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Demand Response Alert Service Based on Appliance Modeling

Author

Listed:
  • 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)

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%.

Suggested Citation

  • Ioanna-M. Chatzigeorgiou & Christos Diou & Kyriakos C. Chatzidimitriou & Georgios T. Andreou, 2021. "Demand Response Alert Service Based on Appliance Modeling," Energies, MDPI, vol. 14(10), pages 1-15, May.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:10:p:2953-:d:558371
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    References listed on IDEAS

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    2. Bugaje, Bilal & Rutherford, Peter & Clifford, Mike, 2022. "Convenience in a residence with demand response: A system dynamics simulation model," Applied Energy, Elsevier, vol. 314(C).

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