Skip to main content

Consumer Participation in Demand Response Programs: Development of a Consumat-Based Toy Model

  • Conference paper
  • First Online:
  • 523 Accesses

Part of the book series: Springer Proceedings in Complexity ((SPCOM))

Abstract

Modeling of the smart grid architecture and its subsystems is a basic requirement for the success of these new technologies to address climate change effects. For a comprehensive research especially on effects of demand response systems, the integration of consumers’ decisions and interactions is essential. To model consumer participation in demand response programs this paper introduces an agent-based approach using the Consumat framework. The implementation in NetLogo provides high scalability and flexibility concerning input parameters and can easily interact with other simulation frameworks. It also forms a possible basis for an overall demand response consumer model. As a so-called toy model, simple correlations in this socio-technical scenario can already be explored.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. European Commission: COM(2014) 15 final: A policy framework for climate and energy in the period from 2020 to 2030, no. 2014, pp. 1–18 (2012)

    Google Scholar 

  2. Federal Energy Regulatory Commission: Assessment of Demand Response & Advanced Metering (2006)

    Google Scholar 

  3. Comstock, O.: Demand response saves electricity during times of high demand (2016). https://www.eia.gov/todayinenergy/detail.php?id=24872. Accessed 10 Jun 2020

  4. Schwarzer, J., Kiefel, A., Engel, D.: The role of user interaction and acceptance in a cloud-based demand response model. In: IECON Proceedings (Industrial Electronics Conference) (2013)

    Google Scholar 

  5. Miller, M.Z., Griendling, K., Mavris, D.N.: Exploring human factors effects in the Smart Grid system of systems Demand Response. In: 2012 7th International Conference on System of Systems Engineering (SoSE), pp. 1–6 (2012)

    Google Scholar 

  6. Schwarzer, J., Engel, D., Lehnhoff, S.: Conceptual design of an agent-based socio-technical demand response consumer model. In: International Conference on Industrial Informatics, pp. 680–685 (2018)

    Google Scholar 

  7. Moglia, M., Cook, S., McGregor, J.: A review of agent-based modelling of technology diffusion with special reference to residential energy efficiency. Sustain. Cities Soc. 31, 173–182 (2017)

    Article  Google Scholar 

  8. Bonabeau, E.: Agent-Based Modeling: Methods and Techniques for Simulating Human Systems, vol. 99, pp. 7280–7287 (2002)

    Google Scholar 

  9. Le Page, C., Bazile, D., Becu, N., Bommel, P.: Agent-based modelling and simulation applied to environmental management. In: Edmonds, B., Meyer, R. (Eds.) Simulating Social Complexity. Springer (2013)

    Google Scholar 

  10. Schwarzer, J., Engel, D.: Agent-based modeling of consumer participation in demand response programs with the consumat framework. In: Abstracts from the 9th DACH+ Conference on Energy Informatics, vol. 3, no. 27, pp. 13–15 (2020)

    Google Scholar 

  11. Jager, W., Janssen, M.A., Vlek, C.A.J.: Consumats in a commons dilemma: testing the behavioural rules of simulated consumers (1999)

    Google Scholar 

  12. Schaat, S., Jager, W., Dickert, S.: Psychologically plausible models in agent-based simulations of sustainable behavior. In: Alonso-Betanzos, A., Sánchez-Maroño, N., Fontenla-Romero, O., Polhill, J.G., Craig, T., Bajo, J., Corchado, J.M. (eds.) Agent-Based Modeling of Sustainable Behaviors, pp. 1–25. Springer International Publishing, Cham (2017)

    Google Scholar 

  13. Moglia, M., Podkalicka, A., Mcgregor, J.: An agent-based model of residential energy efficiency adoption. J. Artif. Soc. Soc. Simul. 21(3), 26 (2018)

    Article  Google Scholar 

  14. Janssen, M.A., Jager, W.: Stimulating diffusion of green products—co-evolution between firms and consumers. J. Evol. Econ. 12(3), 283–306 (2002)

    Article  Google Scholar 

  15. Vardakas, J.S., Zorba, N., Verikoukis, C.V.: A survey on demand response programs in smart grids: pricing methods and optimization algorithms. IEEE Commun. Surv. Tutorials 17(1), 152–178 (2015)

    Article  Google Scholar 

  16. Kim, H., Kim, Y.J., Yang, K., Thottan, M.: Cloud-based demand response for smart grid: Architecture and distributed algorithms. In: 2011 IEEE International Conference on Smart Grid Communication, pp. 398–403 (2011)

