Information on individual educational components (ECTS-Course descriptions) per semester

Degree programme: Master Sustainable Energy Systems
Type of degree: FH Master´s Degree Programme
Part-time
Winter Semester 2022

Course unit title Fundamentals of Optimisation
Course unit code 072722010501
Language of instruction German
Type of course unit (compulsory, optional) Compulsory optional
Semester when the course unit is delivered Winter Semester 2022
Teaching hours per week 2
Year of study 2022
Number of ECTS credits allocated Second Cycle (Master)
Number of ECTS credits allocated 3
Name of lecturer(s) Babette HEBENSTREIT


Prerequisites and co-requisites

None


Course content

Application examples: Demand side management; PV self consumption; load management in e-mobility; producer and consumer mix; data development analysis; optimization for costs, energy, CO2, time, efficiency, failure etc. Methods:

  • Linear optimization: geometric solution, matrix formulation, sensitivity analysis (shadow prices), mixed integer linear programming, modeling tricks
  • Modeling and solving on the computer using a Modeling Language and a Solver
  • Square optimization: Least squares (regression) and least norm
  • Non-linear optimization (introductory only): Lagrange multipliers, solution with iterative methods

Learning outcomes

After completing the course, students will be able to apply standard optimization techniques to typical techno-economic problems. Students can

  • give an overview of standard optimization methods and assess their suitability for problems.
  • apply standard optimization techniques to typical techno-economic problems, implement them in a scripting language, and interpret their results and sensitivity.
  • assess the computational complexity of a problem
  • assess which problem information is useful and to what extent (model accuracy, data effort, value of information)

Planned learning activities and teaching methods

Integrated Course


Assessment methods and criteria
  • Assessment of exercises
  • Oral or written exam

Comment

None


Recommended or required reading
  • Boyd, Stephen; Vandenberghe, Lieven (2018): Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares. 1. Aufl. Cambridge, UK ; New York, NY: Cambridge University Press.
  • Sioshansi, Ramteen; Conejo, Antonio J. (2017): Optimization in Engineering: Models and Algorithms. 1st ed. 2017. New York, NY: Springer.
  • Holland, Heinrich; Holland, Doris (2016): Mathematik im Betrieb: Praxisbezogene Einführung mit Beispielen. 12., wesentl. überarb. Aufl. 2016. Wiesbaden: Gabler Verlag.
  • Sierksma, Gerard; Zwols, Yori (2015): Linear and Integer Optimization: Theory and Practice, Third Edition. CRC Press.
  • Bronson, Richard; Naadimuthu, Govindasami (1997): Schaum’s Outline of Operations Research. 2 ed. New York: McGraw-Hill Education - Europe.
  • Williams, H. Paul (2013): Model Building in Mathematical Programming 5e. 5. Hoboken, N.J: Wiley.

Mode of delivery (face-to-face, distance learning)

Presence course. Students will be informed of the lecturer's attendance requirements before the start of the course.