Energy Systems Analysis

  • Prof. Dr. Valentin Bertsch
  • Leonie Plaga
  • Sophie Pathe
  • Course (3 SWS)
  • Exercise (1 SWS)
  • every Winter Semester
  • english

90 h self-study

Contact time:
60 h (4 SWS)


  • Examination
    (90 Minutes)

Requirements for the award of credits

  • Passed examination
    (Note: The grade results exclusively from the exam)
  • Successful completion of the computer exercises
    (Details will be announced at the beginning of the semester)

Energy Systems Analysis


  • Modelling and Simulation of Energy Systems
    • Introduction and overview of energy systems analysis
    • Fundamental optimisation models for power systems analysis
      • Optimal unit commitment (short-term planning)
      • Optimal capacity expansion (long-term planning)
      • Scenario planning approaches
          • Introduction to scenario planning
          • Combination of scenario planning and power systems analysis
      • Investment appraisal
      • Selected case studies
  • Decision Analysis and Assessment of Strategies
    • Types of decision environments and models
    • Structuring decision problems
      • Generating objectives and hierarchies
      • Generating and preselecting alternatives
      • Preference elicitation
      • Aggregation functions and sensitivity analysis
      • Selected case studies

During the exercises, students work on concrete case studies using an open source energy systems model to be installed on their (mobile) computers, and practise preparing input data, processing model results and drawing conclusions.


Learning goals and competences

After successful completion of this module the students are able to

    • name categories of energy systems models and explain the methodological concepts behind the different categories.
    • explain and apply approaches for generating energy systems model input data in a structured way.
    • apply selected methods and models to practical problems (e.g. unit commitment optimisation).
    • interpret results from energy systems models and draw conclusions to support decision making.
    • discuss strengths and weaknesses of the methods and models used and to discuss and derive potential for improvement.

Moreover, the students will have

  • developed the ability to think in a networked and critical way and are able to select and apply established methods and procedures,
  • acquired in-depth and interdisciplinary methodological competence and are able to apply it in a situationally appropriate manner.

The students practice scientific learning and thinking and can

  • develop complex problems in technical systems in a structured way and solve them in an interdisciplinary way using suitable methods,
  • transfer knowledge/skills to concrete systems engineering problems.