Operational Research

, Volume 17, Issue 2, pp 633–647 | Cite as

Coupling techno-economic energy models with behavioral approaches

  • Emmanuel Fragnière
  • Roman Kanala
  • Francesco Moresino
  • Adriana Reveiu
  • Ion Smeureanu
Original Paper

Abstract

Classical energy planning models assume that consumers are rational, which is obviously rarely the case. This paper proposes an original method to take into account the consumer’s real behavior in an energy model. This new hybrid model combines technical methods from operations research with behavioral approaches from social sciences and couples a classical energy model with a Share of Choice model.

Keywords

Consumer behavior Energy and environmental planning model Share of choice 

Mathematics Subject Classification

90 91F 

Supplementary material

12351_2016_246_MOESM1_ESM.dat (38 kb)
Supplementary material 1 (dat 38 KB)
12351_2016_246_MOESM2_ESM.mod (36 kb)
Supplementary material 2 (mod 37 KB)
12351_2016_246_MOESM3_ESM.run (16 kb)
Supplementary material 3 (run 17 KB)

References

  1. Abilock H, Fishbone LG (1979) User’s Guide for MARKAL (BNL Version). BNL 27075Google Scholar
  2. Agarwal J, Desarbo WS, Malhotra NK, Rao VR (2015) An interdisciplinary review of research in conjoint analysis: recent developments and directions for future research. Cust Needs Sol 2:19–40CrossRefGoogle Scholar
  3. Albers S, Brockhoff K (1977) A procedure for new product positioning in an attribute space. Eur J Oper Res 1(4):230–238CrossRefGoogle Scholar
  4. Amorim F, Pina A, Gerbelová H, da Silva PP, Vasconcelos J, Martins V (2014) Electricity decarbonisation pathways for 2050 in Portugal: a TIMES (the integrated MARKAL-EFOM system) based approach in closed versus open systems modelling. Energy 69:104–112CrossRefGoogle Scholar
  5. Arbuthnott KD (2009) Education for sustainable development beyond attitude change. Int J Sustain High Educ 10(2):152–163CrossRefGoogle Scholar
  6. Camm JD, Cochran JJ, Curry DJ, Kannan S (2006) Conjoint optimization: an exact branch-and-bound algorithm for the share-of-choice problem. Manag Sci 52(3):435–447CrossRefGoogle Scholar
  7. Cayla J-M, Maïzi N (2015) Integrating household behavior and heterogeneity into the times-households model. Appl Energy 139:56–67CrossRefGoogle Scholar
  8. Cho Y, Koo Y, Huh S-Y, Lee M (2015) Evaluation of a consumer incentive program for an energy-efficient product in South Korea. Energy Effic 8(4):745–757CrossRefGoogle Scholar
  9. Daly HE, Ramea K, Chiodi A, Yeh S, Gargiulo M (2014) Incorporating travel behaviour and travel time into TIMES energy system models. Appl Energy 135:429–439CrossRefGoogle Scholar
  10. Daly HE, Ramea K, Chiodi A, Yeh S, Gargiulo M, Ó Gallachóir B (2015) Modal shift of passenger transport in a TIMES model: application to Ireland and California, vol 30., Lecture notes in energySpringer, DavisGoogle Scholar
  11. Debreu G (1960) Topological methods in cardinal utility theory. In: Arrow KJ, Karlin S, Suppes P (eds) Mathematical methods in the social sciences, vol 73. University Press, Stanford, pp 16–26Google Scholar
  12. Fishbone LG, Abilock H (1981) MARKAL, a linear-programming model for energy systems analysis: technical description of the BNL version. Int J Energy Res 5(4):353–375CrossRefGoogle Scholar
  13. Fragnière E, Kanala R, Lavigne D, Moresino F, Nguene G (2010) Behavioral and technological changes regarding lighting consumptions: a MARKAL case study. Low Carbon Econ 1(1):8–17CrossRefGoogle Scholar
  14. Fragnière E, Lombardi A, Moresino F (2012) Designing and pricing services based on customer-perceived value: an airline company feasibility study. Serv Sci 4(4):320–330CrossRefGoogle Scholar
  15. Giraudet L-G, Guivarch C, Quirion P (2012) Exploring the potential for energy conservation in french households through hybrid modeling. Energy Econ 34(2):426–445CrossRefGoogle Scholar
  16. Gollwitzer PM, Sheeran P (2006) Implementation intentions and goal achievement: a meta-analysis of effects and processes. In: Advances in experimental social psychology. Academic Press, pp 69–119Google Scholar
  17. Green PE, Krieger AM, Agarwal MK (1993) A cross validation test of four models for quantifying multiattribute preferences. Market Lettrs 4(4):369–380CrossRefGoogle Scholar
  18. Green PE, Rao VR (1971) Conjoint measurement for quantifying judgmental data. J Market Res 8:355–363CrossRefGoogle Scholar
  19. Gruca TS, Klemz BR (2003) Optimal new product positioning: a genetic algorithm approach. Eur J Oper Res 146(3):621–633CrossRefGoogle Scholar
