Current electricity tariffs do not reflect the real costs that a customer incurs to a supplier, as units are charged at the same rate, regardless of the consumption pattern. In this paper, we propose a prediction-of-use tariff that better reflects these costs, which asks customers to predict a baseline consumption, and charges them based both on their actual consumption, and the deviation from their prediction. We show how under this tariff no customer would have an incentive to consume in excess of their actual needs, and derive closed form expressions for their optimal prediction and expected payments. Second, using principles from cooperative game theory, we study how customers can collectively reduce their potential deviation by aggregating under a group-buying scheme. We prove that the associated cost game is concave, which means grouping reduces the total expected bill and that this payment can be fairly allocated among customers by their Shapley values. Third, considering a model where customers can join the group online, we propose marginal payment allocation schemes that incentivise them to commit early, thus preventing start-up inertia. Finally, we validate our model using real data from a set of 3000 consumers from the UK.@en

Current electricity tariffs do not reflect the real costs that a customer incurs to a supplier, as units are charged at the same rate, regardless of the consumption pattern. In this paper, we propose a prediction-of-use (POU) tariff that better reflects the predictability cost of a customer. Our tariff asks customers to pre-commit to a baseline consumption, and charges them based on both their actual consumption and the deviation from the anticipated baseline. First, we study, from a cooperative game theory perspective, the cost game induced by a single such tariff, and show customers would have an incentive to minimize their risk, by joining together when buying electricity as a grand coalition. Second, we study the efficient (i.e., cost-minimizing) structure of buying groups for the more realistic setting when multiple, competing POU tariffs are available. We propose a polynomial time algorithm to compute the efficient buyer groups, and validate our approach experimentally, using a large-scale data set of domestic consumers in the U.K.@en

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 (Vinyals et al. 2014) 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.@en