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Determining profits for solar energy with remote sensing data

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  • Wang, Shifeng
  • Koch, Barbara

Abstract

The objects of the article are to determine the profits for solar energy integrating remote sensing data: the optimal locations of photovoltaic and the base price of electricity resulting from solar energy. An illustrated experiment with five European countries data sets is taken. Results indicate that Germany is the only optimal region to set up photovoltaic so as to satisfy the electricity demand of the five considered. Results also show that solar energy is a promising energy source since the highest base price of electricity resulting from solar energy is only 0.35$/kWh. The base electricity price for Germany is the lowest whereas the base electricity price for Italy is the highest. Moreover, the results further indicate that the photovoltaic module price plays a key role in determining the best appropriate region(s) to install photovoltaic and the base electricity price.

Suggested Citation

  • Wang, Shifeng & Koch, Barbara, 2010. "Determining profits for solar energy with remote sensing data," Energy, Elsevier, vol. 35(7), pages 2934-2938.
    • Handle: RePEc:eee:energy:v:35:y:2010:i:7:p:2934-2938
    DOI: 10.1016/j.energy.2010.03.025
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    Cited by:

    1. Rohit Babu & Saurav Raj & Bugatha Ram Vara Prasad, 2021. "A Review at the Utilization of Renewable Energy in an Agricultural Operation," Biophysical Economics and Resource Quality, Springer, vol. 6(4), pages 1-13, December.
    2. Rusu, Eugen & Onea, Florin, 2013. "Evaluation of the wind and wave energy along the Caspian Sea," Energy, Elsevier, vol. 50(C), pages 1-14.
    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. Akyuz, E. & Coskun, C. & Oktay, Z. & Dincer, I., 2012. "A novel approach for estimation of photovoltaic exergy efficiency," Energy, Elsevier, vol. 44(1), pages 1059-1066.
    5. 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.
    6. Wang, Sicong & Wang, Shifeng, 2011. "Spatial interaction models for biomass consumption in the United States," Energy, Elsevier, vol. 36(11), pages 6555-6558.
    7. Lukač, Niko & Seme, Sebastijan & Dežan, Katarina & Žalik, Borut & Štumberger, Gorazd, 2016. "Economic and environmental assessment of rooftops regarding suitability for photovoltaic systems installation based on remote sensing data," Energy, Elsevier, vol. 107(C), pages 854-865.

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