dg location in matlab
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I WANT TO GET THE CODE OF dg location in Matlab?
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dg location in matlab

Abstract

Genetic algorithm (GA) is utilized to select most suitable Distributed Generator (DG) technology for optimal operation of power system as well as determine the optimal location and size of the DG to minimize power loss on the network. Three classes of DG technologies, synchronous generators, asynchronous generators, and induction generators, are considered and included as part of the variables for the optimization problem. IEEE 14-bus network is used to test the applicability of the algorithm. The result reveals that the developed algorithm is able to successfully select the most suitable DG technology and optimally size and place the DGs to minimize power loss in the network. Furthermore, optimum multiple placement of DG is considered to see the possible impact on power loss in the network. The result reveals that multiple placements can further reduce the power loss in the network.

Introduction

Recent researches have revealed that installation of Distributed Generators (DGs) in the power network has some advantages which include the improvement in voltage profile and reduction in the power loss on the power network . The extent to which DGs reduce power system loss and improves voltage profile depends on the size and location of the DGs . The different modes through which DGs affect the reactive power in a network enable it to provide voltage support . This support however depends on the deliberate placement and sizing of DG to improve the voltage profile of the network. Hence, to maximize these benefits, it is crucial to find the optimal size of DGs and their appropriate locations in the network, as sitting of DG units in improper locations could jeopardizes the system operation .

Several models and methods have been suggested for the solution of the optimal sizing and location of DGs: selection of optimal location and sizing of multiple DGs have been performed by Kumar using Kalman Filter Algorithm. It was similarly reported that the algorithm is effective for determining the size and location of DG. Moreover, it has the advantage in that it runs on fewer samples compared to other algorithms, thereby reducing the computational burden usually experienced during the optimization process. Rani and Davi have optimally determined the location and the size of DG on IEEE 33-bus system using the exact loss formula approach. The result reveals that the method was able to achieve reduction in power losses and improved the voltage profile of the system. A novel algorithm which uses economic dispatch approach was developed by Kamel and Kermanshahi . The algorithm was used to determine the optimum size and location of the DGs embedded in the distribution network. The algorithm also takes into account the power cost and the available rating of DGs if the DGs exist in a competitive market. The technique was applied to three test distribution systems with different sizes (6 buses, 18 buses, and 30 buses). The results indicated that if the DGs are located at their optimal locations and have optimal sizes the total losses in the distribution network will be reduced by nearly 85%.

The optimum size and location of capacitors and distributed generations (DGs) are determined simultaneously in a radial distribution network. The objective function includes power losses reduction and voltage profile improvement using ant colony algorithm. The proposed method was tested on IEEE 33-bus test system. The results show a considerable reduction in the total power loss in the system and improved voltage profiles of all the buses. Similarly, Allocating of DGs and optimal locations and size of Solid State Fault Current Limiters (SSFCLs) have been implemented by Shahriari and Samet using genetic algorithm (GA). Optimal placement and sizing of multiple distributed generation in radial distribution feeders have been performed by Nagireddy et al using combined differential evaluation, HPSO method, with the objective of reducing the real power loss and improving the voltage profile of the network. Backtracking Search Algorithm has been used by Ishak et al. for optimal DG placement and sizing for voltage stability improvement and power loss reduction. The applicability of the proposed method was verified using the IEEE 30-bus transmission network. It was revealed that the proposed method is effective in optimally sizing and locating DG in distribution systems.

Genetic algorithm (GA) has been proposed by Kotb et al. for optimum sizing and placement of DGs in a distributed network. The total active and reactive power losses were minimized and voltage profile was improved. GA fitness function introduced includes the active power losses, reactive power losses, and the cumulative voltage deviation variables. It was argued that GA can be used as a better tool than traditional methods to enable the planners to choose the best size and location of DGs. It was also revealed that the addition of DG to the distribution system reduces the active and reactive power loss and improves the system voltage. Particle Swarm Optimization (PSO) algorithm has been proposed by researchers for optimal allocation and sizing of DGs for loss reduction. Similarly, a Multiobjective Particle Swarm Optimization (MOPSO) algorithm was used to find the optimal number, size, and location of DG units in the radial distribution systems in order to minimize the real power losses and reduce the voltage deviation in . The proposed method was tested on standard IEEE 33-bus test system and it was reported that by installing DGs, the total power loss of the system was reduced and the system’s voltage profile also improved. A Pareto-based Nondominated Sorting Genetic Algorithm II (NSGAII) was proposed in to determine locations and sizes of specified number of DG units within the primary distribution system. In their work, three objective functions were considered as the indices of the system performance: average Load Voltage Deviation (LVD), minimization of the system real power loss, and minimization of the annualized investment costs of DG. A fuzzy decision making analysis was used to obtain the final trade-off optimal solution. The proposed methodology was tested on modified IEEE 33-bus radial system. The test results indicate that NSGA-II is a viable planning tool for practical DG placement and useful contribution of DG in improving the steady state system performance of the distribution system by the optimal allocating, setting, and sizing multitype DG.

Several algorithms have been proposed to optimize the size and the placement of DGs in a network as reviewed in the aforementioned studies. However, none considered or reported how the type of DG technologies affects the optimization problem. Different DG technologies have different reactive power characteristics which can have different effect on the power loss and voltage profile of a power system . It is therefore intuitive to think that selection of appropriate DG technology alongside the optimal placement and sizing of the DG may further reduce the loss experienced in a network. This paper therefore includes DG technology as part of the optimization objective function in addition to its size and location using genetic algorithm.
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