Optimal Network Reconfiguration Research Articles

A novel integration technique for optimal network reconfiguration and distributed generation placement in power distribution networks

This paper presents a novel integration technique for optimal network reconfiguration and distributed generation (DG) placement in distribution system with an objective of power loss minimization and voltage stability enhancement. Fireworks Algorithm (FWA) is used to simultaneously reconfigure and allocate optimal DG units in a distribution network. FWA is a new swarm intelligence based optimization algorithm which is conceptualized using the fireworks explosion process of searching for a best location of sparks. The radial nature of the network is secured by generating proper parent node-child node path of the network during power flow. Voltage Stability Index (VSI) is used to pre-identify the optimal candidate locations for DG installation. Six different scenarios are considered during DG placement and reconfiguration of network to assess the performance of the proposed technique. Simulations are carried out on well-known IEEE 33- and 69-bus test systems at three different load levels. The simulated results demonstrate well the performance and effectiveness of the proposed method.

Application of Hopfield neural network for distribution network’s reconfiguration

In power systems, power losses of system distribution networks take large proportion. In order to reduce power waste we have applied a variety of methods such as: improving the grid, setting the capacitor capacity and suitable location, optimizing operating system, etc. This paper refers to a reconfiguration method for reducing such losses. Reconfiguration method is a method of finding an optimal network reconfiguration to operate the electricity network to the smallest losses. The restructuring of the classical method would require a lot of time for the distribution networks having many nodes, and unsuitably for the fast response. This paper presents the use of Hopfield neural network to find an optimal reconfiguration of the distribution network. The proposed method is tested on the data grid form of the IEEE. The result of the proposed method is compared with the similar results by the other method and shows that optimal function has a minimum.

Comparative study of accuracy and computation time for optimal network reconfiguration techniques via simulation

The objective of this work is to examine the effect of load flow analysis type, horizon length and discount factor, as well as switch ordering on the accuracy and speed of dynamic programming-based optimal network reconfiguration studies for minimizing loss. The author employs simulation of a 118-bus power system to obtain results. These results demonstrate that strong relationships exist between the parameters of an optimal network reconfiguration study and its performance. The author discusses how these relationships may be used to predict performance of future studies as well as, in turn, help power system operators make better decisions regarding parameterization and obtain maximum benefit from simulation-based analyses with minimum effort.

Application of binary group search optimization to distribution network reconfiguration

This paper presents the binary group search optimization algorithm (BGSO) for solving the optimal distribution network reconfiguration problem. The objective of the optimization is loss reduction in distribution system. The proposed GSO algorithm is introduced with fundamental modifications, such as introducing binary form of the GSO in order to be fit for reconfiguration and all binary form problems. All formulation of conventional GSO has been modified for accessing a novel powerful binary searching algorithm. Additionally, the forward–backward sweep, load flow is used in this paper, due to its accuracy. The Comparative studies are conducted on standard networks to verify the validity of the proposed algorithm. The simulations on different test cases confirm validity of the BGSO in solving optimal distribution network reconfiguration problem.

Optimal Reconfiguration in Radial Distribution System Using Gravitational Search Algorithm

This article presents an innovative technique for solving network reconfiguration problems with an objective of minimizing network I2R losses for an explicit set of loads. Amid many performance standards considered for optimal network reconfiguration, voltage constraint is an important one. This problem calls for determining the best combination of feeders to be opened in the radial distribution system so it provides optimal performance in the preferred settings. In solving this problem, the gravitational search algorithm is used to reconfigure the radial distribution system; this algorithm practices an optimal pattern for sustaining the radial nature of the network at every stage of the solution, and it further allows proficient exploration of the solution space. The anticipated scheme minimizes the objective function, which has been given in the problem formulation to reduce I2R losses in addition to balancing loads in the feeders. The solution technique involves determination of the best switching combinations and calculation of power loss and voltage profile. The practicality of the anticipated technique is validated in two distribution networks, where attained results are compared by means of available literature. Correspondingly, it is seen from the results that network losses are reduced when voltage stability is enriched through network reconfiguration.

