Optimal Placement and Sizing of Distributed Generators in Balanced/Unbalanced Distribution Systems Using Backtracking Search Optimization (BSA)
Author(s):
Shaik Imran Basha, R. Mruthyunjaya Reddy
Keywords:
MATLAB, Backtracking Search Optimization, Algorithm
Abstract
Over the last quarter of 20th century, many developments have been made in the power generation and major transmission networks optimization. For achieving this, digital computers have been used in the study of load flows, system stability and in optimum load allocation of generators. Until recently, concentration is confined to optimize the distribution systems, which represent some 50-60% of the overall cost of an electric power system. Often, sub-transmission and distribution systems were designed with a rule of thumb approach which can result in the system becoming more expensive. This thesis presents various methods for power loss reduction of unbalanced radial distribution systems. The base case load flow solution becomes the initial solution in realization of the algorithms presented here for transformer modeling, network reconfiguration, optimal sizing and sitting of capacitors as well as distributed generators in unbalanced radial distribution systems. Distributed Generation (DG) sources are becoming more prominent in distribution systems due to the incremental demands for electrical energy. Proper locations and sizing of DGs in Distribution systems is important for obtaining their maximum potential benefits. In this paper, finding the optimal location and size of DGs is dealt keeping active power loss as the objective. A very recent swarm optimization technique namely backtracking search optimization algorithm (BSOA) is considered and compared with conventional Big Bank Big Crunch Method (BBBC). DGs supplying both active and reactive power have been studied. The proposed methodology has been verified on IEEE 33 distributed test system coded in MATLAB.
Article Details
Unique Paper ID: 144963

Publication Volume & Issue: Volume 4, Issue 6

Page(s): 234 - 244
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