Units In Distribution Networks Research Articles

Strategic Integration and Configuration of Distributed Generators Units in Distribution Networks: An Overview

Strategic Integration and Configuration of Distributed Generators Units in Distribution Networks: An Overview

Impacts of Distributed Generation on Power System Protection

Distributed generation (DG) offershuge benefits to the power system network to cater tothe rapidly growing demand for electric power. The integration of DG units intoexisting power networks is beneficial to both utilities and consumers. However, the installation of DG units in distribution networks might impose some operational, technical,and economic impacts. Increased short-circuitlevel and changes in load flow direction are examples of the technical issues arising from the high penetration of DG. Such issues will affect the existing protection system, mainly overcurrent relays. Thus, to ensure reliable and selective operation of protection relays, the impacts of DG should be considered. Thispaper discusses the impacts of DG on the protection systems byidentifying various protection problems.The paperis complemented by a simulation case study on the impactsof DG on fault levels, protection coordination, and distance relays.The simulatedsystem was modeledusingDIgSILENT PowerFactory®software.

Identification of Sphere of Influence of Single and Multiple DG Units in Distribution Networks: A Visualisation Approach Involving Sankey Diagrams and Geospatial Mapping

Identification of Sphere of Influence of Single and Multiple DG Units in Distribution Networks: A Visualisation Approach Involving Sankey Diagrams and Geospatial Mapping

Optimal allocation and sizing of DG and FCL units in distribution networks to ensure protection coordination and cost reduction

Optimal allocation and sizing of DG and FCL units in distribution networks to ensure protection coordination and cost reduction

Correction: Parallel operated hybrid Arithmetic-Salp swarm optimizer for optimal allocation of multiple distributed generation units in distribution networks

Correction: Parallel operated hybrid Arithmetic-Salp swarm optimizer for optimal allocation of multiple distributed generation units in distribution networks

Optimal Allocation and Energy Management of Units in Distribution Networks with Multiple Renewable Energy Sources and Battery Storage Based on Computational Intelligence

The paper deals with an optimization problem in an electricity distribution network with different types of distributed generation and a battery storage system in terms of a smart grid concept. The optimization problem considers two objectives, namely, the annual energy losses and the exchange of energy with the higher-level power grid. The decision variables of the problem are the allocation of the different distributed generation units and the battery storage system, the annual power profiles of the controllable distributed generation and the battery storage system, and the power factor profiles of the controllable and noncontrollable distributed generation. All decision variables are solved simultaneously in a single optimization problem. The variable load shapes of the grid consumers and the profiles of the photovoltaic and wind power systems are considered in the study. All data are observed at the annual level with hourly resolution. The problem solving method uses computational intelligence techniques, namely, metaheuristic optimization methods and artificial neural networks. The study proposes a framework for optimizing the decision variables in the planning phase of distributed generation and battery storage, and for controlling the variable power and power factor profiles based on an artificial neural network in the implementation phase. The optimization problem is solved with a power system simulation program and a metaheuristic optimizer in cosimulation synergy. The three cases of distributed generation and battery storage are considered simultaneously. The proposed method is applied to the test grid operator IEEE with 37 buses, and reductions in annual energy losses and energy exchange are obtained in the ranges 34–86% and 41–99%, respectively.

Introducing a New Method for DPMU in Detecting the Type and Location of the Fault

Introduction: Nowadays, due to the increasing development of distribution networks, their safety and high-reliability performance are of particular importance. One of the most important problems that endanger the security and reliability of these networks is the creation of some faults in them. In case of a fault in the network, identifying its location and type can be of great help in repairing faulty equipment. Also, by detecting the disconnection of one of the equipment or lines, it is possible to prevent accidents in the network. Methods: Phasor Measurement Unit (PMU) has been widely and successfully used as Transmission- Phasor Measurement Unit (T-PMU). The reporting time of PMUs is much shorter than the old Supervisory Control and Data Acquisition (SCADA) systems. They can provide synchronized phasor measurements that can generate voltage phasors of different network nodes. This study aimed to investigate the various applications of phasor measurement units in distribution networks and present a new method for detecting and analyzing the location and type of fault and topology fault of the distribution network using the Internet of Things (IOT) analysis method. Results: To implement this method, it is necessary to measure different parameters of the distribution network before and after the occurrence of a fault, which is used by the DPMU for measurement. The simulation results indicate that for both single-topology and multi-topology faults, the proposed method has higher accuracy and better detection than the remaining common methods and effectively detects single-topology and multi-topology faults in the distribution network. Conclusion: This method can provide a more accurate network topology to estimate the state of the distribution network, which improves the accuracy of the state estimation and is suitable for implementing various advanced functions of the distribution management system.

