Location Of Distributed Generation Research Articles

Mathematical morphology‐based islanding detection for distributed generation

This study proposes a novel method based on mathematical morphology (MM) for islanding detection in micro-grid integrated with distributed generation (DG). The method uses basic MM operators like dilate erode difference filter (DEDF) to operate on three-phase voltage and current signals on target DG location. These filter output values are fed to a new operator defined as the average of the difference between maximum and minimum value of DEDF at each structuring element called KV and KI. Accordingly, a new operator called the MM ratio index (MM RI) of KI to KV is derived. The MM RI computed is used to track the islanding condition from non-islanding condition. Additionally, to further aid the islanding detection, a mathematical morphological operator close open difference filter on negative sequence voltage is computed, which is passed through a difference operator D. The proposed method is analysed extensively to efficiently detect islanding in electrical power distribution network integrated with DG.

Optimizing distributed generation parameters through economic feasibility assessment

To meet the fast growth of electricity demand, the traditional network solution tends to expand existing substations, build more new substations, and build transmission lines. Distributed Generation (DG) is posed as an alternative method for the network providers not only to accommodate the load increase and relieve network overload, but also to offer other additional technical and economic benefits. This paper addresses the issue of DG planning and has proposed a technique for optimizing the DG size and location to minimize the overall investment and operational cost of the system. The proposed optimization methodology assesses the compatibility of different generation schemes in terms of their cost factors that can be significantly contributed by a DG. The direct and indirect costs of power supply quality, reliability, energy loss, total power operation, and DG investment are used as key cost components of the DG siting and sizing strategy. The Particle Swarm Optimization (PSO) method is applied to obtain the optimal DG planning solutions. Finally, the proposed approach is tested on a distribution feeder of an Australian power network. Simulation results are presented to illustrate the feasibility and effectiveness of the proposed method.

Multiple distributed generation units allocation in distribution network for loss reduction based on a combination of analytical and genetic algorithm methods

Many methods have been proposed to determine the optimal location and capacities of distributed generation (DG) units to reach the lowest value for system losses. In this study, the combination of analytical and genetic algorithm methods is used for optimal allocation of multiple DGs in a distribution network to minimise the system losses. This combination guarantees the convergence accuracy and speed in multiple DG units allocation. In this study, the DGs active power, power factor, and location are simultaneously considered during distribution network losses minimisation. The utility will dictate only the maximum DG power generation if the DG is installed by DG owner. However, both of the size and the location of DG will be determined by the utility if the DG is installed by it. The proposed method is applied to 33-bus and 69-bus test distribution systems. Simulation results show that the proposed method results in lower losses compared with the other methods.

Multiple DG Placements in Distribution System for Power Loss Reduction Using PSO Algorithm

Optimal placement and optimal sizing of Distributed Generation (DG) for the sake of reduction in power loss and improvement of voltage profile in distribution networks are investigated in this paper. Particle Swarm Optimization (PSO) algorithm is used to find out the best location and optimal size of DG. Complete analysis is carried out on IEEE 33-bus and IEEE 69-bus radial distribution systems. Each system is considered for two different cases and comparative results obtained will demonstrate the effectiveness of the proposed method as far as placement, sizing of DG and minimization of power losses are concerned.

Economic Analysis for different Types of DG Placement in Radial Distribution Network

The installation of distributed generation (DG) in the distribution network produces the benefits such as the reinforcement of grid, power quality improvement, voltage regulation, line loss reductions, enhanced utility structure reliability, congestion management, reduction in fuel costs and reserve requirements. To achieve these benefits, the DG should be properly sized and placed in the distribution networks. In this work the performance of various types of DG systems in the radial distribution network (RDN) for loss reduction and voltage profile improvement is compared by inserting them at the optimal location. In the restructured power system environment, the choice about the type of DG for connection is taken by their investors or GENCOS depending on the location, availability of main fuel, transport facilities and climatic situation. In developing countries the investors are more concern about the investment cost and profit obtained. Hence in this paper the cost profit factor analysis for the optimal placement of various types of DG is projected. Here, a new Distributed Generation Sitting Index (DGSI) ranking method is projected to find the optimum DG Size and location for real power loss minimization in radial distribution network. The proposed DGSI method is compared with the existing loss minimization method and Bus Voltage Sensitivity Index (BVSI) ranking method based on real power loss minimization, voltage profile improvement and cost profit factor analysis. To validate the effectiveness of the proposed DGSI ranking method it is tested on 15 bus RDN and IEEE 33 bus RDN. The results show the efficacy of the proposed method.

