Load Concentration Factor Based Analytical Method for Optimal Placement of Multiple Distribution Generators for Loss Minimization and Voltage Profile Improvement

Abstract

This paper presents novel separate methods for finding optimal locations, sizes of multiple distributed generators (DGs) simultaneously and operational power factor in order to minimize power loss and improve the voltage profile in the distribution system. A load concentration factor (LCF) is introduced to select the optimal location(s) for DG placement. Exact loss formula based analytical expressions are derived for calculating the optimal sizes of any number of DGs simultaneously. Since neither optimizing the location nor optimizing the size is done iteratively, like existing methods do, the simulation time is reduced considerably. The exhaustive method is used to find the operational power factor, and it is shown with the results that the losses are further reduced and voltage profile is improved by operating the DGs at operational power factor. Results for power loss reduction and voltage profile improvement in IEEE 37 and 119 node radial distribution systems are presented and compared with the the loss sensitivity factor (LSF) method, improved analytical (IA) and exhaustive load flow method (ELF). The comparison for operational power factor and other power factors is also presented.

A time‐varying load‐based analytical approach for DG optimization in the distribution network

“…These methods include Loss Sensitivity Factor (LSF), which is used to estimate optimal bus number, analytical expression methods, improvement analytical (IA) method, and efficient analytical (EA) method . Also, other methods have been proposed for siting multiple DG units in distribution networks to minimize active loss . A multi objective function taking the active and reactive loss into account have also been proposed to find optimal size of DG .…”

“…19 Also, other methods have been proposed for siting multiple DG units in distribution networks to minimize active loss. [20][21][22] A multi objective function taking the active and reactive loss into account have also been proposed to find optimal size of DG. 3 The common point between most of these methods and algorithms is the sizing and siting of DG units based on peak or average demands, with a few researches considering time varying demands.…”

Minimizing Power Losses in Distribution Systems Using GA in Planning and Operation: A Case Study for DGs and SCs

“…In [16], a Genetic Algorithm combined with a sequential quadratic programming-based algorithm has been proposed for minimizing the sum of costs, power losses, network upgrading, carrying out the reactive power service, and installation costs. In [17], a load concentration factor based analytical method was proposed to find the optimal solution able for minimizing power losses and improving the voltage profile.…”

Maximum Permissible Integration Capacity of Renewable DG Units Based on System Loads

“…In the optimum value approach, the most appropriate sizing and/or siting is determined according to the criteria considered in the study. The most important optimization studies can be given as grouped according to their objectives on which they are based; minimization of power losses [20,[23][24][25][26], minimization of energy losses [27], increasing system reliability based on various reliability indices [28,29] and minimization of costs [29,30]. There are also approaches to define the maximum permissible capacity value based on a single system criterion other than the hosting capacity and optimum values approaches [31][32][33][34][35][36][37].…”