Demand Response Alert Service Based on Appliance Modeling

Ioanna-M. Chatzigeorgiou, Christos Diou, Kyriakos C. Chatzidimitriou and Georgios T. Andreou
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Ioanna-M. Chatzigeorgiou: School of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Christos Diou: Department of Informatics and Telematics, Harokopio University of Athens, 17778 Athens, Greece
Kyriakos C. Chatzidimitriou: School of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Georgios T. Andreou: School of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece

Energies, 2021, vol. 14, issue 10, 1-15

Abstract: Demand response has been widely developed during recent years to increase efficiency and decrease the cost in the electric power sector by shifting energy use, smoothening the load curve, and thus ensuring benefits for all participating parties. This paper introduces a Demand Response Alert Service (DRAS) that can optimize the interaction between the energy industry parties and end users by sending the minimum number of relatable alerts to satisfy the transformation of the load curve. The service creates appliance models for certain deferrable appliances based on past-usage measurements and prioritizes households according to the probability of the use of their appliances. Several variations of the appliance model are examined with respect to the probabilistic association of appliance usage on different days. The service is evaluated for a peak-shaving scenario when either one or more appliances per household are involved. The results demonstrate a significant improvement compared to a random selection of end users, thus promising increased participation and engagement. Indicatively, in terms of the Area Under the Curve (AUC) index, the proposed method achieves, in all the studied scenarios, an improvement ranging between 41.33% and 64.64% compared to the baseline scenario. In terms of the F 1 score index, the respective improvement reaches up to 221.05%.

Keywords: data analytics; artificial intelligence applied to power systems; demand side management; flexibility; demand response; appliance modeling; peak shaving; smart grid (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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