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
Summer Semester 2022

Course unit title Decisions under Uncertainty
Course unit code 072722020501
Language of instruction German
Type of course unit (compulsory, optional) Compulsory optional
Semester when the course unit is delivered Summer 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) Klaus RHEINBERGER


Prerequisites and co-requisites

None


Course content

Typical applications: Price, demand and resource uncertainties; quantitative risk management; control of stochastic energy systems such as charge load management in electromobility; optimal management of energy generation plants and energy storage facilities with uncertain data using forecasts; optimal trading strategies on markets. Methods:

  • Modelling of decision situations under uncertainty: decision tree, strategies, scenarios
  • Estimating probability distributions from data
  • Scenario generation from distributions
  • Quantification of risk
  • Conditional probabilities, aggregation effects, Bayesian inference
  • Optimization of benefit vs. risk: portfolio optimization, hedging
  • Stochastic optimization by linear optimization, value of perfect and incomplete information

Learning outcomes

After completing the course, students will be able to identify, model and quantify uncertainties in technical, economic and ecological systems. Students can

  • Structure decision situations under uncertainty.
  • Explain the basics of probability calculation and statistics and apply them to the computer.
  • Estimate probability distributions from data and generate scenarios for the simulation of a stochastic system.
  • Calculate aggregation effects.
  • Calculate a posterior distribution using Bayesian inference from previous knowledge and measured data, which contains new findings.
  • Quantify risk and minimize it through diversification.
  • Determine the value of (un)complete information.

Planned learning activities and teaching methods

Integrated Course


Assessment methods and criteria
  • Evaluation of assignments in small groups and individual work
  • Final examination

Comment

None


Recommended or required reading
  • Schiller, John J.; Srinivasan, R. Alu; Spiegel, Murray R. (2013): Schaum’s Outline of Probability and Statistics: 897 Solved Problems + 20 Videos. 4 ed. New York: McGraw-Hill Education Ltd.
  • Bertsimas, Dimitris (2004): Data, Models, and Decisions: The Fundamentals of Management Science. Belmont, Mass: Dynamic Ideas Llc.
  • Cornuéjols, Gérard; Peña, Javier; Tütüncü, Reha (2018): Optimization Methods in Finance. 2. Aufl. Cambridge, United Kingdom ; New York, NY: Cambridge University Press.
  • Martin, Osvaldo (2018): Bayesian Analysis with Python: Introduction to statistical modeling and probabilistic programming using PyMC3 and ArviZ, 2nd Edition. 2nd Revised edition. Packt Publishing.
  • Kovacevic, Raimund M.; Pflug, Georg Ch; Vespucci, Maria Teresa (2013): Handbook of Risk Management in Energy Production and Trading. 2013. Aufl. New York: Springer.
  • Birge, John R.; Louveaux, François (2011): Introduction to Stochastic Programming. 2nd ed. 2011. New York: Springer.

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.