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Online Demand Response Characterization Based on Variability in Customer Behavior

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This paper proposes an online framework to char-acterize demand response(DR)over time.The proposed frame-work facilitates obtaining and updating the daily consumption patterns of customers.The essential concept of response profile class(RPC)is introduced for characterization and complement-ed by the measure of the variability in customer behavior.This paper uses a modified version of the incremental clustering by fast search and find of density peaks(CFSFDP)algorithm for daily profiles,considering the multivariate normal kernel densi-ty 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 com-mercial Chilean end-users have demonstrated how the proposed framework can continuously characterize DR.The proposed framework is proven to achieve realistic customer models for ef-fective energy management by estimating the customer response to price signals at the distribution system operator(DSO)level.

Demand response(DR)incremental indiceon-line characterizationonline clusteringresponse profile class(RPC)

Lester Marrero、Daniel Sbárbaro、Luis García-Santander

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Department of Electrical Engineering,University of Concepción,Con-cepción 4070409,Chile

National Agency for Research and Development(ANID)partial support of ANIDFondap SERC

ANID-PFCHA/Doctorado-Nacional/2120224615110019

2024

10.35833/MPCE.2023.000516

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现代电力系统与清洁能源学报(英文版)

ISSN：
年,卷(期)：2024.12(3)
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