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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Katz, R.H., et al.: An information-centric energy infrastructure: The Berkeley view. Sustainable Computing: Informatics and Systems 1(1), 7–22 (2011)
Egri, P., Váncza, J.: A distributed coordination mechanism for supply networks with asymmetric information. Eur. J. of Op. Res. 226(3), 452–460 (2013)
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)
Zohar, A., Rosenschein, J.S.: Mechanisms for information elicitation. Artificial Intelligence 172(16-17), 1917–1939 (2008)
Chen, Y., Pennock, D.M.: Designing markets for prediction. AI Magazine 31(4), 42–52 (2010)
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)
Blumsack, S., Fernandez, A.: Ready or not, here comes the smart grid! Energy 37(1), 61–68 (2012)
Jackson, J.: Improving energy efficiency and smart grid program analysis with agent-based end-use forecasting models. Energy Policy 38(7), 3771–3780 (2010)
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)
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)
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)
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)
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)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights 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)