System Reconfiguration Problem Research Articles

Constrained-multiobjective Evolutionary Algorithm for Distribution System Reconfiguration under Severe Constraints

Distribution system reconfiguration problems pose significant challenges due to their multiple objectives, high computational complexity, and severe constraints. These challenges become even more pronounced with the integration of renewable energy sources, which introduce uncertainties and further tighten the constraints. This paper proposes a constrained multiobjective optimization evolutionary algorithm (CMOEA) based on a dual-stage approach with a dual-population genetic operator. The proposed CMOEA is applied to a case study of a distribution system reconfiguration problem with severe constraints, uncertainties, and high computational demands, and it shows good performance in finding infeasible solutions with small constraint violations and feasible solutions with small objectives.

Efficient energy valley optimization approach for reconfiguring thermoelectric generator system under non-uniform heat distribution

A thermoelectric generator (TEG) suffers from low conversion efficiency, especially when it has been operated under non-uniform temperature distribution. Dynamic reconfiguration is an effective method to enhance the TEG overall efficiency although it requires many electrical switches that make the process more complex. This paper proposes a new approach of energy valley optimizer (EVO) to optimally reconfigure the TEG array operated at non-uniform temperature distribution and enhance the harvested electrical energy. The selection of EVO is due to fast convergence rate, parameter-free, and avoiding stuck in local optima. Minimization of absolute error between the TEG system current at maximum power and the actual one is the proposed fitness function. The analysis is performed on small 9 × 9 TEG array and large 15 × 15 TEG array. Eight temperature distribution patterns of non-uniform row, non-uniform column, short wide, long wide, diagonal, external, internal, and random are analyzed. The proposed EVO is assessed via comparing to reported immune genetic algorithm (IIGA) and other programmed approaches of osprey optimization algorithm (OOA), chef-based optimization algorithm (CBOA), artificial hummingbird algorithm (AHA), and northern goshawk optimizer (NGO). Moreover, statistical investigation is implemented via performing Kruskal-Wallis test, Friedman test, ANOVA test, and Wilcoxon rank test. The fetched results revealed that, the best enhanced power from small 9 × 9 array is 9.143% compared to the original arrangement occurred at long wide temperature distribution. Regarding large 15 × 15 TEG array, the best enhanced power happened during diagonal pattern with 3.359%. The results confirmed the preference of the proposed EVO in solving the TEG system reconfiguration problem with great efficiency.

Distribution system reconfiguration using water cycle algorithm

High power losses are a great concern in operating electric distribution system. Reconfiguration is one of the most economic approaches for reducing power losses of the system. This study suggests a technique for dealing with the distribution system reconfiguration problem based on a water cycle algorithm for minimizing active power loss. The water cycle algorithm is a recently developed metaheuristic algorithm that inspired the process of water circulation for solving optimization problems. The effectiveness and performance of the water cycle algorithm were tested on the 33-node and 69-node systems. The water cycle algorithm was applied to determine the best configuration of the distribution system for active power loss minimization. The results yielded by the water cycle algorithm were compared with other optimization algorithms in the literature and the comparisons showed that the water cycle algorithm obtained good quality of solution for the problem. Therefore, the water cycle algorithm is the potential method for the distribution system reconfiguration problem.

Reduced search space combined with particle swarm optimization for distribution system reconfiguration

This paper presents a methodology based on a mesh analysis technique to reduce the search space of the distribution system reconfiguration problem. After reducing the search space, the metaheuristic particle swarm optimization (PSO) was used to solve the problem, finding the best values presented in the literature. In the proposed methodology, the subsets of candidate switches were initially established through a two-stage heuristics. In the first stage, the number of open switches required to keep the radial configuration was calculated, which is the number of meshes on the system, and in the sequence the switches that make up each mesh were identified. In each subset, only one switch is opened, which makes the search process more efficient, compared to the analysis using all the switches. In the second stage of heuristic, the number of switches of the subsets was decreased with the help of an optimal power flow (OPF), which determines the switches from the subsets of first stage more propitious to be opened, this being the main contribution of the work. The PSO was developed for minimizing power losses in the distribution network lines subject to the following constraints: (a) minimum and maximum voltage limits; (b) radial network topology; and (c) balance of active and reactive power in the network buses. The algorithm was validated in four radial distribution systems: 5 nodes with 7 lines, 16 nodes with 21 lines, 33 nodes with 37 lines and 70 nodes with 74 lines.

