Modelling long term uncertainties in generation investment projects in competitive electricity markets

Abstract

In the scope of the restructuring process of the electricity sector, typical vertically integrated companies were unbundled leading to agents dedicated to the generation, transmission, distribution and retailing activities. As a consequence, the traditional integrated planning is no longer in use and so the generation expansion-planning problem has to be reshaped. This paper describes a decision aid approach to help generation companies to decide whether to invest on new assets. The developed model aims at analyzing the available expansion options considering the possible behaviors of other companies. In this approach, it is maximized the expected revenue of a generation company provided that the safe operation of the power system is guaranteed. On the other hand, the expansion planning exercise is more risky and so it becomes important to internalize uncertainties related with the price volatility, with the reliability of generation units, with the demand evolution and with investment and operation costs. These uncertainties are modeled by pdf functions and the solution approach is based on Genetic Algorithms. Finally, the paper includes a Case Study to illustrate the application and interest of the developed approach.