Hybrid genetic algorithm based combined economic and emission dispatch for utility system

This paper presents new hybrid genetic algorithm (GA) to solve the combined economic and emission dispatch (CEED) problem. Focus is on the reduction of single pollutant nitrogen oxide (NO/sub x/). The equality constraints of power balance; the inequality generator capacity constraints and the prohibited zone constraints are considered. Here real coded GA is used for global search, Tabu search (TS) a part of the proposed algorithm which is a simple short-term memory procedure is used to counter the danger of entrapment at a local optimum and the premature convergence of the GA. The fast decoupled load flow (FDLF) is conducted to find the transmission losses by substituting the generation value to the respective PV buses. 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 results are compared with classical method and fuzzy controlled GA method. The algorithm is tested on six generator system and 66-bus utility system. It is observed that the proposed algorithm is reliable, fast and superior.