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The impacts of meeting a tight CO2 performance standard on the electric power sector

Author

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  • Hanson, Donald
  • Schmalzer, David
  • Nichols, Christopher
  • Balash, Peter

Abstract

This paper presents innovative modeling of complex interactions among gas-fired generators, coal-fired power plants, and renewables (wind and solar) when pushed hard to reduce CO2 emissions. A hypothetical CO2 technology performance standard, giving rise to a shadow price on CO2 emissions, was specified as part of the study design. In this work we see gas generation rapidly replacing coal generation. To understand the fate of coal-based generation, it is important to examine trends at a granular level. An important feature of our model, the Electricity Supply and Investment Model (ESIM) is that it contains a unit inventory with unit characteristics and a memory of how each unit is operated over time. Cycling damages that individual coal units incur are a function of cumulative wear and tear over time. The expected remaining life of a cycled coal unit will depend on the severity of the cycling and for how many years. Deteriorating operating characteristics of a cycled unit over time results in higher operating costs, slipping down the dispatch loading order, and hence an acceleration of cycling damage, that is, a viscous circle of decline. The rate of CFPP retirements will increase for lower gas prices, higher price on CO2 emissions, and greater penetration of variable and intermittent renewables.

Suggested Citation

  • Hanson, Donald & Schmalzer, David & Nichols, Christopher & Balash, Peter, 2016. "The impacts of meeting a tight CO2 performance standard on the electric power sector," Energy Economics, Elsevier, vol. 60(C), pages 476-485.
  • Handle: RePEc:eee:eneeco:v:60:y:2016:i:c:p:476-485
    DOI: 10.1016/j.eneco.2016.08.018
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    References listed on IDEAS

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    1. Donald Hanson & David Schmalzer, 2013. "An adoption scenario for carbon capture in pulverized coal power plants in the USA," Greenhouse Gases: Science and Technology, Blackwell Publishing, vol. 3(4), pages 303-308, August.
    2. repec:aen:journl:ej37-3-hirth is not listed on IDEAS
    3. Michael G. Shelby & Allen A. Fawcett & O. Eric Smith & Donald A. Hanson & Ronald D. Sands, 2008. "Representing technology in CGE models: a comparison of SGM and AMIGA for electricity sector CO 2 mitigation," International Journal of Energy Technology and Policy, Inderscience Enterprises Ltd, vol. 6(4), pages 323-342.
    4. Donald A. Hanson & Yaroslav Kryukov & Sven Leyffer & Todd S. Munson, 2010. "Optimal control model of technology transition," International Journal of Global Energy Issues, Inderscience Enterprises Ltd, vol. 33(3/4), pages 154-175.
    5. Ueckerdt, Falko & Brecha, Robert & Luderer, Gunnar & Sullivan, Patrick & Schmid, Eva & Bauer, Nico & Böttger, Diana & Pietzcker, Robert, 2015. "Representing power sector variability and the integration of variable renewables in long-term energy-economy models using residual load duration curves," Energy, Elsevier, vol. 90(P2), pages 1799-1814.
    6. Edenhofer, Ottmar & Hirth, Lion & Knopf, Brigitte & Pahle, Michael & Schlömer, Steffen & Schmid, Eva & Ueckerdt, Falko, 2013. "On the economics of renewable energy sources," Energy Economics, Elsevier, vol. 40(S1), pages 12-23.
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    Cited by:

    1. Carlo Mari & Carlo Lucheroni & Nabangshu Sinha & Emiliano Mari, 2025. "Power System Portfolio Selection and CO 2 Emission Management Under Uncertainty Driven by a DNN-Based Stochastic Model," Mathematics, MDPI, vol. 13(9), pages 1-23, April.
    2. Carlo Mari, 2018. "CO 2 Price Volatility Effects on Optimal Power System Portfolios," Energies, MDPI, vol. 11(7), pages 1-18, July.
    3. Xu, Jin-Hua & Yi, Bo-Wen & Fan, Ying, 2020. "Economic viability and regulation effects of infrastructure investments for inter-regional electricity transmission and trade in China," Energy Economics, Elsevier, vol. 91(C).
    4. Spiller, Elisheba & Sopher, Peter & Martin, Nicholas & Mirzatuny, Marita & Zhang, Xinxing, 2017. "The environmental impacts of green technologies in TX," Energy Economics, Elsevier, vol. 68(C), pages 199-214.
    5. Carlo Mari, 2020. "Stochastic NPV Based vs Stochastic LCOE Based Power Portfolio Selection Under Uncertainty," Energies, MDPI, vol. 13(14), pages 1-18, July.

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    Keywords

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    JEL classification:

    • Q21 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Demand and Supply; Prices
    • Q28 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Government Policy
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

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