  20. Homburg C, Koschate N, Hoyer WD (2005) Do satisfied customers really pay more? A study of the relationship between customer satisfaction and willingness to pay. J Market 69(2):84–96CrossRefGoogle Scholar
  21. Howells M, Rogner H, Strachan N, Heaps C, Huntington H, Kypreos S, Hughes A, Silveira S, DeCarolis J, Bazillian M, Roehrl A (2011) OSeMOSYS: the open source energy modeling system: an introduction to its ethos, structure and development. Energy Policy 39(10):5850–5870CrossRefGoogle Scholar
  22. Jaffe AB, Stavins RN (1994) The energy paradox and the diffusion of conservation technology. Resour Energy Econ 16(2):91–122CrossRefGoogle Scholar
  23. Kannan R (2011) The development and application of a temporal MARKAL energy system model using flexible time slicing. Appl Energy 88(6):2261–2272CrossRefGoogle Scholar
  24. Kim J-Y, Natter M, Spann M (2009) Pay what you want: a new participative pricing mechanism. J Market 73(1):44–58CrossRefGoogle Scholar
  25. Leijten FRM, Bolderdijk JW, Keizer K, Gorsira M, van der Werff E, Steg L (2014) Factors that influence consumers acceptance of future energy systems: the effects of adjustment type, production level, and price. Energy Effic 7(6):973–985CrossRefGoogle Scholar
  26. Loulou R, Remme U, Kanudia A, Lehtila A, Goldstein G (2005) Documentation for the TIMES modelGoogle Scholar
  27. Luce RD, Tukey JW (1964) Simultaneous conjoint measurement: a new scale type of fundamental measurement. J Math Psychol 1(1):1–27CrossRefGoogle Scholar
  28. Manne A, Mendelsohn R, Richels R (1995) Merge: a model for evaluating regional and global effects of GHG reduction policies. Energy Policy 23(1):17–34CrossRefGoogle Scholar
  29. Maros I, Arabatzis G, Sifaleras A (2009) Special issue on optimization models in environment and sustainable development. Oper Res Int J 9(3):225–227CrossRefGoogle Scholar
  30. Murphy R, Jaccard M (2011) Energy efficiency and the cost of GHG abatement: a comparison of bottom-up and hybrid models for the US. Energy Policy 39(11):7146–7155CrossRefGoogle Scholar
  31. Nguene G, Fragnière E, Kanala R, Lavigne D, Moresino F (2011) Socio-MARKAL : integrating energy consumption behavioral changes in the technological optimisation framework. Energy Sustain Dev 15(1):73–83CrossRefGoogle Scholar
  32. Ramea K, Yang C, Yeh S, Bunch D, Ogden J (2013) Incorporation of consumer demand in energy systems models and their implications for climate policy analysis. International Energy Workshop 2013, ParisGoogle Scholar
  33. Rivers N, Jaccard M (2005) Combining top-down and bottom-up approaches to energy-economy modeling using discrete choice methods. Energy J 26(1):83–106CrossRefGoogle Scholar
  34. Sarbassov Y, Kerimray A, Tokmurzin D, Tosato G, Miglio RD (2013) Electricity and heating system in Kazakhstan: exploring energy efficiency improvement paths. Energy Policy 60:431–444CrossRefGoogle Scholar
  35. Shogren JF, Taylor LO (2008) On behavioral-environmental economics. Rev Environ Econ Policy 2(1):26–44CrossRefGoogle Scholar
  36. Srinivasan V, Shocker AD (1973) Linear programming techniques for multidimensional analysis of preferences. Psychometrika 38(3):337–369CrossRefGoogle Scholar
  37. Sundstrom E, Bell PA, Busby PL, Asmus C (1996) Environmental psychology 1989–1994. Annu Rev Psychol 47(1):485–512CrossRefGoogle Scholar
  38. Wang XJ, Camm JD, Curry DJ (2009) A branch-and-price approach to the share-of-choice product line design problem. Manag Sci 55(10):1718–1728CrossRefGoogle Scholar
  39. Welsch M, Deane P, Howells M, Gallachóir BÓ, Rogan F, Bazilian M, Rogner H-H (2014) Incorporating flexibility requirements into long-term energy system models a case study on high levels of renewable electricity penetration in Ireland. Appl Energy 135:600–615CrossRefGoogle Scholar
  40. Wu J, Zhu Q, Yin P, Song M (2015) Measuring energy and environmental performance for regions in China by using DEA-based Malmquist indices. Oper Res, 1–21Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Emmanuel Fragnière
    • 1
    • 2
  • Roman Kanala
    • 4
  • Francesco Moresino
    • 3
  • Adriana Reveiu
    • 5
  • Ion Smeureanu
    • 5
  1. 1.University of Bath, School of ManagementBathUK
  2. 2.University of Applied Sciences Western SwitzerlandSierreSwitzerland
  3. 3.University of Applied Sciences Western SwitzerlandGenevaSwitzerland
  4. 4.Université de GenèveGenevaSwitzerland
  5. 5.Bucharest University of Economic StudiesBucharestRomania

Personalised recommendations