IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i24p6674-d463755.html
   My bibliography  Save this article

Modelling End-User Behavior and Behavioral Change in Smart Grids. An Application of the Model of Frame Selection

Author

Listed:
  • Sebastian Hoffmann

    (Technology Studies Group, Faculty of Social Sciences, TU Dortmund University, 44227 Dortmund, Germany)

  • Fabian Adelt

    (Technology Studies Group, Faculty of Social Sciences, TU Dortmund University, 44227 Dortmund, Germany)

  • Johannes Weyer

    (Technology Studies Group, Faculty of Social Sciences, TU Dortmund University, 44227 Dortmund, Germany)

Abstract

This paper presents an agent-based model (ABM) for residential end-users, which is part of a larger, interdisciplinary co-simulation framework that helps to investigate the performance of future power distribution grids (i.e., smart grid scenarios). Different modes of governance (strong, soft and self-organization) as well as end-users’ heterogeneous behavior represent key influential factors. Feedback was implemented as a measure to foster grid-beneficial behavior, which encompasses a range of monetary and non-monetary incentives (e.g., via social comparison). The model of frame selection (MFS) serves as theoretical background for modelling end-users’ decision-making. Additionally, we conducted an online survey to ground the end-user sub-model on empirical data. Despite these empirical and theoretical foundations, the model presented should be viewed as a conceptual framework, which requires further data collection. Using an example scenario, representing a lowly populated residential area (167 households) with a high share of photovoltaic systems (30%), different modes of governance were compared with regard to their suitability for improving system stability (measured in cumulated load). Both soft and strong control were able to decrease overall fluctuations as well as the mean cumulated load (by approx. 10%, based on weekly observation). However, we argue that soft control could be sufficient and more societally desirable.

