Determination of design parameters to minimize LCOE, for a 1 MWe CSP plant in different sites
Fayrouz El Hamdani,
Sébastien Vaudreuil,
Souad Abderafi and
Tijani Bounahmidi
Renewable Energy, 2021, vol. 169, issue C, 1013-1025
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/m2/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.
Keywords: LCOE; Parabolic trough collector plant; Modeling; ANN; RSM; Optimization of design parameters (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960148121000677
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:169:y:2021:i:c:p:1013-1025
DOI: 10.1016/j.renene.2021.01.060
Access Statistics for this article
Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides
More articles in Renewable Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().