Course content

Focus on the energy resource markets for coal, oil, natural gas, and hydrogen. This course offers detailed insights into the markets’ value chains and the challenges arising from climate policy. It links the technical characteristics of the value chains to the markets’ organization. Some exemplary numerical models of the markets are introduced together with some recent economic analyses of global and European markets.

Learning outcome

Position and function in the PhD programme:

The course is meant for PhD students in management science, broadly defined. We assume that students have a foundation in selected areas of finance and optimization, comparable to a Master of Science in Industrial Economics and Technology Management. Specifically we build on courses having subjects such as microeconomics, including equilibrium models. The course audience is PhD students having an interest in computational equilibrium models, energy and environment in the interface between energy economics, and technical systems analysis. It will give the students in-depth knowledge and a competence foundation making them able to participate in research processes so they can develop new knowledge within the area.

After following this course the student should:

- have knowledge about regulation and market mechanisms in electricity, natural gas, coal and oil markets.

- be able to discuss uses and models for advanced analysis of energy systems and markets and be familiar with model classes, features and drivers.

- be able to model and discuss the links between markets and the physical characteristics of underlying technologies.

Learning methods and activities

The class will be a mixture of lectures, small assignments, and discussions of topical subjects. Required reading must be prepared for each class and recommended reading is additionally provided. In addition to the final written exam, students are expected to read the materials. The participating PhD students are required to prepare homework assignments and to give a presentation to earn full credits.

Homework assignments:

For each course day / course topic, the PhD students are given assignments. The master students can also work on the assignments, but they are not mandatory. In general, the assignments are questions related to the readings or to current news topics and they may require some additional reading and/or short research.

All assignments will be made available in due time before lecture starts. Homework assignments must be submitted before the course day by email to the instructor.

All submitted homework assignments are graded pass/fail. Non-submitted assignments are graded "fail". Moreover, all assignments with less than 50% of the expected answers are graded "fail". PhD students need to pass at least ~75% of the homework assignments (i.e. 8 out of 11) to be admitted to the final exam.

Presentation:

Each PhD student must give one short presentation in class. The master students may also choose to give a presentation, but it is not mandatory and not graded. The presentations are graded pass /fail and a successfully passed presentation is required to be admitted to the final exam. The presentation must be no longer than 15 minutes and can be on one of the following topics:

- One of your papers (finished or in progress) if it deals with one of the markets covered in the class, or

- One paper from the reading list in this syllabus, or

- Another scientific paper that relates to the topics of the class, or

- One of the topics from the extra list of topics. These are recent topics from the news where you are required to research a bit.

Make sure to get the lecturer’s agreement to your presentation topic before you start preparing.

Attendance:

Students are expected to be present in the classroom and prepared for every class session. Active participation during lectures and seminar discussions is essential. If unavoidable circumstances arise which prevent attendance or preparation, the instructor should be advised with as much advance notice as possible.

Compulsory assignments

• Homework assignments
• Presentation

Recommended previous knowledge

TIØ4118 Industrial Economic Analysis and TIØ4126 Optimization and Decision Support for Industrial Business Planning.

Required previous knowledge

None

Course materials

Scientific articles and selected literature. Given at course start-up.

Credit reductions