Control of Low-inertia Grids by Energy Strorage Battery using Deep Reinforcement Learning

Student	Vjatšeslav Škiparev
Supervisors	Juri Belikov, Eduard Petlenkov
Keywords	deep learning, reinforcement learning, industry 4.0, power systems, control systems
Degree	PhD

Related publications

V. Skiparev, R. Machlev, N. Chowdhury, Y. Levron, E. Petlenkov, and J. Belikov, “Virtual Inertia Control Methods in Islanded Microgrids,” Energies, vol. 14, no. 6, pp. 1562, 2021. [DOI] [Bib]
V. Skiparev, J. Belikov, and E. Petlenkov, “Reinforcement Learning based Approach for Virtual Inertia Control in Microgrids with Renewable Energy Sources,” 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), 2020. [DOI] [Bib]
V. Skiparev, J. Belikov, and E. Petlenkov, “NN-SANARX model based control of a water tank system using embedded microcontroller Arduino,” in Proceedings of the 2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, (IDAACS). IEEE, 2019. [DOI] [Bib]