Realistic budgeting for complex power plant projects is important for informed investment decisions and avoiding frequent cost overruns. Comparative cost analysis thus provides significant insights for future projects’ investment decisions. This study analyzed the project costs using a regression analysis and Monte Carlo Simulation (MCS) guided by the error analysis for Combined Cycle Power Plants (CCPP) and Natural Gas (NG)-based power plants. The regression analysis shows the cost trend lines with the prediction equation and its R2 value against production capacity in Mega-Watts (MW) of power plant projects in Bangladesh. The MCS presents the cumulative probability function (CDF) and probability density function (PDF) of unit cost in Million U.S. Dollars (MUSD)/MW of both types of projects. The results show that the NG project costs are higher than CCPP power plant projects, where the most frequent project development cost is in a range of 1.01-1.25 MUSD/MW, comparing 0.95-1.15 MUSD/MW of CCPP projects. Among the models, the MCS performs better than the regression model predicting project costs. Finally, MCS is demonstrated in predicting the cost of both types of power plant projects. The study outcomes will assist decision-makers and policy planners in further investment decisions in the power plant and similar infrastructure projects in Bangladesh and economically similar countries.
|Proceedings of International Structural Engineering and Construction
|Published - 2022
Bibliographical notePublisher Copyright:
© 2022 ISEC Press.
- Investment decision
- Realistic budgeting
- Regression model
ASJC Scopus subject areas
- Safety, Risk, Reliability and Quality
- Building and Construction
- Civil and Structural Engineering