Filipe André Barata, José M. Igreja, Rui Neves-Silva

Distributed MPC for Thermal Comfort and Load Allocation with Energy Auction

This paper presents a distributed predictive control methodology for indoor thermal comfort that optimizes the consumption of a limited shared energy resource using an integrated demand-side management approach that involves a power price auction plus an appliance loads allocation scheme. The control objective for each subsystem (house or building) aims to minimize the energy cost while maintaining the indoor temperature inside comfort limits. In a distributed coordinated multi-agent ecosystem, each house or building control agent achieves its objectives while sharing, among them, the available energy through the introduction of particular coupling constraints in their underlying optimization problem. Coordination is maintained by a daily green energy auction bring in a demand-side management approach. The implemented distributed MPC algorithm is described and validated with simulation studies.

Keywords:

renewable energy; energy savings; control,

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Available on: http://www.storepet-fp7.eu/project- overview. I. Korolija, L. Marjanovic-Halburd, Y. Zhang, and V. I. Hanby, “Influence of building parameters and HVAC systems coupling on building energy performance,” Energy Build., vol. 43, no. 6, pp. 1247–1253, Jun. 2011.
U.S Energy Information Administration: Available on: http://www.eia.gov/ . P. Perrod, R. Critchley, E. Catz, M. Bazargan, “New participants in SmartGrids and associated challenges in the transition towards the grid of the future”, IEEE Bucharest Power Tech Conference, Bucharest. Page(s): 1 – 5, 2009.
A. Kosek, G. Costanzo, H. Bindner and O. Gehrke, “An overview of demand side management control schemes for buildings in smart grids”, IEEE International Conference on Smart Energy Grid Engineering (SEGE), Oshawa, Canada. 2013.
C. Chen, Y. Zhu, Y. Xu, “Distributed generation and Demand Side Management”, Proceedings of International Conference on Electricity Distribution (CICED) 2010.
T. Luo, G. Ault, S. Galloway, “Demand Side Management in a highly decentralized energy future”, Proceedings of 45th International Universities Power Engineering Conference (UPEC), 2010.
D. Callaway, “Tapping the energy storage potential in electric loads to deliver load following and regulation, with application to wind energy”, Energy Conversion and Management. Pp 1389–1400. 2009.
V. Molderink, V. Bakker, M. Bosman, J. Hurink and G. Smith, “Management and Control of Domestic Smart Grid Technology”, IEEE Transactions on Smart Grid, Vol. 1 , Issue: 2, 2010 , Page(s): 109 – 119.
F. Saffre and R. Gedge, “Demand-Side Management for the Smart Grid”, Network Operations and Management Symposium Workshops (NOMS Wksps), 2010 IEEE/IFIP , Page(s): 300 – 303I.
V. Hamidi, F. Li and F. Robinson, “The effect of responsive demand in domestic sector on power system operation in the networks with high penetration of renewable”, IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century, Page(s): 1 – 8. 2008.
S. Gottwalt, W. Ketter, C. Block, J. Collins, and C. Weinhardt, “Demand side management simulation of household behaviour under variable prices”, Energy Policy, Vol. 39, no. 12, pp. 8163 – 8174, 2011.
A. Di Giorgio, L. Pimpinella, F.Liberati, “A Model Predictive Control Approach to the Load Shifting Problem in a Household Equipped with an Energy Storage Unit”, 20th Mediterranean Conference on Control and Automation (MED 2012), Barcelona, 3-6 July 2012.
E. Matallanas, M. Castillo-Cagigal, A. Gutierrez, F. Monasterio Huelin, E. Caamano-Martin, D. Masa, and J. Jimenez-Leube, “Neural network controller for Active Demand-Side Management with PV energy in the residential sector”, Applied Energy, Vol. 91, no. 1, pp.90 – 97, 2012.
