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2022/2023  KAN-CCMVV2429U  Energy System Economics and Modelling

English Title
Energy System Economics and Modelling

Course information

Language English
Course ECTS 7.5 ECTS
Type Elective
Level Full Degree Master
Duration One Semester
Start time of the course Autumn
Timetable Course schedule will be posted at calendar.cbs.dk
Max. participants 80
Study board
Study Board for MSc in Economics and Business Administration
Course coordinator
  • Alexandra Lüth - Department of Economics (ECON)
  • Manuel Llorca - Department of Economics (ECON)
Main academic disciplines
  • Economics
Teaching methods
  • Face-to-face teaching
Last updated on 21-02-2022

Relevant links

Learning objectives
The students will study the basic principles of the energy sector in the current context of restructuring towards a low-carbon energy supply which presents one of humanity’s greatest challenge. The course will consist of two main parts. Every week, students will receive lectures to understand and interpret sound academic methods in energy economics. The lectures focus on techno-economic analysis using numerical optimization and emphasise ambiguity stemming from simplified models. In weekly exercise sessions, the students will adapt the theoretical knowledge and work on programming exercises about sample cases along the course’s material to prepare for their final project. In the programming part, students will learn the basic principles of open science, specifically creating reproducible work using open-source tools, open data, and open results. Aside from programming exercises there will be four additional hours of programming tutorials at the start of the semester, which are voluntary hands-on support and additional practice hours in programming. The aim of this course is to enable the students to:
  • Demonstrate a comprehensive knowledge of the fundamentals of energy system economics
  • Understand and apply different academic methods and models for the analysis of energy systems
  • Use techno-economic analysis to quantitatively assess questions on energy systems using real-world data
  • Apply the basics of energy systems modelling in the programming language Julia
Course prerequisites
Mandatory prerequisites: Knowledge of Microeconomics. Basic experience with a programming language. Interest in Energy Economics, green transition, and quantitative analysis.
Energy System Economics and Modelling:
Exam ECTS 7,5
Examination form Oral exam based on written product

In order to participate in the oral exam, the written product must be handed in before the oral exam; by the set deadline. The grade is based on an overall assessment of the written product and the individual oral performance, see also the rules about examination forms in the programme regulations.
Individual or group exam Oral group exam based on written group product
Number of people in the group 4
Size of written product Max. 20 pages
Groups of 3-4 students

Max. 20 A4-pages

Definition of number of pages:
Groups of
3 students 15 pages max
4 students 20 pages max
Assignment type Project
Written product to be submitted on specified date and time.
15 min. per student, including examiners' discussion of grade, and informing plus explaining the grade
Grading scale 7-point grading scale
Examiner(s) Internal examiner and second internal examiner
Exam period Winter
Make-up exam/re-exam
Same examination form as the ordinary exam
Course content, structure and pedagogical approach

The course covers topics related to the design of energy, and especially electricity markets with a high proportion of renewable electricity generation seen as a key-element to mitigate climate change and protect future generations. Following a general introduction to the energy sector, various special features of electricity as a good and their impact on the functioning of the electricity sector are derived analytically. Different academic methods for energy systems analysis are introduced to the students and main categories of models presented before the students will focus on numerical modelling during the latter part of the course and in their projects. The course teaches the open-source programming language Julia and the use of open data platforms like the Open Power System Data (OPSD) platform.

The structure of the course is as follows. Lectures come before exercises every week. Tutorials are taking place along with the exercises.


  • Introduction to energy system economics
  • Introduction to energy systems modelling
  • Presentation of toy models for analysis of energy systems
  • Presentation of further academic methods in energy economics:
    • System dynamics approaches
    • Efficiency and productivity analysis methods: parametric (e.g., Stochastic Frontier Analysis) and nonparametric (e.g., Data Envelopment Analysis)



  • Introduction to Julia programming language
  • Programming exercises
  • Working with toy models
  • Discussion of projects



  • Training of programming skills
  • Hands-on support with code and software


This module is endorsed by the Copenhagen School of Energy Infrastructure (CSEI) at CBS and it is embedded in its overall strategy of research and education. CSEI is directly supported by the European Commission (DG Energy).

Description of the teaching methods
There will be lectures where students are expected to participate actively, and case-based seminars/lectures.
Feedback during the teaching period
Through oral discussions in class, office hours, voluntary mid-term assignment

Student workload
Lectures 33 hours
Project and exam preparation 90 hours
Reading and preparation 80 hours
Exam 3 hours
Tutorials 4 hours
Expected literature


Expected literature

Selected Chapters from:

Bhattacharyya, S.C. (2019), Energy Economics: Concepts, issues, markets and governance, 2nd ed., Springer.

Coelli, T.J., Rao, D.S.P., O’Donnell, C.J. and Battese, G.E., (2005), An introduction to efficiency and productivity analysis, 2nd ed., Springer.

Kirschen D.S. and Strbac, G. (2018), Fundamentals of Power System Economics, 2nd ed., John Wiley & Sons, Ltd.


Kwon, C. (2019), Julia Programming for Operations Research: A primer on computing, 2nd ed.


Stoft, S. (2002), Power System Economics: Designing markets for electricity, New York: Wiley.



Algunaibet, I.M., Pozo, C., Galán-Martín, A. and Guillén-Gosálbez, G. (2019), “Quantifying the cost of leaving the Paris Agreement via the integration of life cycle assessment, energy systems modeling and monetization”, Applied Energy, 242, 588-601.


Bezanson, J., Karpinski, S., Shah, V. B., & Edelman, A. (2012). Julia: A fast dynamic language for technical computing. arXiv preprint arXiv:1209.5145.


Dunning, I., Huchette, J., and Lubin, M. (2017), “JuMP: A modeling language for mathematical optimization”, SIAM Review, 59(2), 295-320.


Mutingi, M., Mbohwa, C. and Kommula, V.P. (2017), “System dynamics approaches to energy policy modelling and simulation”, Energy Procedia, 141, 532-539.


Weibezahn, J. and Kendziorski, M. (2019), “Illustrating the benefits of openness: A large-scale spatial economic dispatch model using the Julia language”, Energies, 12(6), 1153.


Course material

Collection of articles and policy reports


Last updated on 21-02-2022