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Electric vehicle charging station (EVCS) infrastructure development is a crucial part of the sustainable growth of the electric vehicle (EV) industry. However, the installation of EVCS may negatively affect the performance of the distribution system. This article presents a novel hybrid optimization approach to solve the optimal allocation problem of EVCS with network reconfiguration in the presence of distributed generation (DG) and distribution static compensator (DSTATCOM), with the objectives of reducing the voltage deviation, real power loss, and investment costs of DGs and DSTATCOMs while improving the voltage stability of the distribution network. A hybrid of African vulture optimization and pattern search (HAVOPS) algorithm is proposed to solve this problem. The impacts of EVCSs are examined for the 33-bus and 136-bus systems with five different cases of DG and DSTATCOM placement with reconfiguration of the network. The efficacy and performance of the HAVOPS algorithm are validated by comparison to the results of the African vulture optimization algorithm, marine predators' algorithm, grey wolf optimizer, artificial gorilla troops optimizer, and genetic algorithm. According to the findings, the HAVOPS algorithm is superior to the other methods in terms of the merit of the solutions it produces. [ABSTRACT FROM AUTHOR]

A mesh type distribution network is often configured into a radial structure to offer a variety of practical benefits, such as lower short-circuit current, low operating costs, and low construction costs. This paper presented a novel technique for optimal planning of radial distribution networks based upon a combination of Graph Theory and Machine Learning (ML) approaches. A network comprises several sectionalizing and tie-type switches. The conversion of a mesh type system into a radial one involves the selection of the best set of switches on the feeder lines that are to be opened or closed. Out of all switching combinations, we have provided the optimal radial combination, consequently the maximum voltage stability of the network with less active and reactive power losses. The proposed system also preserves the system's radial nature, with out islanding any load by a simple coding scheme. To test the capabilities of the proposed technique, we carried out simulations on the 30-node test distribution system and the results are encouraging. [ABSTRACT FROM AUTHOR]

Future distribution systems can be seen with very high penetration of renewable energy sources (RESs) such as solar photovoltaics and wind turbines on account of diverse techno-economic and social concerns. The uncertainty and variability associated with these RESs along with the stochastic nature of load demand imposes real challenges to system operators. More realistic formulations and suitably tailored methodologies can coordinate well-known operational strategies to achieve optimum performance of distribution systems. This article presents a new methodology to optimally coordinate day-ahead scheduling of distributed resources (DRs) with distribution network reconfiguration (DNR). The scheduling problem optimizes economic operation by considering O&M charges of DRs, emission charges of micro turbines, and sale/purchase of electricity to the customers/grid whereas feeder power losses are minimized by solving DNR problem. Proposed methodology coordinates these two key strategies by coarse and fine tuning to optimize several techno-economic and social objectives while duly addressing more realistic scenario of distribution systems. Application results on a modified standard 33-bus distribution system demonstrate the effectiveness of the proposed methodology. [ABSTRACT FROM AUTHOR]

Transmission system expansion leads to excessive short-circuit currents that exceed the capacity of circuit breakers. To avoid over short-circuit current; transmission system operators make various alterations in the transmission system by changing the topology of the system. The feeders can be distributed to different buses in substations through disconnecting coupling circuit breaker between buses. Having been interchanged, the transmission system may face up a lot of problems even in single outage. Therefore, the optimal positions of feeders in substations are important in order to maintain system security. However, this optimization problem has non-convex properties due to AC load flow equations and it has a multi-objective structure to provide the limitation of short-circuit current and security of N-1 contingency. The constraints are the short-circuit current, voltage and transmission line limits in the single contingency. In this paper, the Genetic Algorithm and Binary Particle Swarm Optimization methods were utilized to find a near-optimal bus layout. Algorithms coding was made with Python programming language and PSS/E program was used to obtain power flow and short-circuit data. The results of applying the methods to the IEEE 14-bus test system demonstrated the effectiveness of the methods to take overloads away and restrict short-circuit current and hold voltage in its limit. [ABSTRACT FROM AUTHOR]

In the present work, Binary Particle Swarm Optimization (BPSO) based optimal re-configuration for balanced and unbalanced radial distribution networks using Affine Arithmetic (AA), with uncertainty in generation and load, is proposed to minimize the system losses. An expression for three phase real affine power loss is derived with partial deviations of real power loss in lines with respect to power injections in other buses and also with respect to power injections in other phases in case of unbalanced distribution systems. The major contribution of the present work is the application of AA based optimal network reconfiguration, to both balanced and unbalanced radial distribution networks with uncertainty. The proposed method is tested on IEEE 16, 33, 85 and 119 bus balanced distribution systems and an unbalanced 123 bus system with Distributed Generation (DG) connected at some buses. The optimal loss intervals obtained by the proposed method are compared with that obtained by Interval Arithmetic (IA) and Monte Carlo (MC) simulations based methods. The simulation results show that proposed AA based analysis gives an optimal reconfiguration, for both balanced and unbalanced radial distribution systems with uncertainty as compared to existing IA based method. [ABSTRACT FROM AUTHOR]