Ant colony system algorithm for optimal network reconfiguration

AbstractNetwork reconfiguration of a power distribution system is an operation to alter the topological structure of distribution feeders by changing open/closed status of sectionalizing and tie switches. Network reconfiguration balances feeder loads and helps in managing overload conditions of the network by transferring load from heavily loaded feeders to lightly loaded ones. In this paper we have introduced an ant colony system algorithm for performing network reconfiguration efficiently so as to minimize power losses occurring in a distribution network. The main idea is that of having a set of agents, called ants, which perform search in parallel for good solutions and cooperate through pheromone-mediated indirect and global communication. Informally, each ant constructs a solution path in an iterative way. Validation of the proposed algorithm has been carried using a standard IEEE network. The results found are satisfactory and prove ant colony system algorithm to be an efficient tool for optimal net...

Reconstruction of Distribution Network Based on Binary PSO and Optimal Flow Method

This paper introduces an algorithm based on improved binary PSO and optimal flow to solve the optimal network reconfiguration problem for power loss reduction. The PSO is a relatively new and powerful intelligence evolution method for solving optimization problems. It is a population-based approach that uses exploration of positive feedback as well as greedy search. The algorithm solves reconstruction of distribution network through two stages. Firstly, the binary PSO algorithm get the optimal subspace from the global space, secondly the optimal flow algorithm obtain the local optimal network structure in the subspace, finally through the PSO algorithm iterative, from the local optimal solution to search the global optimal solution. The results of calculation example show that the proposed algorithm is correct and fast.

On the optimal, fair and channel-aware cognitive radio network reconfiguration

In this work, we focus on the Joint Channel Assignment and Routing (JCAR) problem in Cognitive Radio Networks (CRNs) and especially on the optimal reconfiguration of secondary networks under the presence of primary users. Secondary CRN users need to adapt their transmission channels promptly, while effectively limit additional or escalating system modifications triggered by the intertweaved primary user activity. Our approach takes into consideration the underlying spectrum switching dynamics and concurrently aims at a fair resource allocation among the active network flows. We take an optimization perspective and formulate the JCAR and network reconfiguration problems as mixed integer linear programs, addressing fairness concerns as well. We propose a heuristic approach which is based on a sequential reduced search space methodology, in order to obtain efficiently solutions of otherwise tough and demanding reconfiguration problems. The operation, effectiveness and performance of the proposed mechanisms are evaluated through analysis and simulations under various working conditions. The obtained numerical results indicate the benefits of the proposed schemes in terms of overhead performance and their scaling properties with respect to more realistic and thus demanding topologies.

Radial Network Reconfiguration Method in Distribution System using Mutation Particle Swarm Optimization

We present a new Particle Swarm Optimization (PSO) for the optimal radial network reconfiguration. Network reconfiguration is a combinatorial problem. The combination of search space increases exponentially with the number of switches. Fixed loop coding structure is used to reduce the number of combinations. The feature of this method is to introduce mutation operation for discrete decimal problem to PSO. This method, discrete decimal PSO with mutation (DDM-PSO), is applied to both a reconfiguration problem with current constraint and a realistic loss minimization problem. For the former problem, test system with 37 nodes and 63 branches, the proposed method improves the maximum arrival number at the optimal solution from 11 to 18 compared to PSO without mutation in twenty trials. This number of DDM-PSO is also more than that of GA, 11. For the other problem, 112 branches and 102 nodes network, the average losses of the DDM-PSO generated network are less than those of the PSO without mutation by 2.3%. And the proposed method improves the maximum arrival number at the optimal solution from 2 to 17 compared to PSO without mutation in twenty trials. These results show effectiveness of the developed method.

