BYPE2000 Mathematics 2000 Course description

This course, together with Mathematics 1000, will give the students an understanding of mathematical concepts, problems and solution methods with the focus on application. Students will practise using mathematical software in the work on the course, which will enable them to carry out calculations in a work situation.

No requirements over and above the admission requirements.

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

None.

Knowledge

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.

Skills

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

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.

None.

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

All aids are permitted.

Pass or fail.