Lambda based fuel restricted optimal fuel dispatch using hybrid genetic algorithm for utility system

The objective of the paper is to evolve simple, fast and effective algorithm for fuel restricted optimal fuel dispatch (FROFD) using the hybrid intelligence model. A lambda based hybrid genetic algorithm (LBHGA) is used to solve the FROFD problem. In LBHGA, encoding parameter is normalized system incremental cost (λmn). The algorithm has been proposed for minimum cost better quality and time of operating units. Here real coded genetic algorithm (GA) is used for global search and fine tunings are done by tabu search (TS) to direct the search towards the optimal region and local optimization. The fast decoupled load flow (FDLF) is conducted to find the losses by substituting the generation values to the respective PV buses. Then the loss is distributed among all generating units using participation factor method. Applying the results again to the load flow checks the voltage limit violation. The algorithm is tested on six-generator system and 66-bus utility system and compared with other classical methods. Numerical results show that the proposed method is more effective than other previously developed classical methods. It is observed that the proposed algorithm is superior, reliable and fast.