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.

On the Radiality Constraints for Distribution System Restoration and Reconfiguration Problems

Radiality constraints are involved in both distribution system restoration and reconfiguration problems. However, a set of widely used radiality constraints, i.e., the spanning tree (ST) constraints, has its limitations which have not been well recognized. In this letter, the limitation of the ST constraints is analyzed and an effective set of constraints, referred to as the single-commodity flow constraints, is presented. Furthermore, a combined set of constraints is proposed and case studies indicate that the combined constraints can gain computational efficiency in the reconfiguration problem. Recommendations on the use of radiality constraints are also provided.

An initial-point strategy for optimizing distribution system reconfiguration

This study presents a simple and fast strategy for selecting the candidate solutions of the distribution system reconfiguration problem without solving optimization based or load flow based programs. It consists of constructing a node-node adjacency matrix of the original network topology in which specific diagonal entries corresponding to the off-diagonal ones represent the favourable candidate solutions. This strategy needs only primitive network information and is applicable to any optimization method and network dimension without affecting the computational tractability of the problem. Through numerical experiments on various test systems, the efficacy of the proposed scheme is illustrated.

A method to include component condition and substation reliability into distribution system reconfiguration

Component condition and substation (S/S) reliability have a material impact on the cost of customer interruptions in electricity distribution systems (DSs). However, these factors are not usually considered within distribution system reconfiguration (DSR) problem formulations, because of the lack of a readily available methodology. This paper presents such a method, making use of component condition scores which are now mandatory for distribution system operators (DSOs) in the UK. Based on these condition scores, condition-based failure rate can be calculated for each component. S/S reliability is a function of component condition, S/S configuration and the network upstream of the S/Ss. The reliability of the S/S then has an impact on the reliability indices of each load point (LP) it supplies. These factors are combined to deliver a better informed algorithm for DSR, which is verified through its application on two DSs. The annual savings, compared to the formulation that neglects component condition and S/S reliability, can be in the order of tens of thousands of U.S. dollars for a single DS.

Improved Genetic Algorithm for Distribution System Performance Analysis by Taking Advantage of Essential Spanning Trees

Growing interest in the smart grid, increasing use of distributed generation, and classical distribution system reconfiguration (DSR) and restoration problems have led to the search for efficient distribution automation tools. One such tool, the improved Fast Nondominated Sorting Genetic Algorithm (FNSGA), not only is effective in finding system configurations that are optimal with respect to voltages, currents, and losses, but also considered parametric study to determine minimum values of N and Gen. In this paper, the essential spanning tree concept is expanded to improve the computational efficiency of the algorithm. Results of the study show that for relatively small test systems, optimum system configurations are obtained using values of N and Gen that require very small CPU times. In larger systems, optimum values of N and Gen requiring reasonable CPU times can also be found, provided that certain carefully chosen branches are removed from the pool of possibilities when producing the initial population in the algorithm. By using essential trees, the efficiency of the calculation is improved.

Balanced and unbalanced distribution networks reconfiguration considering reliability indices

Abstract Distribution system reconfiguration problem is a complex optimization process to find a structure with minimum losses in which the satisfaction of both sides, those are consumers and distribution system companies, need to be met. One of the most significant parameters in this regard is to increase the reliability of the system. This parameter, on one hand, increases the satisfaction of power consumption and on the other hand, improves the economic benefits of distribution companies. Distribution system reconfiguration, considering the reliability parameters, seems to make the attempts to solve the problem of optimization difficult. In this paper, a modified heuristic approach for distribution system has been presented. Also, in order to consider reliability indexes, a number of new formulas have been presented. The effectiveness of the proposed method is demonstrated on balanced and unbalanced test distribution systems.

