Reconfiguration Of Radial Distribution Systems Research Articles

Analysis of large scale distribution network using Whale Optimization Algorithm

In this study, we use a loop matrix to describe the reorganisation of the RDN's formulation. Calculation time is increased when an optimum reorganisation is determined analytically. More network buses means more time to calculate. Therefore, a technique of optimisation is required to determine the best reorganisation of the radial distribution system. The optimum reorganisation aims to reduce network losses to a minimum and the voltage profile is enhanced. Loop matrices are used to describe the re-formulation of the radial distribution system. The analytical technique of identifying optimum reconfiguration involves additional calculation time. The computational complexity grows as the system's bus count rises. Therefore, a search for the best possible radial distribution system reconfiguration necessitates an optimization technique. The ideal reconfiguration focuses mostly on reducing the system's overall loss. The genetic algorithm (GA), particle swarm optimization (PSO), and whale optimization algorithm (WOA) are the optimization methods used in this paper. Two test systems consisting of 119 buses, and 135 buses are used to evaluate the effectiveness of various optimization strategies. The outcomes are then compared with one another.

Optimal tuning of Reconfiguration of Radial Distribution System using Quasi-Oppositional Moth Flame Optimization

Optimal tuning of Reconfiguration of Radial Distribution System using Quasi-Oppositional Moth Flame Optimization

Reconfiguration of Radial Distribution Systems: An Efficient Mathematical Model

For a few decades, researchers try to reach a better solution by proposing new models or methods for distribution system reconfiguration (DSR) problem. This paper presents an efficient model for reconfiguration of radial distribution systems, aiming loss minimization. The proposed model can be easily solved by every commercial solvers and software. The proposed mixed-integer second-order cone programming (MISOCP) model was tested on 16-, 33-, 69-, 70-, 119-, and 136-bus test systems as well as Taiwan power company network (TPC). The results show high efficiency of the proposed model for reconfiguration of radial distribution systems.

Meta-heuristic matrix moth–flame algorithm for optimal reconfiguration of distribution networks and placement of solar and wind renewable sources considering reliability

Abstract This paper presents optimal multi-criteria reconfiguration of radial distribution systems with solar and wind renewable energy sources using the weight factor method while considering reliability. Minimizing the power loss, improving the voltage profile and stability of the system, as well as, enhancing the reliability are the main objective functions of the problem to address. The reliability index is assumed as the energy not-supplied (ENS) of the end-users. Optimized variables of the problem include opened lines of the system in the reconfiguration process to maintain the radial structure of the network along with finding the optimal place and size of photovoltaic (PV) systems and wind turbine (WT) units in the distribution system, which are determined based on a new meta-heuristic called moth–flame optimization (MFO) algorithm. Simulations for different scenarios are performed utilizing reconfiguration and placement of renewable sources on an IEEE 33-bus radial distribution system. Obtained results in solving the problem indicate the superiority of the presented method compared with some methods in the literature. Furthermore, the results showed that the combined method as the reconfiguration and WT placement simultaneously bring the best performance for the network with lower power loss, improved voltage profile and stability, and enhanced reliability. Moreover, the results showed that considering reliability helps significantly reduce the energy not-supplied of the customers and supply their maximum load demand.

Network reconfiguration and optimal allocation of distributed generations for Mayangone distribution system

This paper focuses on reconfiguration of radial distribution system incorporated with optimal allocation of Distributed Generation units (DGs). The network reconfiguration is to optimize the structure of radial feeders by changing the status of normally closed sectionalizing and normally open tie switches. The optimal allocation of DGs in distribution system has to determine the DGs location and their correspondent sizing for enhancing the system effectively. There are many investigations with both two researches either separately or incorporated simultaneously. But network reconfiguration by using Particle Swarm Optimization (PSO) incorporated with optimal allocation of DGs based on Voltage Stability Index (VSI) and analytical approach of Forward Backward Sweep (FBS) method in radial power flow is the novelty of this research. The main objective is to achieve the minimum losses and improvement of voltage profile while all distribution constraints are satisfied. In proposed algorithm, optimal allocation is determined by finding the best sizing and location of DGs based on VSI and FBS load flow, and then PSO reconfiguration finds out the optimal location of tie and sectionalizing switches simultaneously. The study of test system is carried out on Mayangone Distribution System (MDS) and the obtained results are inefficient for this research.

A New Approach to Load Flow Analysis for Radial Distribution System - Application for Medium Voltage Distribution System in Morocco

This paper presents a load flow algorithm based on the backward/forward sweep principle, flexible for radial distribution system reconfiguration, with an improvement in ensuring a minimum number of searching for connections between nodes in the calculation sequence in the forward and the backward sweep, by organizing the radial distribution system information into a Main-Line and its derivations. The proposed load flow analysis is easy to implement and does not requires the use of any complex renumbering of branches and nodes, or any matrix calculation, with the only use of linear equations based in the Kirchhoff’s formulation.

