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.
|Number of pages||19|
|Publication status||Published - 9 Oct 2021|
- cost model
- power plant