Robust Forecasting of Levelized Cost of Energy Based on a Novel Approach of Uncertain Energy Portfolio Optimization

During the past few years, energy management has become a vital part of the agendas of almost all governments due to its impact on human life. There are some turbulent incidents such as the Covid-19 and the Russia-Ukraine war under different scenarios, influencing the countries’ energy policy and the consumption of the world’s energy resources. To handle uncertainties, there is an urgent need to utilize a robust method. This paper aims to develop a quantitative pragmatic tool called Robust Energy Portfolio Optimization that provides energy policymakers with valuable information to assist them in making decisions. Due to the lack of a unified view of the Levelized Cost of Energy (LCOE) quantity among experts, and its importance to the expected return on dollar for energy production, we employ the Dempster–Shafer theory, a practical and innovative approach to taking into account the experts’ opinions, to calculate the LCOE value of various energy generation technologies under different scenarios. A Polynomial Goal Programming approach is implemented to optimize the suggested multi-objective model which allows energy policymakers to create portfolios based on their preferences. The results confirm that the proposed portfolios are more efficient, sustainable, and resilient than the traditional approaches to selecting energy portfolios.