Dual technique of reconfiguration and capacitor placement for distribution system

Radial Distribution System (RDS) suffer from high real power losses and lower bus voltages. Distribution System Reconfiguration (DSR) and Optimal Capacitor Placement (OCP) techniques are ones of the most economic and efficient approaches for loss reduction and voltage profile improvement while satisfy RDS constraints. The advantages of these two approaches can be concentrated using of both techniques together. In this study two techniques are used in different ways. First, the DSR technique is applied individually. Second, the dual technique has been adopted of DSR followed by OCP in order to identify the technique that provides the most effective performance. Three optimization algorithms have been used to obtain the optimal design in individual and dual technique. Two IEEE case studies (33bus, and 69 bus) used to check the effectiveness of proposed approaches. A Direct Backward Forward Sweep Method (DBFSM) has been used in order to calculate the total losses and voltage of each bus. Results show the capability of the proposed dual technique using Modified Biogeography Based Optimization (MBBO) algorithm to find the optimal solution for significant loss reduction and voltage profile enhancement. In addition, comparisons with literature works done to show the superiority of proposed algorithms in both techniques

Determining optimal location and size of capacitors in radial distribution networks using moth swarm algorithm

In this study, the problem of optimal capacitor location and size determination (OCLSD) in radial distribution networks for reducing losses is unraveled by moth swarm algorithm (MSA). MSA is one of the most powerful meta-heuristic algorithm that is taken from the inspiration of the food source finding behavior of moths. Four study cases of installing different numbers of capacitors in the 15-bus radial distribution test system including two, three, four and five capacitors areemployed to run the applied MSA for an investigation of behavior and assessment of performances. Power loss and the improvement of voltage profile obtained by MSA are compared with those fromother methods. As a result, it can be concluded that MSA can give a good truthful and effective solution method for OCLSD problem

Optimum reactive power compensation for distribution system using dolphin algorithm considering different load models

The distribution system represents the connection between consumers and the entire power network. The radial structure is preferred for distribution system due to its simple design and low cost. The electrical distribution system suffers from problems of rising power losses higher than the transmission system and voltage drop. One of the important solutions to improve the voltage profile and to reduce the electrical distribution system losses is the reactive power compensation which is based on the optimum choice of position and capacitor size in the network. In this paper, different models of electrical loads such as constant power(P), constant current(I), constant impedance(Z), and composite (ZIP) model are implemented with comparisons between them in order to identify the most effective load type that produces the optimal settlement for alleged loss reduction ,enhancement of the voltage profile, and cost savings. To minimize search space, Dolphin Optimization Algorithm (DOA) is applied for selecting the size and location of capacitors. Two case studies (IEEE 16- bus and 33- bus) are employed to evaluate the different load models with optimal reactive power compensation. The results of comparison between the different load models show that ZIP model is the best to produce the optimum solution for capacitor position and size. In addition, comparison of results with literature works are done and showed that DOA is the most robust among other algorithms to achieve the optimum solution for voltage profile enhancement significant reduction of losses, and saving cost

Enhancing radial distribution system performance by optimal placement of DSTATCOM

In this paper, A novel modified optimization method was used to find the optimal location and size for placing distribution Static Compensator in the radial distribution test feeder in order to improve its performance by minimizing the total power losses of the test feeder, enhancing the voltage profile and reducing the costs. The modified grey wolf optimization algorithm is used for the first time to solve this kind of optimization problem. An objective function was developed to study the radial distribution system included total power loss of the system and costs due to power loss in system. The proposed method is applied to two different test distribution feeders (33 bus and 69 bus test systems) using different Dstatcom sizes and the acquired results were analyzed and compared to other recent optimization methods applied to the same test feeders to ensure the effectiveness of the used method and its superiority over other recent optimization mehods. The major findings from obtained results that the applied technique found the most minimized total power loss in system ,the best improved voltage profile and most reduction in costs due power loss compared to other methods

Dual techniques of load shedding and capacitor placement considering load models for optimal distribution system

