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Using modeling to generate alternatives (MGA) to expand our thinking on energy futures

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  • DeCarolis, Joseph F.
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    Abstract

    Energy-economy optimization models - encoded with a set of structured, self-consistent assumptions and decision rules - have emerged as a key tool for the analysis of energy and climate policy at the national and international scale. Given the expansive system boundaries and multi-decadal timescales involved, addressing future uncertainty in these models is a critical challenge. The approach taken by many modelers is to build larger models with greater complexity to deal with structural uncertainty, and run a few highly detailed scenarios under different input assumptions to address parametric uncertainty. The result is often large and inflexible models used to conduct analysis that offers little insight. This paper introduces a technique borrowed from the operations research literature called modeling to generate alternatives (MGA) as a way to flex energy models and systematically explore the feasible, near-optimal solution space in order to develop alternatives that are maximally different in decision space but perform well with regard to the modeled objectives. The resultant MGA alternatives serve a useful role by challenging preconceptions and highlighting plausible alternative futures. A simple, conceptual model of the U.S. electric sector is presented to demonstrate the utility of MGA as an energy modeling technique.

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    Bibliographic Info

    Article provided by Elsevier in its journal Energy Economics.

    Volume (Year): 33 (2011)
    Issue (Month): 2 (March)
    Pages: 145-152

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    Handle: RePEc:eee:eneeco:v:33:y:2011:i:2:p:145-152

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    Web page: http://www.elsevier.com/locate/eneco

    Related research

    Keywords: Mathematical methods (JEL: C02) Optimization Uncertainty Modeling;

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    1. Edmonds, Jae & Clarke, John & Dooley, James & Kim, Son H. & Smith, Steven J., 2004. "Stabilization of CO2 in a B2 world: insights on the roles of carbon capture and disposal, hydrogen, and transportation technologies," Energy Economics, Elsevier, vol. 26(4), pages 517-537, July.
    2. Huntington, Hillard G & Weyant, John P & Sweeney, James L, 1982. "Modeling for insights, not numbers: the experiences of the energy modeling forum," Omega, Elsevier, vol. 10(5), pages 449-462.
    3. Greenblatt, Jeffery B. & Succar, Samir & Denkenberger, David C. & Williams, Robert H. & Socolow, Robert H., 2007. "Baseload wind energy: modeling the competition between gas turbines and compressed air energy storage for supplemental generation," Energy Policy, Elsevier, vol. 35(3), pages 1474-1492, March.
    4. Tschang, F. Ted & Dowlatabadi, Hadi, 1995. "A Bayesian technique for refining the uncertainty in global energy model forecasts," International Journal of Forecasting, Elsevier, vol. 11(1), pages 43-61, March.
    5. Peterson, Sonja, 2006. "Uncertainty and economic analysis of climate change : a survey of approaches and findings," Open Access publications from Kiel Institute for the World Economy info:hdl:10419/3778, Kiel Institute for the World Economy.
    6. DeCarolis, Joseph F. & Keith, David W., 2006. "The economics of large-scale wind power in a carbon constrained world," Energy Policy, Elsevier, vol. 34(4), pages 395-410, March.
    7. E. Downey Brill, Jr. & Shoou-Yuh Chang & Lewis D. Hopkins, 1982. "Modeling to Generate Alternatives: The HSJ Approach and an Illustration Using a Problem in Land Use Planning," Management Science, INFORMS, vol. 28(3), pages 221-235, March.
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