IDEAS home Printed from https://ideas.repec.org/p/arx/papers/1912.03788.html
   My bibliography  Save this paper

Energy Scenario Exploration with Modeling to Generate Alternatives (MGA)

Author

Listed:
  • Joseph F. DeCarolis
  • Samaneh Babaee
  • Binghui Li
  • Suyash Kanungo

Abstract

Energy system optimization models (ESOMs) should be used in an interactive way to uncover knife-edge solutions, explore alternative system configurations, and suggest different ways to achieve policy objectives under conditions of deep uncertainty. In this paper, we do so by employing an existing optimization technique called modeling to generate alternatives (MGA), which involves a change in the model structure in order to systematically explore the near-optimal decision space. The MGA capability is incorporated into Tools for Energy Model Optimization and Analysis (Temoa), an open source framework that also includes a technology rich, bottom up ESOM. In this analysis, Temoa is used to explore alternative energy futures in a simplified single region energy system that represents the U.S. electric sector and a portion of the light duty transport sector. Given the dataset limitations, we place greater emphasis on the methodological approach rather than specific results.

Suggested Citation

  • Joseph F. DeCarolis & Samaneh Babaee & Binghui Li & Suyash Kanungo, 2019. "Energy Scenario Exploration with Modeling to Generate Alternatives (MGA)," Papers 1912.03788, arXiv.org.
    • Handle: RePEc:arx:papers:1912.03788
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/1912.03788
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Peterson, Scott B. & Michalek, Jeremy J., 2013. "Cost-effectiveness of plug-in hybrid electric vehicle battery capacity and charging infrastructure investment for reducing US gasoline consumption," Energy Policy, Elsevier, vol. 52(C), pages 429-438.
    2. Jon C. Liebman, 1976. "Some Simple-Minded Observations on the Role of Optimization in Public Systems Decision-Making," Interfaces, INFORMS, vol. 6(4), pages 102-108, August.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. Horne, Matt & Jaccard, Mark & Tiedemann, Ken, 2005. "Improving behavioral realism in hybrid energy-economy models using discrete choice studies of personal transportation decisions," Energy Economics, Elsevier, vol. 27(1), pages 59-77, January.
    8. Mau, Paulus & Eyzaguirre, Jimena & Jaccard, Mark & Collins-Dodd, Colleen & Tiedemann, Kenneth, 2008. "The 'neighbor effect': Simulating dynamics in consumer preferences for new vehicle technologies," Ecological Economics, Elsevier, vol. 68(1-2), pages 504-516, December.
    9. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Trutnevyte, Evelina, 2016. "Does cost optimization approximate the real-world energy transition?," Energy, Elsevier, vol. 106(C), pages 182-193.
    3. Finke, Jonas & Bertsch, Valentin, 2022. "Implementing a highly adaptable method for the multi-objective optimisation of energy systems," MPRA Paper 115504, University Library of Munich, Germany.
    4. Finke, Jonas & Bertsch, Valentin, 2023. "Implementing a highly adaptable method for the multi-objective optimisation of energy systems," Applied Energy, Elsevier, vol. 332(C).
    5. 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.
    6. Schwaeppe, Henrik & Thams, Marten Simon & Walter, Julian & Moser, Albert, 2024. "Finding better alternatives: Shadow prices of near-optimal solutions in energy system optimization modeling," Energy, Elsevier, vol. 292(C).
    7. Aditya Sinha & Aranya Venkatesh & Katherine Jordan & Cameron Wade & Hadi Eshraghi & Anderson R. Queiroz & Paulina Jaramillo & Jeremiah X. Johnson, 2024. "Diverse decarbonization pathways under near cost-optimal futures," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    8. Steve Pye & Chris Bataille, 2016. "Improving deep decarbonization modelling capacity for developed and developing country contexts," Climate Policy, Taylor & Francis Journals, vol. 16(sup1), pages 27-46, June.
    9. Rhodes, Ekaterina & Hoyle, Aaron & McPherson, Madeleine & Craig, Kira, 2022. "Understanding climate policy projections: A scoping review of energy-economy models in Canada," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    10. Axsen, Jonn & Mountain, Dean C. & Jaccard, Mark, 2009. "Combining stated and revealed choice research to simulate the neighbor effect: The case of hybrid-electric vehicles," Resource and Energy Economics, Elsevier, vol. 31(3), pages 221-238, August.
    11. Hackbarth, André & Madlener, Reinhard, 2016. "Willingness-to-pay for alternative fuel vehicle characteristics: A stated choice study for Germany," Transportation Research Part A: Policy and Practice, Elsevier, vol. 85(C), pages 89-111.
    12. Yue, Xiufeng & Deane, J.P. & O'Gallachoir, Brian & Rogan, Fionn, 2020. "Identifying decarbonisation opportunities using marginal abatement cost curves and energy system scenario ensembles," Applied Energy, Elsevier, vol. 276(C).
    13. Jan-Philipp Sasse & Evelina Trutnevyte, 2023. "A low-carbon electricity sector in Europe risks sustaining regional inequalities in benefits and vulnerabilities," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    14. Sasse, Jan-Philipp & Trutnevyte, Evelina, 2023. "Cost-effective options and regional interdependencies of reaching a low-carbon European electricity system in 2035," Energy, Elsevier, vol. 282(C).
    15. S R Kshirsagar & E D Brill Jr, 1984. "Ideation and Evaluation Methods Applied to Land-Use Planning," Environment and Planning B, , vol. 11(3), pages 313-324, September.
    16. Pfenninger, Stefan & DeCarolis, Joseph & Hirth, Lion & Quoilin, Sylvain & Staffell, Iain, 2017. "The importance of open data and software: Is energy research lagging behind?," Energy Policy, Elsevier, vol. 101(C), pages 211-215.
    17. Traut, Elizabeth & Hendrickson, Chris & Klampfl, Erica & Liu, Yimin & Michalek, Jeremy J., 2012. "Optimal design and allocation of electrified vehicles and dedicated charging infrastructure for minimum life cycle greenhouse gas emissions and cost," Energy Policy, Elsevier, vol. 51(C), pages 524-534.
    18. 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.
    19. Plazas-Niño, F.A. & Ortiz-Pimiento, N.R. & Montes-Páez, E.G., 2022. "National energy system optimization modelling for decarbonization pathways analysis: A systematic literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    20. S-Y Chang & S-L Liaw, 1984. "Evaluation of Methods for Generating Alternatives to Regional Wastewater Treatment Systems," Environment and Planning B, , vol. 11(3), pages 325-337, September.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:1912.03788. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.