Short term complex hydro thermal scheduling using integrated PSO-IBF algorithm

Abstract

In this article, an integrated evolutionary technique such as particle swarm optimization (PSO) algorithm and improved bacterial foraging algorithm (IBFA) have been developed to provide an optimum solution to the scheduling problem with complex thermal and hydro generating stations. PSO algorithm is framed based on the intelligent behavior of the fish school and a flock of birds and the optimal solution in the multidimensional search region is achieved by assigning a random velocity to each potential solution (called the particle). BFA is designed by following the prey-seeking (chemotactic) nature of E. coli bacteria. This technique is followed in an improved manner to get the convergence rate in dynamic for a hyperspace problem by implementing a chemotactic step in a linearly decreased way instead of the static one. The effectiveness of this integrated algorithm is evaluated by using it in a complex thermal and hydro generating system. In this testing system, multiple numbers of cascaded reservoirs in hydro plants have a time coupling effect and thermal power units have a valve point loading effect. The simulation results indicate its merits by comparing it with other meta-heuristic techniques related to the fuel cost required to generate the thermal power.