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.
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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.
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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.
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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
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