Elective Seminar: Market Modeling
| Degree programme | Computer Science - Digital Innovation |
| Subject area | Engineering Technology |
| Type of degree | Bachelor Part-time Summer Semester 2024 |
| Course unit title | Elective Seminar: Market Modeling |
| Course unit code | 083121160302 |
| Language of instruction | English |
| Type of course unit (compulsory, optional) | Elective |
| Teaching hours per week | 2 |
| Year of study | 2024 |
| Level of the course / module according to the curriculum | |
| Number of ECTS credits allocated | 3 |
| Name of lecturer(s) | Klaus RHEINBERGER |
Applied Mathematics, Linear Algebra and Operations Research
- Quantitative market modeling: supply and demand curves, utility and cost function, marginal cost prices, pricing, market power
- Pricing: Price differentiation, versions, bundling, sharing, linear and non-linear pricing
- Decision-making and information evaluatio
- Modern energy markets: forecast supply and demand, planning and implementation of matching
- Demand Side Management in Smart Grids: assessment and aggregation of flexibilities as well as automated optimization through incentive functions
Market trend analyzes only generate qualitative statements about the future market development. By contrast, quantitative market data provides the opportunity to create market analysis with the goal of providing numerical values of market volumes, prices, and volumes for very different purposes. The determination of market volumes and forecasts, however, requires the creation of suitable market models, as not all required data and market figures are available. This course aims to give students an insight into quantitative market modeling and pricing to optimize strategic and operational decisions with quantitative data.
Theoretical and methodological know-how (T/M):
- Students are able to model markets quantitatively and understand the different possibilities of pricing through quantitative evaluations and market-relevant information.
- Students know how to use forecasts for business development decisions. Using the example of energy markets, students can develop and evaluate innovative digital business models.
- Students can use the Python programming language to solve simple but realistic tasks and interpret the results. They select and implement suitable methods based on the task. Furthermore, they graphically represent and interpret relationships in models and data.
- Students are able to solve their tasks independently on time (reliability) and to communicate and reason their created solutions (expressiveness and appearance).
- Students understand the solutions of others and can contribute constructive suggestions for improvement and deal with feedback (critical ability) as well as reflect their own abilities and limitations (self-reflection ability).
- Ability and readiness to acquire new knowledge independently and to learn from successes and failures (learning competence and motivation).
Integrated course: 2 THW
Lecture with exercises, programming project.
- Term paper (30%)
- Written exam and Computer exam (70%)
For a positive grade, a minimum of 50% of the possible points must be achieved across all parts of the examination.
None
- Biggar, Darryl R.; Hesamzadeh, Mohammad Reza (2014): The Economics of Electricity Markets. 1. Chichester, West Sussex, United Kingdom: Wiley.
- Luenberger, David G. (2006): Information Science. Princeton: University Press Group.
- Morales, Juan M. u.a. (2013): Integrating Renewables in Electricity Markets: Operational Problems. 2014. Aufl. New York: Springer.
- Python Software Foundation (o. J.): python. Online im Internet: URL: https://www.python.org/ (Zugriff am: 21.05.2018).
- Vohra, Professor Rakesh V.; Krishnamurthi, Professor Lakshman (2012): Principles of Pricing: An Analytical Approach. New. Cambridge; New York: Cambridge University Press.
- Wilson, Robert B. (1993): Nonlinear Pricing. Revised. New York, NY: Oxford University Press USA.
In-class lecture: Compulsory attendance.