Managing load contract restrictions with online learning

Rodrigo Henriquez, Antoine Lesage-Landry, Joshua A. Taylor, Daniel Olivares, Matias Negrete-Pincetic

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

1 Cita (Scopus)

Resumen

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.

Idioma originalInglés
Título de la publicación alojada2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings
EditorialInstitute of Electrical and Electronics Engineers Inc.
Páginas1035-1039
Número de páginas5
ISBN (versión digital)9781509059904
DOI
EstadoPublicada - 7 mar. 2018
Publicado de forma externa
Evento5th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Montreal, Canadá
Duración: 14 nov. 201716 nov. 2017

Serie de la publicación

Nombre2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings
Volumen2018-January

Conferencia

Conferencia5th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017
País/TerritorioCanadá
CiudadMontreal
Período14/11/1716/11/17

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