Fast electrical demand optimization under real-time pricing

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Real-time pricing (RTP) is an effective scheme for reducing peak demand, but it can lead to load synchronization, where a large amount of consumption is shifted from a typical peak time to a non-peak time, without reducing the peak demand. To address this issue, this paper presents a demand management method under RTP for the smart grid, that solves a large-scale of energy scheduling problem for households in an area. This is a distributed optimization method that finds the optimal consumption levels to minimize the total electricity cost while meeting the demands and preferences of households. Moreover, we propose to compute the probability distributions of start times for tasks, with which smart meters can quickly schedule tasks in practice, while matching the aggregate demand to the optimal consumption levels. The complexity of the optimization method is independent of the number households, which allows it to be applied to problems with realistic scales.

Original languageEnglish
Title of host publicationProceedings of the Thirty-First AAAI Conference on Artificial Intelligence, Twenty-Ninth Innovative Applications of Artificial Intelligence Conference, Seventh Symposium on Educational Advances in Artificial Intelligence
EditorsSatinder Singh, Shaul Markovitch
Place of PublicationPalo Alto CA USA
PublisherAssociation for the Advancement of Artificial Intelligence (AAAI)
Number of pages7
ISBN (Print)9781577357803
Publication statusPublished - 1 Jan 2017
EventAAAI Conference on Artificial Intelligence 2017 - Hilton San Francisco Union Square, San Francisco, United States of America
Duration: 4 Feb 201710 Feb 2017
Conference number: 31st


ConferenceAAAI Conference on Artificial Intelligence 2017
Abbreviated titleAAAI 2017
CountryUnited States of America
CitySan Francisco
Internet address

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