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Combining Behavioral Approaches with Techno-Economic Energy Models: Dealing with the Coupling Non-Linearity Issue

Author

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  • Francesco Moresino

    (Geneva School of Business Administration, University of Applied Sciences Western Switzerland (HES-SO Genève), Carouge 1227, Switzerland)

  • Emmanuel Fragnière

    (Service Design Lab (IEM), University of Applied Sciences Western Switzerland (HES-SO Valais), Sierre 3960, Switzerland
    School of Management, University of Bath, Bath BA2 7AY, UK)

Abstract

Consumer behaviour is often complex and even sometimes not economically rational. Wrongly, the first techno-economic energy planning models assumed the economic rationality hypothesis and, therefore, represented consumers’ behaviour incorrectly. Nevertheless, the current trend is to couple these models with behavioural approaches that were specially developed to describe the real consumer choices. A novel approach was recently proposed, where a classical energy model is coupled with a share of choice model. This new approach has however two weaknesses. First, the share of choice increases the computational complexity as it necessitates additional binary variables for the modelling. Second, for complex models, the inclusion of the share of choice can lead to non-linearity and hence to severe computational problems. In the present paper, we propose to improve this method by externalizing the share of choice. Doing so, the number of binary variable will be reduced and the linearity property will be kept even for complex models.

Suggested Citation

  • Francesco Moresino & Emmanuel Fragnière, 2018. "Combining Behavioral Approaches with Techno-Economic Energy Models: Dealing with the Coupling Non-Linearity Issue," Energies, MDPI, vol. 11(7), pages 1-14, July.
  • Handle: RePEc:gam:jeners:v:11:y:2018:i:7:p:1787-:d:156808
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    Cited by:

    1. Francesco Moresino, 2021. "A Robust Share-of-Choice Model," Mathematics, MDPI, vol. 9(3), pages 1-10, February.
    2. Dennis Dreier & Mark Howells, 2019. "OSeMOSYS-PuLP: A Stochastic Modeling Framework for Long-Term Energy Systems Modeling," Energies, MDPI, vol. 12(7), pages 1-26, April.

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