Hybrid Ant Colony Robust Genetic Algorithm for Optimal Placement of Renewable Distributed Generation and Storage units in Distribution Networks

This paper presents a multi-objective algorithm to support sizing and placement of Renewable Distributed Generation with storage units (RDG&S) in radial distribution networks. Two objectives are considered in the model, the first one is focused in the minimization of the RDG&S units capital costs and the second one in the minimization of system losses. This approach uses a hybrid Ant Colony Genetic Algorithm (ACGA) divided in two steps. At the first step of the approach an Ant Colony (AC) acts to face with the uncertainty of the problem and to deal with instabilities of the initial data. This way a good Pareto front, which is used to feed the initial population of da Genetic Algorithm (GA). At the second step, an Elitist Robust Genetic Algorithm with a secondary population is used, to characterize the non-dominated Pareto Optimal Frontier. In this algorithm the concept of robustness is operationalized in the computation of the fitness value assigned to solutions. The results presented in this approach demonstrates the real capabilities of the proposed algorithm to generate a well-spread and more robust effective non-dominated Pareto Optimal Frontier.

Parallel operated hybrid Arithmetic-Salp swarm optimizer for optimal allocation of multiple distributed generation units in distribution networks.

The installation of Distributed Generation (DG) units in the Radial Distribution Networks (RDNs) has significant potential to minimize active power losses in distribution networks. However, inaccurate size(s) and location(s) of DG units increase power losses and associated Annual Financial Losses (AFL). A comprehensive review of the literature reveals that existing analytical, metaheuristic and hybrid algorithms employed on DG allocation problems trap in local or global optima resulting in higher power losses. To address these limitations, this article develops a parallel hybrid Arithmetic Optimization Algorithm and Salp Swarm Algorithm (AOASSA) for the optimal sizing and placement of DGs in the RDNs. The proposed parallel hybrid AOASSA enables the mutual benefit of both algorithms, i.e., the exploration capability of the SSA and the exploitation capability of the AOA. The performance of the proposed algorithm has been analyzed against the hybrid Arithmetic Optimization Algorithm Particle Swarm Optimization (AOAPSO), Salp Swarm Algorithm Particle Swarm Optimization (SSAPSO), standard AOA, SSA, and Particle Swarm Optimization (PSO) algorithms. The results obtained reveals that the proposed algorithm produces quality solutions and minimum power losses in RDNs. The Power Loss Reduction (PLR) obtained with the proposed algorithm has also been validated against recent analytical, metaheuristic and hybrid optimization algorithms with the help of three cases based on the number of DG units allocated. Using the proposed algorithm, the PLR and associated AFL reduction of the 33-bus and 69-bus RDNs improved to 65.51% and 69.14%, respectively. This study will help the local distribution companies to minimize power losses and associated AFL in the long-term planning paradigm.

Optimal Allocation and Sizing of PV Generation Units in Distribution Networks via the Generalized Normal Distribution Optimization Approach

The problem of optimal siting and dimensioning of photovoltaic (PV) generators in medium-voltage distribution networks is addressed in this research from the perspective of combinatorial optimization. The exact mixed-integer programming (MINLP) model is solved using a master–slave (MS) optimization approach. In the master stage, the generalized normal distribution optimization (GNDO) with a discrete–continuous codification is used to represent the locations and sizes of the PV generators. In the slave stage, the generalization of the backward/forward power method, known as the successive approximation power flow method, is adopted. Numerical simulations in the IEEE 33-bus and 69-bus systems demonstrated that the GNDO approach is the most efficient method for solving the exact MINLP model, as it obtained better results than the genetic algorithm, vortex-search algorithm, Newton-metaheuristic optimizer, and exact solution using the General Algebraic Modeling System (GAMS) software with the BONMIN solver. Simulations showed that, on average, the proposed MS optimizer reduced the total annual operative costs by approximately 27% for both test feeders when compared with the reference case. In addition, variations in renewable generation availability showed that from 30% ahead, positive reductions with respect to the reference case were obtained.

