Large neighborhood search for temperature control with demand response

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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 languageEnglish
Title of host publicationPrinciples and Practice of Constraint Programming
Subtitle of host publication26th International Conference, CP 2020 Louvain-la-Neuve, Belgium, September 7–11, 2020 Proceedings
EditorsHelmut Simonis
Place of PublicationCham Switzerland
Number of pages17
ISBN (Electronic)9783030584757
ISBN (Print)9783030584740
Publication statusPublished - 2020
EventInternational Conference on Principles and Practice of Constraint Programming 2020 - Louvain-la-Neuve, Belgium
Duration: 7 Sep 202011 Sep 2020
Conference number: 26th (Proceedings) (Website)

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceInternational Conference on Principles and Practice of Constraint Programming 2020
Abbreviated titleCP2020
Internet address


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

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