| Course title |
Smart grids |
| Acronym |
13M021IEM |
| Study programme |
Electrical Engineering and Computing |
| Module |
Applied Mathematics, Audio and Video Communications, Audio and Video Technologies, Biomedical and Environmental Engineering, Biomedical and Nuclear Engineering, Computer Engineering and Informatics, Electronics, Electronics and Digital Systems, Energy Efficiency, Information and Communication Technologies, Microwave Engineering, Nanoelectronics and Photonics, Power Systems - Networks and Systems, Power Systems - Renewable Energy Sources, Power Systems - Substations and Power Equipment, Signals and Systems, Software Engineering, System Engineering and Radio Communications |
| Type of study |
master academic studies |
| Lecturer (for classes) |
|
| Lecturer/Associate (for practice) |
|
| Lecturer/Associate (for OTC) |
|
| ESPB |
6.0 |
Status |
elective |
| Condition |
Power System Analysis |
| The goal |
Introducing students to the concept, architecture, and role of smart grids in modern power systems. Acquiring knowledge about the integration of RES, consumer management, electric vehicles, and energy storage. Special focus is placed on applying artificial intelligence algorithms for system control and optimization. |
| The outcome |
The student will be able to explain smart grid concepts and architecture, analyze the role of RES, storage, and EVs, apply principles of demand-side management and prosumer operation, describe ICT and communication protocols, use algorithms for monitoring and control, and apply software tools for real-time simulation and analysis of smart grids. |
| Contents of lectures |
The content includes: introduction to smart grids, architecture and components, integration of RES, storage systems and EVs, demand management and prosumer participation, communication protocols (e.g., IEC 61850), ICT and cybersecurity, monitoring and automation, AI applications, smart meters, standards, and future development trends in smart grids. |
| Contents of exercises |
Practical training includes distribution grid modeling, simulation of RES and storage integration, demand and prosumer management, AMI data analysis, application of communication protocols and optimization algorithms, as well as real-time work with HIL simulators and a final project focused on hybrid energy system control. |
