PENG9620 Smart cities for a Sustainable Energy Future - From Design to Practice Course description

Course name in Norwegian
Smart cities for a Sustainable Energy Future - From Design to Practice
Study programme
PhD Programme in Engineering Science
Year of study
Course history


This course covers contemporary topics in smart energy systems such as smart power grid, smart buildings, vehicle-to-grid (V2G) and communication technologies for and network security in smart energy systems, including emerging approaches towards energy intelligence such as machine learning and blockchain.

The course will be offered once a year, provided 5 or more students sign up for the course. If less than 5 students sign up for a course, the course will be cancelled for that year

Recommended preliminary courses

Bachelor's or master's degree in engineering or science.

Required preliminary courses


Learning outcomes


On successful completion of the course, the student:

  • is at the forefront of knowledge about smart energy systems, both at the system level and at the specific component/application level.
  • understands what different technologies can be used at what level in energy generation, transmission, distribution and consumption networks.
  • knows about communication technologies and their performance limits for enabling energy intelligence in smart energy systems.


On successful completion of the course, the student can:

  • solve resource optimisation problems for the energy information network.
  • apply optimisation techniques and machine learning-based approaches for residential demand response management and vehicle-to-grid.

General competence

On successful completion of the course, the student can:

  • communicate and collaborate with experts from other disciplines on larger interdisciplinary and multidisciplinary research projects.
  • Recognise and assess a project's potential and value
  • participate in debates and communicate results through recognised international channels, such as academic conferences.
  • can construct and develop relevant models and discuss the model's validity.
  • Disseminate knowledge to broader audiences

Teaching and learning methods

Module 1 and 2 will take the form of a series of lectures. Module 3 will be a combination of hands-on sessions along with the project assignment.

Practical training

The students will solve specific problems using optimisation or machine learning techniques. The students will submit a brief report with results for the problem in the assignment, also describing the process they used for solving the assignment, including the code.

Course requirements



The results for the project assignment, process description, and the code will be assessed by the course leader. The exam can be appealed.

Permitted exam materials and equipment

All aids are permitted.

Grading scale

Pass or fail.


One examiner. External examiner is used periodically.