Optimal Allocation of DGs and Reconfiguration of Radial Distribution Systems Using an Intelligent Search-based TLBO

This paper addresses an application of Teaching-Learning-Based Optimization method for the optimal allocation of Distributed Generations (DGs) in radial distribution systems. The problem is formulated to maximize annual energy loss reduction while maintaining better node voltage profiles using penalty function approach. A piecewise linear multi-level load pattern is considered, and the distribution network is reconfigured after optimal placement of DGs in the distribution network. A probability-based heuristic intelligent search (IS) is suggested to enhance the accuracy and convergence of the optimization techniques. IS directs optimization techniques to efficiently scan the problem search space in such a way that a fair candidature is available to all decision variables of the problem. It virtually squeezes the search space while maintaining adequate diversity in population. The proposed method is investigated on the benchmark IEEE 33-bus, 69-bus test distribution systems, and 83-bus real distribution system. The application results show that the proposed optimization methodology provides substantial improvement in convergence characteristics and quality of solutions.

Reconfiguration of Radial Distribution Systems with Variable Demands Using the Clonal Selection Algorithm and the Specialized Genetic Algorithm of Chu–Beasley

This paper presents two new approaches to solve the reconfiguration problem of electrical distribution systems (EDSs) with variable demands, using the CLONALG and the SGACB algorithms. The CLONALG is a combinatorial optimization technique inspired by biological immune systems, which aims at reproducing the main properties and functions of the system. The SGACB is an optimization algorithm inspired by natural selection and the evolution of species. The reconfiguration problem with variable demands is a complex combinatorial problem that aims at identifying the best radial topology for an EDS, while satisfying all technical constraints at every demand level and minimizing the cost of energy losses in a given operation period. Both algorithms were implemented in C++ and test systems with 33, 84, and 136 nodes, as well as a real system with 417 nodes, in order to validate the proposed methods. The obtained results were compared with results available in the literature in order to verify the efficiency of the proposed approaches.

Multi-Objective Invasive Weed Optimization – An application to optimal network reconfiguration in radial distribution systems

Abstract Optimal Network Reconfiguration (ONR) is one of the technique utilized by distribution operators in normal and emergency condition for optimal operation of the distribution system. In this article, a Multi-Objective Invasive Weed Optimization (MOIWO) Algorithm is proposed to solve the Optimal Network Reconfiguration (ONR). While solving ONR of the radial distribution system, minimization of active power loss, maximum node voltage deviation, number of switching operations and the load balancing index are considered as the objectives simultaneously. For adopting Invasive Weed Optimization (IWO) Algorithm for solving this multi-objective problem, a non-dominated sorting technique and crowding distance are used to rank the weeds. To investigate the feasibility of the proposed algorithm, it is tested on standard IEEE 33-Bus Test Radial Distribution System and 84 Bus Taiwan Power Company (TPC) Practical Distribution Network. For an analysis of the load flows in a radial distribution system Backward/Forward sweep load flow algorithm is implemented. The performance of the proposed algorithm is compared with results available in recent literature and it is observed that the proposed method produces a high quality Pareto solution and finds a global optimum configuration.

MVMO for Optimal Reconfiguration in Smart Distribution Systems

This paper introduces an approach based on the mean-variance mapping optimization algorithm (MVMO) to solve the optimal reconfiguration of radial distribution systems (ORRDS). MVMO is an emerging evolutionary algorithm, whose search procedure performs within a normalized range of the search space for all optimization variables, and by following either a single parent-offspring pair or a population based procedure. Remarkably, its main trait resides in the use of a special mapping function for mutation operation, which allows a controlled shift from exploration priority at early stages of the search process to exploitation at later stages. Numerical tests on the IEEE 33-bus test system, including performance comparison with other state-of-art algorithms, show the effectiveness of the MVMO-based approach to get the optimal switching combination in the network which results in total minimum active power losses.

A novel codification and modified heuristic approaches for optimal reconfiguration of distribution networks considering losses cost and cost benefit from voltage profile improvement

Network reconfiguration is the process of changing the topology of distribution systems by altering the open/closed status of switches. Because there are many candidate-switching combinations in the distribution system, network reconfiguration is a complicated combinatorial, non-differentiable constrained optimization problem. In addition, the radiality constraint typically increases the intricacy of the problem. In this paper, to avoid create infeasible configuration, a new codification is proposed. The proposed codification is computationally efficient and guarantees to generate only feasible radial topologies all times. Also, in this paper, a modified heuristic approach for optimal reconfiguration in radial distribution systems is presented. Additionally, in order to economize voltage profile improvement, a number of new formulas have been represented. The effectiveness of the proposed method is demonstrated on balanced and unbalanced test distribution systems.

