A DNR and DG Sizing Simultaneously by Using EPSO

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 efficient hybridization of both Particle Swarm Optimization (PSO) and Evolutionary Programming (EP) methods which is called the Evolutionary Particle Swarm Optimization (EPSO). The proposed method is used to find the optimal network reconfiguration and optimal size of Distribution Generation (DG) simultaneously. The main objective of this paper is to gain the lowest result of real power losses in the distribution network while improve the computational time as well as satisfying other operating constraints. A comprehensive performance analysis is carried out on IEEE 33 bus distribution system. The proposed method is applied and its impact on the network reconfiguration for real power loss is investigated. The results are presented and compared with the strategy of separated DG sizing and network reconfiguration

A 16kV Distribution Network Reconfiguration by Using Evolutionaring Programming for Loss Minimizing

In the worldwide trend toward restructuring the electricity network; there are a lot of problems. Where with the increasing of electricity demand, intelligence algorithm is one of the optimization search engine that may help in minimizing the power losses in the power distribution network. This paper presents a method of 16kV Distribution Network Reconfigurtion (DNR) by using Evolutionary Programming (EP). The main objectives of this study are to minimize the power losses and improve the voltage profile while analyzing the consistency and computing time effectively. The performance of the Evolutionary Programming method will be investigated and the impact to the 16kV distribution network will be analyzed. Thereal result will be compared with the conventional initial network and other optimization technique which is Genetic Algorithm (GA). The results of this study is hoped to help the power system engineers in Malaysia in order to solve the losses problem in the plant at the same time increasing the efficiency of the real 16-bus distribution system

Multi-Objective Network Reconfiguration with Optimal DG Output Using Meta-Heuristic Search Algorithms

Network reconfiguration is a well-known technique for the distribution system to reduce power losses. However, the reconfiguration technique, by itself, could only minimize power losses up to a certain point. Further power losses reduction could be realized via the application of distributed generation (DG). However, the integration of DG to the distribution system at a non-optimal value could instead increase power losses and voltage fluctuation. Therefore, it is vital to develop an effective optimization strategy to determine the optimal output of the DG and simultaneously ensure optimal configuration. This paper presents a simultaneous optimal network reconfiguration with optimal DG output to minimize power losses and improve the voltage profile. Different objectives are discussed in this paper: (1) to minimize power losses, (2) to improve voltage profile index, (3) to maximize DG output. Evolutionary programming, particle swarm optimization, firefly, and gravitational search algorithm methods have been applied for optimal distribution network reconfiguration with optimal DG output. To evaluate the possibilities of the suggested method, simulations using MATLAB software are carried out on an IEEE 33-bus radial distribution system. The obtained outcomes prove the efficiency of the proposed strategy to find an optimal network configuration and optimal output of DG units

Minimum switching losses for solving distribution NR problem with distributed generation

Power losses in a distribution system are commonly minimised via optimal network reconfiguration (NR). Previously, research on NR was focused on planning, where the final configuration reporting the lowest power losses being the main goal. However, power losses during switching operations from the original state to the optimal state of configuration were not considered. This study discusses the optimal switching path for minimising power losses when reconfiguring a network. The simultaneous optimal NR and distributed generation (DG) output was also proposed. The proposed methodology involves: (i) optimal NR and DG output simultaneously and (ii) optimal switching path to convert the network from the initial configuration to the final configuration obtained from (i). The selected optimisation technique in this study is the firefly algorithm. The proposed method was tested using IEEE 33-bus, 69-bus, and 118-bus radial distribution networks, while also accounting for static and dynamic loads. The results confirmed the effectiveness of the proposed method in determining the optimal path of switching operations, as well as the optimal network configuration and optimal output of DG units

DG Sizing and DNR Based on REPSO for Power Losses Reduction

The detrimental of losses in network can be solved by using Distribution Network Reconfiguration (DNR) and sizing the Distribution Generation (DG) concurrently. In determining the optimal sizing of DG and identifying the switching operation plan for network reconfiguration, an optimization method which is called as Rank Evolutionary Particle Swarm (REPSO) will be introduced. The main objectives of this paper are to minimize the total power losses in a radial distribution network and to find the most accurate and acceptable size of DG. A comprehensive performance analysis will be carried out on IEEE-33 bus system to show the effectiveness of the REPSO over conventional PSO and hybridization EPSO method. The reliability of proposed method will hope to help the power system engineer in reducing the distribution feeder losses and improve system security in the future

Power losses reduction via simultaneous optimal distributed generation output and reconfiguration using ABC optimization

Optimal Distributed Generation (DG) output and reconfiguration are among the well accepted approach to reduce power loss in a distribution network. In the past, most of the researchers employed optimal DG output and reconfiguration separately. In this work, a simultaneous DG output and reconfiguration analysis is proposed to maximize power loss reduction. The impact of the separated analysis and simultaneous analysis are investigated. The test result on the 33 bus distribution network with 3 units of DG operated in PV mode showed the simultaneous analysis gave the lowest power loss (global optimal) and faster results compared to other combined methods. All the analyses for optimizing the DG as well as reconfiguration are used the Artificial Bee Colony Optimization technique

Simultaneous Network Reconfiguration with Distributed Generation Sizing and Tap Changer Adjustment for Power Loss Reduction Using Imperialist Competitive Algorithm

This paper presents a simultaneous network reconfiguration with DG sizing and tap changer adjustment to minimize power loss in a distribution system. Imperialist Competitive Algorithm is used to identify the best set combination of reconfiguration, DG size and tap changer. The performance of the proposed method has been tested on 33 bus distribution system at different load levels and different scenarios of DGs location. The test results indicated an improvement of power loss reduction as compared to existing method. Furthermore, the simultaneous approach is able to produce lowest power loss than any combination of two components simultaneously. At the same time, voltage also maintains within allowable limit