AGC Unit Selection Based on Hybrid Particle Swarm Optimization

AGC unit selection problem has been getting more attention due to economical operation of the power industry. This problem is formulated as a constrained nonlinear mixed integer programming problem with variable unit regulation capacity. Due to the problem including continuous and integral variables, it is difficult to solve the problem using integer programming. PSO has been successfully applied to a wide range of applications, mainly in solving continuous nonlinear optimization problems. Therefore, this paper presents an improved hybrid PSO algorithm to solve the AGC unit selection problem. And then a novel PSO algorithm using dynamic inertial weight was introduced, which enhanced the global search ability of the algorithm and improved its convergence speed. The hybrid algorithm was successfully validated for a test system consisting of 15 units. Numerical simulation results show that the improved hybrid PSO algorithm outperformed standard PSO algorithm and genetic algorithm on the same problem and can save considerable cost of AGC ancillary service. It is concluded that the algorithm is supposed to be an effective way to deal with the optimization problems in the power market, and has a wide potential application in power system planning and operation.