The distributed generation (DG) planning with the varying pattern of the practical load is difficult as it calls for the frequent changes in DG size and system configuration, which is neither feasible nor permissible. Rather such a DG size and a configuration, which can be utilized over a wider load pattern, are more acceptable. This work presents a coordinated approach for DG planning and system reconfiguration. While to operate a particular DG size and the system configuration over a wide range of loading pattern, the configurations are ranked under different probabilistic loading patterns. Based upon the ranking of the new configuration, the energy performance of the coordinated planning is evaluated. Further, the observations from coordinated planning are imposed on coordinated operation using harmony search algorithm (HSA). The proposed approach is tested for single as well as multi-objectives on a 33-node system. A significant improvement in the computational efforts and energy performance of the resulting configuration have been observed where losses have reduced to 81.11 and 53.77 kW with single DG and multi-DG allocation respectively. [ABSTRACT FROM AUTHOR]

This paper presents a fuzzy multi-objective based heuristic algorithm for network reconfiguration of distribution systems considering distributed generations (DGs). The objectives of reduction of real power loss, branch current carrying capacity limit, maximum and minimum voltage constraints, and feeder load balancing are considered for performance enhancement of the distribution system. Since these objectives are non-commensurable and difficult to solve simultaneously using conventional approaches, they are converted into fuzzy domain and a fuzzy multi-objective function is formulated. A sensitivity analysis based on voltage profile improvement and real power loss reduction is used for obtaining optimal locations of DGs and genetic algorithm is used for optimal sizing of DGs. The proposed reconfiguration algorithm is implemented in two stages, initially in the first stage without incorporating DGs and in the second stage incorporating DGs for obtaining an optimal distribution system network reconfiguration. The advantage of the proposed method is demonstrated through a seventy node four feeders and a sixteen node three feeders distribution systems. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]

This article proposes a new evolutionary algorithm for power distribution system reconfiguration considering a multi-objective optimization approach. The proposed algorithm is based on the use of local searches distributed strategically along the Pareto front. The combination of local searches with elitism improves the convergence speed, diversity, and quality of the solutions found. The performance of the algorithm was successfully tested on several distribution systems, and the results show an improvement over those provided by other techniques. The objective functions to optimize are typical of distribution systems: power losses, voltage deviation, system average interruption frequency index, system average interruption duration index, energy not supplied, and customer average interruption duration index. [ABSTRACT FROM AUTHOR]

The optimal allocation of distributed energy resources is one of the most important and challenging task toward realizing smart grid objectives. Smart grid initiatives may be realized after obtaining integrated solutions of distributed energy resources while taking into account the realistic operational strategy of distribution network reconfiguration. This article addresses improved variants of three meta-heuristic techniques—the improved genetic algorithm, improved particle swarm optimization, and improved teaching–learning-based optimization—to efficiently handle the problem of simultaneous allocation of distributed energy resources, such as shunt capacitors and distributed generators in radial distribution networks. The problem is formulated to maximize annual energy loss reduction and to maintain a better node voltage profile while considering network reconfiguration under a variable load scenario. Several algorithm specific modifications are suggested in the standard forms of genetic algorithm, particle swarm optimization, and teaching–learning-based optimization to overcome their intrinsic flaws. In addition, an intelligent search algorithm is proposed to further enhance the performance of optimizing techniques. The proposed methods are investigated on the benchmark IEEE 33-bus test distribution system, and a comparative analysis is carried out to judge the suitability of the proposed techniques. The application results obtained are promising when compared with other established methods. [ABSTRACT FROM AUTHOR]

This article proposes a combined methodology for network reconfiguration and optimal placement of shunt capacitors in radial distribution systems to reduce real power loss and enhance bus voltages. The power loss is reduced by network reconfiguration and capacitor placement, which in turn reduces a utility's loss of revenue. To ensure radial structure and avoid islanding of nodes, feasible tie-switch combinations are formed prior to the optimization process using a graph theory based method. The modified flower pollination algorithm uses a flower pollination process, an improved local neighborhood search method, and a dynamic switching probability approach to enhance the global search for optimization. The performance of this approach is tested on standard 33-bus, 69-bus, and 118-bus radial distribution test feeders. Results have been compared with previous methods reported in the literature, indicating the effectiveness of the combinatorial approach in terms of loss reduction, voltage enhancement, and cost saving. [ABSTRACT FROM AUTHOR]

Abstract—Alteration of power network topology is often required to meet important objectives, such as restoring connectivity, minimizing power losses, maintaining stability, maximizing power transfer capability etc., and is achieved by switching of circuit breakers and other switching devices in the power network. Primary power distribution networks are often interconnected and meshed but should be transformed to radial topology to achieve various operational advantages. Distribution networks also need to be reconfigured after faults to restore power at all the load points. Reconfiguring a power network, however, is a complicated multi-constrained optimization problem, as there may exist many feasible switching combinations in a large power network. This article proposes a novel application of graph theory, supported by Kruskal's maximal spanning tree algorithm, to search for the optimal network topology and to optimally convert an interconnected meshed network into a radial system to achieve best operational characteristics, cost, and control. The proposed technique has been demonstrated on a 30-node primary distribution network originally having a mesh topology, and the results indicate significant performance improvement after transformation into optimal radial topology. [ABSTRACT FROM PUBLISHER]

