Optimal distributed generation allocation in distribution network using Hereford Ranch algorithm

The necessity for flexible electric systems, energy saving, loss reducing and environmental impact are providing impetus to the development of distributed generation (DG). DG includes the application of small generators, scattered throughout a power system, to provide the electric power needed by electrical customer. Such locally distributed generation, has several merits from the viewpoint of environmental restriction and location limitations, as well as transient and voltage stability in the power system. The exact solution of the DG allocation can be obtained by a complete enumeration of all feasible combinations of sites and sizes of DGs in the network. The number of alternatives could be very large, however load flow should be performed for each feasible combination and selection of the optimized solution among these alternatives is an important task. The problem is to determine site and size of DG that minimizes the distribution power losses under the condition that number of DGs and total capacity of DGs are known. Artificial intelligence techniques have come to be most widely used tool for solving optimal DG allocation. Genetic algorithm (GA) is an efficient tool to solve optimization problems. Generally, GA's ability to find a correct solution in a variety of problems, to preserve diversity for preventing premature convergence and to improve convergence time is affected by parent selection algorithm for generating offspring. In this paper, to overcome the defects of existing simple genetic algorithm (SGA), Hereford Ranch algorithm (HRA), is applied to search optimal site and size of DG in distribution feeders. HRA uses sexual differentiation and selective breeding in choosing parents for genetic string. The proposed method was tested for 34-node IEEE distribution test feeder