Optimal Configuration of DG in Distribution System: An Overview

Distributed generation (DG) has increased ever attention in the distribution system from last few years. The main reason for DG in distribution system is increasing electric demand, deregulated power system and congested transmission network, which eventually declines the system performance. There is also increasing pressure of greenhouse gas emissions. For proper utilization of DG, the optimal placement and sizing is of main concern. Because improper DG location and size will increase the losses and decrease the system performance than existing. On the contrary, proper placement will maintain voltage profile, reduce power loss, and increase voltage stability in the distribution system. This paper presents overview of DG, the advances in DG technology and different optimization methods used for optimal placement and sizing problem. The key issues and challenges offered in the development of DG is also presented in this paper.

Optimized Planning for Hybrid Micro-grid in Grid-connected Mode

The emergence of the Distributed Generation (DG) units along with their application in the distribution system level has led to the establishment of microgrids. microgrids are a part of the distribution network in which, in addition to the loads, there are micro sources operating in two modes, i.e. grid-connected mode and island also known as standalone mode. In the grid-connected mode, the load is supplied through local DG units, and if necessary, power is exchanged with the upstream grid, concerned as well. Considering the development of DG units technology in recent years which in turn, has led to the expansion of microgrid concept, microgrid planning meaning determining the capacities of local DG units assumes an extraordinary significance, taking the technical, economic and environmental considerations into account. This research tries to study the optimized planning for a grid-connected hybrid microgrid. The case study was conducted in Razi University in Kermanshah, Iran (34°23'Nlatitude and 47°6'E longitude). Due to the stochastic behavior of renewable energies based DG units, the uncertainties about the amount of generated power from these resources are considered using appropriate probability density functions. The problem is a Mixed Integer Non-linear Program (MINLP), to be solved by means of GAMS software. In order to bring about optimized results, microgrid simulation is processed in assorted scenarios, both in probabilistic and deterministic modes. In the end, the results are compared to those resulted from Homer Energy software. KeywordsOptimized Planning, Hybrid microgrid, Probability Functions, Uncertainty, Distributed Generation

Krill herd algorithm for optimal location of distributed generator in radial distribution system

Distributed generator (DG) is recognized as a viable solution for controlling line losses, bus voltage, voltage stability, etc. and represents a new era for distribution systems. This paper focuses on developing an approach for placement of DG in order to minimize the active power loss and energy loss of distribution lines while maintaining bus voltage and voltage stability index within specified limits of a given power system. The optimization is carried out on the basis of optimal location and optimal size of DG. This paper developed a new, efficient and novel krill herd algorithm (KHA) method for solving the optimal DG allocation problem of distribution networks. To test the feasibility and effectiveness, the proposed KH algorithm is tested on standard 33-bus, 69-bus and 118-bus radial distribution networks. The simulation results indicate that installing DG in the optimal location can significantly reduce the power loss of distributed power system. Moreover, the numerical results, compared with other stochastic search algorithms like genetic algorithm (GA), particle swarm optimization (PSO), combined GA and PSO (GA/PSO) and loss sensitivity factor simulated annealing (LSFSA), show that KHA could find better quality solutions.

