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CPES Testing with mosaik: Co-Simulation Planning, Execution and Analysis

1
OFFIS—Institute for Information Technology, Escherweg 2, 26121 Oldenburg, Germany
2
Department of Computer Science, University of Oldenburg, Ammerländer Heerstraße 114-118, 26129 Oldenburg, Germany
3
Group Energy Informatics, Leibniz University Hannover, Appelstraße 9a, 30167 Hannover, Germany
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Appl. Sci. 2019, 9(5), 923; https://doi.org/10.3390/app9050923
Received: 19 January 2019 / Revised: 24 February 2019 / Accepted: 26 February 2019 / Published: 5 March 2019
(This article belongs to the Special Issue Cyber Physical Energy Systems)
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Abstract

The complex nature of cyber-physical energy systems (CPES) makes systematic testing of new technologies for these setups challenging. Co-simulation has been identified as an efficient and flexible test approach that allows consideration of interdisciplinary dynamic interactions. However, basic coupling of simulation models alone fails to account for many of the challenges of simulation-based multi-domain testing such as expert collaboration in test planning. This paper illustrates an extended CPES test environment based on the co-simulation framework mosaik. The environment contains capabilities for simulation planning, uncertainty quantification and the development of multi-agent systems. An application case involving virtual power plant control is used to demonstrate the platform’s features. Future extensibility of the highly modular test environment is outlined. View Full-Text
Keywords: testing; co-simulation; mosaik; multi-agent systems; simulation planning; uncertainty quantification testing; co-simulation; mosaik; multi-agent systems; simulation planning; uncertainty quantification
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Steinbrink, C.; Blank-Babazadeh, M.; El-Ama, A.; Holly, S.; Lüers, B.; Nebel-Wenner, M.; Ramírez Acosta, R.P.; Raub, T.; Schwarz, J.S.; Stark, S.; Nieße, A.; Lehnhoff, S. CPES Testing with mosaik: Co-Simulation Planning, Execution and Analysis. Appl. Sci. 2019, 9, 923.

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