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This study develops scenarios aiming to transition the Chilean energy system in 2050 to 100% renewable energy; taking into account local resource potentials, demands, cross-sectoral integration of the electricity, heating, transport, and industrial sectors, and synergies in their related infrastructures. The energy system model EnergyPLAN is used to simulate the hourly operation of the energy system. The relationship between potential CO2 emissions reductions and relative costs is estimated using marginal abatement cost curves with the EPLANoptMAC tool to assess the optimal sequence of capacity expansion and carbon abatement alternatives. The analysis demonstrates that it is possible to carry out this transition from a technical perspective more efficiently than what is proposed with current national scenarios while still aligning with climate neutrality targets; and that, in different phases of the Chilean energy transition, specific options could be prioritized based on an improved balance between carbon abatement and costs.

This paper investigates the impact of applying optimisation and simulation approaches for energy system modelling and scenario design in a municipal context. In doing so, the previous understanding of what constitutes “optimisation” and “simulation” is expanded with further distinctions and it is documented how the outlined modelling approaches are applied in the existing literature. In a practical comparison, it is tested how the choice of modelling approach influences the design of future energy system scenarios for a municipal case study. The energy system scenarios and results obtained from a proposed stepwise simulation approach are compared to an established multi-objective optimisation approach. The results show that for the investigated municipal case it is possible to obtain results with a simulation-based approach that are comparable to the results obtained from multi-objective optimisation. Ultimately, the choice of modelling approach is a complex issue in which modellers must consider the necessary degree of modelling freedom, stakeholder involvement, and available system knowledge. Modellers need to consider not only what tool to use, but also how it is used; a tool can be used for both optimisation and simulation, and both can be valid approaches for developing future energy system scenarios.

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.