Evaluating near-optimal scenarios with EnergyPLAN to support policy makers

Abstract

Energy system modelling may support policymakers in their energy planning efforts. Energy system modellers usually identify the optimal system configuration based on an economic objective function, or in multi-objective optimization, a combination of multiple objectives such as greenhouse gas emissions and total system cost. However, there could be political, socio-economic, or environmental reasons justifying a policymaker's selection of a solution that is slightly more costly or greenhouse gas polluting than the uniquely optimal solution. Solely focusing on the uniquely optimal solution disregards potentially diverse alternatives, which based on different evaluation metrics could even be preferable. In response to this challenge, the evaluation of near-optimal solutions is gaining attention in the energy system modelling field as an extension of traditional multi-objective optimization studies and as a way to bridge the gap between simulation and optimization approaches. In this study, we explore near-optimal solutions, outline the diversity of near-optimal solutions, and evaluate the relevance of these solutions in the context of energy planning. The proposed methodology is applied to the Italian case to determine its potential as a tool to support policymakers in evaluating energy system scenarios from a selection of optimal and near-optimal solutions.