Abstract
This paper presents a distributed predictive control methodology for indoor thermal comfort that optimizes the consumption of a limited energy resource using a demand-side management approach. The building divisions are modeled using an electro-thermal modular scheme. For control purposes, this modular scheme allows an easy modeling of buildings with different plans where adjacent areas can thermally interact. The control objective of each subsystem is to minimize the energy cost while maintaining the indoor temperature in the selected comfort bounds. In a distributed coordinated environment, the control uses multiple dynamically coupled agents (one for each subsystem/zone) aiming to achieve satisfaction of available energy coupling constraints. The system is simulated with two zones in a distributed environment.
Chapter PDF
Similar content being viewed by others
References
Moroşan, P., Bourdais, R., Dumur, D., Buisson, J.: Building temperature regulation using a distributed model predictive control. Journal Energy and Buildings, 1445–1452 (2010)
Ma, Y., Kelman, A., Daly, A., Borrelli, F.: Predictive Control for Energy Efficient Buildings with Thermal Storage. IEEE Control System Magazine 32(1), 44–64 (2012)
Freire, R.Z., Oliveira, H.C., Mendes, N.: Predictive controllers for thermal comfort optimization and energy savings. Energy and Buildings 40, 1353–1365 (2008)
Giorgio, A.D., Pimpinella, L., Liberati, F.: A Model predictive Control Approach to the Load Shifting Problem in a household Equipped with an energy Storage Unit. In: Proceedings of the 20th Mediterranean Conference on Control & Automation (MED), Barcelona, Spain, pp. 1491–1498 (2012)
Zong, Y., Kullmann, D., Thavlov, A., Gehrke, O., Bindner, H.W.: Application of predictive control for active load management in a distributed power system with high wind penetration. IEEE Transactions on Smart Grid 3(2), 1055–1062 (2012)
Barata, F., Igreja, J., Neves-Silva, R.: Model Predictive Control for Thermal House Comfort with Limited Energy Resources. In: Proc. of the 10th Portuguese Conference on Automatic Control, pp. 146–151 (July 2012)
Negenborn, R.: Multi-Agent Model Predictive Control with Applications to Power Networks. PhD Thesis, Technische Universiteit Delft. Nederland (2007)
Scattolini, R.: Architectures for distributed and hierarchical Model Predictive Control – A review. Journal of Process Control 19, 723–731 (2009)
Trodden, P., Richards, A.: Distributed model predictive control of linear systems with persistent disturbances. International Journal of Control 83(8), 1653–1663 (2010)
Keviczky, T., Borrelli, F., Balas, G.: Decentralized Receding Horizon Control for Large Scale Dynamically Decoupled Systems. Automatica 42, 2105–2115 (2006)
Siano, P.: Demand response and smart grids—A survey. Renewable and Sustainable Energy Reviews 30, 461–478 (2014)
Hazyuk, I., Ghiaus, C., Penhouet, D.: Optimal temperature control of intermittently heated buildings using Model Predictive Control: Part I e Building modelling. Building and Environment 51, 379–387 (2012)
Luyben, W.: Process Modeling, Simulation and control for Chemicals Engineers, 2nd edn. McGrawHill
Barata, F., Neves-Silva, R.: Distributed Model Predictive Control for Thermal House Comfort with Auction of Available Energy. In: Proceedings of SG-TEP 2012: International Conference on Smart Grid Technology, Economics and Policies (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 IFIP International Federation for Information Processing
About this paper
Cite this paper
Barata, F.A., Neves-Silva, R. (2014). Distributed MPC for Thermal Comfort in Buildings with Dynamically Coupled Zones and Limited Energy Resources. In: Camarinha-Matos, L.M., Barrento, N.S., Mendonça, R. (eds) Technological Innovation for Collective Awareness Systems. DoCEIS 2014. IFIP Advances in Information and Communication Technology, vol 423. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54734-8_34
Download citation
DOI: https://doi.org/10.1007/978-3-642-54734-8_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-54733-1
Online ISBN: 978-3-642-54734-8
eBook Packages: Computer ScienceComputer Science (R0)