A fast and scalable algorithm for scheduling large numbers of devices under real-time pricing

Shan He; Mark Wallace; Graeme Gange; Ariel Liebman; Campbell Wilson

doi:10.1007/978-3-319-98334-9_42

A fast and scalable algorithm for scheduling large numbers of devices under real-time pricing

Shan He^*
, Mark Wallace
, Graeme Gange
, Ariel Liebman
, Campbell Wilson

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › peer-review

13 Citations (Scopus)

Abstract

Real-time pricing (RTP) is a financial incentive mechanism designed to encourage demand response (DR) to reduce peak demand in medium and low voltage distribution networks but also impacting the generation and transmission system. Though RTP is believed to be an effective mechanism, challenges exist in implementing RTP for residential consumers wherein manually responding to a changing price is difficult and uncoordinated responses can lead to undesired peak demand at what are normally off-peak times. Previous research has proposed various algorithms to address these challenges, however, they rarely consider algorithms that manage very large numbers of houses and devices with discrete consumption levels. To optimise conflicting objectives under RTP prices in a fast and highly scalable manner is very challenging. We address these issues by proposing a fast and highly scalable algorithm that optimally schedules devices for large numbers of households in a distributed but non-cooperative manner under RTP. The results show that this algorithm minimises the total cost and discomfort for 10,000 households in a second and has a constant computational complexity.

Original language	English
Title of host publication	Principles and Practice of Constraint Programming - 24th International Conference, CP 2018, Proceedings
Editors	John Hooker
Publisher	Springer Verlag Italia
Pages	649-666
Number of pages	18
ISBN (Print)	9783319983332
DOIs	https://doi.org/10.1007/978-3-319-98334-9_42
Publication status	Published - 2018
Externally published	Yes
Event	24th International Conference on the Principles and Practice of Constraint Programming, CP 2018 - Lille, France Duration: 27 Aug 2018 → 31 Aug 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11008 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	24th International Conference on the Principles and Practice of Constraint Programming, CP 2018
Country/Territory	France
City	Lille
Period	27/08/18 → 31/08/18

Access to Document

10.1007/978-3-319-98334-9_42

Cite this

He, S., Wallace, M., Gange, G., Liebman, A., & Wilson, C. (2018). A fast and scalable algorithm for scheduling large numbers of devices under real-time pricing. In J. Hooker (Ed.), Principles and Practice of Constraint Programming - 24th International Conference, CP 2018, Proceedings (pp. 649-666). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11008 LNCS). Springer Verlag Italia. https://doi.org/10.1007/978-3-319-98334-9_42

He, Shan ; Wallace, Mark ; Gange, Graeme et al. / A fast and scalable algorithm for scheduling large numbers of devices under real-time pricing. Principles and Practice of Constraint Programming - 24th International Conference, CP 2018, Proceedings. editor / John Hooker. Springer Verlag Italia, 2018. pp. 649-666 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "A fast and scalable algorithm for scheduling large numbers of devices under real-time pricing",

abstract = "Real-time pricing (RTP) is a financial incentive mechanism designed to encourage demand response (DR) to reduce peak demand in medium and low voltage distribution networks but also impacting the generation and transmission system. Though RTP is believed to be an effective mechanism, challenges exist in implementing RTP for residential consumers wherein manually responding to a changing price is difficult and uncoordinated responses can lead to undesired peak demand at what are normally off-peak times. Previous research has proposed various algorithms to address these challenges, however, they rarely consider algorithms that manage very large numbers of houses and devices with discrete consumption levels. To optimise conflicting objectives under RTP prices in a fast and highly scalable manner is very challenging. We address these issues by proposing a fast and highly scalable algorithm that optimally schedules devices for large numbers of households in a distributed but non-cooperative manner under RTP. The results show that this algorithm minimises the total cost and discomfort for 10,000 households in a second and has a constant computational complexity.",

author = "Shan He and Mark Wallace and Graeme Gange and Ariel Liebman and Campbell Wilson",

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year = "2018",

doi = "10.1007/978-3-319-98334-9\_42",

language = "English",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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He, S, Wallace, M, Gange, G, Liebman, A & Wilson, C 2018, A fast and scalable algorithm for scheduling large numbers of devices under real-time pricing. in J Hooker (ed.), Principles and Practice of Constraint Programming - 24th International Conference, CP 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11008 LNCS, Springer Verlag Italia, pp. 649-666, 24th International Conference on the Principles and Practice of Constraint Programming, CP 2018, Lille, France, 27/08/18. https://doi.org/10.1007/978-3-319-98334-9_42

A fast and scalable algorithm for scheduling large numbers of devices under real-time pricing. / He, Shan; Wallace, Mark; Gange, Graeme et al.
Principles and Practice of Constraint Programming - 24th International Conference, CP 2018, Proceedings. ed. / John Hooker. Springer Verlag Italia, 2018. p. 649-666 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11008 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › peer-review

TY - GEN

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AU - He, Shan

AU - Wallace, Mark

AU - Gange, Graeme

AU - Liebman, Ariel

AU - Wilson, Campbell

PY - 2018

Y1 - 2018

N2 - Real-time pricing (RTP) is a financial incentive mechanism designed to encourage demand response (DR) to reduce peak demand in medium and low voltage distribution networks but also impacting the generation and transmission system. Though RTP is believed to be an effective mechanism, challenges exist in implementing RTP for residential consumers wherein manually responding to a changing price is difficult and uncoordinated responses can lead to undesired peak demand at what are normally off-peak times. Previous research has proposed various algorithms to address these challenges, however, they rarely consider algorithms that manage very large numbers of houses and devices with discrete consumption levels. To optimise conflicting objectives under RTP prices in a fast and highly scalable manner is very challenging. We address these issues by proposing a fast and highly scalable algorithm that optimally schedules devices for large numbers of households in a distributed but non-cooperative manner under RTP. The results show that this algorithm minimises the total cost and discomfort for 10,000 households in a second and has a constant computational complexity.

AB - Real-time pricing (RTP) is a financial incentive mechanism designed to encourage demand response (DR) to reduce peak demand in medium and low voltage distribution networks but also impacting the generation and transmission system. Though RTP is believed to be an effective mechanism, challenges exist in implementing RTP for residential consumers wherein manually responding to a changing price is difficult and uncoordinated responses can lead to undesired peak demand at what are normally off-peak times. Previous research has proposed various algorithms to address these challenges, however, they rarely consider algorithms that manage very large numbers of houses and devices with discrete consumption levels. To optimise conflicting objectives under RTP prices in a fast and highly scalable manner is very challenging. We address these issues by proposing a fast and highly scalable algorithm that optimally schedules devices for large numbers of households in a distributed but non-cooperative manner under RTP. The results show that this algorithm minimises the total cost and discomfort for 10,000 households in a second and has a constant computational complexity.

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Principles and Practice of Constraint Programming - 24th International Conference, CP 2018, Proceedings

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Y2 - 27 August 2018 through 31 August 2018

ER -

He S, Wallace M, Gange G, Liebman A, Wilson C. A fast and scalable algorithm for scheduling large numbers of devices under real-time pricing. In Hooker J, editor, Principles and Practice of Constraint Programming - 24th International Conference, CP 2018, Proceedings. Springer Verlag Italia. 2018. p. 649-666. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-98334-9_42

A fast and scalable algorithm for scheduling large numbers of devices under real-time pricing

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Fast and Scalable Electrical Demand Scheduling for a Large Number of Residential Consumers

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