Dispatching thermostatically controlled loads for frequency regulation using adversarial multi-armed bandits

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 lear...

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Bibliographic Details
Published inEPEC : 2017 IEEE Electrical Power and Energy Conference : 22-25 October 2017 pp. 1 - 6
Main Authors Mohamed, Amr, Lesage-Landry, Antoine, Taylor, Joshua A.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2017
Subjects
demand response
frequency regulation
Generators
Load management
Load modeling
multi-armed bandit
online learning
Power demand
Sociology
Statistics
Temperature control
thermostatically controlled loads
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DOI10.1109/EPEC.2017.8286168

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Summary: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.
DOI:10.1109/EPEC.2017.8286168