Section outline

  • 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. concerning power plants)

  • 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 File

  • Real options analysis for energy sector

    • The most important characteristics of the investment projects in the energy sector are large sunk investment costs (irreversibility), flexibility of investment timing, and uncertainty related to the existence of commodity derivatives.

      In that context, the main goal of an investor in the energy sector is to determine the right time to invest, expand, switch, or abandon the investment decision. Unfortunately, the traditional investment evaluation methods are no longer sufficient to deal properly with risk and uncertainty but real options analysis can successfully deal with this specific characteristic of investments in light of irreversibility, managerial and flexibility. Real options analysis (ROA) is based on analytical techniques originally developed for financial derivatives pricing and follows the fundamental principle of market value maximization.

      This case study is designed to determine the optimal timing to invest (option to invest) in a specific project. The first step is to identify the most suitable type of real option for the project, develop a model, and select an appropriate ROA solution method.

      Within the framework of this case study, the investment project value (discounted net cash flow) should be defined first. For this purpose, the technical characteristics of the project (technology), as well as stochastic financial parameters, are applied to the project value simulation (Monte Carlo Simulation). Applying the real options theory from the lecture and additional information from the exercise unit, an adequate ROA solution method (for the defined type of option) is used and programmed in Python. As a result, the decision to invest will be made when the present value of the project for the given discounted cash flow turns out to be higher than the optimal continuation value (deferral).

      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  File
    • Real options analysis and its application in the energy sector - exercise File
    • Case study Pagina

      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

  • Case study submission

    • Case study submission Compito
      Opened: mercoledì, 16 ottobre 2024, 10:30
      Due: martedì, 5 novembre 2024, 23:59

      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

  • 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). Energiewende at Risk: On the Continuation of Renewable Power Generation at the End of Public Policy Support, Energies, 12: 3616, https://doi.org/10.3390/en12193616
      • 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
      • Kroniger D., Madlener R. (2014). Hydrogen storage for wind parks: A real options evaluation for an optimal investment in more flexibility, Applied Energy, 136: 931-946, https://doi.org/10.1016/j.apenergy.2014.04.041
      • Weibel S., Madlener R. (2015). Cost-effective design of ringwall storage hybrid power plants: A real options analysis, Energy Conversion and Management, 103: 871-885, DOI: 10.1016/j.enconman.2015.06.043

  • 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.

  • Licensing

    • Case study “Application of real options analysis for investment in energy sector”

      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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      ERC FCN
       

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