Energy system optimization models (ESOMs) should be used in an interactive way to uncover knife-edge solutions, explore alternative system configurations, and suggest different ways to achieve policy objectives under conditions of deep uncertainty. In this paper, we do so by employing an existing optimization technique called modeling to generate alternatives (MGA), which involves a change in the model structure in order to systematically explore the near-optimal decision space. The MGA capability is incorporated into Tools for Energy Model Optimization and Analysis (Temoa), an open source framework that also includes a technology rich, bottom up ESOM. In this analysis, Temoa is used to explore alternative energy futures in a simplified single region energy system that represents the U.S. electric sector and a portion of the light duty transport sector. Given the dataset limitations, we place greater emphasis on the methodological approach rather than specific results. Modeling to generate alternatives (MGA) is applied to an energy system model.Temoa, an open source energy model, and a two sector U.S. dataset are utilized.MGA is used to explore different cost- and CO2-constrained futures.Results highlight the value of iterative analysis to probe the model decision space.
['Joseph F. DeCarolis', 'Samaneh Babaee', 'Bailian Li', 'S. Kanungo']
Modelling to generate alternatives with an energy system optimization model