This study addresses the application of an enhanced binary particle swarm optimization algorithm to generate optimal switching topology along radial distribution networks. The objective function is established with a weighting factor to offer flexibility consistent with the user decision. The active power loss minimization, voltage profile improvement, and enhancements of fast voltage stability indices are approached. Various S- and V-shaped transfer functions are attempted and analyzed to guarantee good performance of the proposed approach. The proposed method is applied to two well-known systems: the 33- and the 118-node radial distribution networks, to validate its significance and applicability. The realized results are compared to those reported for other recent heuristic competing techniques in the literature. The comparisons and subsequent discussions prove that the proposed methodology is able to generate high-quality solutions to the optimal switching schemes. [ABSTRACT FROM PUBLISHER]

This article presents an innovative technique for solving network reconfiguration problems with an objective of minimizing networkI2Rlosses 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 reduceI2Rlosses 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. [ABSTRACT FROM PUBLISHER]

Motivated by the challenge of efficiently reconfiguring distribution networks for power loss reduction, this study presents an approach for finding a minimum loss radial configuration for a power network using ordinal optimization. Ordinal optimization relies on order comparison and goal softening to make the problem solution easier and the computation more efficient. The successful application of ordinal optimization to such a complex optimization problem required the investigation of several algorithmic parameters. The solution algorithm was implemented in a software package, where an acceptable solution is considered good enough if it is in the top m% of the solutions with a probability P. Testing it on 33- and 136-bus systems, minimal power loss results were obtained on the 33-bus system that are in the top 0.03% of the search space. Comparing the experimental results with other recently published methods showed the effectiveness of ordinal optimization for minimum loss calculations and motivated further studies in smart-grid-like scenarios, where the results obtained for different load levels were in the top 0.13% of the search space. [ABSTRACT FROM AUTHOR]

The issue of protection system coordination in reconfigurable distribution systems is dealt with in this article. The protection coordination requirement restricts the changes in the power-flow direction through the distribution lines and drastically reduces the search space to be explored by the network reconfiguration algorithms. Moreover, the protection system for the reconfigurable distribution network has to be designed considering all permissible configurations of the network. This article illustrates the protection design procedure through a test network and presents a network reconfiguration algorithm incorporating the protection coordination constraints. [ABSTRACT FROM AUTHOR]

Particle swarm optimization (PSO) has emerged as a useful optimization tool for handling non-linear programming problems. Various modifications to the basic method have been proposed with a view to enhance speed and robustness. This method has been applied successfully on some benchmark mathematical problems. A few PSO applications have been reported on real-world problems, such as network reconfiguration for loss reduction, load balancing, and even capacitor placement in distribution systems being discrete in nature. PSO is modified to discrete particle swarm optimization (DPSO). The results of DPSO are compared with the results of the genetic algorithm. [ABSTRACT FROM AUTHOR]

Network reconfiguration of a 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 for time varying loads is very complex and extremely non-linear optimization, which can be effectively solved using genetic algorithm (GA). By transferring loads from the heavily loaded feeders to the lightly loaded feeders, network reconfiguration can balance feeder loads and lighten overload conditions of the network. This article presents a novel approach to solve the radial distribution system reconfiguration problem for load balancing using GA. A load flow technique is proposed for solving radial distribution systems using sparsity technique. The proposed method is simple, as it requires only two equations for load balancing. This method is demonstrated through a 69-node test system. The test results reveal that the proposed method yields optimal configuration with reduced computational burden. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Network reconfiguration for maximizing voltage stability is the determination of switching-options that maximize voltage stability the most for a particular set of loads on the distribution systems, and is performed by altering the topological structure of distribution feeders. Network reconfiguration for time-varying loads is a complex and extremely nonlinear optimization problem which can be effectively solved by Artificial Neural Networks (ANNs), as ANNs are capable of learning a tremendous variety of pattern mapping relationships without having a priori knowledge of a mathematical function. In this paper a generalized ANN model is proposed for on-line enhancement of voltage stability under varying load conditions. The training sets for the ANN are carefully selected to cover the entire range of input space. For the ANN model, the training data are generated from the Daily Load Curves (DLCs). A 16-bus test system is considered to demonstrate the performance of the developed ANN model. The proposed ANN is trained using Conjugate Gradient Descent Back-propagation Algorithm and tested by applying arbitrary input data generated from DLCs. The test results of the ANN model are found to be the same as that obtained by off-line simulation. The enhancement of voltage stability can be achieved by the proposed method without any additional cost involved for installation of capacitors, tap-changing transformers, and the related switching equipment in the distribution systems. The developed ANN model can be implemented in hardware using the neural chips currently available. [ABSTRACT FROM AUTHOR]