Energy System Modeling with Python
Details zur Veranstaltung
| Level | Master |
| Unterrichtssprache | Englisch |
| Semester | Summer |
| ECTS (SWS) | 6 (4) |
| Dozentin | Dr. Ramiz Qussous et al. |
| Vorlesung in HISinOne | Link |
Inhalt
This lecture introduces the principles and methods for modeling and analyzing complex energy systems using Python. A special emphasis is placed on working with real-world data from current research sources such as open databases, scientific datasets, and publicly available energy system statistics. Participants will not only learn to build their own computational models but also to interpret and critically discuss model results in the context of actual energy system challenges.
Throughout the course, students will develop the toolkit necessary to approach questions and challenges in modern energy systems. They will gain experience in formulating abstract representations of energy systems, translating them into executable Python code, and testing their behavior under varying conditions and assumptions. By combining fundamental modeling concepts with hands-on programming practice, the course builds a bridge between theoretical understanding and applied analysis.
At the end of the course, participants complete a small project that brings together the ideas and methods covered in the lectures and exercises. Working under the guidance of the teaching team, they design and carry out a modeling study on a selected energy topic and present their results. This project allows students to apply their knowledge in a practical setting and demonstrate their ability to carry out and communicate an independent analysis.
Inhalte der Vorlesung:
- Introduction to Python and Data Types
- Overview of Python programming concepts relevant to energy system modeling
- Data types, variables, and data structures
- Flow control, loops, and iteration
- Defining and using functions
- Global vs local variables
- Electricity Markets
- Fundamentals of electricity market operation and market design
- Building and analyzing a simple merit‑order model
- Discussion of different market structures and pricing mechanisms
- Case studies of current European market examples
- Linear Optimization in Energy System Modeling
- Introduction to mathematical optimization in energy systems
- Formulating linear programming problems (LP) and interpreting results
- Using Python tools (e.g. Pyomo/Linopy) for modeling and solving optimization cases
- Hands‑on exercises linking real datasets to optimization problems
- Power Flow Analysis
- Basic principles of power flow in electric networks
- Implementing DC‑power‑flow models in Python
- Understanding the connection between network constraints and market outcomes
- Energy System Expansion Modeling
- Modeling long‑term planning and infrastructure expansion
- Integrating multiple energy carriers and technologies
- Scenario analysis for investment and system development
- Applying optimization and data‑driven methods to assess future system configurations