Author
Abstract
The importance and relevance of sustainability and climate neutrality is related to the transformation of energy, mobility and industry sectors. Decision-oriented, quantitative predictive or prescriptive methods (e.g., linear programming, multi-criteria decision support) are required to evaluate and design these complex systems. These are provided particularly by operations research. The relevance and need for these methods in the context of sustainable development is evidenced by many research projects, as well as the application of the operation research methods in the different industry sectors. That are also central topics in many study programs. The goal of the project “Operations Research (OR) for Sustainability: Energy, Mobility, Industry” founded by OERContent.nrw is to develop, implement and disseminate a digital, model- and application-oriented teaching/learning offer in the subject area of “Operations Research for Sustainable Development” using open source software. This project opens also new possibilities for students to learn how the real business problems can be solved with methods from finance and operations research applied to the energy domain. In the case study presented, the students are offered to learn the basics of mean-variance portfolio theory, the economics of selected power plants, and how to apply the theory to optimize power plant portfolios. In the context of real assets, the main challenge is to define the rate of return as a portfolio selection criterion and to simulate it in the modeling. The analysis is conducted in moodle learning platform by using the object-oriented programming language Python, which creates some specific challenges that are discussed in the case study as well.
Suggested Citation
Barbara Glensk & Qinghan Yu & Reinhard Madlener, 2025.
"Application of Portfolio Theory to Optimize Power Plant Mixes in Classroom Teaching,"
Lecture Notes in Operations Research, in: Guido Voigt & Malte Fliedner & Knut Haase & Wolfgang Brüggemann & Kai Hoberg & Joern Meissner (ed.), Operations Research Proceedings 2023, chapter 0, pages 431-437,
Springer.
Handle:
RePEc:spr:lnopch:978-3-031-58405-3_55
DOI: 10.1007/978-3-031-58405-3_55
Download full text from publisher
To our knowledge, this item is not available for
download. To find whether it is available, there are three
options:
1. Check below whether another version of this item is available online.
2. Check on the provider's
web page
whether it is in fact available.
3. Perform a
for a similarly titled item that would be
available.
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:lnopch:978-3-031-58405-3_55. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.