Display language

#31027 / #1

Seit WiSe 2022/23

English

Data Science for Energy System Modelling

6

Brown, Thomas William

benotet

Portfolioprüfung

Zugehörigkeit


Fakultät III

Institut für Energietechnik

33371400 FG Digitaler Wandel in Energiesystemen

MSc Regenerative Energiesysteme

Kontakt


TA 8

Neumann, Fabian

t.brown@tu-berlin.de

PORD-Nr.ModultitelLPBenotungPrüfungsformPNr. (POS)Modulprüfung PORDModulprüfung PNr.
44113

Learning Outcomes

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

Content

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 and energy demands. - GIS-based evaluation of renewable energy potentials. - Modelling of daily and seasonal energy storage. - Modelling of 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, PyPSA and atlite). - Visualization and communication of energy system analysis.

Module Components

Pflichtgruppe:

All Courses are mandatory.

Course NameTypeNumberCycleLanguageSWSVZ
Data Science for Energy System ModellingIVWSEnglish4

Workload and Credit Points

Data Science for Energy System Modelling (IV):

Workload descriptionMultiplierHoursTotal
180.0h(~6 LP)
Attendance15.04.0h60.0h
Class preparation and follow-up15.02.0h30.0h
Preparation of assignments5.018.0h90.0h
The Workload of the module sums up to 180.0 Hours. Therefore the module contains 6 Credits.

Description of Teaching and Learning Methods

The course will follow a hands-on didactic approach. Introductory lectures will familiarize the students with each covered topic. Workshops will then introduce open-source tooling to address the topic and offer guidance on programming in Python. The students will demonstrate their learning progress individually throughout the semester in assignments that include programming as well as analysis. During this process, students will successively build, run and communicate the results of their own energy system models.

Requirements for participation and examination

Desirable prerequisites for participation in the courses:

- Basic knowledge of mathematics, linear algebra, and statistics is assumed. - Basic knowledge of programming in Python or other languages is helpful, but not required. - The course is complementary to the courses "Energy Systems" and "Energy Economics".

Mandatory requirements for the module test application:

No information

Module completion

Grading

graded

Type of exam

Portfolio examination

Type of portfolio examination

100 points in total

Language

English

Test elements

NamePointsCategorieDuration/Extent
Individual Homework 1 (renewable potentials)20written18h
Individual Homework 2 (storage and networks)20written18h
Individual Homework 3 (electricity markets)20written18h
Individual Homework 4 (system planning)20written18h
Group Presentation (sector-coupling, uncertainty analysis)20oral18h

Grading scale

1.01.31.72.02.32.73.03.33.74.0
90.085.080.075.070.066.062.058.054.050.0

Test description (Module completion)

Students program their own energy system models individually in four homework assignments with guiding tasks and questions. The final examination element is a presentation on one of the advanced topics in small groups of up to 3 students.

Duration of the Module

The following number of semesters is estimated for taking and completing the module:
1 Semester.

This module may be commenced in the following semesters:
Wintersemester.

Maximum Number of Participants

This module is not limited to a number of students.

Registration Procedures

Course materials and announcements will be distributed through the ISIS platform.

Recommended reading, Lecture notes

Lecture notes

Availability:  unavailable

 

Electronical lecture notes

Availability:  unavailable

 

Literature

Recommended literature
Strbac, G., Kirschen, D., 2019. Fundamentals of Power System Economics, 2 ed. WILEY.
Taylor, J.A., 2015. Convex Optimization of Power Systems. Cambridge University Press.

Assigned Degree Programs


This module is used in the following Degree Programs (new System):

Studiengang / StuPOStuPOsVerwendungenErste VerwendungLetzte Verwendung
Energie- und Verfahrenstechnik (M. Sc.)11WiSe 2022/23WiSe 2022/23
Process Energy and Environmental Systems Engineering (M. Sc.)11WiSe 2022/23WiSe 2022/23
Regenerative Energiesysteme (M. Sc.)11WiSe 2022/23WiSe 2022/23

Students of other degrees can participate in this module without capacity testing.

Miscellaneous

No information