Performance Enhancement of Radial Distributed System with Distributed Generators by Reconfiguration Using Binary Firefly Algorithm

The extent of real power loss and voltage deviation associated with overloaded feeders in radial distribution system can be reduced by reconfiguration. Reconfiguration is normally achieved by changing the open/closed state of tie/sectionalizing switches. Finding optimal switch combination is a complicated problem as there are many switching combinations possible in a distribution system. Hence optimization techniques are finding greater importance in reducing the complexity of reconfiguration problem. This paper presents the application of firefly algorithm (FA) for optimal reconfiguration of radial distribution system with distributed generators (DG). The algorithm is tested on IEEE 33 bus system installed with DGs and the results are compared with binary genetic algorithm. It is found that binary FA is more effective than binary genetic algorithm in achieving real power loss reduction and improving voltage profile and hence enhancing the performance of radial distribution system. Results are found to be optimum when DGs are added to the test system, which proved the impact of DGs on distribution system.

Placement of DG for reliability improvement and loss minimization with reconfiguration of radial distribution systems

Purpose – The purpose of the paper is to find the best distributed generators (DGs) location for improving reliability and reducing power loss using distribution system reconfiguration. This is implemented in the presence of the tie-switches. It proposes a search-based algorithm for the reconfiguration problem. Individual DG placement is obtained for all system configurations, and analytical hierarchy process tool is used for finding the overall best location. This is carried out for various system loadings. Design/methodology/approach – This paper proposes a knowledge-based search algorithm which needs the base conditions of the distribution system. A detailed analysis is carried out for finding the best DG locations from the obtained DG placements for various system configurations. Simulations are rigorously carried out with the help of programming. Results from these simulations are further given to analytical hierarchy tool for obtaining the DGs location. Findings – The findings of the paper are the DG placement for various system loadings and various system configurations and to obtain the best DG location for any system configuration. A search-based algorithm is designed for accomplishing it. Originality/value – The proposed method identifies the placement of distributed generation at distribution systems for reliability improvement and power loss reduction which is one of the present day needs for fulfilling the raising power consumers.

Reconfiguration of Radial Distribution Systems with Fuzzy Multi-Objective Approach Using Modified Big Bang-Big Crunch Algorithm

This paper presents an efficient method for the multi-objective reconfiguration of distribution networks in fuzzy framework using modified Big Bang-Big Crunch algorithm. The considered objectives are minimization of network real power, minimization of the deviation of nodes voltage, minimization of the branch current constraint violation and minimization of switching operations number. All the objectives are fuzzified with trapezoidal membership function and the max-geometric mean operator is applied to determine the best compromising solution among the four objectives. The 33-bus and 69-bus distribution networks are used to demonstrate effectiveness of the proposed method. The obtained results show that proposed method is powerful and promising for reconfiguration of multi-objective distribution systems.

Distribution System Reconfiguration for Loss Minimization Using Modified Artificial Neural Network Approach of 16 Bus and 33 Bus Standard Test Systems with an Compensator

– This paper presents a new method for identifying the best switching option for the reconfiguration of Radial Distribution Systems (RDS). Feeder reconfiguration is the technique to alter the topological structure of the distribution feeder by changing the open/close status of sectionalizing and tie switches. The reconfiguration involves in selection of the set of sectionalizing switches to be opened and tie switch to be closed such that the resulting RDS has the desirable performance. Amongst the several criteria considered for optimal network configuration, loss minimization criterion is very widely used. In this project a novel method is presented which utilizes feeder reconfiguration as a planning and real time control tool in order to restructure the primary feeders for the loss minimization. The mathematical formulation of the proposed method is given; the solution procedure is illustrated with an example. Owing to the discrete nature of the solution space, a neural network approach for optimal reconfiguration of distribution network is proposed.

