Identifying Key Uncertainties in Energy Transitions: A Puerto Rico Case Study

Published in Nature Communications (2025).

Energy transition planning often relies on deterministic forecasts despite deep technological, economic, and climate uncertainty. This study introduces a computational framework that integrates nested Monte Carlo simulation with surrogate-based global sensitivity analysis to identify the dominant sources of uncertainty driving long-term system costs.

Applied to Puerto Rico’s hurricane-prone power system, the analysis shows that uncertainty in hurricane frequency and organizational inefficiency dominate total expected cost variance, while fuel price uncertainty drives operational cost variance. Results demonstrate that deterministic cost comparisons can misrepresent transition risk, with fully renewable and fully decarbonized pathways having substantial probability of being less costly than business-as-usual.

Keywords: energy transition, uncertainty quantification, global sensitivity analysis, Monte Carlo simulation, resilience

Recommended citation: Khayambashi, K., Clarens, A. F., Shobe, W. M., & Alemazkoor, N. (2025). Identifying key uncertainties in energy transitions: A Puerto Rico case study. Nature Communications, 16(1), 9064.
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