Different Geothermal Power Cycle Configurations Cost Estimation Models

Moein Shamoushaki, Giampaolo Manfrida, Lorenzo Talluri, Pouriya H. Niknam and Daniele Fiaschi
Additional contact information
Moein Shamoushaki: Department of Industrial Engineering, University of Florence, 50134 Firenze, Italy
Giampaolo Manfrida: Department of Industrial Engineering, University of Florence, 50134 Firenze, Italy
Lorenzo Talluri: Department of Industrial Engineering, University of Florence, 50134 Firenze, Italy
Pouriya H. Niknam: Department of Industrial Engineering, University of Florence, 50134 Firenze, Italy
Daniele Fiaschi: Department of Industrial Engineering, University of Florence, 50134 Firenze, Italy

Sustainability, 2021, vol. 13, issue 20, 1-19

Abstract: An economic assessment of different geothermal power cycle configurations to generate cost models is conducted in this study. The thermodynamic and exergoeconomic modeling of the cycles is performed in MATLAB coupled to Refprop. The models were derived based on robust multivariable regression to minimize the residuals by using the genetic algorithm. The cross-validation approach is applied to determine a dataset to examine the model in the training phase for validation and reduce the overfitting problem. The generated cost models are the total cost rate, the plant’s total cost, and power generation cost. The cost models and the relevant coefficients are generated based on the most compatibilities and lower error. The results showed that one of the most influential factors on the ORC cycle is the working fluid type, which significantly affects the final economic results. Other parameters that considerably impact economic models results, of all configurations, are geothermal fluid pressure and temperature and inlet pressure of turbine. Rising the geothermal fluid mass flow rate has a remarkable impact on cost models as the capacity and size of equipment increases. The generated cost models in this study can estimate the mentioned cost parameters with an acceptable deviation and provide a fast way to predict the total cost of the power plants.

Keywords: cost model; power plant; geothermal; optimization; ORC (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2071-1050/13/20/11133/pdf (application/pdf)
https://www.mdpi.com/2071-1050/13/20/11133/ (text/html)

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:gam:jsusta:v:13:y:2021:i:20:p:11133-:d:652189

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().