A Distribution feeder reconfiguration method has been developed. feeder reconfiguration is an easy and in-expensive method, for Distribution system loss reduction, which is done by maneuver in the network. In this work, Graph Theory approach has been adopted for analysis and optimization of Distribution system. Distribution system partitioning and optimization is modeled as a linear programming problem, using this theory. The Distribution system is modeled as a hyper-graph, and specific weights are assigned to every branch of the hyper-graph. Then the Distribution system is partitioned to sub-graphs. feeder reconfiguration for loss reduction can be performed in all of the sub-graphs, simultaneously. The most important feature of the presented method, is improving the performance of reconfiguration for large scale networks. Noticeable reduction in calculation time is the other advantage of network partitioning that makes it suitable for fast and real-time applications. Performance of the method has been presented on a standard Distribution system. Solution algorithm and discussion is included.

In this paper, to solve the multi-objective problem of Distribution feeder reconfiguration (DFR) in the presence of distributed generation (DG), the hybrid particle swarm optimization and gravitational search algorithm (PSOGSA) has been proposed, which is a combination of particle optimization (PSO) and gravitational (GSA) optimization algorithm. In this field, the power losses and operating costs are the two most used objective functions in the literature. In addition to the mentioned objective functions, this paper also considers the optimal generation capacity of DG resources and energy not supplied (ENS), which is one of the basic reliability indexes of Distribution networks. In this paper, the values of different objective functions are normalized by the fuzzy method, and also the Fuzzy decision-maker is used to determine the most optimal solution among the Pareto-optimal solutions. The proposed algorithm is implemented on IEEE 70-bus and 119-bus test systems. The simulation results show the efficiency of the proposed PSOGSA in improving the considered objective functions. The proposed method, by establishing a suitable fit between different objective functions has introduced a more efficient structure with lower losses and operating costs, as well as greater reliability, compared to other optimization algorithms.

This article presents a network feeder reconfiguration in balanced Distribution networks using Multi Verse Optimization (MVO) to optimize the total network power losses and reduce emissions by means of step by step switching. reconfiguration is a considerable manner of altering the power flows through the lines from the main substation to load ends, while maintaining radial structure. The main objective of this paper is to solve feeder reconfiguration problem to reduce the total line losses and emission reduction for an open loop Distribution system. MVO is a population based method to resolve the network reconfiguration problem. A precise power flow solution is applied and the objective is formulated. A nature inspired Multi Verse Optimization is utilized to restructure the power Distribution system and identify the optimal tie switches for lower line losses in the Distribution network. The reduction of resistive losses leads to reduction of emissions. The suggested MVO method has carried out on two standard 16-node and 69-node Distribution systems for normal load and overload conditions and results show the performance of the anticipated MVO method. The final outcomes prove a significant reduction in real power losses and emissions.

Using distributed generations (DGs) with optimal scheduling and optimal Distribution feeder reconfiguration (DFR) are two aspects that can improve efficiency as well as technical and economic features of microgrids (MGs). This work presents a stochastic copula scenario-based framework to jointly carry out optimal scheduling of DGs and DFR. This framework takes into account non-dispatchable and dispatchable DGs. In this paper, the dispatchable DG is a fuel cell unit and the non-dispatchable DGs with stochastic generation are wind turbines and photovoltaic cells. The uncertainties of wind turbine and photovoltaic generations, as well as electrical demand, are formulated by a copula-based method. The generation of scenarios is carried out by the scenario tree method and representative scenarios are nominated with scenario reduction techniques. To obtain a weighted solution among the various solutions made by several scenarios, the average stochastic output (ASO) index is used. The objective functions are minimization of the operational cost of the MG, minimization of active power loss, maximization of voltage stability index, and minimization of emissions. The best-compromised solution is then chosen by using the fuzzy technique. The capability of the proposed model is investigated on a 33-bus MG. The simulation results show the efficiency of the proposed model to optimize objective functions, while the constraints are satisfied.

A novel intelligent neural optimizer with two objective functions is designed for electrical Distribution systems. The presented method is faster than alternative optimization methods and is comparable with the most powerful and precise ones. This optimizer is much smaller than similar neural systems. In this work, two intelligent estimators are designed, a load flow program is coded, and a special modified heuristic optimization algorithm is developed and used too. The load pattern concept is used for training ANNs. Finally, the designed optimizer is tested on an example Distribution system; simulation results are presented, and compared with similar systems.

Distribution network reconfiguration is one of the well-known and effective strategies in the Distribution networks which performs by the status management of the network switches in order to obtain a new optimal configuration for the feeders. This study formulates multi-objective Distribution feeder reconfiguration in the presence of distributed generators and capacitors. Common objective functions in the Distribution network reconfiguration problem include power losses and voltage deviations, which are important goals in traditional Distribution systems. Usually, less attention has been paid to the reliability and voltage security target functions. Therefore, the main objectives of this study are to improve the reliability and maintenance of voltage by solving the problem of Distribution network reconfiguration. The inherent complexities of the Distribution network rearrangement problem have made it a serious challenge to provide a practical and robust solution to overcome the complexities of this problem, therefore, the improved gravitational search optimization algorithm to solve this problem Has been. In order to show the efficiency of the proposed method, it has been tested on a 33-bus test system.and the results are compared with theresults of using other evolutionary algorithms, such as particleswarm optimization and shuffled frog leaping