Optimal deployment of feeder remote terminal units in distribution networks to improve power supply reliability

Deploying feeder remote terminal units (FRTUs) in the distribution network and then integrating them into existing manual switches (MSs) can facilitate automatic locating of faulted sections and rapid isolation from faults to improve the power supply reliability. However, the fault management process, including fault location, fault isolation, and service restoration, is usually simplified in traditional FRTUs deployment methods. In particular, the fault isolation and service restoration operations are usually modeled individually without considering the FRTUs operation during the whole fault management process in these traditional methods. In this paper, the FRTUs in the potential service restoration paths for the customers are ruled out to avoid unnecessary power outage, and the effect of FRTUs operation on the fault management process is analyzed and discussed in branched distribution networks. Secondly, a quantification analysis model considering FRTUs operation is built to obtain the accurate interruption time of the distribution network. Thirdly, the quantification model is used to obtain the revenue generated by interruption cost reduction for the deployed FRTUs. Finally, an optimal deployment model of FRTUs in mixed-integer nonlinear format is constructed and solved efficiently by the software LocalSolver, which combines heuristic techniques and stochastic search algorithms. The new method is compared with the traditional FRTUs deployment method in the CSG 53-node test system, and the results show that the proposed method can achieve greater return on investment and higher power supply reliability.

Multi-Objective Optimization of Distributed Generation Despite Energy Storage Systems for Optimal Management

The influence of distributed generation resources and energy storage units in distribution networks is increasing. Therefore, it is essential to examine their effect on network reliability. In this study, in order to present the optimal energy management strategy in the smart distribution network, the problem of multi-objective optimization of rearrangement of distribution feeders with the presence of distributed generation sources and energy storage units has been solved in a dynamic framework. Objective functions in this study include minimization of energy losses, redistributed energy and operating costs. In order to simultaneously optimize the reliability index and other objective functions, another optimal design for the production of distributed generation units, charging and discharging of energy storage systems as well as the topology of distribution network feeders is presented. In order to solve the multi-objective optimization problem in this study, a combination of particle social algorithms and frog mutation has been used. In order to show the ability of the method in providing an optimal energy management plan in a 95-bus network has been tested.

Flower pollination-based optimal placement of distributed generation units in distribution networks

Flower pollination-based optimal placement of distributed generation units in distribution networks

Flower pollination-based optimal placement of distributed generation units in distribution networks

The sharp increasing power demand and configuration changes in distribution networks (DNs) may operate the network more closely to voltage stability (VS) boundaries. Under critical operating condit...

A Review of Metaheuristic Techniques for Optimal Integration of Electrical Units in Distribution Networks

The optimal integration of electrical units, such as distributed generation units, power electronic devices, and electric vehicles, is a significant development of smart grids. This development has effectively transformed the traditional grid system, promising numerous advantages for economic values and autonomous energy source control. In smart grids development, metaheuristic algorithms are one of the optimization algorithms that have been applied extensively to mitigate the accompanying problems such as voltage instability, power loss, and high installation cost. This paper presents a comprehensive review of metaheuristic techniques for the optimal integration of electrical units in distribution networks, considering different phases of the optimization process. These include the approaches for handling of crucial objective functions and the optimal integration methods for different electrical units. This review shows a need for more research on developing efficient metaheuristic algorithms and the effective handling of multiple objective functions.

Distributed Generation: A Critical Review of Technologies, Grid Integration Issues, Growth Drivers and Potential Benefits

Owing to liberalization of electricity market, technology evolution, energy security, environmental issues and growing concerns of energy cost, the penetration of distributed energy units in distribution network is increasingly observed worldwide. Penetratingrembedded generation, or distributedrgeneration (DG), in powerrdistribution grid requires asnumber of issues to be considered, such as definition of DG, rating of DG, the best DG technology etc. This paper presents an extensive critical review of various dimensions of distributed generation (DG) including definitions, generation technologies and their status, impact on distribution network performance etc. The study also presents comparative study between the various technologies in terms of most important technological characteristics of each DG technology. The policy makers, utility regulators and DG planning engineers can use this critical review, withoutigoing through complicatedicomputations, as guidelines to makeipolicies, standards andidecisions in DG penetration andirelated issues.

