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Energy transition in transportation under cost uncertainty- an assessment based on robust optimization

Author

Listed:
  • Claire Nicolas

    (EconomiX - EconomiX - UPN - Université Paris Nanterre - CNRS - Centre National de la Recherche Scientifique)

  • Stéphane Tchung-Ming
  • Emmanuel Hache

Abstract

To improve energy security and ensure the compliance with stringent climate goals, the European Union is willing to step up its efforts to accelerate the development and deployment of electrification, and in general, of alternative fuels and propulsion methods. Yet, the costs and benefits of imposing norms on vehicle or biofuel mandates should be assessed in light of the uncertainties surrounding these pathways, in terms of e.g. cost of these new technologies. By using robust optimization, we are able to introduce uncertainty simultaneously on a high number of cost parameters without notably impacting the computing time of our model (a French TIMES paradigm model). To account for the different nature of the uncertain parameters we model two kinds of uncertainty propagation with time. We then apply this formal setting to French energy system under carbon constraint. As uncertainty increases, as does technology diversification to hedge against it. In the transportation sector, low-carbon alternatives (CNG, electricity) appear consistently as hedges against cost variations, along with biofuels. Policy implications of diversification strategies are of importance; in that sense, the work undertaken here is a step towards the design of robust technology-oriented energy policies.

Suggested Citation

  • Claire Nicolas & Stéphane Tchung-Ming & Emmanuel Hache, 2016. "Energy transition in transportation under cost uncertainty- an assessment based on robust optimization," Working Papers hal-04141579, HAL.
  • Handle: RePEc:hal:wpaper:hal-04141579
    Note: View the original document on HAL open archive server: https://hal.science/hal-04141579
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    References listed on IDEAS

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    1. Levi, Peter G. & Pollitt, Michael G., 2015. "Cost trajectories of low carbon electricity generation technologies in the UK: A study of cost uncertainty," Energy Policy, Elsevier, vol. 87(C), pages 48-59.
    2. Schade, Burkhard & Wiesenthal, Tobias, 2011. "Biofuels: A model based assessment under uncertainty applying the Monte Carlo method," Journal of Policy Modeling, Elsevier, vol. 33(1), pages 92-126, January.
    3. Claire Nicolas & Valérie Saint-Antonin & Stéphane Tchung-Ming, 2014. "(How) does sectoral detail affect the robustness of policy insights from energy system models? The refining sector's example," Working Papers hal-02475035, HAL.
    4. Dimitris Bertsimas & Melvyn Sim, 2004. "The Price of Robustness," Operations Research, INFORMS, vol. 52(1), pages 35-53, February.
    5. Yeh, Sonia & Rubin, Edward S., 2012. "A review of uncertainties in technology experience curves," Energy Economics, Elsevier, vol. 34(3), pages 762-771.
    6. Menten, Fabio & Tchung-Ming, Stéphane & Lorne, Daphné & Bouvart, Frédérique, 2015. "Lessons from the use of a long-term energy model for consequential life cycle assessment: The BTL case," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 942-960.
    7. Papineau, Maya, 2006. "An economic perspective on experience curves and dynamic economies in renewable energy technologies," Energy Policy, Elsevier, vol. 34(4), pages 422-432, March.
    8. Dimitris Bertsimas & Aurélie Thiele, 2006. "A Robust Optimization Approach to Inventory Theory," Operations Research, INFORMS, vol. 54(1), pages 150-168, February.
    9. Elmar Kriegler & John Weyant & Geoffrey Blanford & Volker Krey & Leon Clarke & Jae Edmonds & Allen Fawcett & Gunnar Luderer & Keywan Riahi & Richard Richels & Steven Rose & Massimo Tavoni & Detlef Vuu, 2014. "The role of technology for achieving climate policy objectives: overview of the EMF 27 study on global technology and climate policy strategies," Climatic Change, Springer, vol. 123(3), pages 353-367, April.
    10. Rozakis, S. & Sourie, J. -C., 2005. "Micro-economic modelling of biofuel system in France to determine tax exemption policy under uncertainty," Energy Policy, Elsevier, vol. 33(2), pages 171-182, January.
    11. Dimitris Bertsimas & David B. Brown, 2009. "Constructing Uncertainty Sets for Robust Linear Optimization," Operations Research, INFORMS, vol. 57(6), pages 1483-1495, December.
    12. Karthik Natarajan & Dessislava Pachamanova & Melvyn Sim, 2009. "Constructing Risk Measures from Uncertainty Sets," Operations Research, INFORMS, vol. 57(5), pages 1129-1141, October.
    13. Claire Nicolas & Valérie Saint-Antonin & Stéphane Tchung-Ming, 2014. "(How) does sectoral detail affect the robustness of policy insights from energy system models? The refining sector’s example," Working Papers hal-04141280, HAL.
    14. Nathalie Alazard-Toux & Patrick Criqui & Jean-Guy Devezeaux de Lavergne & Sandrine Mathy & Philippe Menanteau, 2014. "Scénarios de l’Ancre pour la transition énergétique : rapport 2013," Working Papers hal-01849390, HAL.
    15. Poss, Michael, 2014. "Robust combinatorial optimization with variable cost uncertainty," European Journal of Operational Research, Elsevier, vol. 237(3), pages 836-845.
    16. Rubin, Edward S. & Azevedo, Inês M.L. & Jaramillo, Paulina & Yeh, Sonia, 2015. "A review of learning rates for electricity supply technologies," Energy Policy, Elsevier, vol. 86(C), pages 198-218.
    17. ,, 2000. "Problems And Solutions," Econometric Theory, Cambridge University Press, vol. 16(2), pages 287-299, April.
    18. Nathalie Alazard-Toux & Patrick Criqui & Jean-Guy Devezeaux de Lavergne & Laetitia Chevallet & Sylvie Gentier & Emmanuel Hache & Elisabeth Le Net & Philippe Menanteau & Frederic Thais, 2015. "Decarbonization Wedges report," Post-Print hal-01241910, HAL.
    19. Gritsevskyi, Andrii & Nakicenovi, Nebojsa, 2000. "Modeling uncertainty of induced technological change," Energy Policy, Elsevier, vol. 28(13), pages 907-921, November.
    20. A. L. Soyster, 1973. "Technical Note—Convex Programming with Set-Inclusive Constraints and Applications to Inexact Linear Programming," Operations Research, INFORMS, vol. 21(5), pages 1154-1157, October.
    21. Henri-David Waisman & Celine Guivarch & Franck Lecocq, 2013. "The transportation sector and low-carbon growth pathways: modelling urban, infrastructure, and spatial determinants of mobility," Climate Policy, Taylor & Francis Journals, vol. 13(sup01), pages 106-129, March.
    22. Patrick Criqui, 2015. "Decarbonization Wedges and the Electricity Sector," Post-Print hal-01849892, HAL.
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    More about this item

    Keywords

    Robust optimization; Climate change; Energy transition; Transportation policy.;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • R40 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - General

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