Omar Aliman

Transmission loss and load flow allocations via genetic algorithm technique

Transmission loss and load flow allocations become important issues under deregulation system. Due to nonlinear nature of power flow, tracing the loss and power flow through the mesh network becomes more complicated. Since the complexity of electricity transmission system, it is not straightforward to determine the contribution of particular generator to a particular line loss and/ or load. This paper will discuss load flow and loss allocation using Genetic Algorithm (GA) technique. GA is one of the optimization techniques that apply natural phenomena, viz. genetic inheritance and Darwinian strive for survival. Transmission loss and load flow allocations problem will be treated as an optimization problem. In this paper, Ward-Hale 6-bus test system will be used to demonstrate the effectiveness of the technique and validated by IEEE 30-bus test system. Comparison with other method is also given.

Distributed generation installation using particle swarm optimization

This paper presents a particle swarm optimization approach for the placement of distributed generation (DG) in the distribution system. DG installation in the distribution system is very useful in reducing the line losses, as well as improving the voltage profiles. The proposed method combines particle swarm optimization and the Newton-Raphson load flow method to determine the location and size of the DG. The objective function to be minimized in this problem is the total power losses of the system. The proposed approach has been tested on IEEE 69-bus distribution test system and the program was simulated using MATLAB software. Test results show the effectiveness of the developed algorithm.

Firefly Algorithm technique for solving Economic Dispatch problem

This paper presents the implementation of Firefly Algorithm (FA) in solving the Economic Dispatch (ED) problem by minimizing the fuel cost and considering the generator limits and transmission losses. ED is one of the most challenging problems of power system since it is difficult to determine the optimum generation scheduling to meet the particular load demand with the minimum fuel cost and transmission loss. Until now, there are a lot of researches that have been done to seek for closest optimum result in determining the power generation of each generator especially in large scale power system. FA is a meta-heuristic algorithm which is inspired by the flashing behavior of fireflies. The primary purpose of fireflys flash is to act as a signal system to attract other fireflies. In this paper, 26-bus system is utilized to show the effectiveness of the FA in solving the ED problem. Comparison with Continuous Genetic Algorithm (CGA) and conventional method are also given.

Optimal allocation and sizing of Distributed Generation in distribution system via Firefly Algorithm

This paper presents an application of Firefly Algorithm (FA) in determining the optimal location and size of Distributed Generation (DG) in distribution power networks. FA is a meta-heuristic algorithm which is inspired by the flashing behavior of fireflies. The primary purpose of fireflys flash is to act as a signal system to attract other fireflies. In this paper, IEEE 69-bus distribution test system is used to show the effectiveness of the FA. Comparison with other method is also given.

Implementation of DG for loss minimization and voltage profile in distribution system

This paper presents the effects of DG on the performance of an existing distribution network in terms of voltage stability, loss minimization and voltage profile. In this study, a new program was developed based on Artificial Immune System optimization technique in order to determine the optimal size of distributed generator. Various loading conditions were tested in order to evaluate the effectiveness of the proposed technique in determining the optimal size of the distributed generator. The suitable location of distributed generator is identified based on the results from voltage stability index. The proposed technique was tested on IEEE Reliability Test systems namely the IEEE 69-bus and the program was developed using the MATLAB programming software.

Rotation-Elevation of Sun Tracking Mode to Gain High Concentration Solar Energy

Power conversion from solar thermal energy to electrical energy is still very cost-intensive. Serious effort has to be given in the development of the concentrator or heliostat structure expenditure which contributing the most expensive component in a central receiver solar power plant. With current development to find alternatives and lower down the capital, an alternative scheme of sun tracking has been developed and feasibility tested. The proposed rotation-elevation mode of sun tracking has significantly benefits use in high temperature and high concentration solar energy applications. Instead of offering stationary location for large and heavy solar powered Stirling engine application, the process of optical alignment works are reasonably easier and less time consuming. This paper at first will highlight and elaborate its concept and follow with some experimental results.

DG sizing impact for loss minimization considering cost factor

Distributed Generation (DG) is an auspicious solution to many power system problems such as voltage regulation, power loss minimization, effective operating power factor, etc. This paper presents a modified Artificial Bee Colony (ABC) algorithm to suit the searching for the optimal DG sizing and placement in a distribution network. The problem formulation and the proposed technique are tested on a 28-bus Malaysian distribution system. In confirming the effectiveness of this method to determine the minimum power loss which respects to its optimal size and placement, the proposed population-based meta-heuristic approach is compared with the Evolutionary Programming (EP) method for validation and accuracy. The analysis is focused on the sizing impact when considering the cost factor without neglecting the importance to maintain the minimal loss. The results have shown comparatively the same with EP in terms of the computational efficiency. Significance of savings could be achieved while having a low investment of DG installation with a minimum power loss.

Heuristic based placement technique for distributed generation

In this paper, a heuristic DG optimization technique for radial distribution feeders is proposed. Significant search space reductions obtained through a small number of DG tests is offered. A sufficient sensitivity test analysis applicable for DG applications is proposed. Sample test system is examined using the proposed technique and the results are compared with those obtained via other method. The suggested technique succeeds in solving different test cases. A comparative study validates the practical concerns of the proposed method.

Tracing the real power transfer of individual generators to loads using Least Squares Support Vector Machine with Continuous Genetic Algorithm

This paper attempts to trace the real power transfer of individual generators to loads in pool based power system by incorporating the hybridization of Least Squares Support Vector Machine (LS-SVM) with Continuous Genetic Algorithm (CGA)- CGA-LSSVM. The idea is to use CGA to find the optimal values of regularization parameter, γ and Kernel RBF parameter, σ2, and adapt a supervised learning approach to train the LS-SVM model. The technique that uses proportional sharing principle (PSP) is utilized as a teacher. Based on converged load flow and followed by PSP technique for power tracing procedure, the description of inputs and outputs of the training data are created. The CGA-LSSVM will learn to identify which generators are supplying to which loads. In this paper, the 25-bus equivalent system of southern Malaysia is used to illustrate the effectiveness of the CGA-LSSVM technique compared to that of the PSP technique.

Contingency analysis of embedded generation deployment in distribution network

This paper presents a contingency analysis study of Embedded Generation (EG) deployment in distribution network. It is expected of increasing amounts of EG that will be connected to power system distribution in the near future. Due to advances of technology and deregulation in the power market, EG is expected to become more cost effective energy sources that open the opportunity of exploiting renewable energy sources. This paper is started by introducing a new method for determining the optimal location and size of EG that needed to be deployed in the distribution network simultaneously using Genetic Algorithm (GA) technique. It follows by the discussion of evaluating the impact of the location and the size of EG to the system before and after contingency is created in the system due to fault. The proposed allocation methods and contingency analysis are demonstrated using IEEE 69-bus radial distribution system.