Smart Grid Digitalization and Integration

This module provides an overview of the course structure and introduces the basic concepts of intelligent and integrated energy systems that will be covered throughout the course.

This module explores how digitalization is revolutionizing the electricity grid, covering smart cities, digital twins, transactive energy, blockchain technology, and data ownership in digital power systems. Students learn about modern business opportunities in the digital energy sector.

This module focuses on numerical simulation techniques for power systems, including different simulation methods, energy domain coupling, and performance impacts of various modeling approaches. Students gain practical experience through simulation exercises to understand how different methods and models affect system performance.

This module examines decision-making in energy systems, exploring trade-offs between cost, emissions, and reliability. Students learn about capacity planning, optimal scheduling, and the influence of different units on system objectives. The module includes hands-on experience with optimal scheduling problems.

This module covers the application of machine learning in energy systems, including forecasting, security assessment, customer classification, anomaly detection, and reinforcement learning. Students learn to predict system overloads using household power measurements and understand AI deployment in grid operations.

This module addresses the cybersecurity challenges in digital energy systems, covering IT-OT network organization, digital substations, communication protocols, cyber attack analysis, threat mitigation, and blockchain security applications. Students analyze cybersecurity elements through case studies.

This concluding module helps students integrate their learning and understand how to apply their knowledge in real-world scenarios, providing guidance on further development in the field of digital energy systems.