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.
Highlights
Too often those designing portfolios of research and development (R&D) projects fail to adequately address the risk and uncertainty inherent in R&D
Governments are dealing with the challenge of how to efficiently invest in research and development portfolios related to energy technologies
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
Summary
Decision analysis offers a range of tools to evaluate and optimize R&D portfolios, including methods to explicitly represent un certainties and evaluate their effects on portfolio selection. Quantitative treatment of uncertainty and stochastic analysis of R&D portfolios can provide valuable insights into potential benefits and help R&D managers develop portfolios that are more robust and flexible in the light of a wide range of possible futures. Since energy R&D has many downstream effects, modeling the interactions among multiple energy sectors and the impacts on the larger economy provides decision makers with richer information to evaluate R&D investments. We describe an energy-economy modeling framework that has the potential to support and improve public energy R&D decisions by explicitly accounting for risk and uncertainty
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