Contents

• Linear programming
  • Standard form and graphical solution
  • Strong duality
  • Karush-Kuhn Tucker conditions
• Energy system optimisation models
  • Typical formulations and key components (Variables, parameters, constraints, objectives)
  • Selected case studies
• Multi-criteria optimisation approaches
• Linear programming in Python with Linopy (taught in computer exercises)
  • Formulating and solving basic linear problems
  • Formulating and solving small and large energy system models
  • Using multi-criteria optimisation method 
• Students work on a case study in small project groups
  • Students develop an own energy system model to address a selected research question / problem
  • Students evaluate the corresponding results and draw conclusions from it
  • Students communicate their model and findings in a presentation and a short report

After successful completion of this module the students are able to,

• Explain basic concepts of linear programming and solve linear optimisation problems manually and with Python

• Explain and interpret the structure and elements of linear energy system optimisation models by referring to concepts of linear programming and techno-economic characteristics of real energy systems

• Apply the concepts and methods learnt to a case study to answer a research question about the energy transition by developing, implementing and solving an energy system optimisation model using Python

• Analyse and interpret optimisation results and draw conclusions for the energy transition

• Work independently in project groups and present results of their group work in an understandable way, both in an oral and written form

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.