Abstract: Hybrid Optimization using PSO (Particle Swarm Optimization) and GA (Genetic Approach) is the most significant optimization technique which has been used so far for load forecasting amongst Combined Heat and Power (CHP) generators in the power system. This Hybrid Optimization technique is designed in such a way that once we are able to forecast the load, the total fuel cost is reduced as the tendency of this optimization technique is to attain convergence. As compared to the application of using only PSO technique or only GA technique the hybrid approach of both the techniques is much more effective as well as fruitful. In this study the data of six CHP generators has been used for getting the optimum solution. The data from these generators is collated and observed using only Particle Swarm Optimization and the solution was converged as we were able to reach the optimum point. Also the same data was observed after the application of using only Genetic Approach and the solution again got converged although the convergence obtained after using only PSO and GA was not that precise as well as more time consuming. However when we used the hybrid of Particle Swarm Optimization and Genetic Approach the convergence of the solution was far better as compared to only using PSO and GA technique.

Keywords: Hybrid Optimization technique, PSO (Particle Swarm Optimization), GA (Genetic Approach), Economic Load Dispatch (ELD), Economic Dispatch

| DOI: 10.17148/IARJSET.2018.5912