New Study Illuminates the Hidden Uncertainties in Energy Transitions (A Case Study of Puerto Rico)

Researchers unveil a novel framework to identify key drivers of cost uncertainty in shifting to cleaner power systems.

A new article published in Nature Communications offers insight into how government planners can better account for the many uncertainties that accompany the transition from fossil-fuel power systems to renewable or decarbonized systems. The paper, titled “Identifying key uncertainties in energy transitions with a Puerto Rico case study,” was authored by a team from the University of Virginia including Kamiar Khayambashi, Andres Clarens, William Shobe, and Negin Alemazkoor. It makes a compelling case that planners need to do better at taking the right uncertainties into account when readying for an energy transition.

The team developed a three-stage analytical framework and applied it to the electricity system of Puerto Rico, a hurricane-prone island heavily dependent on fossil fuels. The study analyzed three transition pathways: Business as Usual (BAU), Fully Renewable (FR) by 2050, and Fully Decarbonised (FD) by 2050.

Key results show that changes in the frequency and intensity of hurricanes due to climate change emerged as the single largest source of uncertainty in the expected total system cost. Organizational inefficiency (e.g., delays, miscoordination in restoration of infrastructure services) was the second most important uncertainty but is rarely included in energy-system modelling.

Kamiar Khayambashi at the University of Virginia found that planners need to take the right uncertainties into account when it comes to planning for energy transitions. (Photo contributed.)

“In a high-hurricane-risk region with a history of institutional inefficiencies such as Puerto Rico (and the case of grid restoration after Hurricane Maria), the yearly cost of the power grid cannot be accurately judged without explicitly modelling the uncertainties in climate, technology, fuel and institutional performance,” said Kamiar Khayambashi, author of the paper.

Unfortunately, when it comes to planning and investments, the appropriate uncertainties are not considered, which leads to inaccuracies. As utilities and regulators plan long-term investments in clean energy transitions, many models assume a fixed “best-guess” value for fuel prices, technology cost declines, and weather or climate trends. This new research shows that such deterministic planning may miss critical cost-risks from multiple interacting uncertainties, especially in regions vulnerable to extreme weather and with consistent institutional-efficiency challenges.

This study offers a strategic way to focus on data collection, modelling refinement, and risk-mitigation efforts: identify which uncertainties matter most.

“Demonstrating that the high-dimensional uncertainty in long-term planning can be reduced to a few key factors may encourage planners to explicitly account for those critical uncertainties, rather than ignoring them because the problem seems overwhelmingly complex,” said study author Negin Alemazkoor.

For example, in Puerto Rico, energy planners should integrate climate extreme risk models (here, that would be hurricane frequency and intensity) into infrastructure cost and resilience assessments. And organizational risk (such as restoration delays after storms) should be treated as a quantifiable input cost, not just a sidenote.

While the case study is specific to Puerto Rico, the authors emphasize that the framework can be applied in other geographies and under differing policy horizons.

“Our results show that resilience and organizational performance can matter as much as fuel price volatility when designing energy systems for the long term,” stated paper author and associate director of the UVA Environmental Institute, Andres Clarens.

This research paves the way for more robust, uncertainty-aware energy transition planning, especially for regions facing extreme weather and institutional constraints.