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

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Listed:
  • Sasan Maleki
  • Talal Rahwan
  • Siddhartha Ghosh
  • Areej Malibari
  • Daniyal Alghazzawi
  • Alex Rogers
  • Hamid Beigy
  • Nicholas R Jennings

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.

Suggested Citation

  • Sasan Maleki & Talal Rahwan & Siddhartha Ghosh & Areej Malibari & Daniyal Alghazzawi & Alex Rogers & Hamid Beigy & Nicholas R Jennings, 2020. "The Shapley value for a fair division of group discounts for coordinating cooling loads," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-28, January.
  • Handle: RePEc:plo:pone00:0227049
    DOI: 10.1371/journal.pone.0227049
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    References listed on IDEAS

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    Cited by:

    1. Cremers, Sho & Robu, Valentin & Zhang, Peter & Andoni, Merlinda & Norbu, Sonam & Flynn, David, 2023. "Efficient methods for approximating the Shapley value for asset sharing in energy communities," Applied Energy, Elsevier, vol. 331(C).
    2. Miguel A. Ortiz Acuña & Adiel T. Almeida Filho & Francisco S. Ramos, 2024. "Modelling the triple helix system innovation of the main economies from Latin America: a coalitional game theory approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(6), pages 3245-3270, June.
    3. Sunil Tiwari & Joanna Rosak-Szyrocka & Justyna Żywiołek, 2022. "Internet of Things as a Sustainable Energy Management Solution at Tourism Destinations in India," Energies, MDPI, vol. 15(7), pages 1-20, March.

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