Abstract
Prediction-of-use (POU) games [14] address the mismatch between energy supplier costs and the incentives imposed on consumers by fixed-rate electricity tariffs. However, the framework does not address how consumers should coordinate to maximize social welfare. To address this, we develop multiple-profile prediction-of-use (MPOU) games, an extension of POU games in which agents report multiple acceptable electricity use profiles. We show that MPOU games share many attractive properties with POU games attractive (e.g., convexity). However, MPOU games introduce new incentive issues that limit our ability to exploit convexity effectively, a problem we analyze and resolve. We validate our approach with experimental results using utility models learned from real electricity use data.
C. Boutilier—Now at Google Research, Mountain View, CA.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Technically, they define the game as a cost game and show that the game is concave, while we use a profit game, but results from the two perspectives translate directly.
- 2.
Such profiles and values may be explicitly elicited or estimated using past consumption data (see Sect. 6).
- 3.
Our use of zero-expectation payments for risk-neutral agents is mechanically similar to Cremer and McClean’s [5] revenue-optimal auction for bidders with correlated valuations.
- 4.
Publicly available at pecanstreet.org.
- 5.
Our other implementation choices are the same as the valuation model, except we use Dropout of 0.5.
- 6.
This and other tariffs in this section have \(0.2 \leqslant \underline{p} = \bar{p} \leqslant 1.5\).
- 7.
Each instance took around 3Â min on a single thread of 2.6 Ghz Intel i7, 8 GB RAM.
References
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-scale machine learning on heterogeneous systems (2015). http://tensorflow.org/, Software available
Akasiadis, C., Chalkiadakis, G.: Agent cooperatives for effective power consumption shifting. In: Proceedings of the Twenty-seventh AAAI Conference on Artificial Intelligence (AAAI-13), pp. 1263–1269. Bellevue, WA (2013)
Braithwait, S., Hansen, D., O’Sheasy, M.: Retail electricity pricing and rate design in evolving markets, pp. 1–57. Edison Electric Institute, Washington, D.C. (2007)
Castro, J., Gómez, D., Tejada, J.: Polynomial calculation of the Shapley value based on sampling. Comput. Oper. Res. 36(5), 1726–1730 (2009)
Cremer, J., McLean, R.P.: Full extraction of the surplus in Bayesian and dominant strategy auctions. Econometrica 56(6), 1247–1257 (1988)
Kingma, D.P., Ba, J.L.: Adam: a method for stochastic optimization. In: Proceedings of the 3rd ACM SIGKDD International Conference on Learning Representations (ICLR-15), San Diego (2015)
Kota, R., Chalkiadakis, G., Robu, V., Rogers, A., Jennings, N.R.: Cooperatives for demand side management. In: Proceedings of the Twenty-First European Conference on Artificial Intelligence (ECAI-12), pp. 969–974. Montpellier, France (2012)
Lu, T., Boutilier, C.: Matching models for preference-sensitive group purchasing. In: Proceedings of the Thirteenth ACM Conference on Electronic Commerce (EC’12), pp. 723–740. Valencia, Spain (2012)
Manisterski, E., Sarne, D., Kraus, S.: Enhancing cooperative search with concurrent interactions. J. Artif. Intell. Res. 32(1), 1–36 (2008)
Müller, A., Scarsini, M., Shaked, M.: The newsvendor game has a nonempty core. Games Econ. Behav. 38(1), 118–126 (2002)
Osborne, M.J., Rubinstein, A.: A Course in Game Theory. MIT Press, Cambridge (1994)
Perrault, A., Boutilier, C.: Approximately stable pricing for coordinated purchasing of electricity. In: Proceedings of the Twenty-fourth International Joint Conference on Artificial Intelligence (IJCAI-15), Buenos Aires (2015)
Rhodes, J.D., Upshaw, C.R., Harris, C.B., Meehan, C.M., Walling, D.A., Navrátil, P.A., Beck, A.L., Nagasawa, K., Fares, R.L., Cole, W.J., et al.: Experimental and data collection methods for a large-scale smart grid deployment: methods and first results. Energy 65, 462–471 (2014)
Robu, V., Vinyals, M., Rogers, A., Jennings, N.: Efficient buyer groups with prediction-of-use electricity tariffs. IEEE Trans. Smart Grid (2017)
Rose, H., Rogers, A., Gerding, E.H.: A scoring rule-based mechanism for aggregate demand prediction in the smart grid. In: Proceedings of the Eleventh International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS-12), pp. 661–668. Valencia, Spain (2012)
Sarne, D., Kraus, S.: Cooperative exploration in the electronic marketplace. In: Proceedings of the Twentieth National Conference on Artificial Intelligence (AAAI-05), pp. 158–163. Pittsburgh (2005)
Shapley, L.S.: Cores of convex games. Int. J. Game Theory 1, 11–26 (1971)
Simon, C.P., Blume, L.: Mathematics for Economists, vol. 7. Norton, New York (1994)
Srivastava, N., Hinton, G.E., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
Team, G.M.: Electricity and gas supply market report. Technical report. 176/11, The Office of Gas and Electricity Markets (Ofgem), December 2011
Acknowledgments
Perrault was supported by an Ontario Graduate Scholarship. We gratefully acknowledge the support of NSERC. We thank Valentin Robu, Meritxell Vinyals, Marek Janicki, Jake Snell, and the anonymous reviewers for their helpful suggestions.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Perrault, A., Boutilier, C. (2017). Multiple-Profile Prediction-of-Use Games. In: Sukthankar, G., Rodriguez-Aguilar, J. (eds) Autonomous Agents and Multiagent Systems. AAMAS 2017. Lecture Notes in Computer Science(), vol 10642. Springer, Cham. https://doi.org/10.1007/978-3-319-71682-4_17
Download citation
DOI: https://doi.org/10.1007/978-3-319-71682-4_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-71681-7
Online ISBN: 978-3-319-71682-4
eBook Packages: Computer ScienceComputer Science (R0)