Professional Course

Digitalization of Intelligent and Integrated Energy Systems

edX, Online
6 weeks
149 USD
Next course start
Start anytime See details
Self-paced Online
6 weeks
149 USD
Next course start
Start anytime See details
Self-paced Online
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Course description

Digitalization of Intelligent and Integrated Energy Systems

This course will teach you how to digitalize the 'conventional' grid and which digital technologies you can use for this, including but not limited to, AI, machine learning, blockchain and computer simulations.

Even though the power grid has been the main driver of the rapid advancement of technology in the past decades, the engineering of the grid is outdated by today's standards. Equipment is still hardwired and analogue, there is limited data acquisition, and many control actions are performed manually. Subsequently, it becomes harder to ensure similar system quality and efficient operations when more renewables are added to the grid. The next big leap, therefore, is to revamp and digitalize the energy system.

We invited five prominent industry leaders to share industry perspectives, case studies and applications of digitalization in the fields of Grid Operations, Electric Power Systems, Power Distribution, Electrical Systems, Control Systems and Cybersecurity, but also in Consultancy and Software Development. Our guest speakers are: Philip Gladek (CEO of Spectral), Bas Kruimer (Business Director Digital Grid Operations at DNV), Martin Wevers (Grid Planner at TenneT), Evelyn Heylen (Head of research at Centrica) and Antoine Marot (Lead AI Scientist at RTE).

You will also get hands-on experience through solving an optimal scheduling problem, noting the differences between different numerical simulation methods and applying machine learning to predict system overloads.

This course is aimed at professionals in the energy industry who want to broaden their perspective and discover alternative approaches to energy integration in an intelligent way such as:

  • grid operators, electrical systems managers, control systems managers, power engineers
  • cybersecurity consultants, software developers, artificial intelligence managers/ scientists, energy consultants
  • project managers, planners, policy makers etc.

Upcoming start dates

1 start date available

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  • Self-paced Online
  • Online
  • English

Who should attend?



Training content

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)

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)

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)

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)

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)

Course delivery details

This course is offered through The Georgia Institute of Technology, a partner institute of EdX.

3-6 hours per week


  • Verified Track -$149
  • Audit Track - Free

Certification / Credits

What you'll learn

  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

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