A Two Stage Methodology for Siting and Sizing of DG for Minimum Loss in Radial Distribution System using RCGA

Abstract

This paper presents a new methodology using Real Coded Genetic Algorithm (RCGA) for the placement of Distributed Generators(DG) in the radial distribution systems to reduce the real power losses and to improve the voltage profile. A two-stage methodology is used for the optimal DG placement . In the first stage, single DG placement algorithm is used to find the optimal DG locations and in the second stage, Real Coded Genetic Algorithm is used to find the size of the DGs corresponding to maximum loss reduction. The proposed method is tested on standard IEEE 33 bus test system and the results are presented.

Review of Non-Traditional Optimization Methods for Allocation of Distributed Generation and Energy Storage in Distribution System

“…In the literature [20][21][22][23][24][25], the authors investigated the problem of deployment of distributed electricity sources to reduce active power losses and improve voltage characteristics. Lalitha et al [20] used the fuzzy artificial immune system to determine the location of distributed sources, to improve the characteristics of voltage and reduce active power losses.…”

“…In [21] authors determined the optimal location of a distributed generator, using a genetic algorithm, then used a real-coded genetic algorithm to determine the optimal power of distributed generators. By testing it on the IEEE 33 bus test system, the authors showed that the method drastically reduced overall system power losses and significantly improved the characteristics of voltage.…”

“…A genetic algorithm is the most used non-traditional method for determination of the optimal location and size of DGs in distribution network [19,21,26,27,28,36]. Also, the researchers added elitism or used it in combination with other algorithms [27].…”

“…Considering that certain methods for optimization are more appropriate for determining optimal location, [21,24,25,31,36] and other methods for determining the optimal size of DG, the researchers used a combination of methods for optimization to achieve better results, [20,21,22, 24÷30] and [36]. For example, a mix of the fuzzy logic to determine the optimal location and the artificial bee colonies algorithm to determine the optimum power were used in [24].…”

Impact of Distributed Generation on a Distribution Network Voltage Sags in Baghdad City

“…These impacts were studied in both normal steady-state condition (line power losses) and abnormal durations (voltage sag losses). In [9], introduced a new methodology employing RCGA for the situation of DG in the radial distribution systems to decrease real power losses and to get better the voltage profile.…”