Implimentation of Evolutionary Particle Swarm Optimization in Distributed Generation Sizing

The size of Distributed Generation (DG) is very important in order to reduce the impact of installing a DG in the distribution Network. Without proper connection and sizing of DG, it will cause the power loss to increase and also might cause the voltage in the network to operate beyond the acceptable limit. Therefore, most researchers have concentrated on the optimization technique to regulate the DG’s output to compute its optimal size. In this paper, the concept of Evolutionary Particle Swarm Optimization (EPSO) method is implemented in sizing the DG units. By substituting the concept of Evolutionary Programming (EP) in some part of Particle Swarm Optimization (PSO) algorithm process, it will make the process of convergence become faster. The algorithm has been tested in 33bus distribution system with 3 units of DG that operate in PV mode. Its performance was compared with the performance when using the traditional PSO and without using any optimization method. In terms of power loss reduction and voltage profile, the EPSO can give similar performance as PSO. Moreover, the EPSO requires less number of iteration and computing time to converge. Thus, it can be said that the EPSO is superior in term of speed, while maintaining the same performance.DOI:http://dx.doi.org/10.11591/ijece.v2i1.22

Optimal accommodation and management of high renewable penetration in distribution systems

The paper presents a new bi-level optimisation framework for optimal accommodation and operational management of wind power generation and battery energy storage system (BESS) simultaneously, aiming to maximise the renewable hosting capacity of distribution networks. A new objective function is suggested comprising of annual energy loss in feeders, reverse power flow into the grid, non-utilised BESS capacities, round-trip conversion losses of BESSs and node voltage deviation subjected to various system security constraints. An artificial-intelligence-based optimal management of BESS is proposed for effective control of high-renewable power generation. Due to the high investment and running costs of BESS, minimum storage capacity has been ensured in planning stage. In order to show the effectiveness of the proposed model, it is implemented on a benchmark test distribution system of 33-bus. Besides, various test cases are investigated and compared, which shows that the proposed optimisation model is promising

Modelling the effect of applied voltage and frequency on electroluminescence in polymeric material

Electroluminescence method has been used by several researchers to observe the behaviour of an aged polymeric material. Electroluminescence is a phenomenon that occurs when the atoms of a material are being excited due to the application of and external high electrical stresses. The changes in the energy level of these excitation states can be used as an indicator for the initiation of electrical ageing. There are several factors that affect the behaviour of electroluminescence emission such as, among others, applied voltage, applied frequency, ageing of material and types of materials and gases used are discussed in this paper. A mathematical approach relating these factors and the intensity of electroluminescence is proposed through the aid of Dimensional Analysis method. A close relationship is obtained between experimental and simulation that suggests this mathematical approach can be utilized as a tool to predict electrical ageing of insulation material

Optimal multiple distributed generation output through rank evolutionary particle swarm optimization

The total power losses in a distribution network are usually minimized through the adjustment of the output of a distributed generator (DG). In line with this objective, most researchers concentrate on the optimization technique in order to regulate the DG's output and compute its optimal size. In this article, a novel Rank Evolutionary Particle Swarm Optimization (REPSO) method is introduced. By hybridizing the Evolutionary Programming (EP) in Particle Swarm Optimization (PSO) algorithm, it will allow the entire particles to move toward the optimal value faster than usual and reach the convergence value. Moreover, the local best (P-best) and global best (G(best)) values are obtained in simplify manner in the REPSO algorithm. The performance of this new algorithm will be compared to 3 well-known PSO methods, which are Conventional Particle Swarm Optimization (CPSO), Inertia Weight Particle Swarm Optimization (IWPSO), and Iteration Particle Swarm Optimization (IPSO) on 10 mathematical benchmark functions, and solving the optimal DG output problem. From the results, the IWPSO, IPSO and REPSO methods gave the similar "best" value in all functions after being tested 50 times, except for Function 6. However, the REPSO algorithm provided the lowest SD value in all problems. In the power system analysis, the performance of REPSO is similar to IWPSO and IPSO, and better than CPSO, but the REPSO algorithm requires less numbers of iteration and computing time. It can be concluded that the REPSO is a superior method in solving low dimension analysis, either in numerical optimization problems, or DG sizing problems. (C) 2014 Elsevier B.V. All rights reserved

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

Combined voltage stability index for charging station effect on distribution network

This paper presents the effect of Distributed Generation (DG) as well as Charging Station (CS) to the voltage stability index (VSI) on distribution network Various locations for CS within the network are used in order to evaluate the effect of increment of the CS. The DG that is used is operated in PV mode. There are three types of VSI formulae applied to indicated the condition of the network as either collapsed or not which are P-VSI, Q-VSI (known as FVSI) and C-VSI. The study clearly shows that the increment of CS and location of DG has greater impact on Q-VSI and P-VSI towards attaining voltage collapse point. Further study conducted has reviled that voltage collapse indication in radial distribution network is better monitored by use of the Combined VSI ( C-VSI). The analysis was tested on a 33 bus distribution system

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

Improving State Estimation Accuracy Through Incremental Meter Placement Using New Evolutionary Strategy

Power system network is an interconnected web of crucial electrical elements and system monitoring. It is imperative in ensuring system integrity and stability. In regard to this, state estimation is commonly employed to obtain the best estimate of the power system condition based on limited number of measurements. The placement of meters at appropriate locations is crucial in determining the accuracy of the state estimation. Hence, this paper presents a new optimal meter placement strategy for state estimation. A new evolutionary strategy for discrete optimization problem is proposed so that the location of additional meter placement will improve the accuracy of state estimation. The minimization of sum covariance error of state estimation is selected as the objective function to be minimized. Simulation results on the IEEE 30-bus system clearly shows that the proposed approach is able to outperform the conventional heuristic method in determining the optimal meter placement, which enhances the state estimation accuracy

Optimum multi DG units placement and sizing based on voltage stability index and PSO

Optimum DG placement and sizing is one of the current topics in restructured power system. Most of the authors have worked out on their optimum placement base on the power losses reduction concept. However, the improvement on power losses value in the network will not guarantee to the planner to have lower voltage stability index (VSI) for the system. This paper proposes a new approached for multi DG placement and sizing for distribution systems which is based on a voltage stability index. The most optimum DG size will be found out using several types of PSO optimization algorithm. The output results will also compared with EPSO, REPSO, and IPSO. The proposed algorithm is tested on 12-bus, modified 12-bus and 69-bus radial distribution networks