Daily Hydrothermal Generation Scheduling by a new Modified Adaptive Particle Swarm Optimization technique

The fundamental requirement of power system hydrothermal scheduling is to determine the optimal amount of generated powers for the hydro and thermal units of the system in the scheduling horizon of 1 day or few days while satisfying the constraints of the hydroelectric system, thermal plants and electrical power system. Daily Hydrothermal Generation Scheduling (DHGS) is a complicated non-linear, non-convex and non-smooth optimization problem with discontinuous solution space. To deal with this complicated problem, a new Modified Adaptive Particle Swarm Optimization (MAPSO) is proposed in this paper. The inertia weight and acceleration coefficients of the PSO are adaptively changed in the MAPSO owning tree topology. We split-up the cognitive behavior of PSO into the best and not-best parts. The proposed not-best cognitive component, unlike recent methods, retains its dynamic behavior throughout the search process. Personal best position exchanging method is proposed to increase activities of particles to explore broad space. New velocity limiter is also proposed in this paper to enhance exploration capability and convergence behavior of the MAPSO. The proposed MAPSO is tested on six test systems and compared with some recent research works in the area.