Cash Flow Assessment Model for Generation Projects with Alternative Sources

The objective of this project is to propose a methodology and a computational model in Excel to identify and reify the risks of each generation investment alternative so that they can be compared on the same basis, thus assisting agents in their investment decisions. It is proposed to use a methodology based on the risk-return concept to compare all projects, where the IRR under risk conditions is determined, taking into account the complementarity resulting from a diversified portfolio. The focus is on renewable energy projects, but the computational system is generic enough to accommodate other generation sources, as well as synergies with Light’s portfolio.

Initially, a mapping of risk factors and project pricing is carried out: at this stage, the main risk factors of each generation technology are identified, such as hydrological risk, environmental risk, construction risk, technological risk, exchange rate, and fuel. For each of these items, it is proposed to translate the risk factors into scenarios with their respective probabilities of occurrence. In this way, the system allows the user to model the uncertainty of the investment cost through scenarios with different investment cost values, each associated with different probability values. Additionally, the user can generate different scenarios for the evolution of indexers, in order to capture the risk of investment indexing and combine it with other risks also modeled. The regulatory agency (in the Brazilian case, ANEEL) establishes a series of strict financial penalties in case of delay in the plant’s entry into operation. The modeling of these penalties and the simulation of possible delay scenarios are included in this system. The user can model the uncertainty in the plant’s entry-into-operation date through scenarios with different delay periods and different costs associated with this delay (this cost can even be linked to the PLD), each associated with different probability values. With this, the user can simulate the impact on the project’s profitability of these possible delays, combining this risk with other risks also modeled. Subsequently, a model for evaluating investments under uncertainty is proposed, which determines the competitiveness of a generation project by considering its risks, uncertainties, and according to the entrepreneur’s degree of risk aversion. Finally, the last part proposes a methodology for comparing different generation investment technologies by considering their intrinsic uncertainties and risks, illustrating how to calculate the risk premium of each investment alternative and verifying the impact of each project’s intrinsic uncertainty on the variance of its expected return. The methodology can also be extended to analyze a portfolio of projects, determining the gain from combining different generation asset investments in the same portfolio versus the individual candidate projects.