Large neighborhood search for temperature control with demand response

Edward Lam; Frits de Nijs; Peter J. Stuckey; Donald Azuatalam; Ariel Liebman

doi:10.1007/978-3-030-58475-7_35

Large neighborhood search for temperature control with demand response

Edward Lam, Frits de Nijs, Peter J. Stuckey, Donald Azuatalam, Ariel Liebman

Department of Data Science & AI

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

2 Citations (Scopus)

Abstract

Demand response is a control problem that optimizes the operation of electrical loads subject to limits on power consumption during times of low power supply or extreme power demand. This paper studies the demand response problem for centrally controlling the space conditioning systems of several buildings connected to a microgrid. The paper develops a mixed integer quadratic programming model that encodes trained deep neural networks that approximate the temperature transition functions. The model is solved using standard branch-and-bound and a large neighborhood search within a mathematical programming solver and a constraint programming solver. Empirical results demonstrate that the large neighborhood search coupled to a constraint programming solver scales substantially better than the other methods.

Original language	English
Title of host publication	Principles and Practice of Constraint Programming
Subtitle of host publication	26th International Conference, CP 2020 Louvain-la-Neuve, Belgium, September 7–11, 2020 Proceedings
Editors	Helmut Simonis
Place of Publication	Cham Switzerland
Publisher	Springer
Pages	603-619
Number of pages	17
ISBN (Electronic)	9783030584757
ISBN (Print)	9783030584740
DOIs	https://doi.org/10.1007/978-3-030-58475-7_35
Publication status	Published - 2020
Event	International Conference on Principles and Practice of Constraint Programming 2020 - Louvain-la-Neuve, Belgium Duration: 7 Sept 2020 → 11 Sept 2020 Conference number: 26th https://link.springer.com/book/10.1007/978-3-030-58475-7 (Proceedings) https://cp2020.a4cp.org (Website)

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	12241
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	International Conference on Principles and Practice of Constraint Programming 2020
Abbreviated title	CP2020
Country/Territory	Belgium
City	Louvain-la-Neuve
Period	7/09/20 → 11/09/20
Internet address	https://link.springer.com/book/10.1007/978-3-030-58475-7 (Proceedings) https://cp2020.a4cp.org (Website)

Keywords

Sustainability
Energy systems
Power systems
Control
Smart grid
Microgrid
Large neighborhood search
Local search

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-030-58475-7_35

Cite this

Lam, E., de Nijs, F., Stuckey, P. J., Azuatalam, D., & Liebman, A. (2020). Large neighborhood search for temperature control with demand response. In H. Simonis (Ed.), Principles and Practice of Constraint Programming: 26th International Conference, CP 2020 Louvain-la-Neuve, Belgium, September 7–11, 2020 Proceedings (pp. 603-619). (Lecture Notes in Computer Science; Vol. 12241). Springer. https://doi.org/10.1007/978-3-030-58475-7_35

Lam, Edward ; de Nijs, Frits ; Stuckey, Peter J. et al. / Large neighborhood search for temperature control with demand response. Principles and Practice of Constraint Programming: 26th International Conference, CP 2020 Louvain-la-Neuve, Belgium, September 7–11, 2020 Proceedings. editor / Helmut Simonis. Cham Switzerland : Springer, 2020. pp. 603-619 (Lecture Notes in Computer Science).

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title = "Large neighborhood search for temperature control with demand response",

abstract = "Demand response is a control problem that optimizes the operation of electrical loads subject to limits on power consumption during times of low power supply or extreme power demand. This paper studies the demand response problem for centrally controlling the space conditioning systems of several buildings connected to a microgrid. The paper develops a mixed integer quadratic programming model that encodes trained deep neural networks that approximate the temperature transition functions. The model is solved using standard branch-and-bound and a large neighborhood search within a mathematical programming solver and a constraint programming solver. Empirical results demonstrate that the large neighborhood search coupled to a constraint programming solver scales substantially better than the other methods.",

keywords = "Sustainability, Energy systems, Power systems, Control, Smart grid, Microgrid, Large neighborhood search, Local search",

author = "Edward Lam and {de Nijs}, Frits and Stuckey, {Peter J.} and Donald Azuatalam and Ariel Liebman",

year = "2020",

doi = "10.1007/978-3-030-58475-7_35",

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editor = "Simonis, {Helmut }",

booktitle = "Principles and Practice of Constraint Programming",

address = "Switzerland",

note = "International Conference on Principles and Practice of Constraint Programming 2020, CP2020 ; Conference date: 07-09-2020 Through 11-09-2020",

url = "https://link.springer.com/book/10.1007/978-3-030-58475-7, https://cp2020.a4cp.org",