A smart distribution system reconfiguration algorithm with optimal active power scheduling considering various types of distributed generators

This paper presents an efficient way of solving the distribution system reconfiguration (DSR) problem in electrical power systems with consideration of different types of distributed generators (DGs). The objective of a DSR is to minimize the system power loss while satisfying the system constraints and keeping the topology of the system radial. In this paper, a new DSR algorithm based on a modified particle swarm optimization (PSO) is proposed to incorporate DGs with the constant voltage control mode. The proposed method is very efficient because it avoids an extra iteration loop for computing the reactive power at PV buses in order to keep the voltage at a specified magnitude. Furthermore, if the reactive power requirement is not met in between the extreme limits, the proposed algorithm strictly searches for the best possible tie switch combination to simultaneously reduce the power loss and ensure that the DGs operate in PV mode within acceptable reactive power limit. The proposed algorithm also integrates hourly DSR with optimal DG active power scheduling considering the DG type, generation limit constraints, and the allowable DG penetration level. The validity and the effectiveness of the proposed method has been tested using standard IEEE 33‐bus and 69‐bus distribution networks with various case studies. Test results show that the proposed method is robust and delivers a minimal average power loss compared with different methods, and it efficiently models DGs in DSR, demonstrating that the presence of DGs can further reduce the system loss. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

A discrete Teaching–Learning-Based Optimization algorithm to solve distribution system reconfiguration in presence of distributed generation

Reconfiguration is an important way to increase the power distribution systems efficiency. The problem of reconfiguration is a complicated combinatorial optimization problem with discrete decision variables. To solve such problem, a powerful optimization technique is required. This paper presents a discrete Teaching–Learning-Based Optimization (DTLBO) algorithm for solving the distribution system reconfiguration (DSR) problem. The objectives are power loss minimization and voltage profile improvement in presence of distributed generation (DG). The proposed method is applied to 33-bus and 69-bus test systems and a part of the distribution network of Ahvaz, a city in the south of Iran. A comparison between the proposed algorithm and other existing methods shows the effectiveness and capability of the proposed method to reach the global optimum and rapid convergence to the optimal solution.

Reconfiguration of distribution system using a binary programming model

Distribution system reconfiguration aims to choose a switching combination of branches of the system that optimize certain performance criteria of power supply while maintaining some specified constraints. The ability to automatically reconfigure the network quickly and reliably is a key requirement of self-healing networks which is an important part of the future Smart Grid system. We present a unified mathematical framework, which allows us to consider different objectives of distribution system reconfiguration problems in a flexible manner, and investigate its performance. The resulting optimization problem is in quadratic form which can be solved efficiently by using a quadratic mixed integer programming (QMIP) solver. The proposed method has been applied for reconfiguring different standard test distribution systems.

Mathematical representation of radiality constraint in distribution system reconfiguration problem

Abstract Distribution systems are most commonly operated in a radial configuration for a number of reasons. In order to impose radiality constraint in the optimal network reconfiguration problem, an efficient algorithm is introduced in this paper based on graph theory. The paper shows that the normally followed methods of imposing radiality constraint within a mixed-integer programming formulation of the reconfiguration problem may not be sufficient. The minimum-loss network reconfiguration problem is formulated using different ways to impose radiality constraint. It is shown, through simulations, that the formulated problem using the proposed method for representing radiality constraint can be solved more efficiently, as opposed to the previously proposed formulations. This results in up to 30% reduction in CPU time for the test systems used in this study.

Differential evolution using ancestor tree for service restoration in power distribution systems

Problems in power distribution system reconfiguration (PDSR), such as service restoration, power loss reduction, and expansion planning, are usually formulated as complex multi-objective and multi-constrained optimization problems. Several evolutionary algorithms (EAs) have been developed to deal with PDSR problems, but the majority of EAs still demand high running time when applied to large-scale distribution systems (thousands of buses and switches). This paper presents a new approach for service restoration in large scale distribution systems that employs a discrete differential evolution with ancestor tree (DE-Tree). We combine the node-depth encoding (NDE) to represent computationally the electrical topology of the system and the ancestor tree presented here to implement differential evolution for service restoration problems. The ancestor tree is used to build a list of elementary movements that maps one solution in the search space into another, thus capturing the “difference” between forests encoded with the NDE, which is essential in the search engine of differential evolution. The use of an ancestor tree is not only central to implement differential mutation in our algorithm but also can track the sequence of switching operations in the restoration of the system after the optimization process is finished. The proposed approach makes differential evolution suitable for treating combinatorial optimization problems related to PDSR. Results presented on distribution system reconfiguration problems suggest the benefits and fast convergence of the proposed approach.

