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Electric sector investments under technological and policy-related uncertainties: a stochastic programming approach

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  • John Bistline
  • John Weyant

Abstract

Although emerging technologies like carbon capture and storage and advanced nuclear are expected to play leading roles in greenhouse gas mitigation efforts, many engineering and policy-related uncertainties will influence their deployment. Capital-intensive infrastructure decisions depend on understanding the likelihoods and impacts of uncertainties such as the timing and stringency of climate policy as well as the technological availability of carbon capture systems. This paper demonstrates the utility of stochastic programming approaches to uncertainty analysis within a practical policy setting, using uncertainties in the US electric sector as motivating examples. We describe the potential utility of this framework for energy-environmental decision making and use a modeling example to reinforce these points and to stress the need for new tools to better exploit the full range of benefits the stochastic programming approach can provide. Model results illustrate how this framework can give important insights about hedging strategies to reduce risks associated with high compliance costs for tight CO 2 caps and low CCS availability. Metrics for evaluating uncertainties like the expected value of perfect information and the value of the stochastic solution quantify the importance of including uncertainties in capacity planning, of making precautionary low-carbon investments, and of conducting research and gathering information to reduce risk. Copyright Springer Science+Business Media Dordrecht 2013

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  • John Bistline & John Weyant, 2013. "Electric sector investments under technological and policy-related uncertainties: a stochastic programming approach," Climatic Change, Springer, vol. 121(2), pages 143-160, November.
  • Handle: RePEc:spr:climat:v:121:y:2013:i:2:p:143-160
    DOI: 10.1007/s10584-013-0859-4
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    3. Delavane B. Diaz, 2015. "Integrated Assessment of Climate Catastrophes with Endogenous Uncertainty: Does the Risk of Ice Sheet Collapse Justify Precautionary Mitigation?," Working Papers 2015.64, Fondazione Eni Enrico Mattei.
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    6. Simon Parkinson & Ned Djilali, 2015. "Robust response to hydro-climatic change in electricity generation planning," Climatic Change, Springer, vol. 130(4), pages 475-489, June.
    7. Kang, Jidong & Ng, Tsan Sheng & Su, Bin & Milovanoff, Alexandre, 2021. "Electrifying light-duty passenger transport for CO2 emissions reduction: A stochastic-robust input–output linear programming model," Energy Economics, Elsevier, vol. 104(C).
    8. Bistline, John E., 2014. "Natural gas, uncertainty, and climate policy in the US electric power sector," Energy Policy, Elsevier, vol. 74(C), pages 433-442.
    9. Fodstad, Marte & Crespo del Granado, Pedro & Hellemo, Lars & Knudsen, Brage Rugstad & Pisciella, Paolo & Silvast, Antti & Bordin, Chiara & Schmidt, Sarah & Straus, Julian, 2022. "Next frontiers in energy system modelling: A review on challenges and the state of the art," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    10. Pizarro-Alonso, Amalia & Ravn, Hans & Münster, Marie, 2019. "Uncertainties towards a fossil-free system with high integration of wind energy in long-term planning," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    11. Hao Li & Ying Qiao & Zongxiang Lu & Baosen Zhang, 2022. "Power System Transition with Multiple Flexibility Resources: A Data-Driven Approach," Sustainability, MDPI, vol. 14(5), pages 1-25, February.
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    14. Nahmmacher, Paul & Schmid, Eva & Pahle, Michael & Knopf, Brigitte, 2016. "Strategies against shocks in power systems – An analysis for the case of Europe," Energy Economics, Elsevier, vol. 59(C), pages 455-465.

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