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A Clean Energy Standard Analysis with the US-REGEN Model

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  • Geoffrey J. Blanford
  • James H. Merrick
  • David Young

Abstract

A clean energy standard (CES) is a potential policy alternative to reduce carbon emissions in the electric sector. We analyze this policy under a range of technological assumptions, expanding on the Energy Modeling Forum (EMF) 24 study scenarios, using a new modeling tool, US-REGEN. We describe three innovative features of the model: treatment of spatial and temporal variability of renewable resources, cost-of-service electric sector pricing, and explicit representation of energy end-use specific capital. We find that varying technology assumptions results in vastly different futures, with large contrasts in the distribution and scale of inter-regional financial flows, and in the generation mix. We explore regional differences in how the costs of CES credits are passed through with cost-of-service vs. competitive pricing. Finally, we compare the CES to an economy-wide emissions cap. We find that although the two policies result in a similar generation mix, price and electricity end-use results differ.

Suggested Citation

  • Geoffrey J. Blanford & James H. Merrick & David Young, 2014. "A Clean Energy Standard Analysis with the US-REGEN Model," The Energy Journal, , vol. 35(1_suppl), pages 137-164, June.
    • Handle: RePEc:sae:enejou:v:35:y:2014:i:1_suppl:p:137-164
    DOI: 10.5547/01956574.35.SI1.8
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    3. Merrick, James H. & Bistline, John E.T. & Blanford, Geoffrey J., 2024. "On representation of energy storage in electricity planning models," Energy Economics, Elsevier, vol. 136(C).
    4. John E. Bistline & Francisco Chesnaye, 2017. "Banking on banking: does “when” flexibility mask the costs of stringent climate policy?," Climatic Change, Springer, vol. 144(4), pages 597-610, October.
    5. Krakowski, Vincent & Assoumou, Edi & Mazauric, Vincent & Maïzi, Nadia, 2016. "Feasible path toward 40–100% renewable energy shares for power supply in France by 2050: A prospective analysis," Applied Energy, Elsevier, vol. 171(C), pages 501-522.
    6. Merrick, James H., 2016. "On representation of temporal variability in electricity capacity planning models," Energy Economics, Elsevier, vol. 59(C), pages 261-274.
    7. Zachary A. Wendling & David C. Warren & Barry M. Rubin & Sanya Carley & Kenneth R. Richards, 2020. "A Scalable Energy–Economy Model for State-Level Policy Analysis Applied to a Demand-Side Management Program," Economic Development Quarterly, , vol. 34(4), pages 372-386, November.
    8. Mier, Mathias & Weissbart, Christoph, 2020. "Power markets in transition: Decarbonization, energy efficiency, and short-term demand response," Energy Economics, Elsevier, vol. 86(C).
    9. Kittel, Martin & Hobbie, Hannes & Dierstein, Constantin, 2022. "Temporal aggregation of time series to identify typical hourly electricity system states: A systematic assessment of relevant cluster algorithms," Energy, Elsevier, vol. 247(C).
    10. Jayadev, Gopika & Leibowicz, Benjamin D. & Kutanoglu, Erhan, 2020. "U.S. electricity infrastructure of the future: Generation and transmission pathways through 2050," Applied Energy, Elsevier, vol. 260(C).
    11. Krakowski, Vincent & Assoumou, Edi & Mazauric, Vincent & Maïzi, Nadia, 2016. "Reprint of Feasible path toward 40–100% renewable energy shares for power supply in France by 2050: A prospective analysis," Applied Energy, Elsevier, vol. 184(C), pages 1529-1550.
    12. Lion Hirth, 2015. "The Optimal Share of Variable Renewables: How the Variability of Wind and Solar Power affects their Welfare-optimal Deployment," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1).
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    16. Frew, Bethany A. & Becker, Sarah & Dvorak, Michael J. & Andresen, Gorm B. & Jacobson, Mark Z., 2016. "Flexibility mechanisms and pathways to a highly renewable US electricity future," Energy, Elsevier, vol. 101(C), pages 65-78.

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