AAAI Publications, Twenty-Eighth AAAI Conference on Artificial Intelligence

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Efficient Buyer Groups for Prediction-of-Use Electricity Tariffs
Valentin Robu, Meritxell Vinyals, Alex Rogers, Nicholas R. Jennings

Last modified: 2014-06-20


Current electricity tariffs do not reflect the real cost that customers incur to suppliers, as units are charged at the same rate, regardless of how predictable each customer's consumption is. A recent proposal to address this problem are prediction-of-use tariffs. In such tariffs, a customer is asked in advance to predict her future consumption, and is charged based both on her actual consumption and the deviation from her prediction. Prior work {aamas2014} studied the cost game induced by a single such tariff, and showed customers would have an incentive to minimize their risk, by joining together when buying electricity as a grand coalition. In this work we study the efficient (i.e. cost-minimizing) structure of buying groups for the more realistic setting when multiple, competing prediction-of-use tariffs are available. We propose a polynomial time algorithm to compute efficient buyer groups, and validate our approach experimentally, using a large-scale data set of domestic electricity consumers in the UK.


smart grids; electricity tariffs; group buying; coalition structure

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