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Comparative analysis of long-term solar resource and CSP production for bankability

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  • Polo, J.
  • Téllez, F.M.
  • Tapia, C.

Abstract

Feasibility analysis for CSP projects requires quantifying the economic risk associated with the inter-annual variability of the solar resource and uncertainties in the annual energy production estimates. To assess this risk, statistical methods may be applied either before the system simulation (e.g. to create solar resource data in terms of exceedance probabilities such a P50 or P90) or on the system performance to determine the likelihood that a power plant will generate a certain amount of energy in any given year over the plant's life (e.g. the P50 or P90 annual productions). This paper presents and illustrates the long-term analysis of four CSP plants (parabolic trough and central receiver technology) in four sites within the Mediterranean and North Africa region; the analysis includes the exceedance probabilities estimations. Satellite-derived data for 20 years have been used for solar resource datasets in hourly basis, and multiyear simulations of the plant output have been performed with System Advisor Model. The associated uncertainties have been included in the long-term analysis of both DNI and CSP plant production. The results remark the advantages of multiyear analysis of CSP output for long-term analysis against the use of artificial meteorological years which represent the long-term.

Suggested Citation

  • Polo, J. & Téllez, F.M. & Tapia, C., 2016. "Comparative analysis of long-term solar resource and CSP production for bankability," Renewable Energy, Elsevier, vol. 90(C), pages 38-45.
  • Handle: RePEc:eee:renene:v:90:y:2016:i:c:p:38-45
    DOI: 10.1016/j.renene.2015.12.057
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    2. Salazar, Germán & Gueymard, Christian & Galdino, Janis Bezerra & de Castro Vilela, Olga & Fraidenraich, Naum, 2020. "Solar irradiance time series derived from high-quality measurements, satellite-based models, and reanalyses at a near-equatorial site in Brazil," Renewable and Sustainable Energy Reviews, Elsevier, vol. 117(C).
    3. Tomosk, Steve & Haysom, Joan E. & Wright, David, 2017. "Quantifying economic risk in photovoltaic power projects," Renewable Energy, Elsevier, vol. 109(C), pages 422-433.
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    5. Shih‐Chieh Liao & Shih‐Chieh Chang & Tsung‐Chi Cheng, 2022. "Index‐based renewable energy insurance for Taiwan Solar Photovoltaic Power Plants," Risk Management and Insurance Review, American Risk and Insurance Association, vol. 25(2), pages 145-172, June.
    6. Moreno-Tejera, S. & Silva-Pérez, M.A. & Ramírez-Santigosa, L. & Lillo-Bravo, I., 2018. "Evaluation of classification methods according to solar radiation features from the viewpoint of the production of parabolic trough CSP plants," Renewable Energy, Elsevier, vol. 121(C), pages 429-440.
    7. Guindon, A.-A. & Wright, D.J., 2020. "Analytical approach to quantitative risk assessment for solar power projects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    8. Martín-Pomares, Luis & Martínez, Diego & Polo, Jesús & Perez-Astudillo, Daniel & Bachour, Dunia & Sanfilippo, Antonio, 2017. "Analysis of the long-term solar potential for electricity generation in Qatar," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 1231-1246.

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