Suggested Citation

  • Sebastian Hoffmann & Fabian Adelt & Johannes Weyer, 2020. "Modelling End-User Behavior and Behavioral Change in Smart Grids. An Application of the Model of Frame Selection," Energies, MDPI, vol. 13(24), pages 1-26, December.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:24:p:6674-:d:463755
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/24/6674/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/13/24/6674/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jager, Wander, 2006. "Stimulating the diffusion of photovoltaic systems: A behavioural perspective," Energy Policy, Elsevier, vol. 34(14), pages 1935-1943, September.
    2. Wander Jager & Marco A. Janssen, 2002. "Stimulating diffusion of green products," Journal of Evolutionary Economics, Springer, vol. 12(3), pages 283-306.
    3. Stavrakas, Vassilis & Papadelis, Sotiris & Flamos, Alexandros, 2019. "An agent-based model to simulate technology adoption quantifying behavioural uncertainty of consumers," Applied Energy, Elsevier, vol. 255(C).
    4. Nicholas M. Gotts & J. Gareth Polhill, 2017. "Experiments with a Model of Domestic Energy Demand," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 20(3), pages 1-11.
    5. Clemens Kroneberg & Meir Yaish & Volker Stocké, 2010. "Norms and Rationality in Electoral Participation and in the Rescue of Jews in WWII," Rationality and Society, , vol. 22(1), pages 3-36, February.
    6. Behrangrad, Mahdi, 2015. "A review of demand side management business models in the electricity market," Renewable and Sustainable Energy Reviews, Elsevier, vol. 47(C), pages 270-283.
    7. Elinor Ostrom, 2010. "Beyond Markets and States: Polycentric Governance of Complex Economic Systems," American Economic Review, American Economic Association, vol. 100(3), pages 641-672, June.
    8. Friedrich Krebs, 2017. "An Empirically Grounded Model of Green Electricity Adoption in Germany: Calibration, Validation and Insights into Patterns of Diffusion," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 20(2), pages 1-10.
    9. Julija Vasiljevska & Jochem Douw & Anna Mengolini & Igor Nikolic, 2017. "An Agent-Based Model of Electricity Consumer: Smart Metering Policy Implications in Europe," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 20(1), pages 1-12.
    10. Hartmut Esser, 1993. "The Rationality of Everyday Behavior," Rationality and Society, , vol. 5(1), pages 7-31, January.
    11. Fabian Adelt & Johannes Weyer & Sebastian Hoffmann & Andreas Ihrig, 2018. "Simulation of the Governance of Complex Systems (SimCo): Basic Concepts and Experiments on Urban Transportation," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 21(2), pages 1-2.
    12. Hargreaves, Tom & Nye, Michael & Burgess, Jacquelin, 2013. "Keeping energy visible? Exploring how householders interact with feedback from smart energy monitors in the longer term," Energy Policy, Elsevier, vol. 52(C), pages 126-134.
    13. Balcombe, Paul & Rigby, Dan & Azapagic, Adisa, 2015. "Environmental impacts of microgeneration: Integrating solar PV, Stirling engine CHP and battery storage," Applied Energy, Elsevier, vol. 139(C), pages 245-259.
    14. Francesco Pasimeni, 2019. "Community-Based Adoption and Diffusion of Micro-Grids: Analysis of the Italian Case with Agent-Based Model," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 22(1), pages 1-11.
    15. 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.
    16. Han, Q. & Nieuwenhijsen, I. & de Vries, B. & Blokhuis, E. & Schaefer, W., 2013. "Intervention strategy to stimulate energy-saving behavior of local residents," Energy Policy, Elsevier, vol. 52(C), pages 706-715.
    17. Reis, Inês F.G. & Gonçalves, Ivo & Lopes, Marta A.R. & Antunes, Carlos Henggeler, 2020. "A multi-agent system approach to exploit demand-side flexibility in an energy community," Utilities Policy, Elsevier, vol. 67(C).
    18. Hu, Maomao & Xiao, Fu & Wang, Shengwei, 2021. "Neighborhood-level coordination and negotiation techniques for managing demand-side flexibility in residential microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    19. Broman Toft, Madeleine & Schuitema, Geertje & Thøgersen, John, 2014. "Responsible technology acceptance: Model development and application to consumer acceptance of Smart Grid technology," Applied Energy, Elsevier, vol. 134(C), pages 392-400.
    20. Thomas Hoppe & Gerdien De Vries, 2018. "Social Innovation and the Energy Transition," Sustainability, MDPI, vol. 11(1), pages 1-13, December.
    21. Anderson, Kyle & Lee, SangHyun, 2016. "An empirically grounded model for simulating normative energy use feedback interventions," Applied Energy, Elsevier, vol. 173(C), pages 272-282.
    22. Lutz, Lotte Marie & Fischer, Lisa-Britt & Newig, Jens & Lang, Daniel Johannes, 2017. "Driving factors for the regional implementation of renewable energy ‐ A multiple case study on the German energy transition," Energy Policy, Elsevier, vol. 105(C), pages 136-147.
    23. Abrahamse, Wokje & Steg, Linda, 2009. "How do socio-demographic and psychological factors relate to households' direct and indirect energy use and savings?," Journal of Economic Psychology, Elsevier, vol. 30(5), pages 711-720, October.
