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    General information about the case study

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    • With the market liberalization of the energy sector and the resulting increase in market uncertainty, traditional project evaluation techniques (e.g., net present value or discounted cash flow analysis) are no longer sufficient to properly deal with risk and uncertainty. Consequently, the use of more sophisticated and powerful project evaluation methods, such as real options analysis (ROA), has also become more relevant in the energy sector. ROA allows for the application of different types of options (e.g., to defer, abandon, expand, contract, shut down, grow, etc.) with respect to project specification and managerial actions that have to be undertaken. 

      The students are able to:

      ·        … learn about the investment evaluation methods

      ·        … apprehend the basics of real options analysis

      ·        … apply real options analysis to investment decision problems in the energy sector (e.g. power plants, especially renewable technologies)

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    Setting 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

      Furthermore, we provide short introductions to Python and Spyder.

    • Short introduction to Python Datei
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    Real options analysis for energy sector

    • The most important characteristics of the investment projects in the energy sector, especially regarding the renewable technologies, are large sunk investment costs (irreversibility), flexibility of investment or reinvestment timing, and the uncertainty related to the electricity prices. In case of renewable technologies, such as wind power, the public policy support based on the Renewable Energy Law plays very important role. With its expiration, especially onshore wind power plants will have to be scrutinized (after their operation time) as to whether they can economically continue operation, whether they must be repowered, or whether they need to be decommissioned. The evaluation of that kind of decisions can be undertaken applying real options analysis. Opposite to traditional project evaluation techniques, the real options approach takes advantage of the use of uncertain parameters included in the model. Moreover, the development of the electricity prices at the spot market as well as output from renewables can significantly affect the profitability of wind power plants and thus impact the decision about their further optimal operation.

      The main goal of this case study is to find out the decision time to repower (option to extend) the existing wind power plants with respect to project specification.

      Within the framework of this case study the investment project value (cash flow) should be defined first. For this purpose, the technical characteristics of project (technology) as well as stochastic financial parameters will be applied for the project value simulation (Monte Carlo Simulation). Applying the theory from the lecture and additional information from the exercise unit the adequate (for the defied type of option) ROA solution method will be used and program in Python. 

      In this section you will find the presentation with:

      • some basics of financial options as well as real options; different options evaluation methods also for power generation assets,
      • some more information about two selected real options evaluation methods for power generation asset with examples in Python.

      Furthermore, we provide the description of the case study. 

    • Real options analysis and its application in the energy sector  Datei
    • Real options analysis and its application in the energy sector - exercise 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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    Case study submission

    • Case study submission Aufgabe

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

      • Graphical presentation of the results (as .png) with their brief discussion
      • Python code

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    Literature

    • If you are interested in further information on the topic evaluation methods of investment projects, the following publication provide more insights:

      • Brigham E., Ehrhardt M. (2011). Financial Management: Theory and Practice, South-Western CENGAGE Learning, Mason, USA, ISBN-13: 978-1439041473

       

      If you are interested in further literature on the financial options and real options, the following publications provide more insights:

       

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

      • Black F., Scholes M. (1973). The pricing of Options and Corporate Liabilities, The Journal of Political Economy, 81(3): 637-654, https://www.jstor.org/stable/1831029?seq=1
      • Cox J.C., Ross S.A. (1976). The valuation of options for alternative stochastic processes, Journal of Financial Economics, 3: 145-166, https://doi.org/10.1016/0304-405X(76)90023-4
      • Cox J.C., Ross S.A., Rubinstein M. (1979). Option Pricing: A Simplified Approach, Journal of Financial Economics, 7(3): 229-263, https://doi.org/10.1016/0304-405X(79)90015-1
      • Glensk B., Madlener R. (2018). Evaluating the Enhanced Flexibility of Lignite-Fired Power Plants: A Real Options Analysis, Energy Conversion and Management, 177: 737-749, https://doi.org/10.1016/j.enconman.2018.09.062
      • Glensk B., Madlener R. (2019). The Value of Enhanced Flexibility of Gas-Fired Power Plants: A Real Options Analysis. Applied Energy, 251: 113-125, DOI: 10.1016/j.apenergy.2019.04.121
      • Himpler S., Madlener R. (2014). Optimal timing of wind farm repowering: a two-factor real options analysis, Journal of Energy Markets, 7(3): 1-32, DOI: 10.21314/JEM.2014.111
      • Madlener R., Glensk B., Gläsel L. (2019). Optimal Timing of Onshore Wind Repowering in Germany under Policy Regime Changes: A Real Options Analysis, Energies, 12: 4703, https://doi.org/10.3390/en12244703
      • Madlener R., Schumacher M. (2011). Ökonomische Bewertung des Repowering von Onshore-Windenergieanlagen in Deutschland, Energiewirtschaft, 35: 297-320, DOI: 10.1007/s12398-011-0066-9

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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 “Application of real options analysis for repowering of wind power plants”

       

      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 October 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/

       

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