Data Science for Energy System Modelling

Courses

This module will cover the modelling and analysis of future energy systems, with a focus on renewable energy resources and how storage and network infrastructures can aid their integration into the energy system. Directly from the start of the course, students will be exposed to working with real data regarding historical weather data, land eligibility constraints, existing power plant fleets, transmission network data, electricity markets, and demand time series to learn about the challenges and solutions for a successful transition towards climate-neutral energy systems across the globe.

Topics of the course include:

• Time series analysis of wind and solar generation, energy demands, technology costs and prices.
• GIS-based evaluation of renewable energy potentials.
• Modelling of daily and seasonal energy storage.
• Modelling of (linearised) power flows and transmission networks.
• Introduction to mathematical optimization (or repetition thereof).
• Electricity market designs with renewable electricity (merit order, market values, re-dispatch, nodal pricing)
• System planning of renewables deployment, energy storage and transmission infrastructure.
• Modelling of sector-coupling and demand-side management (examples from industry, buildings or transport).
• Modelling under uncertainty and methods of complexity reduction.
• Programming of energy system models in Python (e.g. pandas, geopandas, networkx, pyomo, cartopy, rasterio, PyPSA and atlite).
• Visualization and communication of energy system analysis.

Students are in the position to:

• undertake evaluations of geographical and socio-economic renewable energy potentials
• describe and explain the challenges when integrating renewable energy in energy systems
• critically appraise different concepts for the integration of renewable energy (networks versus storage)
• perform analysis based on techno-economic energy system models independently and interpret the solutions
• process large-scale public datasets to retrieve geographical, meteorological and energy systems information
• program optimization-based energy system models with widely-used open-source tools and public data

To access the full course please visit this page

Other useful links:

• Course website: On this website you will find practical introductions to many Python packages that are useful for dealing with energy data and building energy system models.
• Course GitHub repository