Abstract
The obtained results are put into perspective and discussed in this section. In particular, the development, tuning and computational efforts of the flexibility controllers are discussed, as well as the differences between costs or emissions optimization, and the practical barriers still hindering the large-scale deployment of such controllers.
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References
Killian M, Kozek M (2016) Ten questions concerning model predictive control for energy efficient buildings. Build Environ 105:403–412. ISSN: 03601323. https://doi.org/10.1016/j.buildenv.2016.05.034
Thieblemont H, Haghighat F, Ooka R, Moreau A (2017) Predictive control strategies based on weather forecast in buildings with energy storage system: a review of the state-of-the art. Energy Build 153:485–500. ISSN: 03787788. https://doi.org/10.1016/j.enbuild.2017.08.010
Bacher P, Madsen H (2011) Identifying suitable models for the heat dynamics of buildings. Energy Build 43(7):1511–1522. ISSN: 03787788. https://doi.org/10.1016/j.enbuild.2011.02.005
Reynders G, Diriken J, Saelens D (2014) Quality of grey-box models and identified parameters as function of the accuracy of input and observation signals. Energy Build 82:263–274. ISSN: 03787788. https://doi.org/10.1016/j.enbuild.2014.07.025
Ferracuti F, Fonti A, Ciabattoni L, Pizzuti S, Arteconi A, Helsen L, Comodi G (2017) Datadriven models for short-term thermal behaviour prediction in real buildings. Appl Energy 204:1375–1387. ISSN: 03062619. https://doi.org/10.1016/j.apenergy.2017.05.015
Prívara S, Cigler J, Váňa Z, Oldewurtel F, Sagerschnig C, Žáčeková E (2013) Building modeling as a crucial part for building predictive control. Energy Build 56:8–22. ISSN: 03787788. https://doi.org/10.1016/j.enbuild.2012.10.024
De Coninck R, Magnusson F, Akesson J, Helsen L (2015) Toolbox for development and validation of grey-box building models for forecasting and control. J Build Perform Simul (July):1–16. ISSN: 1940-1493. https://doi.org/10.1080/19401493.2015.1046933
Institute for Housing and Environment (Germany), TABULA project (2016). http://episcope.eu/ (visited on 08/20/2019)
Rouchier S, Jiménez MJ, Castaño S (2019) Sequential Monte Carlo for on-line parameter estimation of a lumped building energy model. Energy Build 187:86–94. ISSN: 03787788. https://doi.org/10.1016/j.enbuild.2019.01.045
Radecki P, Hencey B (2017) Online model estimation for predictive thermal control of buildings. IEEE Trans Control Syst Technol 25(4):1414–1422. ISSN: 10636536. https://doi.org/10.1109/TCST.2016.2587737
Masy G, Georges E, Verhelst C, Lemort V (2015) Smart grid energy flexible buildings through the use of heat pumps and building thermal mass as energy storage in the Belgian context. Sci Technol Built Environ 4731(August):800–811. ISSN: 2374-4731. https://doi.org/10.1080/23744731.2015.1035590
Wood G, Day R, Creamer E, van der Horst D, Hussain A, Liu S, Shukla A, Iweka O, Gaterell M, Petridis P, Adams N, Brown V (2019) Sensors, sense-making and sensitivities: UK household experiences with a feedback display on energy consumption and indoor environmental conditions. Energy Res Soc Sci 55(April):93–105. ISSN: 22146296. https://doi.org/10.1016/j.erss.2019.04.013
Kazanci OB, Olesen BW (2014) Sustainable plus-energy houses (Baeredygtige Energi-Plus huse) final report. Elforsk, Technical Report. https://elforsk.dk/sites/elforsk.dk/files/media/dokumenter/elforsk/Slutrapport
Verhelst C, Logist F, Van Impe J, Helsen L (2012) Study of the optimal control problem formulation for modulating air-to-water heat pumps connected to a residential floor heating system. Energy Build 45:43–53. ISSN: 03787788. https://doi.org/10.1016/j.enbuild.2011.10.015
Camacho EF, Bordons C (2007) Model predictive control. Advanced textbooks in control and signal processing, vol 53. Springer, London, pp 1689–1699. ISBN: 978-1-85233-694-3. https://doi.org/10.1007/978-0-85729-398-5
Klein K, Killinger S, Fischer D, Streuling C, Salom J, Cubi E (2016) Comparison of the future residual load in fifteen countries and requirements to grid-supportive building operation. In: Eurosun 2016, Palma deMallorca, Spain, pp 11–14
Hu M, Xiao F, Jørgensen JB, Wang S (2019) Frequency control of air conditioners in response to real-time dynamic electricity prices in smart grids. Appl Energy 242(March):92–106. ISSN: 03062619. https://doi.org/10.1016/j.apenergy.2019.03.127
De Coninck R, Helsen L (2016) Practical implementation and evaluation of model predictive control for an office building in Brussels. Energy Build 111:290–298. ISSN: 03787788. https://doi.org/10.1016/j.enbuild.2015.11.014
Bundersverband Wärmepumpe, Regularium für das Label “SG Ready” für elektrische Heizungsund Warmwasserw ärmepumpen, Berlin, Germany (2013)
Fischer D, Wolf T, Triebel M-A (2017) Flexibility of heat pump pools: the use of SG-Ready from an aggregator’s perspective. In: 12th IEA heat pump conference, pp 1–12
OpenADR Alliance, OpenADR (2019). https://www.openadr.org/ (visited on 08/19/2019)
SMS-PLC, SmArt BI-directional multi eNergy gAteway (2019). https://sabina-project.eu/mission-objectives/ (visited on 09/15/2019)
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Péan, T. (2021). Discussions, Conclusions and Outlook on Further Research. In: Heat Pump Controls to Exploit the Energy Flexibility of Building Thermal Loads. Springer Theses. Springer, Cham. https://doi.org/10.1007/978-3-030-63429-2_7
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