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 |
|---|---|
| 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 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 |
|---|---|
| Abbreviated title | AAAI 2017 |
| Country | United States |
| City | San Francisco |
| Period | 4/02/17 → 10/02/17 |
| Internet address |
Cite this
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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, Twenty-Ninth Innovative Applications of Artificial Intelligence Conference, Seventh Symposium on Educational Advances in Artificial Intelligence. ed. / Satinder Singh; Shaul Markovitch. Vol. 1 Palo Alto CA USA : Association for the Advancement of Artificial Intelligence (AAAI), 2017. p. 253-259.Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review
TY - GEN
T1 - Fast electrical demand optimization under real-time pricing
AU - He, Shan
AU - Wallace, Mark
AU - Wilson, Campbell
AU - Liebman, Ariel
PY - 2017/1/1
Y1 - 2017/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85046092362&partnerID=8YFLogxK
M3 - Conference Paper
SN - 9781577357803
VL - 1
SP - 253
EP - 259
BT - 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
A2 - Singh, Satinder
A2 - Markovitch, Shaul
PB - Association for the Advancement of Artificial Intelligence (AAAI)
CY - Palo Alto CA USA
ER -