Optimal Distribution Network Reconfiguration Research Articles

Electric eel foraging optimization algorithm for distribution network reconfiguration with distributed generation for power system performance enhancement considerations different load models

Optimizing the placement of distributed generators (DGs) alongside network reconfiguration is crucial for enhancing the efficiency and reliability of modern distribution systems amidst growing energy demands. This paper introduces a novel Electric Eel Foraging Optimization (EEFO) algorithm for the optimal reconfiguration and placement of DGs in radial distribution networks (RDNs), considering different load models. EEFO utilizes an adaptive energy factor to balance exploration and exploitation across four search strategies, such as exploration, resting, hunting, and migrating. Additionally, its free-parameter feature eliminates the need for fixed settings, simplifying the optimization process. The effectiveness of EEFO is demonstrated through its application to the 33-node and practical 84-node RDNs under different DG power factor scenarios with and without network reconfiguration. The primary objective is to minimize active and reactive power losses while reducing voltage deviation. The results indicate that the scenario with DG allocation at optimal power factor and network reconfiguration achieves significant reductions in voltage deviation, active power loss, and reactive power loss, with improvements of up to 92.20 %, 94.03 %, and 92.33 % in the 33-node network and 55.95 %, 60.20 %, and 59.36 % in the 84-node network, respectively. Furthermore, the comparative analysis with the zebra optimizer, whale optimizer, and moth flame optimizer highlights EEFO's superior efficiency and effectiveness in optimal DG allocation and distribution network reconfiguration.

Multi-objective optimal reconfiguration of distribution networks using a novel meta-heuristic algorithm

Reconfiguration strategies are used to reduce power losses and increase the reliability of the distribution systems. Since the optimal reconfiguration problem is a multi-objective optimization problem with non-convex functions and constraints, meta-heuristic algorithms are the most suitable choice for the problem-solving approach. One of the new meta-heuristic algorithms that exhibits excellent performance in solving multi-objective problems is the wild mice colony (WMC) algorithm, which is implemented based on aggressive and mating strategies of wild mice. In this paper, the distribution network reconfiguration problem is solved to reduce power losses, improve reliability, and increase the voltage profile of network buses using the WMC algorithm. In addition, the obtained results are compared with conventional multi-objective algorithms. The optimal reconfiguration problem is applied to the IEEE 33-bus and 69-bus test systems. The comparative study confirms the superior performance of the proposed algorithm in terms of convergence speed, execution time, and the final solution.

Correction to: Optimal reconfiguration of a smart distribution network in the presence of shunt capacitors

Correction to: Optimal reconfiguration of a smart distribution network in the presence of shunt capacitors

Distribution network reconfiguration optimization method based on undirected-graph isolation group detection and the whale optimization algorithm

<abstract> <p>As distributed generation (DG) becomes increasingly integrated into the distribution grid, the structure of the distribution network is becoming more complex. To enhance the safety and cost-effectiveness of distribution systems, distribution network reconfiguration is gaining significant importance. Achieving optimal distribution network reconfiguration entails two key considerations: A feasible topology and economic efficiency. This paper addresses these challenges by introducing a novel approach that combines the potential island detection in undirected-graphs and the application of a whale optimization algorithm (WOA) for network reconfiguration optimization. To begin, we identified island categories based on the type of switchable-branches connected to these islands, allowing for the construction of potential island groups. Subsequently, unfeasible topologies were eliminated based on the conditions under which islands form within these potential island groups. Feasible topologies were then used to construct a model for network reconfiguration optimization. The optimal distribution network topology is determined using the WOA. In the final phase, the proposed method's effectiveness was demonstrated through a case study on the IEEE-33 node distribution network under scenarios with and without DG integration. The results showed that the proposed method exhibited better performance than traditional approaches in distribution network reconfiguration.</p> </abstract>

Active and Reactive Power loss Minimization Along with Voltage profile Improvement for Distribution Reconfiguration

