1. To recognize the digital transformation in the energy sector, identify challenges and solutions, and evaluate its impact on both the power system and society itself.
2. To analyse the IT-OT infrastructure and protocols of the digitalized power system, identify vulnerabilities and learn how to mitigate and recover from these cyber threats.
3. To compare the different model types used for numerical simulations of energy systems and evaluate the influence of individual parameters and system models on the simulation performance.
4. To explain the different objectives of decision-making in energy systems, and the influence of different units and their properties on the decision making.
5. To apply and evaluate machine learning methods for prediction and control in energy systems

Module 0: Introduction

Module 1: The Digital Transformation of the Energy System

This week, you will learn how digitalization and the digital transformation is penetrating the electricity grid and reinventing the way we operate our power system. We will discuss the following elements:

• Digitalization and digital transformation
• Smart cities
• Digital twins
• Transactive energy
• Blockchain
• Data ownership in the digital power system

The CEO of Spectral will tell you how they are leveraging the digital transformation to create new business opportunities.

Finally, you will investigate how digitalization is penetrating and impacting your working environment.

Your instructors of this week are:

• Peter Palensky
• Philip Gladek (Spectral)

Module 2: Computational Methods for Energy Networks

This week will present numerical simulators, computer programs that allow simulations of power system behavior. You will learn the following:

• What is numerical simulation, and why do we need it?
• What methods are there for simulating the electrical power system?
• How can we couple different energy domains?
• How do different modeling approaches impact simulation performance?

An industry expert from TenneT, the Dutch transmission system operator, will explain the role of numerical simulations on one of their key activities, grid planning.

There are multiple simulation exercises where you will be able to see for yourself how different simulation types and methods, solver settings, and network models impact the performance and result of the numerical simulation.

Your instructors of this week are:

• Cornelis Vuik
• Marieke Kootte
• Martin Wevers (TenneT)

Module 3: Decision Support in Integrated Energy Systems

The power system has different types of units, and operators have distinct goals when operating their system. Traditionally, this was low cost, but nowadays, a greater emphasis is placed on emissions and reliability. However, these are at a trade-off with each other, meaning that no golden combination exist. This week, you will be taught:

• What trade-offs there are in energy systems.
• What influence different units and their properties have on decision objectives.
• How capacity planning and other methods can be used in decision making.
• How to use the optimal scheduling problem to meet your decision objectives.

You will also get to see how Centrica, a British multinational energy and services company, is modelling battery systems to support the grid.

At the end of the week, you will get hands-on experience with the optimal scheduling problem. You will see how the optimal schedule of a small system changes with different model parameters and investigate how to achieve some objectives.

Your instructors of this week are:

• Simon Tindemans
• Pedro P. Vergara
• Evelyn Heylen (Centrica)

Module 4: AI-Based Data and Machine Learning Approaches

This week, you will learn how machine learning can be applied in the energy system, allowing for more accurate predictions and automatic control of the power system. We will discuss the following topics:

• Machine learning basics
• Forecasting with regression models, ensemble methods and neural networks
• Dynamic security assessment with classification and decision trees
• Identification of customer types using k-means clustering
• Anomaly detection using auto-encoders
• Surrogate modeling
• Learning control actions with reinforcement learning

Moreover, we will see two perspectives on the future of machine learning in the power grid, one academic and one from an employee of RTE, the French transmission system operator. The industry speaker will also discuss how artificial intelligence is already being deployed in and around the grid.

At the end of the week, you will get to see how this all works. You will predict the overloading of a cable using the power measurements of several households.

Your instructors of this week are:

• Simon Tindemans
• Jochen Lorenz Cremer
• Antoine Marot (RTE)

Module 5: Cybersecurity of Digital Energy Systems

Although the digital transformation creates many opportunities, it also makes the grid more vulnerable to cyber attacks. This week, you will learn about the cybersecurity of digital energy systems. The following aspects will be highlighted:

• What is cybersecurity?
• How is the IT-OT network of the power grid organized?
• How does a digital substation work?
• What communication protocols are used, and how can these be exploited?
• How can we analyze the impact of cyber attacks on the power grid?
• What can we do to mitigate these cyber threats?
• How can blockchain help to mitigate Internet-of-Things based cyber attacks?

The business director of digital grid operations at DNV will explain how the grid is evolving into a Data Machine because of digitalization. Moreover, he will highlight how you can prepare for the future by improving your cybersecurity.

To conclude this week, you will be tasked with analyzing several cybersecurity elements of a case study.

Your instructors of this week are:

• Alexandru Stefanov
• Raifa Akkaoui
• Bas Kruimer (DNV)

Module 6: Next Steps

“I took this course because I was looking to go deeper in AI, digitalization, and new technology in the electrical sector. This was an excellent tailor-made course, that definitely broaden my vision into these subjects. Everything- from the theory explained by professional experts, to the structure of the course, and the relevant subjects covered- was amazing. I found the practical exercises very helpful to ground all the theory”- Nestor Javier Castro, Electrical Power Engineer, Colombia

“This course invites us to delve into widely developed concepts, specifically applied to the electrical engineering field. It allowed me to apply the knowledge acquired a few years ago during my university engineering career with the current fundamentals of data science. With the help of this course I intend to move towards a professional development directly linked to the implementation of smart grids”- Mario Agustín Hillman, Electrical Engineer, Spain

“I gained better and more detailed insights of the way that energy systems works. I understood more the importance of implementing a more modern and future proof NextGen solution with major focus on the resilience and cybersercurity of such a solution”- Adriaan Tates, Prject Manager, The Netherlands

“The experience with the online learning was great. The videos were very descriptive. Besides, lecturers summarize the key themes of the course well, making it easy to retain the concepts they are trying to covey”- Henock Dandena Dibaba, Junior Researcher, Finland ****