Voltage stability represents one of the main issues in electrical power system. Under voltage load shedding (UVLS) has long been regarded as one of the most successful techniques to prevent the voltage collapse. However, the ordinary load shedding schemes do not consider the different load models and decreasing in the ‎economic cost that resulted from load disconnection, so the dual techniques of load shedding with reactive compensation are needed. Usually loads being modeled as constant power, while in fact of load flow the various load models are utilized. An investigation of optimal dual load shedding with reactive compensation for distribution system based on direct backward forward sweep method (DBFSM) load flow along with a comparison among the other load models are presented in this paper. The teaching learning-based optimization (TLBO) algorithm is executed in order to reduce power losses and enhance the voltage profile. This algorithm is tested and applied to IEEE-16 bus distribution test system to find the optimal superior capacitor size and placement while minimizing load shading for the network. Five different load shedding sequences are considered and the optimization performance of load models demonstrated the comparison through MATLAB program

Optimal selection and location of fixed-step capacitor banks in distribution networks using a discrete version of the vortex search algorithm

This paper deals with the problem of the optimal selection of capacitor banks in electrical AC distribution systems for minimizing the costs of energy losses during a year of operation through a discrete version of the vortex search algorithm (DVSA). This algorithm works with a hypersphere with a variable radius defined by an exponential function where a Gaussian distribution is used to generate a set of candidate solutions uniformly distributed around the center of this hypersphere. This center corresponds to the best solution obtained at the iteration t, which is initialized at the center of the solution space at the iterative search beginning. The main advantage of combining the exponential function with the Gaussian distribution is the correct balance between the exploration and exploitation of the solution space, which allows reaching the global optimal solution of the optimization problem with a low standard deviation, i.e., guaranteeing repeatability at each simulation. Two classical distribution networks composed of 33 and 69 nodes were used to validate the proposed DVSA algorithm. They demonstrated that the DVSA improves numerical reports found in specialized literature regarding the optimal selection and location of fixed-step capacitor banks with a low computational burden. All the simulations were carried out in MATLAB software

Analysis of the Effect of Distributed Generation on Loss Reduction in Electrical Distribution Network

Distribution network is said to be the most visual part of the electric production and the most observed by the utilities for investment, maintenance and operation. The system have been operated under stressed conditions due to limited structure and increasing day to day requirement of power consumption, which have a significant economic and social impact on the system. Due to the system high resistance to impendence ratio, large amount of power loss occur in the network. This loss is the most severity factors affecting the power quality delivered to the end users and depend on power network expansion and load complexity. Among the support methods available for power loss minimization in distribution network, strategic allocation of Distributed Generation (DG) in distribution system is widely considered a viable option. DGs are electrical sources connected to the power network located to consumer’s side but very small when compared with the centralized power plant. They can be in form of wind, mini-hydro, photovoltaic and fuel-based system such as fuel cells and micro-turbines. Therefore, in this study, different approaches for power loss minimization in electrical distribution system with the incorporation of DG by various researchers were reviewed. These approaches have become powerful tools to overcome the problem of power loss minimization in distribution system. Keywords: Distribution System, Power Loss. Distributed Generation, Power Consumption, Photovoltaic System, Centralized Power Plant. DOI: 10.7176/JETP/11-6-02 Publication date: November 30th 202

Distribution network reconfiguration considering DGs using a hybrid CS-GWO algorithm for power loss minimization and voltage profile enhancement

This paper presents an implementation of the hybrid Cuckoo search and Grey wolf (CS-GWO) optimization algorithm for solving the problem of distribution network reconfiguration (DNR) and optimal location and sizing of distributed generations (DGs) simultaneously in radial distribution systems (RDSs). This algorithm is being used significantly to minimize the system power loss, voltage deviation at load buses and improve the voltage profile. When solving the high-dimensional datasets optimization problem using the GWO algorithm, it simply falls into an optimum local region. To enhance and strengthen the GWO algorithm searchability, CS algorithm is integrated to update the best three candidate solutions. This hybrid CS-GWO algorithm has a more substantial search capability to simultaneously find optimal candidate solutions for problem. Furthermore, to validate the effectiveness and performances of the proposed hybrid CS-GWO algorithm is being tested and evaluated for standard IEEE 33-bus and 69-bus RDSs by considering different scenarios