Optimal distribution network reconfiguration (DNR), distributed generations location and sizing (DGs-LS), tap changer adjustment (TCA), and capacitors bank location and sizing (CAs-SL) are different methodologies used to reduce loss and enhance the voltage profile of distribution systems. DNR is the process of changing the network topography by changing both sectionalized and tie switch states. The optimal location looks to find the optimal setting of the DG and CA within the distribution network. Optimal size seeks to find the optimal output generation of both DG and CA. The TCA looks to find the optimal position for TC. These methods are challenging optimization problems and resort to meta-heuristic techniques to find a globally optimal solution. This paper presents a new methodology with which to simultaneously solve the problem of DNR, DGs-LS, TCA, and CAs-SL in distribution networks. This work aims to minimize active and reactive power losses, including voltage profile improvement using a multi-objective decision approach. The firefly algorithm (FA) and analytic hierarchy process (AHP) are used to optimize the fitness function and determine the function weight factors through the use of MATLAB software. Several scenarios were considered on the IEEE 69-bus network. In terms of active power and reactive losses, reductions in the test system of 96.16% and 92.7%, respectively, were achieved, evidencing the positive impact of the proposed methodology on distribution networks.

Optimal Integration of Distribution Network Reconfiguration and Conductor Selection in Power Distribution Systems via MILP

Power distribution systems (PDS) comprise essential electrical components and infrastructure that facilitate the delivery of electrical energy from a power transmission system to end users. Typically, the topology of distribution systems is radial, so that power goes from the substations to end users through main lines or feeders. However, the expansion of new feeders to accommodate new users and ever-growing energy demand have led to higher energy losses and deterioration of the voltage profile. To address these challenges, several solutions have been proposed, including the selection of optimal conductors, allocation of voltage regulators, utilization of capacitor banks, implementation of distributed generation, and optimal reconfiguration. Although reconfiguring the network is the most cost-effective approach, this solution might not be sufficient to completely minimize technical losses and improve system performance. This paper presents a novel approach that combines optimal distribution network reconfiguration (ODNR) with optimal conductor selection (OCS) to minimize power losses and enhance the voltage profiles of PDS. The key contribution lies in the integration of the ODNR and OCS into a single MILP problem, ensuring the attainment of globally optimal solutions. The proposed model was tested with benchmark 33-, 69-, and 85-bus test systems. The results allowed us to conclude that the combined effect of ODNR and OCS presents better results than when any of these approaches are applied either separately or sequentially.

Optimal reconfiguration of a smart distribution network in the presence of shunt capacitors

Optimal reconfiguration of a smart distribution network in the presence of shunt capacitors

Optimal reconfiguration of electric distribution networks in the presence of DG resources based on a cascaded chaotic fruit fly optimizer

Optimal reconfiguration of electric distribution networks in the presence of DG resources based on a cascaded chaotic fruit fly optimizer

Optimal reconfiguration of distribution network based on deep fuzzy c-means clustering algorithm

This study examines the best distribution network reconfiguration to enhance distribution network safety and reduce active power loss. Distribution networks can be reconfigured using a deep fuzzy C-means (FCM) clustering method with the objective of the least amount of active power loss. By creating a novel framework, the size of the neural network is lowered by modifying the number of neurons in input layer. With the simulations tested on IEEE 33-bus distribution network, the outcomes of traditional approaches, such as the Branch-and-Cut and the switching algorithms, are contrasted with the simulation results. The comparative results clearly show advantages to employing the suggested framework for distribution network reconfiguration, such as a quick turnaround time far quicker than the alternatives. These characteristics demonstrate how well the suggested paradigm may be applied to distribution network real-time reconfiguration.

Distribution Network Reconfiguration Based on Hybrid Golden Flower Algorithm for Smart Cities Evolution

Power losses (PL) are one of the most—if not the most—vital concerns in power distribution networks (DN). With respect to sustainability, distribution network reconfiguration (DNR) is an effective course of action to minimize power losses. However, the optimal DNR is usually a non-convex optimization process that necessitates the employment of powerful global optimization methods. This paper proposes a novel hybrid metaheuristic optimization (MO) method called the chaotic golden flower algorithm (CGFA) for PL minimization. As the name implies, the proposed method combines the golden search method with the flower pollination algorithm to multiply their benefits, guarantee the best solution, and reduce convergence time. The performance of the algorithm has been evaluated under different test systems, including the IEEE 33-bus, IEEE 69-bus, and IEEE 119-bus systems and the smart city (SC) network, each of which includes distributed-generation (DG) units and energy storage systems (ESS). In addition, the locations of tie-switches in the DN, which used to be considered as given information in previous studies, are assumed to be variable, and a branch-exchange adaption is included in the reconfiguration process. Furthermore, uncertainty analysis, such as bus and/or line fault conditions, are studied, and the performance of the proposed method is compared with other pioneering MO algorithms with minimal standard deviations ranging from 0.0012 to 0.0101. The case study of SC is considered and the obtained simulation results show the superiority of the algorithm in finding higher PL reduction under different scenarios, with the lowest standard deviations ranging from 0.012 to 0.0432.