Approaches for optimal planning of the energy storage units in distribution network and their impacts on system resiliency — A review

In the recent decade, a significant increase in the penetration level of renewable energy sources (RESs) into the distribution grid is evident due to the world's shift towards clean energy and to increase the reliability or inboard manner resiliency of electrical distribution system. RES based microgrids are the most favorable option available, especially to enhance resiliency. However, the integration of RES over the distribution grid would hamper the grid stability due to its stochastic nature under normal conditions. During extreme weather conditions, RES behavior is completely uncertain. Hence there is a need to eliminate the adverse effects caused by the RES and make the distribution grid more reliable and stable under normal and resilient conditions. To address these issues, many researchers proposed several methods to place energy storage units (ESUs) and microgrids (RES integrated), which can support critical loads at an optimal location in the distribution system during normal and extreme conditions, respectively. The aim of this article is to consolidate and review the research towards various approaches to formulate the problem (optimal location, allocation, and operation of ESU and microgrids to face regular and extreme weather condition) and tools to solve it for enhanced system flexibility and resiliency. Based on the review, a generalized methodology has been designed to adapt the inputs and address both conditions. At the end of the review, future aspects for ESU to strengthen resistance and resiliency of its own are presented, which can be helpful to further improve the reliability and resiliency of the distribution system.

Olympic ranking–based allocation of distributed generation units in distribution networks

Olympic ranking–based allocation of distributed generation units in distribution networks

Impact of Recloser on Protecting Blind Areas of Distribution Network in the Presence of Distributed Generation

The protection relay issues increase with the penetration of distributed generation (DG) units in distribution networks. Blind protection, sympathetic tripping and failure of reclosing are the frequent problems that are created by adding DG to the distribution networks. This research focuses on the impact of reclosers on overcurrent relay blind protection areas with DGs embedded in the distribution network. The protection blinding in overcurrent relays leads to a delay or non-tripping of the relay. The conventional solution for the blinding issue is to increase the sensitivity in the relay settings. The variation of relay settings may cause problems in the distribution network. This paper investigates the effect of reclosers on eliminating blind areas and improves the reliability in the presence of DG. In this way, possible blind points of the case study are detected, and recloser relays are then installed in appropriate locations. The case study is based on realistic data of a distribution network. The DG consists of two generators with a capacity of 4 MW. The simulation is performed in DIgSILENT software.

Efficient optimization technique for multiple DG allocation in distribution networks

In the last few decades, interest in the integration of Distributed Generators (DGs) into distribution networks has been increased due to their benefits such as enhance power system reliability, reduce the power losses and improve the voltage profile. These benefits can be increased by determining the optimal DGs allocation (location and size) into distribution networks. This paper proposes an efficient optimization technique to optimally allocate the multiple DG units in distribution networks. This technique is based on Sine Cosine Algorithm (SCA) and chaos map theory. As any random search-based optimization algorithm, SCA faces some issues such as low convergence rate and trapping in local solutions during the exploration and exploitation phases. This issue can be addressed by developing Chaotic SCA (CSCA). CSCA is mainly based on the iterative chaotic map which used to update the random parameters of SCA instead of using the random probability distribution. The iterative chaotic map is applied for single and multi-objective SCA. The proposed technique is validated using two stranded IEEE radial distribution feeders; 33 and 69-nodes. Comprehensive comparison among the proposed technique, the original SCA, and other competitive optimization techniques are carried out to prove the effectiveness of CSCA. Finally, a complete study is performed to address the impact of the intermittent nature of renewable energy resource on the distribution system. Hence, typical loads and generation (represented in PV power) profiles are applied. The result proves that the CSCA is more efficient to solve the optimal multiple DGs allocation with minimum power loss and high convergence rate.