Course

None

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

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.

Integrated Course

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

None

• 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.

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