Contingency Constrained Economic Load Dispatch Using Particle Swarm Optimization Embedded with Evolutionary Programming for Security Enhancement

摘要

This study presents a Contingency Constrained Economic Load Dispatch (CCELD) using proposed Particle Swarm Optimization embedded with Evolutionary Programming (PSO-EP), conventional Particle Swarm Optimization (PSO), Evolutionary Programming (EP) techniques such as Classical EP (CEP), Fast-EP (FEP) and Mean of Classical and Fast EP (MFEP) to alleviate line overloading. Power system security enhancement deals with the task of taking remedial action against possible network overloads in the system following the occurrences of contingencies. Line overload can be removed by means of generation re-dispatching. The proposed approach employs conventional Particle Swarm Optimization embedded with Evolutionary Programming (PSO-EP) techniques. So that positive features of both techniques are exploited. The proposed method combines the advantages of different EP and PSO techniques to solve the ELD problem with contingency constraints. The solution obtained is quite encouraging and it has stable convergence characteristics. The CCELD problem is a twin-objective function viz. minimization of fuel cost and minimization of severity index. This proposed PSO-EP based CCELD approach generates higher quality solution in terms of optimal cost, minimum severity index than the other methods. Simulation results on IEEE 30 and 118 bus test systems are presented and compared with the results of other approaches.