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PowereX is actively participating in research focused on virtual power plants and smart systems in power engineering.

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Virtual Power Lab

Our microgrid virtual laboratory is the best tool for asset performance comparison.
The performance comparison is powerful method for:

  • Performance studies for planning of energy projects

  • Scientific research of new algorithms for intelligent control

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To ensure we remain current with market developments, local regulations, and new inventions in efficient energy management, we've created a Virtual BESS Laboratory. This innovative platform allows us to explore new approaches and fine-tune our product to suit the unique requirements of each deployment location, whether it involves photovoltaics, small hydro plants, or other customised setups tailored to our customers' needs.

We regularly simulate and analyse various energy market scenarios, identifying potential risks and optimising performance for the best outcomes.

Recently, our laboratory played a crucial role in detecting inefficient charging and discharging patterns, resulting in a significant reduction in annual charging cycles and overall operational costs for BESS systems.


1. M. Chudy, J. Bendik, M. Cenky, “Optimized Power Flows in Microgrids with and without Distributed Energy Storage Systems”, 2019 CIGRE, Grid of the Future Conference, Atlanta 3-6 Nov.


2. M. Trouilloud, M Chudy, “Electric Vehicle Charging Station Daily Load Analysis with a Randomized Algorithm”, INFORMS annual meeting 2019 Seattle, October 20-23


3. M Chudy, J Mwaura, D Walwyn, J Lalk, The effect of increased photovoltaic energy generation on electricity price and capacity in South Africa, AFRICON 2015, Addis Ababa, Ethiopia, 14-17 Sept. 2015


4. Gabriela Grmanová, Peter Laurinec, Viera Rozinajová, Anna Bou Ezzeddine, Mária Lucká, Peter Lacko, Petra Vrablecová, Pavol Návrat, Incremental ensemble learning for electricity load forecasting, 2016 Acta Polytechnica Hungarica 13, 97 – 117


5. Peter Laurinec, Mária Lucká, “Comparison of Representations of Time Series for Clustering Smart Meter Data”, roceedings of the World Congress on Engineering and Computer Science 2016 Vol I  WCECS 2016, October 19-21, 2016, San Francisco, USA


6. Peter Laurinec, Mária Lucká, Clustering-based forecasting method forindividual consumers electricity load using timeseries representations, Open Comput. Sci. 2018; 8:38–50


7. Peter Laurinec, Marek Lóderer, Mária Lucká, Viera Rozinajová, Density-based unsupervised ensemble learning methods for time series forecasting of aggregated or clustered electricity consumption, Journal of Intelligent Information Systems volume 53, pages 219–239 (2019)


8. Tomáš Jarábek, Peter Laurinec, Mária Lucká, Energy load forecast using S2S deep neural networks with k-Shape clustering, 2017 IEEE 14th International Scientific Conference on Informatics, Poprad, Slovakia,

9. Peter Laurinec, Mária Lucká,  Interpretable multiple data streams clustering with clipped streams representation for the improvement of electricity consumption forecasting, Data Mining and Knowledge Discovery volume 33, pages 413–445 (2019)


10. Peter Laurinec, Marek Lóderer, Petra Vrablecová, Mária Lucká, Viera Rozinajová, Anna Bou Ezzeddine , Adaptive Time Series Forecasting of Energy Consumption Using Optimized Cluster Analysis, 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW) Barcelona, Spain


11. Michal Chudy, Lynette Herbst, Jörg Lalk , Wind farms associated with flywheel energy storage plants, IEEE PES Innovative Smart Grid Technologies, Europe , Istambul, Turkey 12-15 Oct. 2014


12. Morne Begemann, George Alex Thopil, Michal Chudy, Electricity load management potential based on the behaviour of consumers in the South African residential sector, 2017 IEEE AFRICON, Cape Town, South Africa, 18-20 Sept. 2017


13. J Veterníková, M Chudý, V Slugeň, M Eisterer, HW Weber, S Sojak, M Petriska, R Hinca, J Degmová, V Sabelová , Positron Annihilation Lifetime Spectroscopy Study of Neutron Irradiated High Temperature Superconductors YBa 2 Cu 3 O 7-δ for Application in Fusion Facilities, Journal of Fusion Energy volume 31, pages 89–95 (2012)

14. M Chudy, J Mwaura, Mitigation of CO2 emissions by optimizing energy storage capacity, 2016 IEEE PES PowerAfrica, Livingstone, Zambia, 28 June-3 July 2016


Dr. Jakub Sevcech


Dr. Jonathan Mwaura

Associate Professor, Northeastern University Khoury College of Computer Sciences

  • Stochastic sampling for battery intelligent energy management systems.

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