Demand Response Alert Service Based on Appliance Modeling

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 F1 score index, the respective improvement reaches up to 221.05%.

Editorial for the Special Issue “Verifying the Targets—Selected Papers from the 55th International Universities Power Engineering Conference (UPEC 2020)”

“…Peakshaving demand response programmes are among the most diffused ones, to obtain a direct demand reduction effect in the relevant time period. Chatzigeorgiou et al [23] address the determination of an appropriate set of alerts to be sent to the users for modifying the demand curve in a peak-shaving demand response programme. Past-usage measurements gathered on many deferrable appliances are used to define priorities to the households based on the probabilities of appliance usage in time.…”