Sparsity-Leveraging Reconfiguration of Smart Distribution Systems

A system reconfiguration problem is considered for three-phase power distribution networks featuring distributed generation. In lieu of binary line selection variables, the notion of group sparsity is advocated to re-formulate the nonconvex distribution system reconfiguration (DSR) problem into a convex one. It is shown that the line selection task boils down to a shrinkage and thresholding operation on the line currents. Further, numerical tests illustrate the ability of the proposed scheme to identify meshed, weakly meshed, or even radial configurations by adjusting a sparsity-tuning parameter in the DSR cost. Constraints on the voltages are investigated and incorporated in the novel DSR problem to facilitate voltage regulation.

Multi-objective optimization using NSGA-II for power distribution system reconfiguration

SUMMARY This study proposes a new strategy to solve the problem of radial power distribution system (RDS) reconfiguration in a multi-objective and constrained environment. Due to the presence of various conflicting objectives and constraints, the proposed strategy uses the Elitist Non-Dominated Sorting Genetic Algorithm-II (NSGA-II), an effective evolutionary multi-objective optimization technique. NSGA-II determines a set of pareto-optimal solutions for the power distribution system topology, considering power losses, reliability and investment in tie-switches. The methodology adopted to evaluate the RDS reliability uses a non-sequential Monte Carlo Simulation and is focused on the impacts of branch failures for interruption energy assessment. The effectiveness of the proposed methodology is demonstrated on a 69 bus RDS. Copyright © 2013 John Wiley & Sons, Ltd.

Radial distribution systems reconfiguration considering power losses cost and damage cost due to power supply interruption of consumers

Distribution system reconfiguration problem is a complex optimization process to find a structure with minimum losses in which the satisfaction of both sides, that is consumers and distribution system companies, need to be met. One of the most significant parameters in this regard is to increase the reliability of system. This parameter, on one hand, increases the satisfaction of power consumption and on the other hand, improves the economic benefits of distribution companies. Distribution system reconfiguration, considering the reliability parameters, seems to make the attempts to solve the problem of optimization difficult. In this paper, a new heuristic approach for distribution system reconfiguration in order to decrease the power losses cost and damage cost due to power supply interruption of consumers has been presented. Radial network construction and all energized nodes constraints are the most important ones that should be considered in distribution system reconfiguration problem. Hence, in this paper a new codification is proposed which is computationally efficient and guarantees to generate only feasible radial topologies all times. In order to illustrate the performance of proposed heuristic method, modified 33-bus and 119-bus distribution networks have been employed which have led to the desired results.

Distribution system reconfiguration for energy loss reduction considering the variability of load and local renewable generation

The interconnection of renewable energy sources with distribution systems is attracting increasing interest because these renewable sources are inexhaustible and nonpolluting. Wind and photovoltaic are among the most mature of these energy sources, and their penetration continues to increase. In this paper a method based on GA (genetic algorithm) is presented to investigate the distribution system reconfiguration problem taking into consideration the effect of load variation and the stochastic power generation of renewable DG (distributed generators units). The presented method determines the annual distribution network reconfiguration scheme considering switching operation costs in order to minimize annual energy losses by determining the optimal configuration for each season of the year. The uncertainties related to DG power and varying load are considered by the creation of a probabilistic generation-load model that combines all possible operating conditions of the renewable DG units with the probability of their occurrence, followed by the incorporation of this model into the reconfiguration problem. The constraints include the voltage limits, the line current limits, the radial topology, and feeding of all loads. In order to evaluate the effectiveness of the proposed method, both balanced and unbalanced distribution systems are used as case studies.

Multi-objective Unbalanced Distribution Network Reconfiguration through Hybrid Heuristic Algorithm

Electrical power distribution systems are critical links between the utility and customer. In general, power distribution systems have unbalanced feeders due to the unbalanced loading. The devices that dependent on balanced three phase supply are affected by the unbalanced feeders. This necessitates the balancing of feeders. The main objective of reconfiguration is to balance the loads among the phases subject to constraints such as load flow equations, capacity and voltage constraints and to reduce the real power loss, while subject to a radial network structure in which all loads must be energized. Therefore, the distribution system reconfiguration problem has been viewed as multi- objective problem. In this paper, the hybrid heuristic algorithm has been used for reconfiguration, which is the combination of fuzzy and greedy algorithms. The purpose of the introduction of greedy is to refrain the searching for the period of phase balancing. The incorporation of fuzzy helps to take up more objectives amid phase balancing in the searching. The effectiveness of the proposed method is demonstrated through modified IEEE 33 bus and modified IEEE 125 bus radial distribution system.