Management of Fault Current Contribution of Synchronous DGs Using Inverter-Based DGs

Of the numerous types of distributed generators (DGs), synchronous DGs represent the most substantial contribution to fault currents and, consequently, have the greatest effect on the operation of the protection system. On the other hand, many types of DGs require power electronic (PE) interfaces at the points where they connect to the grid, which are normally left idle during fault conditions. This paper presents a novel idea for employing the PE interfaces of inverter-based DGs (IBDGs) as a means of managing the fault current contribution of synchronous DGs through the modification of the IBDG current phase angle during fault conditions. This operation enables synchronous DGs and IBDGs to be kept connected to the system during fault conditions but with their contribution to the current having no effect on the magnitude of the fault current. Constraints related to DG locations and capacities are thus relieved. More interestingly, it is demonstrated that larger-capacity IBDGs are more effective for managing the fault current contribution of synchronous DGs, which means that for systems that include synchronous DGs, the availability of more numerous IBDGs would be beneficial.

Voltage Stability Enhancement of Distribution System Using Network Reconfiguration in the Presence Of DG

Voltage stability improvement is an important issue in powersystem planning and operation. This article presents the effect of re-configuration in the presence of distributed generation (DG) in radialdistribution system to improve the voltage stability with different loadmodels by using Artificial Bee Colony (ABC) algorithm. The analysisis accomplished using a voltage stability index which can be evaluatedat each node of the distribution system. The network reconfigurationinvolves a mechanism for selection of the best set of branches to beopened, one from each loop, such that the reconfigured radial distribu -tion system possesses desired performance characteristics. The locationof DG has the main effect of voltage stability on the system. Simulationstudy is conducted on 69-bus radial test system to verify the efficacy ofthe proposed method.

Optimal Location and Sizing of Multiple DGs to Enhance the Voltage Stability in the Distribution System using a Chaotic ABC Algorithm

Electric power is one of the inherent things in the modern world. Recently, the renewable and non-renewable energy based Distributed Generator (DG) units are used for generation of electric power to satisfy the local load of the distribution system. This paper proposes a chaotic artificial bee colony (CABC) algorithm based optimal placement and sizing of various categories of DGs that are simultaneously placed in the distribution system according to their technical benefits and planning aspects. Four different types of DGs, such as capable of supplying real power only, capable of supplying reactive power only, capable of supplying real and reactive power, capable of supplying real power and absorbing reactive power has been considered in this approach. The objective is to reduce the resource cost and network loss in such a way that line flows should be within limit and to improve the voltage profile and stability of the system. The Voltage Stability Index (VSI) is used to identify the most sensitive node in the system. The constant power and other voltage dependent load models such as industrial, residential and commercial are considered for this work. The efficiency of the proposed method is tested on 38-node and 69-node radial distribution systems.

A novel method for optimal DG units capacity and location in Microgrids

Distributed generation (DG) has an overall positive impact on Microgrids. These DGs are usually located close to the load centers which lead to some benefits such as; system power loss and energy cost reduction; voltage profile and stability improvement; environmental friendliness, postponement system upgrading and reliability enhancement. In this paper, a novel combined method based on Genetic Algorithm (GA) and Intelligent Water Drops (IWD) is proposed to find location and capacity of DG in Microgrids for optimizing some objective functions. The objectives are minimizing network power losses, improving voltage regulation and increasing the voltage stability within the framework of system operation and security constraints in Microgrids. In this paper, DG units are modeled as generators that are able to inject just active power to network. The Intelligent Water Drops (IWD) algorithm is a new swarm-based optimization algorithm inspired by observing natural water drops that flow in rivers. A detailed performance analysis is carried out on 69-bus and 33-bus Microgrids to demonstrate the effectiveness of the proposed methodology.

