Energy Markets and Demand

Teacher(s)

Willems Bert;

Language

English

Prerequisites

Undergraduate courses in finance, microeconomics, econometrics, optimization.
Advanced courses  in energy system analysis and energy economics.

Main themes

Some of the topics treated in the course include:
o    Vertical and horizontal market structure in electricity and gas markets
o    Retail markets in gas and electricity
o    Wholesale markets in gas
o    Realtime market in electricity
o    Energy network regulation
o    Demand modelling and behavioral assumptions
o    Energy communities
o    Energy access and energy poverty
o    Investment analyses in markets
o    Power generation analysis under price variability
o    Stochastic modelling of renewable power generation

Learning outcomes

At the end of this learning unit, the student is able to :

The course is devoted to the energy markets, wholesale and retail in electricity and gas, at an international and European level.  The analysis links to earlier courses to analyze the specifics of energy commodity markets, real-time, day-ahead and futures.  The course also includes an introduction to the economics of energy network regulation, the instruments used and their interpretation in the two markets. After the course, the students should be able : to understand the market structure and functioning in energy markets, both deregulated and regulated  In terms of methodology, the students should be able : to perform economic analyses and to interpret data, models and methods used in energy market analyses in the sector.  The student should also be sensitive to the consumption and user perspectives in energy markets, including the energy poverty and energy access dimensions and how they are addressed in regulation.  The course provides the basis for management of energy markets, such as in trading and retail, and local energy regulation, such as load control.

Content

Some of the topics treated in the course include:
o    Vertical and horizontal market structure in electricity and gas markets
o    Retail markets in gas and electricity
o    Wholesale markets in gas
o    Realtime market in electricity
o    Energy network regulation
o    Demand modelling and behavioral assumptions
o    Energy communities
o    Energy access and energy poverty
o    Investment analyses in markets
o    Power generation analysis under price variability
o    Stochastic modelling of renewable power generation
Note: The content of the course might be adjusted based on the availability of guest speakers.

Teaching methods

Ex-cathedra lectures, lectures with active student participation (such as group work, computer simulations, and student presentations), and guest lectures if possible.

Evaluation methods

Grading Structure

• Participation (30 percent)
  Participation includes group work, student presentations, and active involvement in class activities.
  
  • Students are expected to attend company visits organized as part of the course and to actively engage when guest speakers are invited.
    
  • Failure to actively participate will result in a lower participation grade.
    
  • The participation grade is final and cannot be retaken.
    
• Exam (70 percent)
  A written exam will take place at the end of the course.
  
  • A minimum score of 10 out of 20 on the exam is required to pass the course.
    
  • If a resit is necessary, the format may be adapted, for example, the resit may be conducted as an oral exam.
    

Use of AI Tools

AI tools may be used for assignments and preparation unless explicitly stated otherwise for a specific task. If AI is used, students must clearly state this in their submission, briefly describing:

1. Which AI tool or tools were used
   
2. For what purpose they were used, such as drafting text, generating ideas, or running code
   
3. A short description of their own contribution, clarifying what was done by the student themselves versus the AI
   

Recording all prompts or outputs is not required, but students should keep a record of key steps if the instructor requests clarification.
Responsibilities when using AI:
Students remain fully responsible for the quality and integrity of their work. They must:

• Understand and verify all results, calculations, and arguments included in their submission
  
• Be able to present and explain their work, including any AI-generated parts, during discussions or presentations
  
• Ensure they have read and understood all references and source materials cited in their work
  
• Check the correctness of all derivations, code, and factual claims
  
• Avoid entering personal or confidential information into AI systems
  

Failure to meet these responsibilities may negatively affect the assignment grade, even if AI use is properly disclosed.

Late Submission Policy

Late submissions of assignments will result in a grade deduction, with the exact penalty depending on how late the submission is.

• Submissions that are several days late may not be accepted, unless prior arrangements have been made with the instructor.
  
• Exceptions will only be considered in documented cases of illness or other serious circumstances.
  

Free Riding Policy

All group members are expected to contribute actively and fairly to group assignments.

• Free riding will result in a full grade deduction on the assignment for the student concerned.
  
• Instances of free riding should be reported in Moodle.
  
• Groups are encouraged to keep a simple record of contributions such as meeting notes or task lists to clarify responsibilities if disagreements arise.
  

Other information

The communication between the professor and the students takes place through the electronic platform Moodle. You should enroll in the course on Moodle to have access to the online documents such as course notes, slides and additional material that will be posted.

Faculty or entity

> CLSM