A deep learning approach for fairness-based time of use tariff design

Yang Han, Jacqueline C.K. Lam, Victor O.K. Li, David Newbery, Peiyang Guo and Kelvin Chan

Energy Policy, 2024, vol. 192, issue C

Abstract: Time of use (TOU) tariffs aim to shape demand by reducing peak demand. This is significant given the increasing pressure on peak energy supply, particularly in Europe, where peak gas prices have dramatically increased bills. This study defines fairness as a reform measure that carries both distributional and transitional effects on different income users and utilities. A flexible fairness-driven TOU tariff design is proposed, which (1) assigns households to different tariffs by income and estimated price responsiveness calculated from a long short-term memory (LSTM)-based counterfactual electricity consumption model and (2) filters potential tariffs to simultaneously lower total costs, distribute cost savings progressively, and maintain constant utility profitability. Using the 2009-10 Irish Smart Metering Trial based on more than 2000 users, under the proposed frameworks (b) and (c) with fairness constraints, no lower-income users have been observed to pay a higher bill than the higher-income users on average. Specifically, lower-income groups can achieve 8% and 10% bill reduction, respectively, under framework (b) partial bill protection and framework (c) full bill protection for the lower-income users. Our fairness-based TOU framework has successfully achieved fairness of distribution and transition, paving the way for a more equitable and sustainable electricity system.

Keywords: Tariff design; Time of use (TOU); Fairness of distribution; Fairness of transition; Deep learning; Counterfactual estimation; Price responsiveness; Income segmentation (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:enepol:v:192:y:2024:i:c:s0301421524002507

DOI: 10.1016/j.enpol.2024.114230

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