Hybrid genetic algorithm based fuel restricted real power optimization for utility system

The objective is to minimize the production cost of the thermal units. An elegant approach is presented in order to obtain the equivalent cost function of the participating nonfuel restricted units [N. Ramaraj (1993)]. The hybrid genetic algorithm (GA) technique is used for real power optimization. Here real coded GA is used for global search and the fine tunings are done by tabu search (TS) to direct the search towards the optimal region and local optimization. The GA operator´s selection, arithmetic crossover and dynamic mutation are used to generate successive sets of possible operating policies. This technique improves the quality of the solution and reduces the computation time. The optimal solution is obtained neglecting losses. The fast decoupled load flow (FDLF) analysis is conducted to find the losses by substituting the generation values. Then the loss is participated 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 IEEE 6-bus system and a practical 66-bus system. The proposed method is compared with the classical method. It is observed that the proposed algorithm is superior, reliable and fast.