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.
