This Matlab design presents the enhancement and impacts of the system losses, voltage profile, and cost by using distributed generations (DGs) of different size in the distribution network. Distributed Generation (DGs) is expected to play a key role in the residential, commercial and industrial sectors of the power system. Distributed Generation (DG) provides an alternative to the traditional electricity sources and can also be used to enhance the current electrical system. DG produces a series of important influence on the distribution network. It mainly introduces four effect areas, namely, distribution network real power loss, voltage, distribution grid planning, and relay protection. In this analysis, load flow method is used for the optimization of the objective function for minimization of total real power loss and reactive power loss of the system.  The work is focused on analyzing the impact of DG installation on distribution network operation including system voltage profile analysis, real and reactive power losses and cost of the system. Incorporating DG significantly decreases the power loss and maximizes the stability of the voltage profile. To acquire determined compensation from DG resources, it should be synchronized with the best location and size. To make the exploration more useful, the loads are linearly varied in minor steps of 1% since 50% to 150% of the definite value. In every load step sizes, the finest size and position for various types of DGs are calculated and examines the major issues faced by these techniques. The proposed methodology has been tested for IEEE-33 and 69 Bus System using Glow-worm Swarm Optimization (GSO) under different Types of DG’s (TYPE 1, TYPE 2, TYPE 3 and TYPE 4).
Reference Paper-1: Impact Assessment of DG in Distribution Systems from Minimization of Total Real Power Loss Viewpoint by using Optimal Power Flow Algorithms
Author’s Name: Bindeshwar Singh, and Bindu Jee
Source: Elsevier
Reference Paper-2: Glowworm Swarm based Optimization Algorithm for Multimodal Functions with Collective Robotics Applications
Author’s Name: K.N. Krishnanand and Debasish Ghose
Source: Multiagent and Grid Systems

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