@inproceedings{585d0db369d94847be64b3b72c5c84c6,
title = "Dispatching thermostatically controlled loads for frequency regulation using adversarial multi-armed bandits",
abstract = "Utilizing residential Thermostatically Controlled Loads (TCLs) for demand response stands to offer a more economical and environmentally friendly alternative to procuring energy storage and generation facilities for grid ancillary services. We use the adversarial multi-armed bandit framework to learn the signal response of TCLs and determine which TCLs to activate for demand response in real-time. We demonstrate the performance of our proposed approach by invoking theoretical bounds on the performance of an Exp3.M-based algorithm, and comparing the performance with a greedy algorithm. A sub-linear regret shows that the algorithm is able to learn and identify high-performing TCLs, and activate them more frequently as more information is acquired about the TCLs' signal response.",
keywords = "demand response, frequency regulation, multi-armed bandit, online learning, thermostatically controlled loads",
author = "Amr Mohamed and Antoine Lesage-Landry and Taylor, {Joshua A.}",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE Electrical Power and Energy Conference, EPEC 2017 ; Conference date: 22-10-2017 Through 25-10-2017",
year = "2017",
month = jul,
day = "2",
doi = "10.1109/EPEC.2017.8286168",
language = "English (US)",
series = "2017 IEEE Electrical Power and Energy Conference, EPEC 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1--6",
booktitle = "2017 IEEE Electrical Power and Energy Conference, EPEC 2017",
address = "United States",
}