Economic Load Dispatch using Fuzzy Logic Controlled Genetic Algorithm

Abstract

The sizes of the electric power systems are increasing rapidly to meet the energy requirements. A number of power plants are connected in parallel to supply the system load by interconnection of power station. With the development of integrated power system, it becomes necessary to operate the plant units economically. Thus evolves Economic Load Dispatch (ELD) problem.

The ELD problem in a power system is to determine the optimal combination of power outputs for all generating units which will minimize the total fuel cost while satisfying all practical constraints. To solve the problem of ELD there are many methods (traditional) like Lambda Iterative Method (LIM), Newton’s Linear Programming etc. But all those methods are based on the assumption of continuity and differentiability of cost function. Practically, the real power limits of the generators vary between minimum and maximum limits. Hence the optimization of real time ELD problem becomes more non-linear.

In this thesis, fuzzy logic has been applied in combination with Genetic Algorithm (GA) to solve various power system problems. For smooth and better convergence in GA, the crossover probability and mutation rate are adjusted by fuzzy logic strategy leading to an improved GA technique termed as Fuzzy Logic Controlled Genetic Algorithm (FCGA). The proposed FCGA can be applied to a wide range of optimization problem. The computational results reveal that the proposed algorithm has excellent convergence characteristics and is superior to the GA and LIM.