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

  
Degree programme:Bachelor Computer Science - Digital Innovation
Type of degree:FH Bachelor´s Degree Programme
 Part-time
 Summer Semester 2023
  

Course unit titleElective Seminar: Market Modeling
Course unit code083121160302
Language of instructionEnglish
Type of course unit (compulsory, optional)Elective
Semester when the course unit is deliveredSummer Semester 2023
Teaching hours per week2
Year of study2023
Level of course unit (e.g. first, second or third cycle)First Cycle (Bachelor)
Number of ECTS credits allocated3
Name of lecturer(s)


Prerequisites and co-requisites

Applied Mathematics, Linear Algebra and Operations Research

Course content
  • 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
Learning outcomes

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.
Social and communicative competences (S / K) and self-competences (S)
  • 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).
Planned learning activities and teaching methods

Integrated course: 2 THW

Lecture with exercises, programming project.

Assessment methods and criteria
  • Programming Project (50%)
  • Oral Exam of the theoretical contents (50%)

For a positive grade, a minimum of 50% of the possible points must be achieved across all parts of the examination.

Comment

None

Recommended or required reading
  • 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.
Mode of delivery (face-to-face, distance learning)

In-class lecture: Compulsory attendance.

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