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Stochastic techno-economic assessment based on Monte Carlo simulation and the Response Surface Methodology: The case of an innovative linear Fresnel CSP (concentrated solar power) system

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  • Bendato, Ilaria
  • Cassettari, Lucia
  • Mosca, Marco
  • Mosca, Roberto

Abstract

Combining technological solutions with investment profitability is a critical aspect in designing both traditional and innovative renewable power plants. Often, the introduction of new advanced-design solutions, although technically interesting, does not generate adequate revenue to justify their utilization. In this study, an innovative methodology is developed that aims to satisfy both targets. On the one hand, considering all of the feasible plant configurations, it allows the analysis of the investment in a stochastic regime using the Monte Carlo method. On the other hand, the impact of every technical solution on the economic performance indicators can be measured by using regression meta-models built according to the theory of Response Surface Methodology. This approach enables the design of a plant configuration that generates the best economic return over the entire life cycle of the plant. This paper illustrates an application of the proposed methodology to the evaluation of design solutions using an innovative linear Fresnel Concentrated Solar Power system.

Suggested Citation

  • Bendato, Ilaria & Cassettari, Lucia & Mosca, Marco & Mosca, Roberto, 2016. "Stochastic techno-economic assessment based on Monte Carlo simulation and the Response Surface Methodology: The case of an innovative linear Fresnel CSP (concentrated solar power) system," Energy, Elsevier, vol. 101(C), pages 309-324.
    • Handle: RePEc:eee:energy:v:101:y:2016:i:c:p:309-324
    DOI: 10.1016/j.energy.2016.02.048
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    6. Liu, Chunyu & Zheng, Xinrui & Yang, Haibin & Tang, Waiching & Sang, Guochen & Cui, Hongzhi, 2023. "Techno-economic evaluation of energy storage systems for concentrated solar power plants using the Monte Carlo method," Applied Energy, Elsevier, vol. 352(C).
    7. Cuneo, A. & Zaccaria, V. & Tucker, D. & Traverso, A., 2017. "Probabilistic analysis of a fuel cell degradation model for solid oxide fuel cell and gas turbine hybrid systems," Energy, Elsevier, vol. 141(C), pages 2277-2287.
    8. Wu, Yunna & Zhang, Buyuan & Wu, Chenghao & Zhang, Ting & Liu, Fangtong, 2019. "Optimal site selection for parabolic trough concentrating solar power plant using extended PROMETHEE method: A case in China," Renewable Energy, Elsevier, vol. 143(C), pages 1910-1927.
    9. Daabo, Ahmed M. & Mahmoud, Saad & Al-Dadah, Raya K., 2016. "The effect of receiver geometry on the optical performance of a small-scale solar cavity receiver for parabolic dish applications," Energy, Elsevier, vol. 114(C), pages 513-525.
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    11. Coronas, Sergio & Martín, Helena & de la Hoz, Jordi & García de Vicuña, Luis & Castilla, Miguel, 2021. "MONTE-CARLO probabilistic valuation of concentrated solar power systems in Spain under the 2014 retroactive regulatory framework," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).

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