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On choosing the resolution of normative models

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  • Merrick, James H.
  • Weyant, John P.

Abstract

Long time horizon normative models are frequently used for policy analysis, strategic planning, and system analysis. Choosing the granularity of the temporal or spatial resolution of such models is an important modeling decision, often having a first order impact on model results. This type of decision is frequently made by modeler judgment, particularly when the predictive power of alternative choices cannot be tested. In this paper, we show how the implicit tradeoffs modelers make in these formulation decisions, in particular in the tradeoff between the accuracy of representation enabled by the available data and model parsimony, may be addressed with established information theoretic ideas. The paper provides guidance for modelers making these tradeoffs or, in certain cases, enables explicit tests for assessing appropriate levels of resolution. We will mainly focus on optimization based normative models in the discussion here, and draw our examples from the energy and climate domain.

Suggested Citation

  • Merrick, James H. & Weyant, John P., 2019. "On choosing the resolution of normative models," European Journal of Operational Research, Elsevier, vol. 279(2), pages 511-523.
  • Handle: RePEc:eee:ejores:v:279:y:2019:i:2:p:511-523
    DOI: 10.1016/j.ejor.2019.06.017
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    as
    1. Thomas R. Willemain, 1995. "Model Formulation: What Experts Think About and When," Operations Research, INFORMS, vol. 43(6), pages 916-932, December.
    2. Dery, Richard & Landry, Maurice & Banville, Claude, 1993. "Revisiting the issue of model validation in OR: An epistemological view," European Journal of Operational Research, Elsevier, vol. 66(2), pages 168-183, April.
    3. Paul H. Zipkin, 1980. "Bounds on the Effect of Aggregating Variables in Linear Programs," Operations Research, INFORMS, vol. 28(2), pages 403-418, April.
    4. Gunnar Luderer & Volker Krey & Katherine Calvin & James Merrick & Silvana Mima & Robert Pietzcker & Jasper Vliet & Kenichi Wada, 2014. "The role of renewable energy in climate stabilization: results from the EMF27 scenarios," Climatic Change, Springer, vol. 123(3), pages 427-441, April.
    5. Gunnar Luderer & Volker Krey & Katherine Calvin & James Merrick & Silvana Mima & Robert Pietzcker & Jasper van Vliet & Kenichi Wada, 2014. "The role of renewable energy in climate stabilization: results from the EMF27 scenarios," Post-Print halshs-00961843, HAL.
    6. Merrick, James H., 2016. "On representation of temporal variability in electricity capacity planning models," Energy Economics, Elsevier, vol. 59(C), pages 261-274.
    7. Raj Chetty, 2009. "Sufficient Statistics for Welfare Analysis: A Bridge Between Structural and Reduced-Form Methods," Annual Review of Economics, Annual Reviews, vol. 1(1), pages 451-488, May.
    8. Ingmar Schumacher, 2018. "The Aggregation Dilemma In Climate Change Policy Evaluation," Climate Change Economics (CCE), World Scientific Publishing Co. Pte. Ltd., vol. 9(03), pages 1-20, August.
    9. Arthur M. Geoffrion, 1976. "The Purpose of Mathematical Programming is Insight, Not Numbers," Interfaces, INFORMS, vol. 7(1), pages 81-92, November.
    10. Frederic H. Murphy, 2005. "ASP, The Art and Science of Practice: Elements of a Theory of the Practice of Operations Research: Practice as a Business," Interfaces, INFORMS, vol. 35(6), pages 524-530, December.
    11. Frederic H. Murphy, 2005. "ASP, The Art and Science of Practice: Elements of a Theory of the Practice of Operations Research: A Framework," Interfaces, INFORMS, vol. 35(2), pages 154-163, April.
    12. Delavane B. Diaz, 2016. "Estimating global damages from sea level rise with the Coastal Impact and Adaptation Model (CIAM)," Climatic Change, Springer, vol. 137(1), pages 143-156, July.
    13. 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.
    14. Geoffrey J. Blanford, James H. Merrick, John E.T. Bistline, and David T. Young, 2018. "Simulating Annual Variation in Load, Wind, and Solar by Representative Hour Selection," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3).
    15. Frederic H. Murphy, 2005. "ASP, The Art and Science of Practice: Elements of a Theory of the Practice of Operations Research: Expertise in Practice," Interfaces, INFORMS, vol. 35(4), pages 313-322, August.
    16. Costas Arkolakis & Arnaud Costinot & Andres Rodriguez-Clare, 2012. "New Trade Models, Same Old Gains?," American Economic Review, American Economic Association, vol. 102(1), pages 94-130, February.
    17. Hämäläinen, Raimo P. & Luoma, Jukka & Saarinen, Esa, 2013. "On the importance of behavioral operational research: The case of understanding and communicating about dynamic systems," European Journal of Operational Research, Elsevier, vol. 228(3), pages 623-634.
    18. Landry, Maurice & Oral, Muhittin, 1993. "In search of a valid view of model validation for operations research," European Journal of Operational Research, Elsevier, vol. 66(2), pages 161-167, April.
    19. Michael Pidd, 1999. "Just Modeling Through: A Rough Guide to Modeling," Interfaces, INFORMS, vol. 29(2), pages 118-132, April.
    20. Paul H. Zipkin, 1980. "Bounds for Row-Aggregation in Linear Programming," Operations Research, INFORMS, vol. 28(4), pages 903-916, August.
    21. David F. Rogers & Robert D. Plante & Richard T. Wong & James R. Evans, 1991. "Aggregation and Disaggregation Techniques and Methodology in Optimization," Operations Research, INFORMS, vol. 39(4), pages 553-582, August.
    22. John Weyant, 2017. "Some Contributions of Integrated Assessment Models of Global Climate Change," Review of Environmental Economics and Policy, Association of Environmental and Resource Economists, vol. 11(1), pages 115-137.
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    2. James H. Merrick & John E. T. Bistline & Geoffrey J. Blanford, 2021. "On representation of energy storage in electricity planning models," Papers 2105.03707, arXiv.org, revised May 2021.
    3. Bistline, John & Blanford, Geoffrey & Mai, Trieu & Merrick, James, 2021. "Modeling variable renewable energy and storage in the power sector," Energy Policy, Elsevier, vol. 156(C).
    4. Franck Lecocq & Alain Nadaï & Christophe Cassen, 2022. "Getting models and modellers to inform deep decarbonization strategies," Climate Policy, Taylor & Francis Journals, vol. 22(6), pages 695-710, July.
    5. John E. T. Bistline & James Merrick & Victor Niemeyer, 2020. "Estimating Power Sector Leakage Risks and Provincial Impacts of Canadian Carbon Pricing," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 76(1), pages 91-118, May.
    6. Oriol Raventós & Julian Bartels, 2020. "Evaluation of Temporal Complexity Reduction Techniques Applied to Storage Expansion Planning in Power System Models," Energies, MDPI, vol. 13(4), pages 1-18, February.

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