Simulations of smart grids provide a safe means to test new concepts prior to laboratory and field tests. Due to the multi-actor and multi-physics complexity of smart grids, various research institutes have developed “co-simulation” environments which link different models and simulators together. However, the subsequent result analysis of a co-simulation is challenging, due to e.g. various distributed simulations and the large and varied amount of result- and log-data. When results over long time spans are considered, this may become a big data analysis problem. This paper proposes two result processing approaches for co-simulations, of which one is stream-based. A first performance test shows promising data processing rates and we discuss future development steps.
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SmartGrids SRA 2035 (2012) Strategic research agenda update of the SmartGrids SRA 2007 for the needs by the year 2035. http://www.smartgrids.eu/documents/sra2035.pdf. Accessed 12 Jan 2017
Drayer E, Hegemann J, Gehler S, Braun M (2016) Resilient distribution grids cyber threat scenarios and test environment. In: IEEE PES innovative smart grid technologies conference Europe (ISGT-Europe), pp 1–6
Wang H, Stetz T, Marten F, Kraiczy M, Schmidt S, Bock C, Braun M (2015) Controlled reactive power provision at the interface of medium- and high voltage level: first laboratory experiences for a bayernwerk distribution grid using real-time hardware-in-the-loop-simulation. International ETG Congress 2015; Die Energiewende—Blueprints for the new energy age, pp 1–8
Lin H, Smbamoorthy S, Shukla S, Thorp J, Mili L (2011) Power system and communication network co-simulation for smart grid applications. In: ISGT 2011: IEEE PES conference on innovative smart grid technologies
Daily J, Fuller J, Marinovici L, Fisher A, Agarwal K (2014) Framework for network co-simulation. In: 3rd workshop on next-generation analytics for the future power grid
Lin H, Veda SS, Shukla SS, Mili L, Thorp J (2012) Geco: global event-driven co-simulation framework for interconnected power system and communication network. IEEE Trans Smart Grid 3(3):1444–1456
Vogt M, Marten F, Loewer L, Horst KBD, Fetzer D, Menke J-H, Troncia M, Hegemann J, Tobermann C, Braun M (2015)Evaluation of interactions between multiple grid operators based on sparse grid knowledge in context of a smart grid co-simulation environment (Eindhoven), IEEE PowerTech
Ding Y, Morawietz A, Beigl M (2016) Investigation of a grid-driven real-time pricing in a simulation environment. In: IEEE international energy conference, ENERGYCON
Kosek AM, Lnsdorf O, Scherfke S, Gehrke O, Rohjans S (2014) Evaluation of smart grid control strategies in co-simulation integration of ipsys and mosaik. In: Power systems computation conference, pp 1–7
Aydin AA, Anderson KM (2017) Batch to real-time: incremental data collection and analytics platform. In: Proceedings of the 50th Hawaii international conference on system sciences
Peng W, Li Y, Zhu X, Li B (2016) An analysis platform of road traffic management system log data based on distributed storage and parallel computing techniques. In: IEEE international conferences on big data and cloud computing (BDCloud) social computing and networking, pp 585–589
Wu Y, Gong G (2013) A fully distributed collection technology for mass simulation data. In: International conference on computational and information sciences
Chaudhary M (2014) Apache storm: what we learned about scaling and pushing the performance envelope. LOGGLY-Blog. Accessed 4 Aug 2017
This work presents selected results from the project “OpSimEval”, which is performed by Fraunhofer IWES and University of Kassel and is funded by the Federal Ministry for Economic Affairs and Energy under Grant Nos. 032578A and 032578B, on the basis of a decision by the German Bundestag. The development of the “Caro” platform benefited from ideas and feedback of Jaro Habiger, who worked at Fraunhofer IWES during the first prototype development. The authors take full responsibility for the content of this paper.
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Marten, F., Mand, A., Bernard, A. et al. Result processing approaches for large smart grid co-simulations. Comput Sci Res Dev 33, 199–205 (2018). https://doi.org/10.1007/s00450-017-0359-2
- Stream processing
- Big data analysis