    Google Scholar 

  17. Barbato, A., Capone, A., Carello, G., Delfanti, M., Merlo, M., Zaminga, A.: House energy demand optimization in single and multi-user scenarios. In: 2011 IEEE International Conference on Smart Grid Communication 2011, pp. 345–350 (2011)

    Google Scholar 

  18. Schwarzer, J., Engel, D.: Evaluation of data communication requirements for common demand response models. Proc. IEEE Int. Conf. Ind. Technol. (ICIT) 2015, 1311–1316 (2015)

    Google Scholar 

  19. Li, N., Chen, L., Low, S.H.: Optimal demand response based on utility maximization in power networks. IEEE Power Energy Society General Meeting (2011)

    Google Scholar 

  20. Seetharam, D., Bapat, T., Sengupta, N., Ghai, S.K., Shrinivasan, Y.B., Arya, V.: User-sensitive scheduling of home appliances, p. 43 (2011)

    Google Scholar 

  21. Adika, C.W.L.: Autonomous appliance scheduling for household energy management. IEEE Trans. Smart Grid 5(2), 673–682 (2014)

    Article  Google Scholar 

  22. Barbato, A., Capone, A., Rodolfi, M., Tagliaferri, D.: Forecasting the usage of household appliances through power meter sensors for demand management in the smart grid. In: 2011 IEEE International Conference on Smart Grid Communication, pp. 404–409 (2011)

    Google Scholar 

  23. Bandini, S., Manzoni, S., Vizzari, G.: Agent based modeling and simulation: an informatics perspective. J. Artif. Soc. Soc. Simul. 12(4), 4 (2009)

    Google Scholar 

  24. Janssen, M., Ostrom, E.: Empirically based, agent-based models. Ecol. Soc. 11(2) (2006)

    Google Scholar 

  25. Balke, T., Gilbert, N.: How do agents make decisions ? A survey introduction : purpose & goals dimensions of comparison production rule systems, vol. 17, no. 2014, pp. 1–30 (2014)

    Google Scholar 

  26. Jager, W.: Modelling consumer behavior (2000)

    Google Scholar 

  27. Jager, W., Janssen, M.A., De Vries, H.J.M., De Greef, J., Vlek, C.A.J.: Behaviour in commons dilemmas: homo economicus and Homo psychologicus in an ecological-economic model. Ecol. Econ. 35(3), 357–379 (2000)

    Article  Google Scholar 

  28. Jager, W., Janssen, M.: An updated conceptual framework for integrated modeling of human decision making: the Consumat II. ECCS 2012, 10 (2012)

    Google Scholar 

  29. Bravo, G., Vallino, E., Cerutti, A.K., Pairotti, M.B.: Alternative scenarios of green consumption in Italy: an empirically grounded model. Environ. Model. Softw. 47(256), 225–234 (2013)

    Article  Google Scholar 

  30. Natalini, D., Bravo, G.: Encouraging sustainable transport choices in American households: results from an empirically grounded agent-based model. Sustain. 6(1), 50–69 (2014)

    Article  Google Scholar 

  31. Müller, B., et al.: Describing human decisions in agent-based models—ODD+D, an extension of the ODD protocol. Environ. Model. Softw. 48, 37–48 (2013)

    Article  Google Scholar 

  32. Schwarzer, J., Engel, D., Lehnhoff, S.: Conceptual design of an agent-based socio-technical demand response consumer model. In: Proceedings—IEEE 16th International Conference on Industrial Informatics, INDIN 2018 (2018)

    Google Scholar 

  33. Fredersdorf, F., Schwarzer, J., Engel, D.: Die Sicht der Endanwender im Smart Meter Datenschutz. Datenschutz und Datensicherheit - DuD 39(10), 682–686 (2015)

    Article  Google Scholar 

  34. Janssen, M., Jager, W.: Lakeland 2 (2017). https://www.comses.net/codebases/5793/releases/1.0.0/%0A

  35. Bergman, N., Haxeltine, A., Whitmarsh, L., Köhler, J., Schilperoord, M., Rotmans, J.: Modelling socio-technical transition patterns and pathways. J. Artif. Soc. Soc. Simul. 11(3), 7 (2008)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Judith Schwarzer .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Schwarzer, J., Engel, D. (2022). Consumer Participation in Demand Response Programs: Development of a Consumat-Based Toy Model. In: Czupryna, M., Kamiński, B. (eds) Advances in Social Simulation. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-030-92843-8_24

Download citation

Publish with us

Policies and ethics