Identifying key uncertainties in energy transitions with a Puerto Rico case study

Deterministic energy transition planning risks uninformed decisions. Yet, the challenge of high-dimensional uncertainty-encompassing various technological, economic, social, and climatic factors-often leads to a deterministic treatment or simplification of uncertainties in planning. Here, we propose a computationally efficient framework that leverages surrogate-based sensitivity analysis to identify the key uncertainty sources driving the cost of different energy transition scenarios. We applied the proposed approach to Puerto Rico as a hurricane-prone power system that lacks efficient management. We find that changes in the frequency of hurricanes and organizational inefficiency are the two primary sources of uncertainty determining the system's total expected cost. When examining operational costs, different transition scenarios demonstrate unique key uncertainty sources. For example, the price of biofuel would mainly drive the operational cost when transitioning to a fully renewable power system. These findings can help planners by allowing them to focus on a narrower set of uncertainties in planning.