Impacts of the Load Models on Optimal Planning of Distributed Generation in Distribution System

The optimal planning (sizing and siting) of the distributed generations (DGs) by using butterfly-PSO/BF-PSO technique to investigate the impacts of load models is presented in this work. The validity of the evaluated results is confirmed by comparing with well-known Genetic Algorithm (GA) and standard or conventional particle swarm optimization (PSO). To exhibit its compatibility in terms of load management, an impact of different load models on the size and location of DG has also been presented in this work. The fitness evolution function explored is the multiobjective function (FMO), which is based on the three significant indexes such as active power loss, reactive power loss, and voltage deviation index. The optimal solution is obtained by minimizing the multiobjective fitness function using BF-PSO, GA, and PSO technique. The comparison of the different optimization techniques is given for the different types of load models such as constant, industrial, residential, and commercial load models. The results clearly show that the BF-PSO technique presents the superior solution in terms of compatibility as well as computation time and efforts both. The algorithm has been carried out with 15-bus radial and 30-bus mesh system.

The impact of capacity and location of multidistributed generator integrated in the distribution system on electrical line losses, reliability, and interruption cost

The primary purpose of this article is to study the impact of distributed generator (DG) integrated into the distribution system in terms of electrical line losses, reliability and interruption cost. The single‐ and multi‐distributed generators (DG) under this study are connected to a 22 kV distribution system of the Provincial Electricity Authority (PEA) that is a part of Thailand's distribution system. Geographic information systems data, including the lengths of the distribution line and the load locations that are key parameters of PEA, are simulated using digital simulation and electrical network calculation program (DIgSILENT). In addition, the capacity and location of DG installed into the distribution system are considered. The system average interruption frequency index (SAIFI), the system average interruption duration index (SAIDI) and the interruption cost are assessed as reliability indices by comparing the SAIFI, SAIDI, and interruption cost of the base case (without DG) and the cases of single‐ and multi‐DGs connected to the distribution system. Moreover, the electrical line loss is considered in terms of active power line loss, which is also compared with the base case. The results can be summarized by focusing on the location of DG, the capacity of DG, the number of DG, the size of the load, and the distance to the load, which are factors capable of impacting the electrical line loss, SAIFI, SAIDI, and interruption cost. © 2015 American Institute of Chemical Engineers Environ Prog, 34: 1763–1773, 2015

Impact of DG different types on the grid performance

Distributed generation (DG) is playing an important role in power system to improve the grid performance. Optimizing the locations of DGs is necessary to enhance the Grid performance and to avoid the degradation of the power system networks. The type of DG directly influences the penetration level and the placement of DG. In this study, genetic algorithm (GA) technique is proposed to find the optimum location for three different types of distributed generation, these types are synchronous generator, wind turbine and photovoltaic. The obtained results are also validated using Particle Swarm Optimization (PSO). Three different types of DGs will be penetrated individually to find their optimum location and investigate their impact on the grid performance. This approach will be applied on IEEE 13 bus system which is simulated using Power System Computer Aided Design (PSCAD) and Matlab software. GA and PSO are used to find the optimum solution of multi-objective function; the objective function combines the overall number of buses experience voltage sag, the number of buses experience voltage drop, the number of buses experience voltage less than 10%, the overall number of buses experience voltage swell and network power loss. Finally, results are analyzed, discussed and validated which show that the optimum locations of each DG will vary according to the type of DG but all of them will be around the load center. In the mean time, results showed that the highest grid overall performance was for synchronous generator DG and the lowest for photovoltaic DG.

Voltage Stability Enhancement of Distribution System Using Network Reconfiguration in the Presence Of DG

ABSTRACTVoltage stability improvement is an important issue in power system planning and operation. This article presents the effect of reconfiguration in the presence of distributed generation (DG) in radial distribution system to improve the voltage stability with different load models by using Artificial Bee Colony (ABC) algorithm. The analysis is accomplished using a voltage stability index which can be evaluated at each node of the distribution system. The network reconfiguration involves a mechanism for selection of the best set of branches to be opened, one from each loop, such that the reconfigured radial distribution system possesses desired performance characteristics. The location of DG has the main effect of voltage stability on the system. Simulation study is conducted on 69-bus radial test system to verify the efficacy of the proposed method.

