Abstract
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 language | English |
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Title of host publication | Proceedings 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 |
Editors | Satinder Singh, Shaul Markovitch |
Place of Publication | Palo Alto CA USA |
Publisher | Association for the Advancement of Artificial Intelligence (AAAI) |
Pages | 253-259 |
Number of pages | 7 |
Volume | 1 |
ISBN (Print) | 9781577357803 |
Publication status | Published - 1 Jan 2017 |
Event | AAAI Conference on Artificial Intelligence 2017 - Hilton San Francisco Union Square, San Francisco, United States of America Duration: 4 Feb 2017 → 10 Feb 2017 Conference number: 31st http://www.aaai.org/Conferences/AAAI/aaai17.php |
Conference
Conference | AAAI Conference on Artificial Intelligence 2017 |
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Abbreviated title | AAAI 2017 |
Country/Territory | United States of America |
City | San Francisco |
Period | 4/02/17 → 10/02/17 |
Internet address |