  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.
