International Research Journal of Applied and Basic Sciences © 2013 Available online at www.irjabs.com ISSN 2251-838X / Vol, 4 (12): 3601-3610 Science Explorer Publications Optimal Placement of Distributed Generation Resources from the perspective of the private sector by DAPSO Algorithm Mir Mohammad Mir Mousavi 1 , Javad Javidan 2 , Ali Menshsari 1 , Hamid Asadi Bagal 1 1. Science and Research Branch, Islamic Azad University, Tehran, Iran 2.Technical Engineering Department, University of Mohaghegh Ardabili, Iran *Corresponding Author email: m.mousavi515@gmail.com, javidan.javad@gmail.com ali.menshari@gmail.com,h.asadibagal@ieee.org ABSTRACT : Generally, the main goal of designing and developing distribution networks, is responding to the electricity consumption growth with maximum economic efficiency in a way that does not violate the system constraints. On the other hand, the large volume of investment and rising energy and utilities’ prices has caused that distribution network designers use better and more accurate methods to design these networks. The appearance of distributed generation resources in distribution systems, in addition to change in the operation processes of these systems, has provided the opportunity for the companies to design systems with lower costs. To achieve this, the design steps of distribution systems must be reviewed. In this paper a multi-objective model from the perspective of the private sector is offered for optimal placement of distributed generation resources in distribution network that the main objective here is to optimize the voltage profile, system losses and costs. Keywords: Distributed Generation Resources, Multi-Objective Modeling, Wind Turbine, DAPSO Algorithm, Optimization INTRODUCTION Distributed generation resources are actually generation units which usually have low capacity and are connected to the system near the load. Use of DG can cause voltage profiles improvement, system losses reduction, and investment costs reduction by delaying construction of new utilities. In addition, DG can be used to improve the reliability of consumers. According to decrease in the price of DG resources, it is expected that these resources play an important role in the future of distribution systems. On the other hand, the presence of DG in distribution systems increases the complexity of the problem of designing and developing these systems and makes the use of existing methods for the solution inefficient. Thus, in the presence of the DG, the problem of design and development of distribution systems needs to be reviewed and requires developing new models and methods for solving it. For placement of DG, many studies have been done and some of them are mentioned below. In(Tautiva and Cadena,2008) in order to maximize total profit, the author targets and develops a genetic algorithm based heuristic approach to determine the optimal location and size of the distributed generation. In this paper, the technical aspects such as energy losses, voltage level and reliability as well as economic aspects like energy price, investment, distributed generation operating costs, losses cost and not supplied energy cost is noteworthy. In (Celli,2005), multi-objective functions are used for locating and sizing of DG resources in the distribution network. The selected approach allows the designer to choose the best compromise among the cost of network enlargement, the cost of power losses, the cost of energy not supplied and the cost of energy needed by the customer. The implemented technique is based on algorithm (GA) and a constraint method. In (Nara and Hayashi,2001) discusses on the application of TS method for optimal allocation of DGs in the demand side of a distribution system to reduce distribution losses. To determine the location and capacity of DG, Tabu Search algorithm is used. In (Prakomchai,2010), placement of DG on the power network using multi- objective algorithm (MPSO) is done in order to minimize economic cost of distributed generation and emission of environmental pollution throughout the targeted system. In fact, two objective functions are written in this paper, first, minimization of producer costs, and second, minimization of environmental pollution emission.