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On the Use of Causality Inference in Designing Tariffs to Implement More Effective Behavioral Demand Response Programs

1
Faculty of Engineering, University of Porto (FEUP), 4200-465 Porto, Portugal
2
INESC Technology and Science (INESC TEC), 4200-465 Porto, Portugal
*
Author to whom correspondence should be addressed.
Energies 2019, 12(14), 2666; https://doi.org/10.3390/en12142666
Received: 20 May 2019 / Revised: 29 June 2019 / Accepted: 8 July 2019 / Published: 11 July 2019
(This article belongs to the Special Issue Digital Solutions for Energy Management and Power Generation)
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Abstract

Providing a price tariff that matches the randomized behavior of residential consumers is one of the major barriers to demand response (DR) implementation. The current trend of DR products provided by aggregators or retailers are not consumer-specific, which poses additional barriers for the engagement of consumers in these programs. In order to address this issue, this paper describes a methodology based on causality inference between DR tariffs and observed residential electricity consumption to estimate consumers’ consumption elasticity. It determines the flexibility of each client under the considered DR program and identifies whether the tariffs offered by the DR program affect the consumers’ usual consumption or not. The aim of this approach is to aid aggregators and retailers to better tune DR offers to consumer needs and so to enlarge the response rate to their DR programs. We identify a set of critical clients who actively participate in DR events along with the most responsive and least responsive clients for the considered DR program. We find that the percentage of DR consumers who actively participate seem to be much less than expected by retailers, indicating that not all consumers’ elasticity is effectively utilized. View Full-Text
Keywords: consumption elasticity; causal inference; data-driven; demand response; residential consumers consumption elasticity; causal inference; data-driven; demand response; residential consumers
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Ganesan, K.; Tomé Saraiva, J.; Bessa, R.J. On the Use of Causality Inference in Designing Tariffs to Implement More Effective Behavioral Demand Response Programs. Energies 2019, 12, 2666.

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