Capacity at risk: a metric for robust planning decisions under uncertainty in the electric sector

Abstract

Abstract Many decision contexts are characterized by deep uncertainty where there is disagreement about values and probabilities such as policy and technological uncertainties for energy sector investments. Although there are methods for decision analysis in these contexts, there are few simple metrics to guide analysts and decision-makers on whether more sophisticated methods are appropriate, to highlight aspects of robust decision-making, and to prioritize information gathering on uncertainties. Here, we introduce a screening metric called ‘capacity at risk’ and two complementary metrics—robust capacity and risk ratio—for identifying the most decision-relevant uncertainties and for understanding which investments could be robust and which are more uncertain across a range of different futures. The use of deterministic model runs in calculating capacity at risk metrics can lower barriers to entry for modelers and communications with stakeholders. These metrics are applied to an illustrative example of electric sector decarbonization in the United States using a detailed capacity planning and dispatch model. Scenario results demonstrate the importance of climate policy targets and timing on decisions, while uncertainties such as natural gas prices and renewable costs have more moderate impacts on planning. We also apply the capacity at risk framework to other prominent U.S. electric sector scenario analysis. These comparisons suggest that commonly used scenarios may understate uncertainty, giving decision-makers a misleading sense of portfolio risk and understating the value of frameworks that explicitly assess decisions under uncertainty.