A portfolio decision analysis approach to support energy research and development resource allocation. (June 2017)

• Record Type: Journal Article
• Authors: Kurth, Margaret; Keisler, Jeffrey M.; Bates, Matthew E.; Bridges, Todd S.; Summers, Jeffrey; Linkov, Igor
• Is Part Of: Energy policy. Volume 105(2017)
• Page Start: 128
• Page End: 135
• Publication Date: 2017-06
• Subjects: Portfolio decision analysis (PDA) -- Research and development (R&D) -- Technology readiness level (TRL) -- Multi-attribute value model; Energy policy -- Periodicals; Politique énergétique -- Périodiques; Electronic journals; 333.79
• DOI: 10.1016/j.enpol.2017.02.030
• Languages: English
• ISSNs: 0301-4215

Abstract

Abstract: Research sponsored by the US Department of Energy (DOE) aims to facilitate a clean and independent energy future for the nation. Strategic planning for energy research and development (R&D) can be complex and dynamic, in part due to federal budgetary constraints and volatility. Managing R&D funding to advance energy technologies, in spite of these challenges, is a crucial component of the nation's long term energy policy. This study demonstrates a portfolio decision analysis (PDA) approach to support R&D resource allocation decisions for the DOE Office of Fossil Energy's Carbon Capture and Storage R&D program. A multi-attribute value model uses technology readiness levels (TRLs) and other metrics to represent the overall objectives of the R&D program in order to evaluate alternative research portfolios given limited funding. Mathematical optimization identifies efficient funding allocations for each technology program area to maximize the multi-attribute value generated from the total budget. This is especially useful for responding to externally imposed budget changes. As the case study demonstrates, explicitly funding the most value-generating options leads to greater expected R&D programmatic value than typical strategies of equal or proportional distributions of a budget change among technology program areas. Highlights: Decision analysis can minimize the effect of a budget decrement on an R&D program. Greater expected benefits are yielded by differentially funding technologies. Budget scenario testing illustrates factors that influence value generation. Coordinating with US DOE bridges gap between decision research and practice.