Combined PSO and IBF Algorithm for Short Term Hydro Thermal Scheduling

Abstract

Hydro and thermal power plants are planned to reduce the overall operation cost of the thermal power plants by optimally allocating the hydro units and thermal units in the power generation system. In this research work a combined particle swarm optimization (PSO) and improved bacterial foraging algorithm (IBFA) is proposed for short term hydro thermal scheduling (STHTS) with prohibited operating zones (POZs). The PSO algorithm yields the fastest convergence rate and possesses maximum capability of finding the global optimal solutions to the HTS (Hydro Thermal Scheduling) problems. Also BFA has succeeded in solving several issues in optimization, but it demonstrates poor convergence characteristics for large-scale issues such as the STHTS problem. Critical improvements to the basic BFA are implemented to tackle this complex issue in view of its high-dimension search space. The chemotactic step is changed in IBF, so that the convergence becomes dynamic rather than static. The combined PSO-IBF algorithm is assessed on a typical power generation plants consists of a hydroelectric power plant and an equivalent thermal power plant with a time slot of six 12-hour intervals and simulated using the MATLAB software. The simulation result shows that the combined PSO-IBF algorithm yields minimum cost value and optimal convergence rate than the existing algorithms.