Optimal size and siting of multiple DG and DSTATCOM in radial distribution system using Bacterial Foraging Optimization Algorithm

Abstract In this paper, a novel approach to determine the optimal location and sizing of Distribution Generation (DG) and Distribution STATic COMpensator (DSTATCOM) is analyzed, and the objective function is formulated for minimizing power loss, operational costs and voltage profile enhancement of the system subjected to equality and in equality constraints. Loss sensitivity factor (LSF) is used to pre-determine the optimal location of DG and DSTATCOM. The Bacterial Foraging Optimization Algorithm (BFOA) is proposed to determine the optimal size of the DG and DSTATCOM. In this paper, the DG and DSTATCOM are simultaneously allocated in radial distribution system and it is analyzed with different load models. To check the feasibility, the proposed method is tested on IEEE 33-bus and 119-bus radial distribution system and the results were compared with other existing technique.

Optimal placement and sizing of multiple distributed generating units in distribution networks by invasive weed optimization algorithm

Abstract Distributed generation (DG) is becoming more important due to the increase in the demands for electrical energy. DG plays a vital role in reducing real power losses, operating cost and enhancing the voltage stability which is the objective function in this problem. This paper proposes a multi-objective technique for optimally determining the location and sizing of multiple distributed generation (DG) units in the distribution network with different load models. The loss sensitivity factor (LSF) determines the optimal placement of DGs. Invasive weed optimization (IWO) is a population based meta-heuristic algorithm based on the behavior of weeds. This algorithm is used to find optimal sizing of the DGs. The proposed method has been tested for different load models on IEEE-33 bus and 69 bus radial distribution systems. This method has been compared with other nature inspired optimization methods. The simulated results illustrate the good applicability and performance of the proposed method.

Comparación de algoritmos multiobjetivo inspirados en búsqueda armónica, búsqueda cuco y murciélagos para la ubicación de generación distribuida renovable

Las pérdidas eléctricas tienen un impacto significativo en los costos totales de las redes de distribución. El uso de las energías renovables es una gran alternativa para mejorar las pérdidas y los costos, aunque también otros problemas en las magnitudes de las tensiones y la congestión de la red pueden ser mejorados. Sin embargo, determinar la mejor localización y dimensionamiento de generadores eléctricos renovables puede ser a veces una tarea difícil debido al gran número de combinaciones posibles existentes en el espacio de búsqueda. Además, el uso de funciones multiobjetivo incrementa la complejidad del problema y se prefiere usar las metaheurísticas para encontrar soluciones en un tiempo relativamente corto. En este trabajo se evalúa el desempeño de los algoritmos inspirados en búsqueda cuco, búsqueda armónica y murciélagos para la localización y dimensionamiento de la generación distribuida renovable en redes de distribución radiales, usando funciones como la minimización de las pérdidas de energía y los costos de la generación distribuida renovable. Las metaheurísticas fueron programadas en Matlab y evaluadas usando la instancia denominada red de distribución radial de 33 nodos. Los tres algoritmos evaluados obtuvieron resultados similares para los dos objetivos evaluados, encontrando Frentes de Pareto cercanos a las mejores soluciones. La comparación realizada mostró que la búsqueda cuco obtiene los mejores resultados, pero los algoritmos inspirados en murciélagos y búsqueda armónica obtuvieron resultados cercanos a la mejor solución.

Optimal Placement and Sizing of Renewable Distributed Generation via Gravitational Search Algorithm

The installation of distributed generation (DG) gives advantages to the environment such as, it contribute in the reduction of non-peak operating cost, diversification of energy resources, lower losses thus improving overall organization. These advantages might be rescinded if no proper location and sizing of DGs are considered before the DG’s installation. This paper offers an optimal location and sizing of multiple DGs using heuristic method called gravitational search algorithm (GSA). The suggested algorithm is tested on 13-bus radial distribution system. This method is being compared with particle swarm optimization (PSO) in terms of system power loss, voltage deviation and total voltage harmonic distortion (THDv). GSA shows the ability to locate and sized DG optimally with a better performance and more reliable than PSO.