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

Area of Science:

• Energy Systems Analysis
• Decision Science
• Climate Change Adaptation

Background:

• Energy transition planning faces challenges due to high-dimensional uncertainties.
• Simplifying uncertainties can lead to suboptimal or uninformed decisions in energy planning.

Purpose of the Study:

• To develop a computationally efficient framework for identifying key uncertainty sources in energy transition planning.
• To guide decision-makers by highlighting critical factors influencing energy system costs.

Main Methods:

• Leveraged surrogate-based sensitivity analysis to pinpoint uncertainty drivers.
• Applied the framework to Puerto Rico's hurricane-prone power system.
• Analyzed total expected costs and scenario-specific operational costs.

Main Results:

• Hurricane frequency and organizational inefficiency are primary uncertainty sources for Puerto Rico's total expected energy system cost.
• Specific transition scenarios have unique key uncertainty drivers, such as biofuel prices for renewable systems.
• The framework effectively narrows the focus on critical uncertainties.

Conclusions:

• Sensitivity analysis is crucial for robust energy transition planning under uncertainty.
• Understanding specific uncertainty drivers allows for more targeted and efficient planning strategies.
• The proposed framework offers a practical tool for energy planners in complex systems.