TemoaProject: Using Robust Optimization Techniques to Inform US Climate Policy

US energy system development consistent with the Paris Agreement will depend in part on future fuel prices and technology costs, which are highly uncertain. Energy system optimization models (ESOMs) represent a critical tool to examine clean energy futures under different assumptions. While many approaches exist to examine future sensitivity and uncertainty in such models, most assume that uncertainty is resolved prior to the model run. Policy makers, however, must take action before uncertainty is resolved. Robust optimization represents a method that explicitly considers future uncertainty within a single model run, yielding a near-term hedging strategy that is robust to uncertainty. This work focuses on extending and applying robust optimization methods to Temoa, an open source ESOM, to derive insights about low carbon pathways in the United States. Compared to the naïve scenario that ignores future uncertainty, expected savings associated with the robust strategy are 12% and the standard deviation in expected costs is 8% lower in the robust solution. The robust technology deployment strategy entails more diversified technology mixes across all of the energy sectors modeled.