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Economic feasibility of solar power plants based on PV module with levelized cost analysis

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  • Gürtürk, Mert

Abstract

In this study, the cost analysis of solar power system, where is located in Elazığ, Turkey is calculated according to levelized cost analysis method. In the economic feasibility studies carried out by the firms, many parameters such as interest rates, cost of money, detailed sunshine duration, monthly net profit-loss status in one year, cost of investment according to changing interest rates are not taken into consideration. All these parameters, which were not considered by the firms, have been calculated in this study. The payback period of investing in the solar power plant is calculated as 13 years, the payback period of it is calculated as an average of 6.6 years by the firms. The annual profit of a 1 MW solar energy plant is 89,467 US $. Present worth and annual capital cost of the solar power plant are calculated as 1,156,763 US $ and 1,181,875 US $, respectively. The capital cost flow of the investing in solar power plant is determined as 5.628 US $/h. When the results obtained from this study are evaluated in a general framework, the high interest rates in developing countries will have a negative effect on the investments in the solar power plants.

Suggested Citation

  • Gürtürk, Mert, 2019. "Economic feasibility of solar power plants based on PV module with levelized cost analysis," Energy, Elsevier, vol. 171(C), pages 866-878.
  • Handle: RePEc:eee:energy:v:171:y:2019:i:c:p:866-878
    DOI: 10.1016/j.energy.2019.01.090
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    1. Voyant, Cyril & Notton, Gilles & Kalogirou, Soteris & Nivet, Marie-Laure & Paoli, Christophe & Motte, Fabrice & Fouilloy, Alexis, 2017. "Machine learning methods for solar radiation forecasting: A review," Renewable Energy, Elsevier, vol. 105(C), pages 569-582.
    2. Rashwan, Sherif S. & Shaaban, Ahmed M. & Al-Suliman, Fahad, 2017. "A comparative study of a small-scale solar PV power plant in Saudi Arabia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 313-318.
    3. Talavera, D.L. & Pérez-Higueras, P. & Almonacid, F. & Fernández, E.F., 2017. "A worldwide assessment of economic feasibility of HCPV power plants: Profitability and competitiveness," Energy, Elsevier, vol. 119(C), pages 408-424.
    4. Akarslan, Emre & Hocaoglu, Fatih Onur, 2016. "A novel adaptive approach for hourly solar radiation forecasting," Renewable Energy, Elsevier, vol. 87(P1), pages 628-633.
    5. Gürtürk, Mert & Oztop, Hakan F. & Hepbasli, Arif, 2015. "Comparison of exergoeconomic analysis of two different perlite expansion furnaces," Energy, Elsevier, vol. 80(C), pages 589-598.
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