    24. McKenna, Russell, 2018. "The double-edged sword of decentralized energy autonomy," Energy Policy, Elsevier, vol. 113(C), pages 747-750.
    25. Parrish, Bryony & Heptonstall, Phil & Gross, Rob & Sovacool, Benjamin K., 2020. "A systematic review of motivations, enablers and barriers for consumer engagement with residential demand response," Energy Policy, Elsevier, vol. 138(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Niamir, Leila & Filatova, Tatiana & Voinov, Alexey & Bressers, Hans, 2018. "Transition to low-carbon economy: Assessing cumulative impacts of individual behavioral changes," Energy Policy, Elsevier, vol. 118(C), pages 325-345.
    2. Véronique Vasseur & Anne-Francoise Marique & Vladimir Udalov, 2019. "A Conceptual Framework to Understand Households’ Energy Consumption," Energies, MDPI, vol. 12(22), pages 1-22, November.
    3. Liu, Chang & Lin, Boqiang, 2020. "Is increasing-block electricity pricing effectively carried out in China? A case study in Shanghai and Shenzhen," Energy Policy, Elsevier, vol. 138(C).
    4. Anna Borawska & Mariusz Borawski & Małgorzata Łatuszyńska, 2022. "Effectiveness of Electricity-Saving Communication Campaigns: Neurophysiological Approach," Energies, MDPI, vol. 15(4), pages 1-19, February.
    5. Victor Fernández-Guzmán & Edgardo R. Bravo, 2018. "Understanding Continuance Usage of Natural Gas: A Theoretical Model and Empirical Evaluation," Energies, MDPI, vol. 11(8), pages 1-17, August.
    6. Penelope Buckley, 2020. "Prices, information and nudges for residential electricity conservation : A meta-analysis," Post-Print hal-02500507, HAL.
    7. Osunmuyiwa, Olufolahan O. & Peacock, Andrew D. & Payne, Sarah R. & Vigneswara Ilavarasan, P. & Jenkins, David P., 2021. "Divergent imaginaries? Co-producing practitioner and householder perspective to cooling demand response in India," Energy Policy, Elsevier, vol. 152(C).
    8. Ting Yue & Ruyin Long & Junli Liu & Haiwen Liu & Hong Chen, 2019. "Empirical Study on Households’ Energy-Conservation Behavior of Jiangsu Province in China: The Role of Policies and Behavior Results," IJERPH, MDPI, vol. 16(6), pages 1-16, March.
    9. Kendel, Adnane & Lazaric, Nathalie & Maréchal, Kevin, 2017. "What do people ‘learn by looking’ at direct feedback on their energy consumption? Results of a field study in Southern France," Energy Policy, Elsevier, vol. 108(C), pages 593-605.
    10. Bert Willems & Juulia Zhou, 2020. "The Clean Energy Package and Demand Response: Setting Correct Incentives," Energies, MDPI, vol. 13(21), pages 1-19, October.
    11. Lo Piano, S. & Smith, S.T., 2022. "Energy demand and its temporal flexibility: Approaches, criticalities and ways forward," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    12. Wallis, Hannah & Nachreiner, Malte & Matthies, Ellen, 2016. "Adolescents and electricity consumption; Investigating sociodemographic, economic, and behavioural influences on electricity consumption in households," Energy Policy, Elsevier, vol. 94(C), pages 224-234.
    13. Fitiwi, Desta Z. & Lynch, Muireann & Bertsch, Valentin, 2020. "Power system impacts of community acceptance policies for renewable energy deployment under storage cost uncertainty," Renewable Energy, Elsevier, vol. 156(C), pages 893-912.
    14. Zbigniew Bohdanowicz & Beata Łopaciuk-Gonczaryk & Jarosław Kowalski & Cezary Biele, 2021. "Households’ Electrical Energy Conservation and Management: An Ecological Break-Through, or the Same Old Consumption-Growth Path?," Energies, MDPI, vol. 14(20), pages 1-21, October.
    15. Schumacher, K. & Krones, F. & McKenna, R. & Schultmann, F., 2019. "Public acceptance of renewable energies and energy autonomy: A comparative study in the French, German and Swiss Upper Rhine region," Energy Policy, Elsevier, vol. 126(C), pages 315-332.
    16. Ellabban, Omar & Abu-Rub, Haitham, 2016. "Smart grid customers' acceptance and engagement: An overview," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 1285-1298.
    17. Quaglione, Davide & Cassetta, Ernesto & Crociata, Alessandro & Sarra, Alessandro, 2017. "Exploring additional determinants of energy-saving behaviour: The influence of individuals' participation in cultural activities," Energy Policy, Elsevier, vol. 108(C), pages 503-511.
    18. Guntram Pressmair & Christof Amann & Klemens Leutgöb, 2021. "Business Models for Demand Response: Exploring the Economic Limits for Small- and Medium-Sized Prosumers," Energies, MDPI, vol. 14(21), pages 1-28, October.
    19. Batalla-Bejerano, Joan & Trujillo-Baute, Elisa & Villa-Arrieta, Manuel, 2020. "Smart meters and consumer behaviour: Insights from the empirical literature," Energy Policy, Elsevier, vol. 144(C).
    20. Weinand, J.M. & McKenna, R. & Fichtner, W., 2019. "Developing a municipality typology for modelling decentralised energy systems," Utilities Policy, Elsevier, vol. 57(C), pages 75-96.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:13:y:2020:i:24:p:6674-:d:463755. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.