Control, Monitoring and Visualization Center (CMVC)

Markus Breig (KIT)
Research control room of the CMVC in the Energy Lab (Photo: Markus Breig, KIT)

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.

Karl-Heinz Häfele (KIT)
3D Campus model of KIT Campus North (Source: Karl-Heinz Häfele, KIT)

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


  1. High-Frequency 20 ms Power Hardware-in-the-Loop Measurements of Electric Vehicle Charging Control under Voltage Step Perturbations
    Beichter, S.; Friedrich, W.; Jumar, R.; Marvin, D.; Reckel, J. M.; Waczowicz, S.; Veit, H.; Karlsruhe Institute of Technology
    2026. doi:10.5281/zenodo.22965111 
  2. Real World Short Term District Heating Load Forecasting A Machine Learning Benchmark Against an Operational Utility Model
    Beichter, M.; Turowski, M.; Döring, A.; Kreusel, A.; Friederich, N.; Waczowicz, S.; Ordiano, J. Á. G.; Iser, A.; Hitzel, J.; Weschenfelder, R.; Rink, M.; Hagenmeyer, V.; Mikut, R.
    2026. Proceedings. 36. Workshop Computational Intelligence : Berlin, 19. – 20. November 2026. Ed.: H. Schulte, 17–49, KIT Scientific Publishing Full textFull text of the publication as PDF document 
  3. 100 ms-Resolution Electrical Measurement Dataset of an OLTC-Induced Voltage Fluctuation Anomaly Across 0.4 kV, 20 kV, and 110 kV Networks with Diverse Loads and Distributed Energy Resources
    Moster, P.; Beichter, S.; Dorn, M.; Reckel, J. M.; Wachter, J. L.; Jakel, D.; Jumar, R.; Wiegel, F.; Waczowicz, S.; Hagenmeyer, V.
    2026. doi:10.5281/zenodo.22826984 
  4. Automated Metadata Acquisition in Energy Research: BPMN-driven Workflows at the KIT Energy Lab
    Reckel, J. M.; Holzhäuer, M.; Moser, T.; Moster, P.; Waczowicz, S.; Hagenmeyer, V.
    2026, April 30. Helmholtz Metadaten Collaboration : Metadata in Action (HMC 2026), Heidelberg, Germany, April 28–30, 2026. doi:10.5281/zenodo.20035852 Full textFull text of the publication as PDF document 
  5. 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 
  6. 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 Full textFull text of the publication as PDF document 
  7. 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 Full textFull text of the publication as PDF document 
  8. 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 Full textFull text of the publication as PDF document 
  9. 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 Full textFull text of the publication as PDF document 
  10. 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 

Contact persons

N. N.

Dr.-Ing. Simon Waczowicz
Dr.-Ing. Simon Waczowicz
Head of Research Platform Energy

+49 721 608-24918simon waczowicz ∂does-not-exist.kit eduwww.iai.kit.edu/english/RPE.php207668 Campus Nord