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• W3120207856 abstract "The levelized cost of energy (LCOE) from a parabolic trough collector power plant is influenced directly by its design parameters. In the present paper, the impact of five design parameters (solar multiple, direct normal irradiation, cycle efficiency, mirrors efficiency and absorber efficiency) is investigated in order to minimize the LCOE for a 1 MWe power plant. The artificial neural networks (ANN) and the response surface methodology (RSM) are used to model the LCOE as function of these five parameters. The methodology is applied to six cities of Morocco, and the results show that the ANN is the best model to predict the LCOE using the best topology of 5-7-1 neurons. This structure allows satisfactory prediction of LCOE obtained with Mean Absolute Error (MAE) of about 2% and Maximum Error (ME) of 9%. A minimal cost of 8.74 cents is obtained for Tata city with a direct normal irradiation of 7.09 Wh/m 2 /day, by coupling ANN model to desirability function. The optimal plant design point is 1.94 for the solar multiple, 20.88% for the cycle efficiency, 0.8673 for the mirror efficiency and 0.8537 for the absorber efficiency. • Two methodologies are applied to optimize design parameters of a CSP plant. • Response surface methodology and Artificial Neural network are used to optimize the parameters. • The choice of design parameter impacts the levelized cost of energy directly. • An optimum cost is calculated for each studied site in Morocco as function of five design parameters." @default.
• W3120207856 created "2021-01-18" @default.
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• W3120207856 date "2021-05-01" @default.
• W3120207856 modified "2023-09-27" @default.
• W3120207856 title "Determination of design parameters to minimize LCOE, for a 1 MWe CSP plant in different sites" @default.
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• W3120207856 doi "https://doi.org/10.1016/j.renene.2021.01.060" @default.
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