Economic emission short-term hydrothermal scheduling using a dynamically controlled particle swarm optimization

In this study a Dynamically Controlled Particle Swarm Optimization (DCPSO) method has been developed to solve Economic Emission Short-Term Hydrothermal Scheduling (EESTHS) problem of power system with a variety of operational and network constraints. The inertial, cognitive and social behavior of the swarm is modified by introducing exponential functions for better exploration and exploitation of the search space. A new concept of preceding and aggregate experience of particle is proposed which makes PSO highly efficient. A correction algorithm is suggested to handle various constraints related to hydrothermal plants. The overall methodology efficiently regulates the velocity of particles during their flight and results in substantial improvement. The effectiveness of the proposed method is investigated on two standard hydrothermal test systems considering various operational constraints. The application results show that the proposed DCPSO method is very promising.