Energy sector portfolio analysis with uncertainty. (15th January 2022)

• Record Type: Journal Article
• Authors: Milford, James; Henrion, Max; Hunter, Chad; Newes, Emily; Hughes, Caroline; Baldwin, Samuel F.
• Is Part Of: Applied energy. Volume 306:Part A(2022)
• Publication Date: 2022-01-15
• Subjects: Portfolio analysis -- Energy system -- Expert elicitation -- Uncertainty -- Stochasticity -- Stochastic Energy Deployment System; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2021.117926
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: Stochastic Energy Deployment System assesses research and development portfolios. Outcomes of federal goals for technology cost and performance are evaluated. Interviews obtained estimates of research and development uncertainty. Lessons learned on conveying stochastic results to diverse audiences are discussed. Abstract: Governments are dealing with the challenge of how to efficiently invest in research and development portfolios related to energy technologies. Research and development investment decisions in the energy space are especially difficult due to numerous risks and uncertainties, and due to the complexity of energy's interactions with the broad economy. Historically, much of the U.S. Department of Energy's in-depth research and development analyses focused on assessing the impact of a research and development activity in isolation from other available opportunities and did not substantially consider risk and uncertainty. Endeavoring to combine integrated energy-economy modeling with uncertainty analysis and technology-specific research and development activities, the U.S. Department of Energy commissioned the development of the Stochastic Energy Deployment System to support and improve public energy research and development decision-making. The Stochastic Energy Deployment System draws from expert-elicited probability distributions for research and development-driven improvements in technology cost and performance, and it uses Monte Carlo simulations to evaluate the likelihood of outcomes within a system dynamics energy-economy model. The framework estimates the uncertain benefits and costs of various research and development portfolios and provides insight into the probability of meeting national technology goals, while accounting for interactions with the larger economy and for interactions among research and development investments spanning many energy sectors.