In this paper, a genetic algorithm and constriction factor based particle swarm optimization technique are proposed for solving the short term pumped storage hydro thermal scheduling problem. The performance efficiency of the proposed techniques is demonstrated on hydrothermal test system comprising of five thermal units and one pumped storage power plant. A wide rang of thermal and hydraulic constraints are taken into consideration such as real power balance constraint, minimum and maximum limits of thermal units and pumped storage power plant, water discharge and water pumping rate limits and reservoir storage volume constraints. The simulation results obtained from the constriction factor based particle swarm optimization technique are compared with the outcomes obtained from the genetic algorithm in terms of cost saving and execution time to reveal the validity and verify the feasibility of the proposed methods. The test results show that the constriction factor based particle swarm optimization technique performs better than genetic algorithm in solving this problem in terms of cost saving and computational |
