Online Demand Response Characterization Based on Variability in Customer Behavior

This paper proposed an online framework to characterize demand response (DR) over time. The proposed approach facilitated obtaining and updating the daily consumption patterns of customers. The essential concept of response profile class (RPC) was introduced for characterization, complemented by the measure of the variability in customer behavior. The paper used for daily profiles a modified version of the incremental clustering by fast search and find of density peaks (CFSFDP) algorithm that considered the multivariate normal kernel density estimator and incremental forms of the Davies- Bouldin (iDB) and Xie-Beni (iXB) validity indices. Case studies conducted using real-world and simulated daily profiles of residential and commercial Chilean end-users demonstrated how the proposed approach can continuously characterize DR. Results proved that the proposed framework achieves realistic customer models for effective energy management by estimating the customer response to price signals at the distribution system operator (DSO) level.