Control, Monitoring and Visualization Center (CMVC)
The Control, Monitoring and Visualization Center of the Energy Lab is a research-oriented infrastructure for the design, development and testing of new software for planning and operating smart energy system solutions. Based on a computer cluster with cloud and big data technologies, innovative services provide highly scalable data management, analysis, forecasting, simulation, optimization and visualization functionalities that can be used directly via the web. A SCADA instrumentation ensures the data and control connection of local and remote Energy Lab plants and networks.
The co-simulation environment of the CMVC allows models of new components in the energy system as well as the SCADA interfaces of existing plants to be linked in an overall model. By adding data prediction modules, market models and optimization tools to determine operating schedules, future operating options can be simulated and evaluated in the model.
Research topics
- Data acquisition, analysis and visualization of system-related data of the plants in Energy Lab
- Research of new automated operation management mechanisms for microgrids
- Integration of research-oriented applications for data prediction modules, market models and optimization tools
Equipment
- Research control room and IT infrastructure consisting of Big Data and virtualization clusters
- SCADA communication hardware and field devices for data acquisition and control of plants in Energy Lab
- Research-oriented software solutions for cloud technology-based data management and the execution of complex scientific calculation workflows
Selected scientific publications
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Automation Level Taxonomy for Time Series Forecasting Services: Guideline for Real-World Smart Grid Applications
Meisenbacher, S.; Galenzowski, J.; Förderer, K.; Suess, W.; Waczowicz, S.; Mikut, R.; Hagenmeyer, V.
2024. Energy Informatics – 4th Energy Informatics Academy Conference, EI.A 2024, Kuta, Bali, Indonesia, October 23–25, 2024, Proceedings, Part I. Ed.: B. Jørgensen, 277–297, Springer Nature Switzerland. doi:10.1007/978-3-031-74738-0_18 -
A new Data-Driven Approach for Comparative Assessment of Baseline Load Profiles Supporting the Planning of Future Charging Infrastructure
Galenzowski, J.; Waczowicz, S.; Hagenmeyer, V.
2023. Companion Proceedings of the 14th ACM International Conference on Future Energy Systems (e-Energy ’23), 8–20, Association for Computing Machinery (ACM). doi:10.1145/3599733.3600245 -
Probabilistic forecasts of the distribution grid state using data-driven forecasts and probabilistic power flow
González-Ordiano, J. Á.; Mühlpfordt, T.; Braun, E.; Liu, J.; Çakmak, H.; Kühnapfel, U.; Düpmeier, C.; Waczowicz, S.; Faulwasser, T.; Mikut, R.; Hagenmeyer, V.; Appino, R. R.
2021. Applied energy, 302, Art.-Nr.: 117498. doi:10.1016/j.apenergy.2021.117498 -
Chancen der Digitalisierung für die Energiewende
Waczowicz, S.; Müller-Langer, F.; Kröner, M.; Steubing, M.; Fischedick, M.; Weigel, P.; Hagenmeyer, V.
2019. GWF / Gas+Energie, (1), 60–64 -
Concept and benchmark results for Big Data energy forecasting based on Apache Spark
González Ordiano, J. Á.; Bartschat, A.; Ludwig, N.; Braun, E.; Waczowicz, S.; Renkamp, N.; Peter, N.; Düpmeier, C.; Mikut, R.; Hagenmeyer, V.
2018. Journal of Big Data, 5 (1), Art.Nr. 11. doi:10.1186/s40537-018-0119-6 -
Information and communication technology in energy lab 2.0: Smart energies system simulation and control center with an open-street-map-based power flow simulation example
Hagenmeyer, V.; Cakmak, H. K.; Düpmeier, C.; Faulwasser, T.; Isele, J.; Keller, H. B.; Kohlhepp, P.; Kühnapfel, U.; Stucky, U.; Waczowicz, S.; Mikut, R.
2016. Energy Technology, 4 (1), 145–162. doi:10.1002/ente.201500304
Head of Research Platform Energy
+49 721 608-24918
simon waczowicz ∂does-not-exist.kit edu
www.iai.kit.edu/english/RPE.php
207
668 Campus Nord