Advertisement

Large Neighborhood Search for Temperature Control with Demand Response

Conference paper
  • 621 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12333)

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 

References

  1. 1.
    Achterberg, T., Koch, T., Martin, A.: Branching rules revisited. Oper. Res. Lett. 33(1), 42–54 (2005)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Anderson, R., Huchette, J., Tjandraatmadja, C., Vielma, J.P.: Strong mixed-integer programming formulations for trained neural networks. In: Lodi, A., Nagarajan, V. (eds.) IPCO 2019. LNCS, vol. 11480, pp. 27–42. Springer, Cham (2019).  https://doi.org/10.1007/978-3-030-17953-3_3CrossRefzbMATHGoogle Scholar
  3. 3.
    Azuatalam, D., Mhanna, S., Chapman, A., Verbič, G.: Optimal HVAC scheduling using phase-change material as a demand response resource. In: 2017 IEEE Innovative Smart Grid Technologies-Asia (ISGT-Asia). IEEE (2017)Google Scholar
  4. 4.
    Bartolini, A., Lombardi, M., Milano, M., Benini, L.: Neuron constraints to model complex real-world problems. In: Lee, J. (ed.) CP 2011. LNCS, vol. 6876, pp. 115–129. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-23786-7_11CrossRefGoogle Scholar
  5. 5.
    Boyd, S., Vandenberghe, L.: Convex Optimization. Cambridge University Press, Cambridge (2004)CrossRefGoogle Scholar
  6. 6.
    Chassin, D.P., Fuller, J.C., Djilali, N.: GridLAB-D: an agent-based simulation framework for smart grids. J. Appl. Math. 2014 (2014) Google Scholar
  7. 7.
    Cybenko, G.: Approximation by superpositions of a sigmoidal function. Math. Control Signals Syst. 2(4), 303–314 (1989).  https://doi.org/10.1007/BF02551274
  8. 8.
    Feydy, T., Stuckey, P.J.: Lazy clause generation reengineered. In: Gent, I.P. (ed.) CP 2009. LNCS, vol. 5732, pp. 352–366. Springer, Heidelberg (2009).  https://doi.org/10.1007/978-3-642-04244-7_29CrossRefGoogle Scholar
  9. 9.
    FICO: MIP formulations and linearizations (2009). https://www.fico.com/en/resource-download-file/3217
  10. 10.
    Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016). http://www.deeplearningbook.org
  11. 11.
    Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: ICLR 2015 (2015)Google Scholar
  12. 12.
    Kohlhepp, P., Harb, H., Wolisz, H., Waczowicz, S., Müller, D., Hagenmeyer, V.: Large-scale grid integration of residential thermal energy storages as demand-side flexibility resource: a review of international field studies. Renew. Sustain. Energy Rev. 101, 527–547 (2019)CrossRefGoogle Scholar
  13. 13.
    Motegi, N., Piette, M.A., Watson, D.S., Kiliccote, S., Xu, P.: Introduction to commercial building control strategies and techniques for demand response. Technical report California Energy Commission, PIER (2006)Google Scholar
  14. 14.
    de Nijs, F., Stuckey, P.J.: Risk-aware conditional replanning for globally constrained multi-agent sequential decision making. In: Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems. IFAAMAS (2020)Google Scholar
  15. 15.
    Pérez-Lombard, L., Ortiz, J., Pout, C.: A review on buildings energy consumption information. Energy Build. 40(3), 394–398 (2008)CrossRefGoogle Scholar
  16. 16.
    Pisinger, D., Røpke, S.: Large neighborhood search. In: Gendreau, M., Potvin, J.Y. (eds.) Handbook of metaheuristics, chapter 13, pp. 399–419. Springer (2010).  https://doi.org/10.1007/978-1-4419-1665-5_13
  17. 17.
    Pratt, R.G., Taylor, Z.T.: Development and testing of an equivalent thermal parameter model of commercial buildings from time-series end-use data. Technical report Pacific Northwest Laboratory, Richland, Washington (1994)Google Scholar
  18. 18.
    Say, B., Wu, G., Zhou, Y.Q., Sanner, S.: Nonlinear hybrid planning with deep net learned transition models and mixed-integer linear programming. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI-17, pp. 750–756 (2017)Google Scholar
  19. 19.
    Shaw, P.: Using constraint programming and local search methods to solve vehicle routing problems. In: Maher, M., Puget, J.-F. (eds.) CP 1998. LNCS, vol. 1520, pp. 417–431. Springer, Heidelberg (1998).  https://doi.org/10.1007/3-540-49481-2_30CrossRefGoogle Scholar
  20. 20.
    Vázquez-Canteli, J.R., Nagy, Z.: Reinforcement learning for demand response: A review of algorithms and modeling techniques. Appl. Energy 235, 1072–1089 (2019)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Monash UniversityMelbourneAustralia
  2. 2.CSIRO Data61MelbourneAustralia

Personalised recommendations