}

Lam, E , de Nijs, F , Stuckey, PJ, Azuatalam, D & Liebman, A 2020, Large neighborhood search for temperature control with demand response. in H Simonis (ed.), Principles and Practice of Constraint Programming: 26th International Conference, CP 2020 Louvain-la-Neuve, Belgium, September 7–11, 2020 Proceedings. Lecture Notes in Computer Science, vol. 12241, Springer, Cham Switzerland, pp. 603-619, International Conference on Principles and Practice of Constraint Programming 2020, Louvain-la-Neuve, Belgium, 7/09/20. https://doi.org/10.1007/978-3-030-58475-7_35

Large neighborhood search for temperature control with demand response. / Lam, Edward ; de Nijs, Frits ; Stuckey, Peter J. et al.
Principles and Practice of Constraint Programming: 26th International Conference, CP 2020 Louvain-la-Neuve, Belgium, September 7–11, 2020 Proceedings. ed. / Helmut Simonis. Cham Switzerland: Springer, 2020. p. 603-619 (Lecture Notes in Computer Science; Vol. 12241).

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

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T1 - Large neighborhood search for temperature control with demand response

AU - Lam, Edward

AU - de Nijs, Frits

AU - Stuckey, Peter J.

AU - Azuatalam, Donald

AU - Liebman, Ariel

N1 - Conference code: 26th

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N2 - Demand response is a control problem that optimizes the operation of electrical loads subject to limits on power consumption during times of low power supply or extreme power demand. This paper studies the demand response problem for centrally controlling the space conditioning systems of several buildings connected to a microgrid. The paper develops a mixed integer quadratic programming model that encodes trained deep neural networks that approximate the temperature transition functions. The model is solved using standard branch-and-bound and a large neighborhood search within a mathematical programming solver and a constraint programming solver. Empirical results demonstrate that the large neighborhood search coupled to a constraint programming solver scales substantially better than the other methods.

AB - Demand response is a control problem that optimizes the operation of electrical loads subject to limits on power consumption during times of low power supply or extreme power demand. This paper studies the demand response problem for centrally controlling the space conditioning systems of several buildings connected to a microgrid. The paper develops a mixed integer quadratic programming model that encodes trained deep neural networks that approximate the temperature transition functions. The model is solved using standard branch-and-bound and a large neighborhood search within a mathematical programming solver and a constraint programming solver. Empirical results demonstrate that the large neighborhood search coupled to a constraint programming solver scales substantially better than the other methods.

KW - Sustainability

KW - Energy systems

KW - Power systems

KW - Control

KW - Smart grid

KW - Microgrid

KW - Large neighborhood search

KW - Local search

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SN - 9783030584740

T3 - Lecture Notes in Computer Science

SP - 603

EP - 619

BT - Principles and Practice of Constraint Programming

A2 - Simonis, Helmut

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T2 - International Conference on Principles and Practice of Constraint Programming 2020

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Lam E , de Nijs F , Stuckey PJ, Azuatalam D, Liebman A. Large neighborhood search for temperature control with demand response. In Simonis H, editor, Principles and Practice of Constraint Programming: 26th International Conference, CP 2020 Louvain-la-Neuve, Belgium, September 7–11, 2020 Proceedings. Cham Switzerland: Springer. 2020. p. 603-619. (Lecture Notes in Computer Science). doi: 10.1007/978-3-030-58475-7_35

Large neighborhood search for temperature control with demand response

Abstract

Publication series

Conference

Keywords

UN SDGs

Access to Document

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Cite this