Evaluating Economic Load Dispatch Problem by Hybrid Particle Swarm Optimization for Three Thermal Generating Units

The modern power system around the world has grown in complexity of interconnection and power demand. The focus has shifted towards the enhanced performance, increased customer focus, low cost, reliable and clean power. In this changed perspective, scarcity of energy resources, increasing power generation cost, environmental concern necessitates optimal economic dispatch. In reality power stations neither are at equal distances from load nor have similar fuel cost functions. Hence for providing cheaper power, load has to be distributed among the various power stations in a way which results in lowest cost of generation. Practical economic dispatch (ED) problems have highly non-linear objective function with equality and inequality constraints. Conventional methods such as lambda iteration method and gradient method have been applied to solve the Economic Load Dispatch (ELD) problem. However, this techniques don’t give optimal solution because they require incremental fuel cost curves which are piecewise linear and monotonically increasing to find the global optimal solution. Artificial Intelligent (AI) techniques like Particle Swarm Optimization (PSO) method do give optimal solution. PSO is applied to allocate the active power among the generating stations satisfying the system constraints and minimizing the cost of power generated. In present work, GLSPSA is proposed for solving ELD problems. The efficiency and effectiveness of the proposed technique is benchmarked for test case of three generating units. The results of the GLSPSA compared with that of other intelligence algorithms.