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.
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 | |
| Publication status | Published - 2020 |
| Event | International Conference on Principles and Practice of Constraint Programming 2020 - Louvain-la-Neuve, Belgium Duration: 7 Sep 2020 → 11 Sep 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 | Belgium |
| City | Louvain-la-Neuve |
| Period | 7/09/20 → 11/09/20 |
| Internet address |
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Keywords
- Sustainability
- Energy systems
- Power systems
- Control
- Smart grid
- Microgrid
- Large neighborhood search
- Local search
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