Contents I. Introduction
Distributed system operations and planning have been facing with the major changes in recent decades. Distributed Generation (DG) has gained extensive acceptance within the present-day in decentralized framework. It has been designed to provide reliable energy to loads by using a relatively small number of well-understood generators with a varying type of technologies. The propose is to enhance the reliability, security of the distributed system. A hybrid resource that consists of multiple energy technologies is a strong strategy for transforming the grid that provides significant flexibility and adaptability [1]. Moreover, during connect DG to electrical system a comprehensive DG can provide the resiliency, operation efficiency, environment, and fast delivery [2]. System operators can monitor and manage the optimal power flow in the distributed system. However, in reality, many regions lack effective management and planning for operating DG, resulting is uncontrol the active power from each DG unit. Additionally, there is an uncertainty of the appropriate active power from DG against the power demand. Scheduling with optimization for multiple objectives along with the operating capacity of DG [3], and the active power of DG in distributed system should be optimize when the electrical system is integrated by a high penetration of DG [4]. The voltage deviation and the active power loss are 2 important factors that indicate power quality of the distributed system [5]–[7]. Then, the objective function will contain 2 parts. Firstly, the active power loss is calculated for evaluating the minimum total active power loss in the system and the last one is the voltage deviation, it is calculated the voltage variation when DG connect to the grid for ensuring the stability of the distributed network [8] by using particle swarm optimization (PSO). The PSO is a computational intelligence method that it is not largely affected by the size and the nonlinearity of the problem. It can effectively reach the best solution whereas the conventional analytical methods encounter difficulties in convergence. Moreover, the PSO exhibits certain advantages compared to other similar optimization methodologies such as, simple to execute, there are fewer parameters requiring adjustment, each particle recalls of both its individual previous best value and the best value within its neighborhood, and the diversity of the swarm is maintained more efficiently [9], [10] so, the results demonstrated that the maximum active power in each DG was injected to distributed network by PSO operation.
