Abstract
The recent surge in electricity prices has increased the demand for cost-effective and sophisticated heat pump controllers. As domestic floor heating systems are becoming increasingly popular, there is an urgent need for more efficient control systems that include also heat buffer tanks to account for fluctuating energy prices. We propose a scalable thermal model of the hot water buffer tank together with a mixing loop and evaluate its operation and performance on an experimental Danish house from the OpSys project. We experimentally assess the buffer tank’s quality by selecting the proper size and number of virtual layers using an industry-standard controller. Finally, we integrate the buffer tank and mixing loop into the heating system and create an intelligent Stratego controller to examine their performance. We analyze the tradeoff between cost and comfort for different buffer tank sizes to determine when a buffer tank or a mixing loop should be included in the system. By providing a detailed understanding of the buffer tank and mixing loop, our study enables the clients to make better decisions regarding the appropriate buffer tank size and when to install a mixing loop based on their specific heating needs.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Energy consumption in households, April 2023. https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Energy_consumption_in_households
Daryabari, M.K., Keypour, R., Golmohamadi, H.: Stochastic energy management of responsive plug-in electric vehicles characterizing parking lot aggregators. Appl. Energy 279, 115751 (2020)
Agesen, M.K., et al.: Toolchain for user-centered intelligent floor heating control. In: IECON 2016–42nd Annual Conference of the IEEE Industrial Electronics Society, pp. 5296–5301 (2016)
Larsen, K.G., Mikučionis, M., Muñiz, M., Srba, J., Taankvist, J.H.: Online and compositional learning of controllers with application to floor heating. In: Chechik, M., Raskin, J.-F. (eds.) TACAS 2016. LNCS, vol. 9636, pp. 244–259. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-662-49674-9_14
Vogler-Finck, P.J.C., Wisniewski, R., Popovski, P.: Reducing the carbon footprint of house heating through model predictive control - a simulation study in Danish conditions. Sustain. Cities Soc. 42, 558–573 (2018)
Hasrat, I.R., Jensen, P.G., Larsen, K.G., Srba, J.: End-to-end heat-pump control using continuous time stochastic modelling and uppaal stratego. In: Aït-Ameur, Y., Crăciun, F. (eds.) TASE 2022. LNCS, vol. 13299, pp. 363–380. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-10363-6_24
Juhl, R., Møller, J.K., Madsen, H.: CTSMR - Continuous Time Stochastic Modeling in R. arXiv (2016)
David, A., Jensen, P.G., Larsen, K.G., Mikučionis, M., Taankvist, J.H.: Uppaal Stratego. In: Baier, C., Tinelli, C. (eds.) TACAS 2015. LNCS, vol. 9035, pp. 206–211. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-662-46681-0_16
Hermansen, R., Smith, K., Thorsen, J.E., Wang, J., Zong, Y.: Model predictive control for a heat booster substation in ultra low temperature district heating systems. Energy 238, 121631 (2022)
Sepulveda, A., Paull, L., Morsi, W.G., Li, H., Diduch, C.P., Chang, L.: A novel demand side management program using water heaters and particle swarm optimization. In: 2010 IEEE Electrical Power and Energy Conference, pp. 1–5. IEEE (2010)
Paull, L., MacKay, D., Li, H., Chang, L.: Awater heater model for increased power system efficiency. In: 2009 Canadian Conference on Electrical and Computer Engineering, pp. 731–734. IEEE (2009)
Lu, S., et al.: Centralized and decentralized control for demand response. In: ISGT 2011, pp. 1–8. IEEE (2011)
Nehrir, M.H., Jia, R., Pierre, D.A., Hammerstrom, D.J.: Power management of aggregate electric water heater loads by voltage control. In: 2007 IEEE Power Engineering Society General Meeting, pp. 1–6. IEEE (2007)
Hock, C., Goh, K., Apt, J.: Consumer strategies for controlling electric water heaters under dynamic pricing. In: Carnegie Mellon Electricity Industry Center Working Paper (2004)
Dolan, P.S., Nehrir, M.H., Gerez, V.: Development of a Monte Carlo based aggregate model for residential electric water heater loads. Electr. Power Syst. Res. 36(1), 29–35 (1996)
Laurent, J.C., Malhame, R.P.: A physically-based computer model of aggregate electric water heating loads. IEEE Trans. Power Syst. 9(3), 1209–1217 (1994)
Lane, I.E., Beute, N.: A model of the domestic hot water load. IEEE Trans. Power Syst. 11(4), 1850–1855 (1996)
Jia, R., Nehrir, M.H., Pierre, D.A.: Voltage control of aggregate electric water heater load for distribution system peak load shaving using field data. In: 2007 39th North American Power Symposium, pp. 492–497 (2007)
