Study location	Germany, Berlin
Type	On Campus, full-time
Nominal duration	2 weeks
Study language	English
Awards	Summer School
Accreditation	3 ECTS

Tuition fee	€1,050 per programme The program price consists of the course/tuition fee (student or working professional, see details below) plus the registration fee (€60). Student course/tuition fee: €990 Working professional course/tuition fee: €1190 This course/tuition fee covers the course, course materials and a cultural program.
Registration fee	€60 one-time The registration fee is in addition to the course/tuition fee and covers the processing of your application. It is payable upon registration. Please note that the registration fee is non-refundable.

Entry qualification	The entry qualification documents are accepted in the following languages: English / German. Please upload one of the following documents: University degree Transcript of records University enrollment certificate

Language requirements

English All applicants are required to upload a document or certificate to demonstrate their proficiency in English language. If you are a non-native English speaker, you must prove you have a score equivalent to the level B2 or above in the European system (the Common European Framework of Reference for Languages, or CEFR), or provide evidence that you’ve undertaken an equivalent degree/studies in English. A list of scores from the main providers is included for reference. Certificates from other providers are also accepted. CEFR: B2 IELTS: 5-6 Cambridge exam: First certificate in English FCE (A-C) TOEFL iBT: 87 TOEFL Paper based: 600 Chinese CET – 4: 493 Chinese CET – 6: 450 TOEIC: 685 More details: www.tu.berlin/en/international/summer-school/requirements If you are a native English speaker, please select this during registration. You will then be exempt from having to upload proof of English level.

Other requirements	Required: Basic understanding of energy systems and markets. Beneficial: Knowledge of any programming language. Knowledge of Python (variables, functions, loops). Familiarity with linear algebra and optimization.

More information	tu.berlin/..school/summer-school-on-campus

Overview

This course provides a hands-on introduction to Python for energy system modeling, focusing on real-world applications such as renewable energy integration, electricity, heating and hydrogen networks, as well as energy storage. Participants will learn to model, simulate, and optimize energy systems while gaining skills in data processing, scenario analysis, and solving optimization problems. Through practical case studies, students will develop coding expertise and the modeling skill set to solve real-world problems coming up in our transition to a net-zero carbon neutral future energy system.

Learning Goals

By the end of the course, participants will be able to:

• Understand modeling tasks in the energy system space
• Read and write Python code and understand important programming concepts
• Analyze and handle datasets including geodata, graph data and time series data using various Python libraries
• Set up and solve optimization problems relevant to energy systems
• Use Python tools to visualize and interpret energy simulation results

Programme structure

Main Course Components

1. Introduction to Energy System Modeling
Overview of the course and the importance of modeling tasks in the energy systems space, including key concepts and methodologies.

2. Python Programming Fundamentals
Introduction to reading and writing Python code, focusing on important programming concepts necessary for energy modeling. Participants will learn best practices and efficient coding techniques.

3. Data Handling and Analysis
Techniques for analyzing and managing datasets, including geodata, graph data, and time series data, using Python libraries such as Pandas, NumPy and more. Participants will work with real datasets to build practical skills.

4. Optimization in Energy Systems
Setting up and solving optimization problems relevant to energy systems using Pyomo. Participants will learn how to model and optimize various scenarios within energy networks.

5. Data Visualization and Interpretation
Participants will learn how to effectively communicate their findings using Python libraries such as Matplotlib and Plotly to visualize and interpret simulation results, including interactive charts.

6. Project
A final project where participants apply the knowledge and skills acquired throughout the course to a specific case study in energy systems modeling, integrating programming, data analysis, optimization, and visualization.