Skip to main content

Efficient Mechanism for Aggregate Demand Prediction in the Smart Grid

  • Conference paper
Multiagent System Technologies (MATES 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8076))

Included in the following conference series:

  • 1395 Accesses

Abstract

This paper studies an aggregate demand prediction problem relevant in smart grids. In our model, an aggregator agent is responsible for eliciting the demand forecasts of a number of self-interested home agents and purchasing electricity for them. Forecasts are given in form of probability distributions, and generating them incurs costs proportional to their precision. The paper presents a novel scoring rule based mechanism which not only makes the agents interested in reporting truthfully, but also inspires them to achieve the socially optimal forecast precision. Hence, the aggregator agent is then able to optimise the total expected cost of electricity supply. Therefore the mechanism becomes efficient, contrarily to prior works in this field. Empirical studies show that it is beneficial to join to the mechanism compared to purchasing electricity directly from the market, even if the mechanism consists only of a few agents.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Katz, R.H., et al.: An information-centric energy infrastructure: The Berkeley view. Sustainable Computing: Informatics and Systems 1(1), 7–22 (2011)

    Article  Google Scholar 

  2. Egri, P., Váncza, J.: A distributed coordination mechanism for supply networks with asymmetric information. Eur. J. of Op. Res. 226(3), 452–460 (2013)

    Article  Google Scholar 

  3. Apt, K.: A primer on strategic games. In: Apt, K.R., Graedel, E. (eds.) Lectures in Game Theory for Computer Scientists, pp. 1–37. Cambridge University Press (2011)

    Google Scholar 

  4. Zohar, A., Rosenschein, J.S.: Mechanisms for information elicitation. Artificial Intelligence 172(16-17), 1917–1939 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  5. Chen, Y., Pennock, D.M.: Designing markets for prediction. AI Magazine 31(4), 42–52 (2010)

    MathSciNet  Google Scholar 

  6. Papakonstantinou, A., Rogers, A., Gerding, E., Jennings, N.: Mechanism design for the truthful elicitation of costly probabilistic estimates in distributed information systems. Artificial Intelligence 175(2), 648–672 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  7. Blumsack, S., Fernandez, A.: Ready or not, here comes the smart grid! Energy 37(1), 61–68 (2012)

    Article  Google Scholar 

  8. Jackson, J.: Improving energy efficiency and smart grid program analysis with agent-based end-use forecasting models. Energy Policy 38(7), 3771–3780 (2010)

    Article  Google Scholar 

  9. Rose, H., Rogers, A., Gerding, E.H.: A scoring rule-based mechanism for aggregate demand prediction in the smart grid. In: 11th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2012), pp. 661–668 (2012)

    Google Scholar 

  10. Egri, P., Váncza, J.: Supply network coordination by vendor managed inventory – a mechanism design approach. In: Proc. of the 2nd Workshop on Artificial Intelligence and Logistics (AILog 2011), 22nd International Joint Conference on Artificial Intelligence (IJCAI), pp. 19–24 (2011)

    Google Scholar 

  11. Qin, Y., Wang, R., Vakharia, A.J., Chen, Y., Seref, M.M.: The newsvendor problem: Review and directions for future research. European Journal of Operational Research 213(2), 361–374 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  12. Ketzenberg, M.E., Rosenzweig, E.D., Marucheck, A.E., Metters, R.D.: A framework for the value of information in inventory replenishment. European Journal of Operational Research 182(3), 1230–1250 (2007)

    Article  MATH  Google Scholar 

  13. Oliveira, F.S., Ruiz, C., Conejo, A.J.: Contract design and supply chain coordination in the electricity industry. Eur. J. of Op. Res. 227(3), 527–537 (2013)

    Article  MathSciNet  Google Scholar 

  14. Chalkiadakis, G., Robu, V., Kota, R., Rogers, A., Jennings, N.: Cooperatives of distributed energy resources for efficient virtual power plants. In: 10th Int, Conf. on Autonomous Agents and Multiagent Systems (AAMAS 2011), pp. 787–794 (2011)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Egri, P., Váncza, J. (2013). Efficient Mechanism for Aggregate Demand Prediction in the Smart Grid. In: Klusch, M., Thimm, M., Paprzycki, M. (eds) Multiagent System Technologies. MATES 2013. Lecture Notes in Computer Science(), vol 8076. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40776-5_22

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-40776-5_22

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40775-8

  • Online ISBN: 978-3-642-40776-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics