Project number:
1/0490/23
Title of the project:
Economically Efficient Predictive Control of Microgrids
Grant scheme:
VEGA
Project type:
VEGA Research Projects
Project duration (start):
01.01.2023
Project duration (end):
31.12.2026
Principal investigator for STU:
Michal Kvasnica
Deputy investigator:
Martin Klaučo
Investigators:
Tereza Ábelová, Peter Bakaráč, Kristína Fedorová, Matúš Furka, Juraj Holaza, Michaela Horváthová, Karol Kiš, Roman Kohút, Amirhosein Mosavi, Rudolf Trautenberger, Patrik Valábek, Marek Wadinger

The content of the project, which falls into the scientific field of automation, is basic research in the development of a comprehensive concept of design, synthesis and implementation of an automated system for cost-effective management of local power systems (microgrids) where own consumption is partially or completely covered by own production sources (often renewable energy sources such as solar panels and wind turbines), supplemented by energy storage systems and rotating assets. The proposed system will ensure the optimal utilization of individual assets so that the economy of operation is maximized and adverse effects on the environment are minimized. The main advantage of the proposed solution compared to the existing approaches is the integrity and conceptuality of the entire system, where the individual modules communicate with each other and increase the economic profitability of the entire system through a synergistic effect.

Publications

2025

  1. R. KohútM. KlaučoM. Kvasnica: Unified carbon emissions and market prices forecasts of the power grid. Applied Energy, vol. 377, 2025.

2024

  1. D. Efremov – T. Haniš – M. Klaučo: Vehicle and Wheels Stability Defined Using Driving Envelope Protection Algorithm. IEEE Transactions on Intelligent Transportation Systems, pp. 1–13, 2024.
  2. J. HolazaP. BakaráčJ. Oravec: Revisiting Reachability-Driven Explicit MPC for Embedded Control. European Journal of Control, vol. 78, pp. 101019, 2024.   Zenodo
  3. M. KlaučoP. Valábek: Application of Machine Learning in Accelerating MPC for Chemical Processes. In 12th IFAC Symposium on Advanced Control of Chemical Processes, 2024.   Zenodo
  4. P. ValábekM. FikarM. Klaučo: Enhancing Closed-Loop Performance in Manufacturing Processes Using Universal Controller Tuning for Industrial Practice. In 12th IFAC Symposium on Advanced Control of Chemical Processes, pp. 375–380, 2024.   Zenodo
  5. M. WadingerM. Kvasnica: Adaptable and Interpretable Framework for Anomaly Detection in SCADA-based industrial systems. Expert Systems with Applications, no. 123200, vol. 246, 2024.   Zenodo

2023

  1. T. ÁbelováK. FedorováM. Kvasnica: Optimization-Based Power Distribution Method for State-of-Charge Balancing of Battery Storage Systems. Editor(s): R. Paulen and M. Fikar, In Proceedings of the 24th International Conference on Process Control - Summaries Volume, Slovak Chemical Library, Slovak University of Technology in Bratislava, Radlinského 9, SK812-37, Bratislava, Slovakia, 2023.
  2. T. ÁbelováR. KohútK. FedorováM. Kvasnica: Risk-Aware Stochastic Energy Management of Microgrid with Battery Storage and Renewables. In IFAC World Congress 2023, Yokohama, Japan, 2023.   Zenodo
  3. K. FedorováT. ÁbelováM. Kvasnica: Dynamic Power Purchase Agreement. Editor(s): R. Paulen and M. Fikar, In Proceedings of the 2023 24th International Conference on Process Control, IEEE, Slovak University of Technology in Bratislava, Radlinského 9, 81237, Bratislava, Slovakia, 2023.   Zenodo
  4. K. Fedorová – Y. Jiang – J. Oravec – C. Jones – M. Kvasnica: A Generalized Stopping Criterion for Real-Time MPC with Guaranteed Stability. In 62nd IEEE Conference on Decision and Control, IEEE, Singapore, pp. 4705–4710, 2023.   Zenodo
  5. R. KohútM. Kvasnica: Power Output Reconstruction of Photovoltaic Curtailment. Editor(s): R. Paulen and M. Fikar, In Proceedings of the 2023 24th International Conference on Process Control, IEEE, Slovak University of Technology in Bratislava, Radlinského 9, 81237, Bratislava, Slovakia, 2023.   Zenodo
  6. R. KohútE. PavlovičováK. FedorováJ. OravecM. Kvasnica: Real-Time Deep-Learning-Driven Parallel MPC. In 62nd IEEE Conference on Decision and Control, IEEE, Singapore, 2023.   Zenodo
  7. P. ValábekM. Klaučo: Generation of MPC-like Explicit Control Laws with Reinforcement Machine Learning. Editor(s): R. Paulen and M. Fikar, In Proceedings of the 24th International Conference on Process Control - Summaries Volume, Slovak Chemical Library, Slovak University of Technology in Bratislava, Radlinského 9, SK812-37, Bratislava, Slovakia, 2023.   Zenodo

Investigators


Responsibility for content: prof. Ing. Michal Kvasnica, PhD.
Last update: 29.04.2022 8:15
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