Advertisement

Demand response optimized heat pump control for service sector buildings

A Modular Framework for Simulation and Building Operation
  • Edith Birrer
  • Cyril Picard
  • Patrick Huber
  • Daniel Bolliger
  • Alexander Klapproth
Special Issue Paper
  • 266 Downloads

Abstract

With an increasing amount of volatile renewable electrical energy, the balancing of demand and supply becomes more and more demanding. Demand response is one of the emerging tools in this new landscape. Targeting service sector buildings, we investigated a tariff driven demand response model as a means to shave electrical peak loads and thus reducing grid balancing energy. In this paper is presented a software framework for load shifting which uses a tariff signal for the electric energy as minimization target. The framework can be used both on top of an existing building management system to shift heat generation towards low-tariff times, as well as to simulate load shifting for different buildings, heat pumps and storage configurations. Its modular architecture allows us to easily replace optimizers, weather data providers or building management system adapters. Our results show that even with the current TOU tariff system, up to 34 % of cost savings and up to 20 % reduction in energy consumption can be achieved. With Sub-MPC, a modified MPC optimizer, we could reduce computing times by a factor 50, while only slightly affecting the quality of the optimization.

Keywords

ICT in buildings and housing Building energy operating systems Heating devices and energy networks Demand response Dynamic electricity prices Load shifting Simulation Building automation 

Notes

Acknowledgments

Without the knowledge and financial support of our partners from SFOE (Swiss Federal Office of Energy), BKW, MeteoSchweiz, Siemens and Swissgrid, this work would not have been realized. This complete set of stakeholders in the field of DR gives iHomeLab the chance to derive simulation and controller software and corresponding models. Special thanks go to our colleagues Thierry Prud’homme and Stefan Ineichen from the CC Electronics of the Lucerne University of Applied Sciences and Arts, who provided the thermal building model.

References

  1. 1.
    Mabey N, Burke T, Gallagher L, Born C, and Kewley B (2015) COP21 outcome and what’s next for climate action [Online]. Available: https://www.e3g.org/library/judging-cop21-outcome-and-whats-next-for-climate-action. Accessed 13 Mar 2016
  2. 2.
    Domigall Y, Albani A, Winter R (2013) Effects of demand charging and photovoltaics on the grid. In: Industrial electronics society, IECON 2013-39th annual conference of the IEEE, pp 4739–4744Google Scholar
  3. 3.
    Koch S, Wiederkehr M (2010) Lokales Lastmanagement [Online]. Available: http://www.iast.ch/lastmanagement_ch/. Accessed 14 Mar 2016
  4. 4.
    Lendi D (2011), Microgrids und deren Möglichkeiten, In: Presented at Smart Grid Circle, ReussbühlGoogle Scholar
  5. 5.
    Müller EA, Graf E, Kobel B, Humi A, Wenger R, Frei U, Christen C, Moser R, Fritzsche C, Mathys O (2013) Potential der Schweizer Infrastrukturanlagen zur Lastverschiebung, Bern Bundesamt Für Energ. BFEGoogle Scholar
  6. 6.
  7. 7.
    Palensky P, Dietrich D (2011) Demand side management: demand response, intelligent energy systems, and smart loads. Ind Inform IEEE Trans 7(3):381–388CrossRefGoogle Scholar
  8. 8.
    Loock M, Kuenzel K, Wüstenhagen R (2010) IMPROSUME-the impact of prosumers in a smart grid based energy market [Online]. Available: https://www.alexandria.unisg.ch/id/project/70172. Accessed 14 Mar 2016
  9. 9.
    Coquoz J, Hoffmann VH, Girod B (2012) Potential contribution of households’ demand response for integration of distributed solar photovoltaic in Switzerland. Zür. ETH ZürGoogle Scholar
  10. 10.
    econcept AG (2009) Smart Metering für die Schweiz: Potenziale, Erfolgsfaktoren und Massnahmen für die Steigerung der Energieeffizienz. Bundesamt Für Energ. BFEGoogle Scholar
  11. 11.
    SUPSI, BFH, Bacher Energie AG (2014) Swiss2G—Pilot-and demonstration project; an innovative concept for the decentralized management of distributed energy generation, storage and consumption and consumer acceptance. Bundesamt für Energie BFE, BernGoogle Scholar
  12. 12.
    von Roon S, Gobmaier T (2010) Demand response in der industrie-Status und Potenziale in Deutschland. Münch, Forschungsstelle Für Energiewirtschaft EV FfEGoogle Scholar
  13. 13.
    Birrer E, Bolliger D, Kyburz R, Klapproth A, Summermatter S (2015) Load Shift potential analysis using various demand response tariff models on swiss service sector buildings. In: Presented at the 8th international conference on energy efficiency in domestic appliances and lighting—EEDAL’15, LucerneGoogle Scholar
  14. 14.
    Široký J, Oldewurtel F, Cigler J, Prívara S (2011) Experimental analysis of model predictive control for an energy efficient building heating system. Appl Energy 88(9):3079–3087CrossRefGoogle Scholar
  15. 15.
    Mayer B, Killian M, Kozek M (2015) Management of hybrid energy supply systems in buildings using mixed-integer model predictive control. Energy Convers Manag 98:470–483CrossRefGoogle Scholar
  16. 16.
    Castilla M, Álvarez JD, Berenguel M, Rodríguez F, Guzmán JL, Pérez M (2011) A comparison of thermal comfort predictive control strategies. Energy Build 43(10):2737–2746CrossRefGoogle Scholar
  17. 17.
    Schwarzer K (2004) Lacasa: Einsatz von MATLAB-Simulink zur energetischen Analyse und Optimierung von Alt-und Neubauten inklusive Heizungs-, Lüftungs-und Klimatechnik/Hrsg.: FIA-Projekt, Forschungs-Informations-Austausch im Fachinstitut Gebäude-Klima eV Projekt: Solar-Institut Jülich an der Fachhochschule AachenGoogle Scholar
  18. 18.
    MATLAB and Simulink, R2015a ed. The MathWorks, Inc., Natick, R2015aGoogle Scholar
  19. 19.
    Oldewurtel F, Parisio A, Jones CN, Morari M, Gyalistras D, Gwerder M, Stauch V, Lehmann B, Wirth K (2010) Energy efficient building climate control using stochastic model predictive control and weather predictions. In: American Control Conference (ACC), 2010, pp 5100–5105Google Scholar
  20. 20.
    Löfberg J (2004) YALMIP: a toolbox for modeling and optimization in MATLAB, pp 284–289Google Scholar
  21. 21.
    Dott R, Haller MY, Ruschenburg J, Ochs F, Bony J (2013) The reference framework for system simulations of the IEA SHC Task 44/HPP Annex 38 Part B: buildings and space heat load. In: A Technical Report of Subtask C, Report C1 Part BGoogle Scholar
  22. 22.
    Feist W (2005) First steps: what can be a passive house in your region with your climate. Passive House Inst, Www Passiv DarmstadtGoogle Scholar
  23. 23.
    I. MINERGIE (2008) The MINERGIE-standard for buildings. Ver. Minergie Bern SwitzGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Edith Birrer
    • 1
  • Cyril Picard
    • 1
  • Patrick Huber
    • 1
  • Daniel Bolliger
    • 1
  • Alexander Klapproth
    • 1
  1. 1.Lucerne University of Applied Sciences and Arts–Engineering & Architecture, CC-iHomeLabLucerneSwitzerland

Personalised recommendations