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


  • DeCarolis, Joseph F.


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.

Suggested Citation

  • DeCarolis, Joseph F., 2011. "Using modeling to generate alternatives (MGA) to expand our thinking on energy futures," Energy Economics, Elsevier, vol. 33(2), pages 145-152, March.
  • Handle: RePEc:eee:eneeco:v:33:y:2011:i:2:p:145-152

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    References listed on IDEAS

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    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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    Cited by:

    1. Dodds, Paul E., 2014. "Integrating housing stock and energy system models as a strategy to improve heat decarbonisation assessments," Applied Energy, Elsevier, vol. 132(C), pages 358-369.
    2. repec:eee:appene:v:195:y:2017:i:c:p:356-369 is not listed on IDEAS
    3. repec:eee:rensus:v:80:y:2017:i:c:p:1389-1398 is not listed on IDEAS
    4. Koppelaar, Rembrandt H.E.M. & Keirstead, James & Shah, Nilay & Woods, Jeremy, 2016. "A review of policy analysis purpose and capabilities of electricity system models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 1531-1544.
    5. Li, Francis G.N. & Trutnevyte, Evelina, 2017. "Investment appraisal of cost-optimal and near-optimal pathways for the UK electricity sector transition to 2050," Applied Energy, Elsevier, vol. 189(C), pages 89-109.
    6. DeCarolis, Joseph & Daly, Hannah & Dodds, Paul & Keppo, Ilkka & Li, Francis & McDowall, Will & Pye, Steve & Strachan, Neil & Trutnevyte, Evelina & Usher, Will & Winning, Matthew & Yeh, Sonia & Zeyring, 2017. "Formalizing best practice for energy system optimization modelling," Applied Energy, Elsevier, vol. 194(C), pages 184-198.
    7. Pfenninger, Stefan & Keirstead, James, 2015. "Renewables, nuclear, or fossil fuels? Scenarios for Great Britain’s power system considering costs, emissions and energy security," Applied Energy, Elsevier, vol. 152(C), pages 83-93.
    8. Merrick, James H., 2016. "On representation of temporal variability in electricity capacity planning models," Energy Economics, Elsevier, vol. 59(C), pages 261-274.
    9. repec:eee:energy:v:144:y:2018:i:c:p:1107-1118 is not listed on IDEAS
    10. Trutnevyte, Evelina, 2016. "Does cost optimization approximate the real-world energy transition?," Energy, Elsevier, vol. 106(C), pages 182-193.
    11. Trutnevyte, Evelina, 2013. "EXPANSE methodology for evaluating the economic potential of renewable energy from an energy mix perspective," Applied Energy, Elsevier, vol. 111(C), pages 593-601.
    12. Hunter, Kevin & Sreepathi, Sarat & DeCarolis, Joseph F., 2013. "Modeling for insight using Tools for Energy Model Optimization and Analysis (Temoa)," Energy Economics, Elsevier, vol. 40(C), pages 339-349.
    13. DeCarolis, Joseph F. & Hunter, Kevin & Sreepathi, Sarat, 2012. "The case for repeatable analysis with energy economy optimization models," Energy Economics, Elsevier, vol. 34(6), pages 1845-1853.
    14. Arie ten Cate, 2012. "The socially optimal energy transition in a residential neighbourhood in the Netherlands," CPB Discussion Paper 222, CPB Netherlands Bureau for Economic Policy Analysis.
    15. repec:eee:energy:v:126:y:2017:i:c:p:886-898 is not listed on IDEAS


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