TY - GEN
T1 - Managing load contract restrictions with online learning
AU - Henriquez, Rodrigo
AU - Lesage-Landry, Antoine
AU - Taylor, Joshua A.
AU - Olivares, Daniel
AU - Negrete-Pincetic, Matias
N1 - Funding Information:
This research was partially funded by the Complex Engineering Systems Institute, ISCI (ICM-FIC: P05-004-F, CONICYT: FB0816), by CONI-CYT/FONDECYT/11140621, by the Fonds de recherche du Québec – Nature et technologies and by the National Science and Engineering Research Council (NSERC).
Publisher Copyright:
© 2017 IEEE.
PY - 2018/3/7
Y1 - 2018/3/7
N2 - Demand Response (DR) is an effective means of providing flexibility in power systems facing increased variability from renewables. Aggregators must dispatch loads for demand response which provide the most useful services while respecting each load's constraints. In this work, we propose an online learning model where a DR aggregator has to manage a portfolio of curtailable loads subject to several types of restrictions, such as the number of times each load may be curtailed and the total budget. We address this problem with the recent bandits with knapsacks framework. We test the algorithm on numerical examples and discuss the resulting behavior of the algorithm.
AB - Demand Response (DR) is an effective means of providing flexibility in power systems facing increased variability from renewables. Aggregators must dispatch loads for demand response which provide the most useful services while respecting each load's constraints. In this work, we propose an online learning model where a DR aggregator has to manage a portfolio of curtailable loads subject to several types of restrictions, such as the number of times each load may be curtailed and the total budget. We address this problem with the recent bandits with knapsacks framework. We test the algorithm on numerical examples and discuss the resulting behavior of the algorithm.
KW - Demand response
KW - Load aggregator
KW - Multi-armed bandits
KW - Online learning
UR - http://www.scopus.com/inward/record.url?scp=85047978844&partnerID=8YFLogxK
U2 - 10.1109/GlobalSIP.2017.8309118
DO - 10.1109/GlobalSIP.2017.8309118
M3 - Conference contribution
AN - SCOPUS:85047978844
T3 - 2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings
SP - 1035
EP - 1039
BT - 2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017
Y2 - 14 November 2017 through 16 November 2017
ER -