Technical Journal of Engineering and Applied Sciences Available online at www.tjeas.com ©2014 TJEAS Journal-2014-4-1/29-35 ISSN 2051-0853 ©2014 TJEAS Optimal Allocation of DG by using Improved Genetic for IEEE 33 Bus Systems Ahmadali Ahmadi, Hamid reza Khademi,Hossein Mousavi, Amin Hajizadeh abbarerfan@gmail.com, hamid.khademi2012@gmail.com, h.moussavi.k@gmail.com, aminhajizadeh@gmail.com Department of Electrical Engineering, Islamic Azad University, Damghan Branch, Damghan, Iran. ABSTRACT: Recently, distributed generation (DG) in distribution system has been increasing to high penetration levels. The impact of DG on different aspects of distribution system operation, such as reliability and energy loss depend highly on DG location in distribution feeder. Optimal DG placement plays an important role. This article presents an Improved genetic (GA) - based approach to optimal DG placement. The proposed algorithm has been implemented to the IEEE 33-bus system to find the optimal reactive power control variables to decrease the real power losses and to improve the voltage profile to be more effective for this task. Keywords: Distributed Optimal Reactive Power Dispatch, IEEE 33 Bus Systems, Improved Genetic Algorithm, Radial distribution. INTRODUCTION With increasing concerns about decreasing the possible fossil fuels energy and with concerning about rising environmental population, the installation of Distributed Generation (DG) is increasing annually. In order to enhance voltage profile, stability, power losses reduction and etc, it is important that this increasing of installation of DGs in Distribution system should be systematically (Hedayati et al., 2008). The best selecting size and site of DGs in a distribution system is a complicated optimization problem and if this problem includes the Multi Objective Function (MOF), this problem become more complicated. Recently, meta-heuristics optimization techniques are being successfully implemented to complex optimization problems in distribution systems. Gandomkar et al. proposed the optimum location of the DG in the distribution network (Gandomkar, 2005). The work was led towards analyzing different factors depended on the network and the DG itself like the overall system performance, the system reliability, the voltage profile, the load variation, network losses, and the DG loss regulating factors. Katsigiannis and Georgilakis proposed a Tabu search (TS) search method to find the optimal solution of their problem was explained (Katsigiannis et al., 2008); but the tabu search is known to be time consuming algorithm; furthermore, it is may be trapped in a local minimum. For minimizing the real power losses of power system, Lalitha et al. proposed a Particle Swarm Optimization (PSO) algorithm to characterize the optimum size and location of a single DG unit (El-Zonkoly and A. M, 2005). The problem was turned into an optimization program and the real power loss of the system was the only aspect considered in this research to describe the location and size of only one DG unit optimally. Lately, population based optimization approaches like: These algorithms are: Genetic algorithm (GA) (Q. H. Wu and J. T. Ma, 1995), Evolutionary programming (EP) (Lee and Xin, 2004) and Evolutionary strategy (Hansen et al., 1996) are being successfully implemented to solve the distributed generation optimal reactive power problems. WANG et al. modeled normal DGs node type; their work proposes three-phase power flow algorithm in distribution network base on N-R method with DGs (Zhu and Tmsovick, 2002). Due to the large value of R/X, N-R method has problem of difficult to convergence. In reference, back/forward sweep is utilized to solve radial distributed network power flow calculation, the final results showed that the calculate speed and convergence speed is slow (Ishwarya and Surya, 2014). Improved Genetic Algorithm (IGA) is used in this article in order to find solution to optimization problems, optimal size and site of DG in 33 bus \ system. The goal of this article is to reduce the real power losses, reactive power and to develop the voltage profile. The simulation test systems were simulated in MATLAB. Total Real Power Loss The total power loses in a distribution system with N number of branches can be achieved by: