Simulation of Smart-Grid Models using Quantization-Based Integration Methods

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 artikelhttp://dx.doi.org/10.3384/ecp14096787

Ingår i: Proceedings of the 10th International Modelica Conference; March 10-12; 2014; Lund; Sweden

Linköping Electronic Conference Proceedings 96:82, s. 787-797

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Publicerad: 2014-03-10

ISBN: 978-91-7519-380-9

ISSN: 1650-3686 (tryckt), 1650-3740 (online)


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


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