Modul

Students are in the position to:

• describe and explain the challenges when integrating renewable energy in energy systems
• critically evaluate different concepts for the integration of renewable energy (networks versus storage)
• understand the challenges when modelling large-scale energy systems, as well as complexity reduction techniques
• do model calculations for energy system analysis
• describe the basics of electricity market theory and operation

program energy system models using standard open source tools

This module will cover the modelling and analysis of future energy systems, with a focus on renewable energies and their interactions with energy networks.

Topics include:

• Time series analysis of wind, solar and energy demand in Europe.
• Complex network theory.
• Analysis of power flow in electrical networks.
• Modelling storage, the role of storage versus networks.
• Basics of optimisation, Karush-Kuhn-Tucker conditions.
• Basics of microeconomics.
• Economics of electricity markets.
• Short-run versus long-run efficiency.
• Network optimisation, storage optimisation.
• Programming energy system models.
• Model reduction techniques.
• Coupling electricity to other energy sectors.
• Role of renewables in electricity markets.

Additional topics may also include:

• Dynamics in power networks.
• Contingency analysis.

Effects of climate change on energy systems.

Basic knowledge of mathematics, linear algebra, differential equations, statistics and programming is assumed.

If you are not familiar with Python, it is recommended to take an online tutorial in Python before the course starts, since the exercise classes involve Python programming.

Basic knowledge of network theory and optimisation theory are helpful, but not required.