Xenofon Floros
Department of Computer Science, ETH Zurich, Switzerland
Federico Bergero
CIFASIS-CONICET, Rosario, Argentina
Nicola Ceriani
Politecnico di Milano, Dipartimento di Elettronica, Informazione e Bioingegneria, Italy
Francesco Casella
Politecnico di Milano, Dipartimento di Elettronica, Informazione e Bioingegneria, Italy
Ernesto Kofman
CIFASIS-CONICET, Rosario, Argentina
François Cellier
Department of Computer Science, ETH Zurich, Switzerland
Ladda ner artikel
http://dx.doi.org/10.3384/ecp14096787Ingår i: Proceedings of the 10th International Modelica Conference; March 10-12; 2014; Lund; Sweden
Linköping Electronic Conference Proceedings 96:82, s. 787-797
Concepts such as smart grids; distributed generation and micro–generation of energy; market–driven as well as demand–side energy management; are becoming increasingly important and relevant as emerging trends in the design; management and control of energy systems. Appropriate modeling and design; efficient management and control strategies of such systems are currently being studied. In this line of research a very important enabling component is efficient and reliable simulation. However those energy models are typically large; stiff and exhibiting heavy discontinuities; and at the same time consist of interconnected multi–domain subsystems encompassing electrical; thermal; and thermo-fluid models. Object-Oriented (O–O) languages such as Modelica are obviously well-suited for the modeling of such systems; however; traditional state-ofthe-art hybrid differential algebraic equation solvers cannot efficiently simulate these systems especially when their size grows to the order of hundreds; thousands; or even more interconnected units.
The goal of this paper is to show; through a couple of exemplary case studies; that Quantized State System (QSS) integration methods are ideally suited to solve models of such systems; as they scale up better than traditional methods with the system size; and provide time savings of several orders of magnitude; while achieving comparable numerical precision.
Quantization–Based Integration Methods; QSS; DASSL; Smart–Grids; EnergyMarket; Modelica
[1] http://people.inf.ethz.ch/florosx/modelica2014/.
[2] CIBSE Guide A: Environmental Design. CIBSE
Publications, Norwich, UK, 2006.
[3] F. Bergero, X. Floros, J. Fernández, E. Kofman, and F. E. Cellier. Simulating Modelica models with a Stand–Alone Quantized State Systems Solver. In 9th International Modelica Conference, 2012.
[4] F. E. Cellier and E. Kofman. Continuous System Simulation. Springer, New York, 2006.
[5] N. M. Ceriani, R. Vignali, L. Piroddi, and M. Prandini. An approximate dynamic programming approach to the energy management of a building cooling system. In European Control Conference, Zurich (Switzerland), July 17-19, pages 2026–2031, 2013.
[6] A. Elsheikh, E. Widl, and P. Palensky. Simulating complex energy systems with modelica: A primary evaluation. In Digital Ecosystems Technologies (DEST), 2012 6th IEEE International Conference on, pages 1–6, 2012.
[7] F. Felgner, S. Agustina, R. C. Bohigas, R. Merz, and L. Litz. Simulation of thermal building behaviour in modelica. In 2nd International Modelica Conference, March 2002.
[8] F. Felgner, R. Merz, and L. Litz. Modular modelling of thermal building behaviour using modelica. Mathematical and Computer Modelling of Dynamical Systems, 12(1):35–49, 2006.
[9] J. Fernández and E. Kofman. Implementación autónoma de metodos de integración numérica QSS. Technical report, FCEIA - UNR, Rosario, Argentina, 2012.
[10] X. Floros, F. Bergero, F. E. Cellier, and E. Kofman. Automated simulation of modelica models with qss methods - the discontinuous case -. In 8th International Modelica Conference, March 2011.
[11] X. Floros, F. Cellier, and E. Kofman. Discretizing time or states? a comparative study between dassl and qss. In 3rd International Workshop on Equation-Based Object-Oriented Modeling Languages and Tools, EOOLT, Oslo, Norway, October 3, 2010, pages 107–115, 2010.
[12] P. Fritzson, P. Aronsson, H. Lundvall, K. Nystrom, A. Pop, L. Saldamli, and D. Broman. The OpenModelica Modeling,Simulation, and Development Environment. Proceedings of the 46th Conference on Simulation and Modeling (SIMS’05), pages 83–90, 2005.
[13] P. Fritzson and V. Engelson. Modelica - A Unified Object-Oriented Language for System Modelling and Simulation. In ECOOP, pages 67–90, 1998.
[14] E. Kofman. Discrete Event Simulation of Hybrid Systems. SIAM Journal on Scientific Computing, 25(5):1771–1797, 2004.
[15] E. Kofman. A Third Order Discrete Event Simulation Method for Continuous System Simulation. Latin American Applied Research, 36(2):101–108, 2006.
[16] E. Kofman and S. Junco. Quantized State Systems. A DEVS Approach for Continuous System Simulation. Transactions of SCS, 18(3):123–132, 2001.
[17] G. Migoni, M. Bortolotto, E. Kofman, and F. E. Cellier. Linearly implicit quantization-based integration methods for stiff ordinary differential equations. Simulation Modelling Practice and Theory, 35:118 – 136, 2013.
[18] C. Perfumo, E. Kofman, J. Braslavsky, and J. Ward. Load Management: Model-Based Control of Aggregate Power for Populations of Thermostatically Controlled Loads. Energy Conversion and Management, 55:36–48, 2012.
[19] A. Sodja and B. Zupancic. Modelling thermal processes in buildings using an object-oriented approach and modelica. Simulation Modelling Practice and Theory, 17(6):1143 – 1159, 2009.
[20] M. Wetter. Modelica-based modelling and simulation to support research and development in building energy and control systems. Journal of Building Performance Simulation, 2(2):143–161, 2009.
[21] M. Wetter. Co-simulation of building energy and control systems with the building controls virtual test bed. Journal of Building Performance Simulation, 4(3):185–203, 2011.
[22] B. Zeigler, H. Praehofer, and T. G. Kim. Theory of Modeling and Simulation - Second Edition. Academic Press, 2000.