Distribution Network Reconfiguration for Loss Reduction by Hybrid Differential Evolution

This article introduces a hybrid differential evolution (HDE) method for dealing with optimal network reconfiguration aiming at power loss reduction. The network reconfiguration of distribution systems is to recognize beneficial load transfers so that the objective function composed of power losses is minimized and the prescribed voltage limits are satisfied. The proposed method determines the proper system topology that reduces the power loss according to a load pattern. Mathematically, the problem of this research is a nonlinear programming problem with integer variables. This article presents a new approach that employs the HDE algorithm with integer variables to solve the problem. One three-feeder distribution system from the literature and one practical distribution network of Taiwan Power Company are used to exemplify the performance of the proposed method. Two other methods, the genetic algorithm and the simulated annealing, are also employed to solve the problem. Numerical results show that the proposed method is better than the other two methods.

Optimal network reconfiguration for congestion management by deterministic and genetic algorithms

In this paper, the problem of finding the optimal topological configuration of a power transmission system is considered with the aim of providing system operators with a tool suited for congestion management. Network reconfiguration looks particularly appealing since it allows transmission system operators to alleviate overloads by means of switching operations that may avoid costly generation or load curtailments. The techniques of corrective switching proposed in the 1980s are profitably employed to formulate the problem of network reconfiguration for the purpose of congestion management. The solution of the resulting large-scale mixed-integer programming problem is carried out both by a deterministic branch-and-bound algorithm included in the CPLEX optimization package and by a genetic algorithm. Tests were performed on a 33-bus CIGRE test system and on an actual 432-bus network of Italian origin.

Distribution network reconfiguration for loss reduction by ant colony search algorithm

This paper introduces an ant colony search algorithm (ACSA) to solve the optimal network reconfiguration problem for power loss reduction. The ACSA is a relatively new and powerful intelligence evolution method for solving optimization problems. It is a population-based approach that uses exploration of positive feedback as well as greedy search. The ACSA was inspired from natural behavior of the ant colonies on how they find the food source and bring them back to their nest by building the unique trail formation. By applying the ACSA, the near-optimal solution to the network reconfiguration problem can be effectively achieved. The ACSA applies the state transition rule, local pheromone-updating rule, and global pheromone-updating rule to facilitate the computation. The network reconfiguration problem of one three-feeder distribution system from the literature and one practical distribution network of Taiwan Power Company (TPC) are, respectively, solved using the proposed ACSA method, the genetic algorithm (GA), and the simulated annealing (SA). Numerical results show that the proposed method is better than the other two methods.

Optimal Reconfiguration of Radial Distribution Systems to Maximize Loadability

This paper presents a new method for optimal reconfiguration of radial distribution systems (RDS). Optimal reconfiguration involves selection of the best set of branches to be opened, one each from each loop, such that the resulting RDS has the desired performance. Amongst the several performance criteria considered for optimal network reconfiguration, maximizing loadability is an important one. Owing to the discrete nature of the solution space, a fuzzy adaptation of the evolutionary programming algorithm for optimal reconfiguration of RDS to maximize loadability is proposed in this paper. This method maximizes a fuzzy index developed using a maximum loadability index. A 33-bus RDS is optimally reconfigured by the proposed method and the results are presented.

Application assessments of distribution network minimum loss reconfiguration

Application aspects of optimal distribution network reconfiguration are highlighted in this paper. The problem is raised from the theoretical solution level to the practical implementation assessment level. For this purpose, a complex methodology for operational and short-term planning analyses of distribution systems is created. It mainly regards nonautomated or low level automated distribution systems. Optimization of a distribution network configuration for a given loading condition is done by a heuristic method. However, the methodology appropriateness to practical applications is attained by introducing a method for load estimation in distribution systems with minimum information. Furthermore, the optimization results are evaluated throughout an appropriate cost/benefit analysis. In this way, the composed tool becomes flexible, allowing appraisal of the network reconfiguration potential benefits, as well as the economically approved frequency of switching. The results of the study on Skopje's distribution network are presented.