.2012.01.061

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    References listed on IDEAS

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    1. 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.
    2. repec:eee:rensus:v:81:y:2018:i:p1:p:1154-1165 is not listed on IDEAS
    3. Mahtta, Richa & Joshi, P.K. & Jindal, Alok Kumar, 2014. "Solar power potential mapping in India using remote sensing inputs and environmental parameters," Renewable Energy, Elsevier, vol. 71(C), pages 255-262.
    4. Slednev, Viktor & Bertsch, Valentin & Ruppert, Manuel & Fichtner, Wolf, 2017. "Highly resolved optimal renewable allocation planning in power systems under consideration of dynamic grid topology," MPRA Paper 79706, University Library of Munich, Germany.
    5. Mekonnen, Addisu D. & Gorsevski, Pece V., 2015. "A web-based participatory GIS (PGIS) for offshore wind farm suitability within Lake Erie, Ohio," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 162-177.
    6. Höltinger, Stefan & Salak, Boris & Schauppenlehner, Thomas & Scherhaufer, Patrick & Schmidt, Johannes, 2016. "Austria's wind energy potential – A participatory modeling approach to assess socio-political and market acceptance," Energy Policy, Elsevier, vol. 98(C), pages 49-61.
    7. repec:gam:jeners:v:10:y:2017:i:12:p:2072-:d:121878 is not listed on IDEAS
    8. Gass, Viktoria & Schmidt, Johannes & Strauss, Franziska & Schmid, Erwin, 2013. "Assessing the economic wind power potential in Austria," Energy Policy, Elsevier, vol. 53(C), pages 323-330.
    9. Watts, David & Durán, Pablo & Flores, Yarela, 2017. "How does El Niño Southern Oscillation impact the wind resource in Chile? A techno-economical assessment of the influence of El Niño and La Niña on the wind power," Renewable Energy, Elsevier, vol. 103(C), pages 128-142.
    10. Shafiullah, Md & Rahman, Syed Masiur & Mortoja, Md. Golam & Al-Ramadan, Baqer, 2016. "Role of spatial analysis technology in power system industry: An overview," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 584-595.
    11. Siyal, Shahid Hussain & Mörtberg, Ulla & Mentis, Dimitris & Welsch, Manuel & Babelon, Ian & Howells, Mark, 2015. "Wind energy assessment considering geographic and environmental restrictions in Sweden: A GIS-based approach," Energy, Elsevier, vol. 83(C), pages 447-461.
    12. Ribeiro, Alan Emanuel Duailibe & Arouca, Maurício Cardoso & Coelho, Daniel Moreira, 2016. "Electric energy generation from small-scale solar and wind power in Brazil: The influence of location, area and shape," Renewable Energy, Elsevier, vol. 85(C), pages 554-563.
    13. Bossavy, Arthur & Girard, Robin & Kariniotakis, Georges, 2016. "Sensitivity analysis in the technical potential assessment of onshore wind and ground solar photovoltaic power resources at regional scale," Applied Energy, Elsevier, vol. 182(C), pages 145-153.
    14. Atici, Kazim Baris & Simsek, Ahmet Bahadir & Ulucan, Aydin & Tosun, Mustafa Umur, 2015. "A GIS-based Multiple Criteria Decision Analysis approach for wind power plant site selection," Utilities Policy, Elsevier, vol. 37(C), pages 86-96.
    15. Meschede, Henning & Holzapfel, Peter & Kadelbach, Florian & Hesselbach, Jens, 2016. "Classification of global island regarding the opportunity of using RES," Applied Energy, Elsevier, vol. 175(C), pages 251-258.
    16. Kucukali, Serhat, 2016. "Risk scorecard concept in wind energy projects: An integrated approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 975-987.
    17. Grassi, Stefano & Junghans, Sven & Raubal, Martin, 2014. "Assessment of the wake effect on the energy production of onshore wind farms using GIS," Applied Energy, Elsevier, vol. 136(C), pages 827-837.


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