P. Moroşan, R. Bourdais, D. Dumur and J. Buisson, “Building temperature regulation using a distributed model predictive control”, American Control Conference (ACC). Pp. 3174 – 3179. 2010.
Y. Ma, A. Kelman, A. Daly and F. Borrelli, “Predictive Control for Energy Efficient Buildings with Thermal Storage”, IEEE Control System Magazine, February 2012. Vol 32, nº1, pp. 44 – 64.
R. Balan, S. Stan, C. Lapusan, “A Model Based Predictive Control Algorithm for Building Temperature Control”, 3rd IEEE International Conference on Digital Ecosystems and Technologies, DEST '09, pp. 540 – 545. R. Freire, G. Oliveira, N. Mendes, “Non-linear Predictive Controllers for Thermal Comfort Optimization and energy Saving.”, IFAC WS ESC’06 Energy Saving Control in Plants and Buildings, pp. 87-92. 2006.
F. Barata J. Igreja and R. Neves-Silva “Model Predictive Control for Thermal House Comfort with Limited Energy Resources”, Proceedings of the 10th Portuguese Conference on Automatic Control, Madeira, July 2012, pp. 146-151.
M. Maasoumy, M. Razmara, M. Shahbakhti, and a. S. Vincentelli, “Handling model uncertainty in model predictive control for energy efficient buildings,” Energy Build., vol. 77, pp. 377–392, Jul. 2014.
J. Cigler, P. Tomáško, and J. Široký, “BuildingLAB: A tool to analyze performance of model predictive controllers for buildings,” Energy Build., vol. , pp. 34–41, Feb. 2013.
R. Negenborn, Multi-Agent Model Predictive Control with Applications to Power Networks. In: PhD Thesis, Technische Universiteit Delft. Nederland, 2007.
R. Scattolini, “Architectures for distributed and hierarchical Model Predictive Control – A review”, Journal of Process Control, Vol.19, pp 723–731. 2009.
V. Chandan and A. G. Alleyne, “Decentralized predictive thermal control for buildings,” J. Process Control, pp. 1–16, Apr. 2014.
P. Trodden and A. Richards, “Distributed model predictive control of linear systems with persistent disturbances”, International Journal of Control, Vol. 83, No. 8, August 2010, pp. 1653–1663. 2010. T. Keviczky, F. Borrelli, and G. Balas,
“Decentralized Receding Horizon Control for Large Scale Dynamically Decoupled Systems”, Automatica, Vol. 42, pp. 2105–2115. 2006.
Q. Xu, X. Jia, L. He, “The control of Distributed Generation International Conference On Electronics and Information Engineering (ICEIE), Vol. 1 On page(s): V1-30 - V1-33. Multi-Agent System”, K. Mets, M. Strobbe, T. Verschueren, T. Roelens, F. Turck, C. Develder, “Distributed Multi-Agent Algorithm for Residential” Energy Management in Smart Grids”, Proceedings of IEEE IFIP Network Operations and Management Symposium, pp.435-443. 2012.
M. Pipattanasomporn, H. Feroze, S. Rahman, “Multi-agent systems in a distributed smart grid: Design and implementation”, PSCE '09, IEEE/PES Power Systems Conference and Exposition. 2009.
F. Barata and R. Neves-Silva, “Distributed model predictive control for thermal house comfort with auction of available energy”, Proceedings of International Conference on Smart Grid Technology, Economics and Policies (SG-TEP 2012). 2012.
I. Hazyuk, C. Ghiaus, D. Penhouet, “Optimal temperature control of intermittently heated buildings using Model Predictive Control: Part I - Building modeling”, Environment, Vol. 51, pp. 379-387. 2012. Journal Building and
B. Bequette, Process Control, Modeling, Design and Simulation, Prentice Hall, pp.58. 2003. Annexes
Equation (23) represents the continuous space-state model for a whole house with Nd divisions and Nd heating/cooling sources, obtained using the thermal model of Section 2.2.     u  iNd   iNd C     CiNd      ReqiNdiNd    TiNd       CiNd RiNdiNd   i C  CiNd RiNdiNd CiNd ReqiNdiNd CiNd CiNd RiNdiNd RiNdNd CNd  iNdi R C eqii R i RC  i C iNdi R i RC C iNdi R     . (23)