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Inter-temporal R&D and Capital Investment Portfolios for the Electricity Industry’s Low Carbon Future

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  • Nidhi R. Santen
  • Mort D. Webster
  • David Popp
  • Ignacio Pérez-Arriaga

Abstract

ABSTRACT A pressing question facing policy makers today in developing a long-term strategy to manage carbon emissions from the electric power sector is how to appropriately balance investment in R&D for driving innovation in emerging low- and zerocarbon technologies with investment in commercially available technologies for meeting existing energy needs. Likewise, policy makers need to determine how to allocate limited funding across multiple technologies. Unfortunately, existing modeling tools to study these questions lack a realistic representation of electric power system operations, the innovation process, or both. In this paper, we present a new modeling framework for long-term R&D and electricity generation capacity planning that combines an economic representation of endogenous non-linear technical change with a detailed representation of the power system. The model captures the complementary nature of technologies in the power sector; physical integration constraints of the system; and the opportunity to build new knowledge capital as a non-linear function of R&D and accumulated knowledge, reflective of the diminishing marginal returns to research inherent in the energy innovation process. Through a series of numerical experiments and sensitivity analyses— with and without carbon policy—we show how using frameworks that do not incorporate these features can over- or under-estimate the value of different emerging technologies, and potentially misrepresent the cost-effectiveness of R&D opportunities.

Suggested Citation

  • Nidhi R. Santen & Mort D. Webster & David Popp & Ignacio Pérez-Arriaga, 2017. "Inter-temporal R&D and Capital Investment Portfolios for the Electricity Industry’s Low Carbon Future," The Energy Journal, , vol. 38(6), pages 1-24, November.
    • Handle: RePEc:sae:enejou:v:38:y:2017:i:6:p:1-24
    DOI: 10.5547/01956574.38.6.nsan
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    References listed on IDEAS

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    1. Popp, David & Santen, Nidhi & Fisher-Vanden, Karen & Webster, Mort, 2013. "Technology variation vs. R&D uncertainty: What matters most for energy patent success?," Resource and Energy Economics, Elsevier, vol. 35(4), pages 505-533.
    2. Shawhan, Daniel L. & Taber, John T. & Shi, Di & Zimmerman, Ray D. & Yan, Jubo & Marquet, Charles M. & Qi, Yingying & Mao, Biao & Schuler, Richard E. & Schulze, William D. & Tylavsky, Daniel, 2014. "Does a detailed model of the electricity grid matter? Estimating the impacts of the Regional Greenhouse Gas Initiative," Resource and Energy Economics, Elsevier, vol. 36(1), pages 191-207.
    3. William D. Nordhaus, 2014. "The Perils of the Learning Model for Modeling Endogenous Technological Change," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1).
    4. Nikolaos Kouvaritakis & Antonio Soria & Stephane Isoard, 2000. "Modelling energy technology dynamics: methodology for adaptive expectations models with learning by doing and learning by searching," International Journal of Global Energy Issues, Inderscience Enterprises Ltd, vol. 14(1/2/3/4), pages 104-115.
    5. Baker, Erin & Solak, Senay, 2011. "Climate change and optimal energy technology R&D policy," European Journal of Operational Research, Elsevier, vol. 213(2), pages 442-454, September.
    6. Goulder, Lawrence H. & Schneider, Stephen H., 1999. "Induced technological change and the attractiveness of CO2 abatement policies," Resource and Energy Economics, Elsevier, vol. 21(3-4), pages 211-253, August.
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