Abschnittsübersicht

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    General information about the case study

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    • In this section you will find general instructions and the learning objectives of this Case Study

    • In recent years, it has become increasingly popular to take financial risk considerations explicitly into account when deciding about long-term investments in power supply systems. Sensitivity analysis including risk assessment in electricity planning, however, typically does not incorporate portfolio risk and thus cannot replicate the profit/risk or cost/risk relationships. In this context, it makes sense to adopt techniques from finance theory, in particular Mean-Variance Portfolio Theory, which allows investors to create low risk and high return portfolios under various, not only economic criteria, but also technical and social aspects.

      The students are able to:

      ·        … learn the basics of Mean-Variance Portfolio Theory

      ·        … learn about the economics of power plants

      ·        … apply mean-variance portfolio theory to real-world assets from the energy sector (power plants)

    • Announcements Forum/Blog
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    Setting up the work environment

    • In this section, you will find some instructions on how to set up the working environment to work on the Case Study. Students who already have the Python knowledge can skip this section.

      We recommend to use the following software packages:
      - Anaconda (manager for Python environments)
      - Python 3.X (programming language)
      - Spyder (integrated development environment (IDE) for Python)
      - Gurobi (optional)

      It is important not to use Python 2.X versions. The presented program code might not work properly. Installing Anaconda will make it easier for you to set up Python and Spyder.

      Installation instructions

      The following instructions will guide you through all installation steps: 

      Click-by-click installation instructions

      Please use the provided Python Test script for testing.

      Furthermore, we provide short introductions to Python and Spyder.

    • Short introduction to Python Datei
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    Modeling and portfolio optimization of energy assets

    • The main goal of the owners and operators of power plants is the use of different generation technologies to make the power supply secure and sustainable but also profitable. The selection of suitable technology mixes requires a robust analytical framework, such as the Mean-Variance Portfolio Theory. In this framework, specific financial risks related to various technologies as well as the technical, economic and societal aspects of the plants can explicitly be considered.

      In this section you will find the presentation with:

      • some basics of Mean-Variance Portfolio Theory, and different portfolio optimization methods for power generation assets,
      • some information about the economics of power plants needed for portfolio optimization of power generation asset with examples in Python.

      Furthermore, we provide the description of the case study

    • Portfolio optimization for power generation assets Datei
    • Economics of power plants and example of power plants portfolio optimization in Python Datei
    • Case study Textseite

      Dear students,

      as you work on this assignment, please keep in mind that the instructions provided are meant to serve as a reference and guiding framework. Coding, by its very nature, is a versatile and creative process. There are multiple valid approaches to achieve a solution, and the path you choose might differ from the reference.

      We encourage you to think critically, experiment with different coding techniques, and find an approach that resonates with your understanding. The key is not to replicate the instructions verbatim but to grasp the underlying concepts and apply them effectively. Remember, the journey to the correct result is as valuable as the result itself, and there are many paths that lead to the correct answer.

      All the best, and happy coding!

      FCN-ECO team

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    Literature

    • If you are interested in further information on the topic economics of power plants, the following publication provide more insights:

      • Zweifel P., Praktiknjo A., Erdmann G. (2017). Energy Economics – Theory and Applications, Springer, ISBN: 9783662530221

       

      If you are interested in further literature on the topic portfolio theory, the following publications provide more insights:

      • Elton E.J., Gruber M.J., Brown S.J., Goetzmann W.N. (2007). Modern Portfolio Theory and Investment Analysis, John Wiley and Sons, Inc. ISBN-13: 978-0470050828
      • Brigham E., Ehrhardt M. (2016). Financial Management: Theory and Practice, South-Western CENGAGE Learning, Mason, USA, ISBN-13: 978-1305632295

       

      If you are interested in further literature on the topic portfolio optimization of power generation assets, the following publication provide more insights:

      • Bazilian M., Roques F. (2008). Analytical Methods for Energy Diversity & Security: A tribute to Shimon Awerbuch. Elsevier Science, ISBN: 9780080568874

       

      In the list, you will find selected scientific papers on the topic portfolio optimization of power generation assets:

       

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    Case study submission

    • Case study submission Aufgabe

      Here you should upload the solution of the case study, it means:

      • Graphical presentation of efficient frontiers for both time series (as .png)
      • Tables with the shares of technolgies in efficient portfolios, their expected rate of return and risk
      • Python code 
      • Brief discussion of the results
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    Evaluation of the case study

    • This section contains the evaluation for the case study. The evaluation is not graded, but you will only receive bonus points, if you submit the results of the case study and participate in the evaluation.

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    Licensing

    • Case study “Selection of optimal power plant generation mix”

       

      Chair of Energy Economics and Management

      Institute for Future Energy Consumer Needs and Behavior

      Prof. Dr. Reinhard Madlener, Dr. Barbara Glensk, Qinghan Yu M.Sc.

      RWTH Aachen University April 2023

       

      This work and its contents are – unless stated otherwise – licensed under CC BY-SA 4.0. Excluded from the license are the logos used.

       

      The license agreement is available here: https://creativecommons.org/licenses/by-sa/4.0/deed

      This work is available online at: https://www.orca.nrw/

       

      CC BY-SA Pictogramm
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      Participating universities:

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      Funded by:

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