This tool produces energy transition simulations for the Chilean electricity system to 2050 based on the optimization model ETEM and machine learning models. By changing the input parameters (emission reduction targets, costs, demands, discount factors, etc.), the user can observe and analyze in an interactive way the impacts of these settings on the evolution of the optimal electricity system in terms of investments, generations, emissions, etc. This simulator aims at disseminating simulation results to a wide public of non-experts and to decision makers to evaluate the impact of their decisions.

For more technical information on the ETEM model, on the Machine Learning analysis and/or on the construction of the simulator, we refer to the master thesis report by L. Poblete (2021), a former student who has been part of the ORECC team.