Generation expansion planning based on an advanced evolutionary programming

This paper proposes an efficient evolutionary programming algorithm for solving a generation expansion planning (GEP) problem known as a highly-nonlinear dynamic problem. Evolutionary programming (EP) is an optimization algorithm based on the simulated evolution (mutation, competition and selection). In this paper, some improvements are presented to enhance the efficiency of the EP algorithm for solving the GEP problem. First, by a domain mapping procedure, yearly cumulative capacity vectors are transformed into one dummy vector, whose change can field a kind of trend in the cost value. Next quadratic approximation technique and tournament selection are utilized. To validate the proposed approach, these algorithms are tested on two cases of expansion planning problems. Simulation results show that the proposed algorithm can provide successful results within a reasonable computational time compared with conventional EP and dynamic programming.