Generation Expansion Planning: An Iterative Genetic Algorithm Approach

The generation expansion planning problem (GEP) is a large-scale stochastic nonlinear optimization problem. To handle the problem complexity, decomposition schemes have been used. Usually, such schemes divide the expansion problem into two subproblems: one related to the construction of new plants (investment subproblem) and another dealing with the task of operating the system (operation subproblem). This paper proposes an iterative genetic algorithm (IGA) to solve the investment subproblem. The basic idea is to use a special type of chromosome, christened the pointer-based chromosome (PBC), and the particular structure of that subproblem, to transform an integer-constrained problem into an unconstrained one. IGA's results were compared to those of a branch and bound algorithm (provided by a commercial package) in three different case studles of growing complexity, respectively containing 144, 462, and 1845 decision variables. These results indicate that the IGA is an effective altemative to the solution of the investment subproblem.