In this paper, a genetic algorithm (GA) and particle swarm optimization with constriction factor (CFPSO) are proposed for solving the short term multi chain hydrothermal scheduling problem with non smooth fuel cost objective functions. The performance of the proposed techniques is demonstrated on hydrothermal test system comprising of three thermal units and four hydro power plants. A wide range of thermal and hydraulic constraints such as power balance constraint, minimum and maximum limits of hydro and thermal units, water discharge rate limits, reservoir volume limits, initial and end reservoir storage volume constraint and water dynamic balance constraint are taken into consideration. The simulation results obtained from the constriction factor based particle swarm optimization are compared with the outcomes obtained from the genetic algorithm to reveal the validity and verify the feasibility of the proposed methods. The test results show that the particle swarm optimization technique is better solution than genetic algorithm in terms of solution quality and computational time. |
