The Shapley value for a fair division of group discounts for coordinating cooling loads

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Title: The Shapley value for a fair division of group discounts for coordinating cooling loads
Authors: Maleki, S
Rahwan, T
Ghosh, S
Malibari, A
Alghazzawi, D
Rogers, A
Beigy, H
Jennings, NR
Item Type: Journal Article
Abstract: We consider a demand response program in which a block of apartments receive a discount from their electricity supplier if they ensure that their aggregate load from air conditioning does not exceed a predetermined threshold. The goal of the participants is to obtain the discount, while ensuring that their individual temperature preferences are also satisfied. As such, the apartments need to collectively optimise their use of air conditioning so as to satisfy these constraints and minimise their costs. Given an optimal cooling profile that secures the discount, the problem that the apartments face then is to divide the total discounted cost in a fair way. To achieve this, we take a coalitional game approach and propose the use of the Shapley value from cooperative game theory, which is the normative payoff division mechanism that offers a unique set of desirable fairness properties. However, applying the Shapley value in this setting presents a novel computational challenge. This is because its calculation requires, as input, the cost of every subset of apartments, which means solving an exponential number of collective optimisations, each of which is a computationally intensive problem. To address this, we propose solving the optimisation problem of each subset suboptimally, to allow for acceptable solutions that require less computation. We show that, due to the linearity property of the Shapley value, if suboptimal costs are used rather than optimal ones, the division of the discount will be fair in the following sense: each apartment is fairly "rewarded" for its contribution to the optimal cost and, at the same time, is fairly "penalised" for its contribution to the discrepancy between the suboptimal and the optimal costs. Importantly, this is achieved without requiring the optimal solutions.
Issue Date: 10-Jan-2020
Date of Acceptance: 11-Dec-2019
DOI: 10.1371/journal.pone.0227049
ISSN: 1932-6203
Publisher: Public Library of Science (PLoS)
Journal / Book Title: PLoS One
Volume: 15
Issue: 1
Copyright Statement: © 2020 Maleki et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Keywords: General Science & Technology
Publication Status: Published
Conference Place: United States
Open Access location:
Article Number: e0227049
Appears in Collections:Computing
Faculty of Natural Sciences

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