Network reconfiguration for loss reduction with distributed generations using PSO

This paper presents an effective method based on Particle Swarm Optimization (PSO) to identify the switching operation plan for feeder reconfiguration and optimum value of DG size simultaneously. The main objective is to reduce the real power losses and improve the bus voltage profile in the system while satisfying all the distribution constraints. A method based on PSO algorithm to determine the minimum configuration is presented and their impact on the network real power losses and voltage profiles are investigated. To demonstrate the validity of the proposed algorithm, computer simulations are carried out on 33 bus systems and the results are presented and compare with the Genetic Algorithm (GA) method

The simultaneous application of optimum network reconfiguration and distributed generation sizing using PSO for power loss reduction

The utilization of Distributed Generation (DG) sources in Distribution Power system is indeed vital as it is capable of solving problems especially pertaining to power losses due to an increasing demand for electrical energy.The location and optimal size of DG has become a prominent issue for the network to have lower power losses value. In order to reduce unnecessary power losses, the use of a combination reconfiguration method and DG units can assist the system to obtain optimal power loss in the network distribution. The primary idea is to have the reconfiguration process embedded with Distributed Generation (DG) and being operated simultaneously to reduce power losses and determine the optimal size of DG by using Particle Swarm Optimization (PSO). The objective of this paper is to focus on reducing the real power losses in the system as well as improving the voltage profile while fulfilling distribution constraints. The simulation results show that the use of simultaneous approach has resulted the lower power losses and better voltage profile of the system. A detail performance analysis is carried out on IEEE 33-bus systems demonstrate the effectiveness of the proposed methodology

Simultaneous Network Reconfiguration and DG Sizing Using Evolutionary Programming and Genetic Algorithm to Minimize Power Losses

Distribution network planning and operation require the identification of the best topological configuration that is able to fulfill the power demand with minimum power loss. This paper presents an effective method based on Evolutionary Programming (EP) and Genetic Algorithm (GA) to identify the switching operation plan for feeder reconfiguration and distributed generation size simultaneously. The main objectives of this paper are to gain the lowest reading of real power losses, upgrade the voltage profile in the system as well as satisfying other operating constraints. Their impacts on the network real power losses and voltage profiles are investigated. A comprehensive performance analysis is carried out on IEEE 33-bus radial distribution systems to prove the efficiency of the proposed methodology. The test result on the system showed the power loss reduction, and voltage profile improvement of the EP is superior to the GA method

Reconfiguration distribution network with multiple distributed generation operation types using simplified artificial bees colony

The power losses in the distribution network are the critical issues that most researchers are trying to solve nowadays. From the passive distribution network in the last decades, the existing of Distributed Generation (DG) in the system will now allow the network to contribute in supplying some of the power to the load. However, selecting the optimal size of DG plays important role to avoid any drawback to the network. The connection of high capacity and excess number of DG units to electrical power system will lead to very high power losses. This factor makes the optimal size of DG become an important issue for the network to have lower power losses value. Furthermore, the use of reconfiguration method in cooperating with the DG units can help the system to have much lower power loss for the distribution system. Since the reconfiguration only required small investment in controlling method, it is very suitable to be used in improving the voltage profile and the power losses after the optimal DG is achievable. Three types of DG modes are used in the study which is constant voltage mode (PV), constant voltage with reactive power mode (PV with VAR constraint) and constant power mode (PQ mode).The Rank Evolutionary Particle Swarm Optimization (REPSO) and a Novel Simplified Artificial Bee Colony (SABC) are used in finding the optimal size of DG and the best configuration of the network respectively. The results show that the use of reconfiguration technique has improved the power losses as well as the voltage profile for the network even after optimal DG sizing has been achieved