Enhanced gravitational search algorithm for multi‐objective distribution feeder reconfiguration considering reliability, loss and operational cost

Power loss reduction can be considered as one of the main purposes for distribution system operators. Reconfiguration is an operation process used for this optimisation by means of changing the status of switches in a distribution network. Recently, all system operators tried their best in order to obtain well-balanced distribution systems to decrease the operation cost, improve reliability and reduce power loss. This study presents an efficient method for solving the multi-objective reconfiguration of radial distribution systems with regard to distributed generators. The conventional distribution feeder reconfiguration (DFR) problem cannot meet the reliability requirements, because it only considers loss and voltage deviation as objective functions. The proposed approach considers reliability, operation cost and loss simultaneously. By adding the reliability objective to the DFR problem, this problem becomes more complicated than before and it needs to be solved with an accurate algorithm. Therefore this study utilises an Enhanced Gravitational Search Algorithm called EGSA which profits from a special mutation strategy in order to reduce the processing time and improve the quality of solutions, particularly to avoid being trapped in local optima. The proposed approach has been applied to two distribution test systems including IEEE 33 and 70-node test systems.

Evolutionary Algorithm for Network Reconfiguration in Distribution Systems Considering Thermal Operational Conditions

This article presents a methodology for the development of an evolutionary algorithm (EA) applied to the reconfiguration of radial distribution systems. The EA uses an integrated thermal and electric model (ITM) to evaluate the fitness function. Initially, the ITM calculates steady-state temperature of each conductor and, consequently, their electric impedance. ITM can be used to assess overhead and underground networks. This assessment is done efficiently to obtain the electric losses in each topology obtained by EA. On the one hand, the ITM is applied to two real distribution networks: an underground of 144 bars and another overhead of 1,311 bars. Results show that there is a divergence from 3 to 17 % when the ITM is used during electric losses evaluation. On the other hand, EA is applied to two overhead distribution networks of 14 and 135 bars. The solutions obtained are feasible and with electric losses approx. 14 % lower than that of the initial topology.

A new multiobjective fuzzy shuffled frog-leaping algorithm for optimal reconfiguration of radial distribution systems in the presence of reactive power compensators

This paper presents a new approach for the optimal reconfiguration of radial distribution systems in the presence of reactive power compensators (RPCs). The proposed method is based on the simultaneous reconfiguration and RPC allocation for the mitigation of losses, equalizing the feeder load balancing as well as improving the voltage profile in power distribution networks. In this paper, the compensators are the capacitor bank and distribution static compensator, which is a type of distribution flexible alternating current transmission system device representative of conventional and modern RPCs. In this regard, the optimal states of the distribution system tie switches are determined taking into consideration the best size and location of the RPCs. In order to facilitate the algorithm for multiobjective search ability, the optimization problem is formulated for minimizing fuzzy performance indices. Since the optimization problem is nonlinear, using intelligent search methods such as the shuffled frog-leaping (SFL) algorithm can overcome the limitations of conventional analytic methods. In addition, to enhance the performance of the standard SFL, the fuzzy frog-leaping rule is used in this paper. This algorithm is more accurate and has an efficient convergence property compared to other intelligent search algorithms. The proposed method is validated using the IEEE 33-bus test system and a Tai-Power 11.4-kV distribution system as a real distribution network. The obtained results indicate that multiobjective simultaneous placement of RPCs along with reconfiguration can be more beneficial than separate single-objective optimization.

Heuristic Approach for Distribution Systems Feeder Reconfiguration to Line Maximum Loadability

This paper presents a heuristic approach for optimal feeder reconfiguration of radial distribution systems (RDS). Optimal feeder reconfiguration involves the selection of the best set of branches to be opened by considering the all tie switches, such that the resulting RDS has the desired performance. Amongst the several performance criteria considered for optimal feeder reconfiguration, line maximizing loadability is an important one. In this paper an algorithm is proposed based on simple heuristic rules and identified an effective switch status configuration of distribution system for maximizing the line maximum loadability of the system. The line maximum loadability, power loss and voltage profile calculation of the best switching combination are found by load flow solution. Compared to other published articles, the proposed method reduces the switching combinations searched and gives the optimum solution in few number of load flow runs. To demonstrate the validity of the proposed algorithm, computer simulations are carried out on 33-bus system and the performance of the proposed method compared with the other existing methods.

Comments on “A new heuristic approach for optimal reconfiguration in distribution systems” in 81 (2011) 282–289

This short communication is a discussion of the paper entitled “A new heuristic approach for optimal reconfiguration in distribution systems” introduced a new approach for optimal reconfiguration of radial distribution systems. Besides, a load flow algorithm based on graph theory was presented in order to give precise branch currents, node voltages and system power losses. In the abstract and in the conclusion section of the above mentioned study, the author stated that the results show that the performance of the proposed method is better than that of several methods available in other published articles, and that the proposed technique represents an improved, more efficient method which can easily solve the distribution network reconfiguration problem compared with those other methods. However, this conclusion can not be deduced from the results given by the author because with the optimal radial configuration given for the test system used, the power losses obtained are higher than those obtained by some of the other methods. So, the proposed final solution is worse.