Elgazzar, K., Li, H., Chang, L.: A centralized fuzzy controller for aggregated control of domestic water heaters. In: 2009 Canadian Conference on Electrical and Computer Engineering, pp. 1141–1146. IEEE (2009)
Paull, L., Li, H., Chang, L.: A novel domestic electric water heater model for a multi-objective demand side management program. Electr. Power Syst. Res. 80(12), 1446–1451 (2010)
Kondoh, J., Lu, N., Hammerstrom, D.J.: An evaluation of the water heater load potential for providing regulation service. In: 2011 IEEE Power and Energy Society General Meeting, pp. 1–8. IEEE (2011)
Diao, R., Lu, S., Elizondo, M., Mayhorn, E., Zhang, Y., Samaan, N.: Electric water heater modeling and control strategies for demand response. In: 2012 IEEE Power and Energy Society General Meeting, pp. 1–8. IEEE (2012)
Yang, X., Svendsen, S.: Improving the district heating operation by innovative layout and control strategy of the hot water storage tank. Energy Build. 224, 110273 (2020)
Farooq, A.A., Afram, A., Schulz, N., Janabi-Sharifi, F.: Grey-box modeling of a low pressure electric boiler for domestic hot water system. Appl. Thermal Eng. 84, 257–267 (2015)
Furbo, S.: Heat storage for solar heating systems. Educational Note, BYG.DTU U-071, ISSN 1396-4046 (2005)
Hessam Golmohamadi and Kim Guldstrand Larsen: Economic heat control of mixing loop for residential buildings supplied by low-temperature district heating. J. Build. Eng. 46, 103286 (2022)
Overgaard, A., Nielsen, B.K., Kallesøe, C.S., Bendtsen, J.D.: Reinforcement learning for mixing loop control with flow variable eligibility trace. In: 2019 IEEE Conference on Control Technology and Applications (CCTA), pp. 1043–1048 (2019)
Volkova, A., et al.: Energy cascade connection of a low-temperature district heating network to the return line of a high-temperature district heating network. Energy 198, 117304 (2020)
Meesenburg, W., Ommen, T., Thorsen, J.E., Elmegaard, B.: Economic feasibility of ultra-low temperature district heating systems in newly built areas supplied by renewable energy. Energy 191, 116496 (2020)
Rahmatmand, A., Vratonjic, M., Sullivan, P.E.: Energy and thermal comfort performance evaluation of thermostatic and electronic mixing valves used to provide domestic hot water of buildings. Energy Build. 212, 109830 (2020)
Jensen, S.Ø.: OPSYS tools for investigating energy flexibility in houses with heat pumps (2018). https://www.annex67.org/media/1838/report-opsys-flexibilitet.pdf
Dayssault systems. dymola (dynamic modeling laboratory) systems engineering), October 2022. https://www.3ds.com/products-services/catia/products/dymola/
Larsen, K.G., Pettersson, P., Yi, W.: Uppaal in a nutshell. Int. J. Softw. Tools Technol. Transf. 1(1-2), 134–152 (1997)
Behrmann, G., et al.: Uppaal 4.0. IEEE Computer Society (2006)
Bulychev, P., Legay, A., Wang, Z.: Uppaal-SMC: statistical model checking for priced timed automata. arXiv preprint arXiv:1207.1272 (2012)
Behrmann, G., Cougnard, A., David, A., Fleury, E., Larsen, K.G., Lime, D.: UPPAAL-Tiga: time for playing games! In: Damm, W., Hermanns, H. (eds.) CAV 2007. LNCS, vol. 4590, pp. 121–125. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-73368-3_14
Jensen, P.G., Larsen, K.G., Legay, A., Nyman, U.: Integrating tools: co-simulation in Uppaal using FMI-FMU. In: 2017 22nd International Conference on Engineering of Complex Computer Systems (ICECCS), pp. 11–19. IEEE (2017)
Hasrat, I.R., Jensen, P.G., Larsen, K.G., Srba, J.: Complete Uppaal Stratego model for “modelling of hot water buffer tank and mixing loop for an intelligent heat pump control", May 2023. https://github.com/ImranRiazAAU/BufferTankModelling.git
Control technology: weather compensated controls (Viessmann: climate of innovation) (2023). https://viessmanndirect.co.uk/files//8e57dbc7-8a10-4065-bcc6-a27700ee752a/weather_comp.pdf
Acknowledgements
We would like to thank Per Printz Madsen and Hessam Golmohammadi for their extensive help in understanding the physics of the buffer tanks. This research is partly funded by the ERC Advanced Grant Lasso, the Villum Investigator Grant S4OS, and DIREC: Digital Research Centre Denmark.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Hasrat, I.R., Jensen, P.G., Larsen, K.G., Srba, J. (2023). Modelling of Hot Water Buffer Tank and Mixing Loop for an Intelligent Heat Pump Control. In: Cimatti, A., Titolo, L. (eds) Formal Methods for Industrial Critical Systems. FMICS 2023. Lecture Notes in Computer Science, vol 14290. Springer, Cham. https://doi.org/10.1007/978-3-031-43681-9_7
Download citation
DOI: https://doi.org/10.1007/978-3-031-43681-9_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-43680-2
Online ISBN: 978-3-031-43681-9
eBook Packages: Computer ScienceComputer Science (R0)