Using robust optimization to inform US deep decarbonization planning

Abstract

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. A robust strategy that explicitly considers future uncertainty has expected savings in total system cost of 12% and an 8% reduction in the standard deviation of expected costs relative to a strategy that ignores uncertainty. The robust technology deployment strategy also entails more diversified technology mixes across the energy sectors modeled.

Keywords

• Robust optimization
• Energy system planning
• Parametric uncertainty
• Energy modeling
• Monte Carlo analysis