A Combined Ranking and Sensitivity Analysis of Power Generation Using Multi-Criteria Decision-Making and Monte-Carlo Simulation

Abstract

This research examined energy sources that can be employed in a region to assist policymakers in determining energy priorities. Three key components were analyzed in this research to rank these energy sources: Levelized Cost of Energy (LCOE), CO2 emissions, and power density. A combination of multi-criteria decision-making (MCDM) methods, namely the Analytical Hierarchy Process (AHP)-Entropy-the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), was used to assess these criteria, which had not been previously applied to rank energy sources. Additionally, the Monte-Carlo method was utilized to detect changes in sensitivity throughout the rankings. Results of the study indicated that gas energy topped the list, followed by Solar Photovoltaic (PV)-crystalline, geothermal, wind, nuclear, Solar PV Commercial & Industrial (C&I), Solar Thermal Tower with Storage, and residential PV rooftop solar. Moreover, nuclear energy ranked the highest when looking at the sensitivity of parameters, while utility-scale Solar PV and wind energy ranked the next highest. Thus, this research can be used to increase objectivity in the assessment and selection of power generation technology to be implemented.