Fast electrical demand optimization under real-time pricing

Research output: Chapter in Book/Report/Conference proceedingConference PaperOther

Abstract

The introduction of smart meters has motivated the electricity industry to manage electrical demand, using dynamic pricing schemes such as real-time pricing. The overall aim of demand management is to minimize electricity generation and distribution costs while meeting the demands and preferences of consumers. However, rapidly scheduling consumption of large groups of households is a challenge. In this paper, we present a highly scalable approach to find the optimal consumption levels for households in an iterative and distributed manner. The complexity of this approach is independent of the number of households, which allows it to be applied to problems with large groups of households. Moreover, the intermediate results of this approach can be used by smart meters to schedule tasks with a simple randomized method.
LanguageEnglish
Title of host publicationProceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17)
EditorsSatinder Singh, Shaul Markovitch
Place of PublicationPalo Alto CA USA
PublisherAssociation for the Advancement of Artificial Intelligence (AAAI)
Pages4935-4936
Number of pages2
StatePublished - 2017
EventAAAI Conference on Artificial Intelligence 2017 - Hilton San Francisco Union Square, San Francisco, United States
Duration: 4 Feb 201710 Feb 2017
Conference number: 31st
http://www.aaai.org/Conferences/AAAI/aaai17.php

Conference

ConferenceAAAI Conference on Artificial Intelligence 2017
Abbreviated titleAAAI 2017
CountryUnited States
CitySan Francisco
Period4/02/1710/02/17
Internet address

Cite this

He, S., Wallace, M., Wilson, C., & Liebman, A. (2017). Fast electrical demand optimization under real-time pricing. In S. Singh, & S. Markovitch (Eds.), Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17) (pp. 4935-4936). Palo Alto CA USA: Association for the Advancement of Artificial Intelligence (AAAI).
He, Shan ; Wallace, Mark ; Wilson, Campbell ; Liebman, Ariel. / Fast electrical demand optimization under real-time pricing. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17). editor / Satinder Singh ; Shaul Markovitch. Palo Alto CA USA : Association for the Advancement of Artificial Intelligence (AAAI), 2017. pp. 4935-4936
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title = "Fast electrical demand optimization under real-time pricing",
abstract = "The introduction of smart meters has motivated the electricity industry to manage electrical demand, using dynamic pricing schemes such as real-time pricing. The overall aim of demand management is to minimize electricity generation and distribution costs while meeting the demands and preferences of consumers. However, rapidly scheduling consumption of large groups of households is a challenge. In this paper, we present a highly scalable approach to find the optimal consumption levels for households in an iterative and distributed manner. The complexity of this approach is independent of the number of households, which allows it to be applied to problems with large groups of households. Moreover, the intermediate results of this approach can be used by smart meters to schedule tasks with a simple randomized method.",
author = "Shan He and Mark Wallace and Campbell Wilson and Ariel Liebman",
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He, S, Wallace, M, Wilson, C & Liebman, A 2017, Fast electrical demand optimization under real-time pricing. in S Singh & S Markovitch (eds), Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17). Association for the Advancement of Artificial Intelligence (AAAI), Palo Alto CA USA, pp. 4935-4936, AAAI Conference on Artificial Intelligence 2017, San Francisco, United States, 4/02/17.

Fast electrical demand optimization under real-time pricing. / He, Shan; Wallace, Mark; Wilson, Campbell; Liebman, Ariel.

Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17). ed. / Satinder Singh; Shaul Markovitch. Palo Alto CA USA : Association for the Advancement of Artificial Intelligence (AAAI), 2017. p. 4935-4936.

Research output: Chapter in Book/Report/Conference proceedingConference PaperOther

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AB - The introduction of smart meters has motivated the electricity industry to manage electrical demand, using dynamic pricing schemes such as real-time pricing. The overall aim of demand management is to minimize electricity generation and distribution costs while meeting the demands and preferences of consumers. However, rapidly scheduling consumption of large groups of households is a challenge. In this paper, we present a highly scalable approach to find the optimal consumption levels for households in an iterative and distributed manner. The complexity of this approach is independent of the number of households, which allows it to be applied to problems with large groups of households. Moreover, the intermediate results of this approach can be used by smart meters to schedule tasks with a simple randomized method.

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He S, Wallace M, Wilson C, Liebman A. Fast electrical demand optimization under real-time pricing. In Singh S, Markovitch S, editors, Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17). Palo Alto CA USA: Association for the Advancement of Artificial Intelligence (AAAI). 2017. p. 4935-4936.