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Demand-response in building heating systems: A Model Predictive Control approach

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  • Bianchini, Gianni
  • Casini, Marco
  • Vicino, Antonio
  • Zarrilli, Donato

Abstract

In this paper we consider the problem of optimizing the operation of a building heating system under the hypothesis that the building is included as an active consumer in a demand response program. Demand response requests to the building operational system come from an external market player or a grid operator. Requests assume the form of price–volume signals specifying a maximum volume of energy to be consumed during a given time slot and a monetary reward assigned to the participant in case it fulfills the conditions. A receding horizon control approach is adopted for the minimization of the energy bill, by exploiting a simplified model of the building. Since the resulting optimization problem is a mixed integer linear program which turns out to be manageable only for buildings with very few zones, a heuristics is devised to make the algorithm applicable to realistic size problems as well. The derived control law is tested on the realistic simulator EnergyPlus to evaluate pros and cons of the proposed algorithm. The performance of the suboptimal control law is evaluated on small- and large-scale test cases.

Suggested Citation

  • Bianchini, Gianni & Casini, Marco & Vicino, Antonio & Zarrilli, Donato, 2016. "Demand-response in building heating systems: A Model Predictive Control approach," Applied Energy, Elsevier, vol. 168(C), pages 159-170.
    • Handle: RePEc:eee:appene:v:168:y:2016:i:c:p:159-170
    DOI: 10.1016/j.apenergy.2016.01.088
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    References listed on IDEAS

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    1. Patteeuw, Dieter & Bruninx, Kenneth & Arteconi, Alessia & Delarue, Erik & D’haeseleer, William & Helsen, Lieve, 2015. "Integrated modeling of active demand response with electric heating systems coupled to thermal energy storage systems," Applied Energy, Elsevier, vol. 151(C), pages 306-319.
    2. Xue, Xue & Wang, Shengwei & Yan, Chengchu & Cui, Borui, 2015. "A fast chiller power demand response control strategy for buildings connected to smart grid," Applied Energy, Elsevier, vol. 137(C), pages 77-87.
    3. Hong, Seung Ho & Yu, Mengmeng & Huang, Xuefei, 2015. "A real-time demand response algorithm for heterogeneous devices in buildings and homes," Energy, Elsevier, vol. 80(C), pages 123-132.
    4. Cardell, J.B. & Anderson, C.L., 2015. "Targeting existing power plants: EPA emission reduction with wind and demand response," Energy Policy, Elsevier, vol. 80(C), pages 11-23.
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