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A Behavioral Economics Approach to Residential Electricity Consumption

Author

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  • Luciano C. Siebert

    (Department of Electricity and Materials, Lactec Institutes, Curitiba 81530-180, Brazil
    Department of Electrical Engineering, Federal University of Parana, Curitiba 82590-300, Brazil)

  • Adriana Sbicca

    (Department of Economics, Federal University of Parana, Curitiba 80210-170, Brazil)

  • Alexandre Rasi Aoki

    (Department of Electricity and Materials, Lactec Institutes, Curitiba 81530-180, Brazil
    Department of Electrical Engineering, Federal University of Parana, Curitiba 82590-300, Brazil)

  • Germano Lambert-Torres

    (Gnarus Institute, Itajuba 37500-052, Brazil)

Abstract

Consumer behavior is complex and is difficult to represent in traditional economic theories of decision-making. This paper focuses on the development of an agent-based approach to analyze people’s behavior in consuming electricity using a behavioral economics framework, where the consumer is the main agent of power systems. This approach may bring useful insights for distribution companies and regulatory agencies, helping to shift thinking to a more user-centric approach. The emergent properties of electricity consumption are modeled by the means of consumer’s heuristics, taking into account the electricity price, consumer’s satisfaction level, willingness to invest in new technologies, social interactions, and marketing strategies by the power utility. Analysis on the emergent behavior of this approach through simulation studies showed that it is indeed valuable, as does not require in-depth data of all details on human behavior. However, it contributes to the understanding of relations among various objects involved in electricity consumption.

Suggested Citation

  • Luciano C. Siebert & Adriana Sbicca & Alexandre Rasi Aoki & Germano Lambert-Torres, 2017. "A Behavioral Economics Approach to Residential Electricity Consumption," Energies, MDPI, vol. 10(6), pages 1-18, June.
  • Handle: RePEc:gam:jeners:v:10:y:2017:i:6:p:768-:d:100259
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    as
    1. Tsvetanov, Tsvetan & Segerson, Kathleen, 2013. "Re-evaluating the role of energy efficiency standards: A behavioral economics approach," Journal of Environmental Economics and Management, Elsevier, vol. 66(2), pages 347-363.
    2. Michael G. Pollitt & Irina Shaorshadze, 2013. "The role of behavioural economics in energy and climate policy," Chapters, in: Roger Fouquet (ed.), Handbook on Energy and Climate Change, chapter 24, pages 523-546, Edward Elgar Publishing.
    3. Saber Talari & Miadreza Shafie-khah & Pierluigi Siano & Vincenzo Loia & Aurelio Tommasetti & João P. S. Catalão, 2017. "A Review of Smart Cities Based on the Internet of Things Concept," Energies, MDPI, vol. 10(4), pages 1-23, March.
    4. Frederiks, Elisha R. & Stenner, Karen & Hobman, Elizabeth V., 2015. "Household energy use: Applying behavioural economics to understand consumer decision-making and behaviour," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 1385-1394.
    5. Luca Ardito & Giuseppe Procaccianti & Giuseppe Menga & Maurizio Morisio, 2013. "Smart Grid Technologies in Europe: An Overview," Energies, MDPI, vol. 6(1), pages 1-31, January.
    6. Tesfatsion, Leigh & Judd, Kenneth L., 2006. "Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics," Staff General Research Papers Archive 10368, Iowa State University, Department of Economics.
    7. Tesfatsion, Leigh, 2006. "Agent-Based Computational Economics: A Constructive Approach to Economic Theory," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 16, pages 831-880, Elsevier.
    8. Herbert A. Simon, 1996. "The Sciences of the Artificial, 3rd Edition," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262691914, December.
    9. Poghosyan, Anush & Greetham, Danica Vukadinović & Haben, Stephen & Lee, Tamsin, 2015. "Long term individual load forecast under different electrical vehicles uptake scenarios," Applied Energy, Elsevier, vol. 157(C), pages 699-709.
    10. Jorge J. Gomez-Sanz & Sandra Garcia-Rodriguez & Nuria Cuartero-Soler & Luis Hernandez-Callejo, 2014. "Reviewing Microgrids from a Multi-Agent Systems Perspective," Energies, MDPI, vol. 7(5), pages 1-28, May.
    11. Herbert A. Simon, 1955. "A Behavioral Model of Rational Choice," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 69(1), pages 99-118.
    12. Lee, Timothy & Yao, Runming, 2013. "Incorporating technology buying behaviour into UK-based long term domestic stock energy models to provide improved policy analysis," Energy Policy, Elsevier, vol. 52(C), pages 363-372.
    13. Hao Bai & Shihong Miao & Xiaohong Ran & Chang Ye, 2015. "Optimal Dispatch Strategy of a Virtual Power Plant Containing Battery Switch Stations in a Unified Electricity Market," Energies, MDPI, vol. 8(3), pages 1-22, March.
    14. Leigh Tesfatsion & Kenneth L. Judd (ed.), 2006. "Handbook of Computational Economics," Handbook of Computational Economics, Elsevier, edition 1, volume 2, number 2.
    15. Peyman Mazidi & Yaser Tohidi & Miguel A. Sanz-Bobi, 2017. "Strategic Maintenance Scheduling of an Offshore Wind Farm in a Deregulated Power System," Energies, MDPI, vol. 10(3), pages 1-20, March.
    16. Simon, Herbert A, 1979. "Rational Decision Making in Business Organizations," American Economic Review, American Economic Association, vol. 69(4), pages 493-513, September.
    17. Lee, Timothy & Yao, Runming & Coker, Phil, 2014. "An analysis of UK policies for domestic energy reduction using an agent based tool," Energy Policy, Elsevier, vol. 66(C), pages 267-279.
    18. Soon-Ryul Nam & Sang-Hee Kang & Joo-Ho Lee & Eun-Jae Choi & Seon-Ju Ahn & Joon-Ho Choi, 2013. "EMS-Data-Based Load Modeling to Evaluate the Effect of Conservation Voltage Reduction at a National Level," Energies, MDPI, vol. 6(8), pages 1-14, July.
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    5. Luciano Cavalcante Siebert & Alexandre Rasi Aoki & Germano Lambert-Torres & Nelson Lambert-de-Andrade & Nikolaos G. Paterakis, 2020. "An Agent-Based Approach for the Planning of Distribution Grids as a Socio-Technical System," Energies, MDPI, vol. 13(18), pages 1-13, September.
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    8. Iztok Podbregar & Sanja Filipović & Mirjana Radovanović & Olga Mirković Isaeva & Polona Šprajc, 2021. "Electricity Prices and Consumer Behavior, Case Study Serbia—Randomized Control Trials Method," Energies, MDPI, vol. 14(3), pages 1-12, January.
    9. Mohseni, Soheil & Brent, Alan C. & Kelly, Scott & Browne, Will N., 2022. "Demand response-integrated investment and operational planning of renewable and sustainable energy systems considering forecast uncertainties: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
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