Learning-Based Topology Optimization of Power Networks

Transitions of power networks demand more powerful solution approaches for optimal transmission switching (OTS) and distribution network reconfiguration (DNR) to tackle complicated cases with high computational efficiency. In this paper, we propose a learning-based solution approach for OTS and DNR, which is established based on an identified common pattern behind existing heuristic algorithms. The original hand-crafted and short-sighted evaluation function is substituted by a parameterized function whose architecture is well-designed by mainly exploiting the gated graph neural network and multilayer perceptrons. For learning the parameterized function that lacks training labels, we further design a learning algorithm that combines the double deep Q-network algorithm and multi-step learning. Then, with the learned parameterized function, the learning-based heuristic algorithm is developed by embedding a feasibility recovery procedure. Finally, numerical studies demonstrate the superiority of the learning-based heuristic algorithm in both solution optimality and computational efficiency.

Optimal distribution network reconfiguration to minimization power loss

With the development of industry, population growth, and suburbanization, load demand is constantly increasing from year to year. Overload demand has greatly strained the distribution network (DN), resulting in increased power losses due to the high-power flow. Therefore, it becomes very important to minimize power losses at the DN to maximize the efficiency of the distribution companies. Network reconfiguration is one of the effective methods distribution companies use to minimize losses in the DN. This paper proposes Dingo Optimization Algorithm (DOA) to solve network reconfiguration problems and minimize the DN's power loss. DOA mimics the social behavior of the Australian dingo dog. IEEE-33 bus DN has been used to demonstrate the effectiveness of the proposed method. The obtained simulation results compared with other methods in the literature and comparison showed that the proposed DOA provides better quality solutions than other methods.

Grey wolf optimisation algorithm for solving distribution network reconfiguration considering distributed generators simultaneously

ABSTRACT This article represents an application of the grey wolf optimisation (GWO) algorithm to solve the most optimistic combinatorial problems for optimal distribution network reconfiguration (DNR) and allocation of distributed generators (DGs) in a system. In this work, a metaheuristics algorithm is utilised to minimise the active power losses (APL) and enhance the voltage profile. Various scenarios were considered in this context to compare the performance of the proposed algorithm under voltage and current capacity constraints. Furthermore, a detailed validation via comparison of the results is being carried out with other methods from the exhaustive literature. The proposed algorithm reduces the APL by 63.13%, 56.19%, and 34.27% with DNR in IEEE 33, 69 and 118-bus systems. Similarly, APL reduction by 69.61%, 82.09%, and 36.08% with DNR considering DGs simultaneously. The results show that the proposed algorithm is an effective and promising method to solve problems similar to this work.

Optimal Reconfiguration of Distribution Network Considering Stochastic Wind Energy and Load Variation Using Hybrid SAMPSO Optimization Method

Due to the stochastic characteristics of wind power generation and following varying demands for load consumption over a planning period, the optimal reconfiguration (OR) of the radial distribution network (RDN) represents a complex problem of a combinatorial nature. This paper evaluates two types of optimal reconfiguration searching for an optimal solution and considering time-varying changes. The first one is a static reconfiguration of RDN (SRRDN) made at a fixed load consumption point and during constant generated renewable power integration. The second one is a dynamic reconfiguration of RDN (DRRDN) made following a stochastic integration of wind energy (WTDG) and/or variation in load demand characteristics. In total, five scenarios are investigated in order to evaluate optimal reconfiguration of RDN (ORRDN) with the aim of reducing total active power losses (TAPL), improving the voltage profile (VP), and minimizing switches’ change costs (SCC). To deal with this, a hybrid optimization technique (SAMPSO) combining the simulated annealing algorithm (SA) with a modified particle swarm optimization (MPSO) is undertaken. This hybrid method coupled with the MATPOWER toolbox is tested on the standard IEEE 69-bus RDN through both SRRDN and DRRDN. The results show the effectiveness of this improved reconfiguration procedure for enhancing the test system performance. A comparison between the proposed optimization method and previous findings’ methods is undertaken in this work. The obtained results proved the superiority and effectiveness of the SAMPSO method in solving the SRRDN and DRRDN problems.

Optimal Planning of Power Distribution Network by a Novel Modified Jaya Algorithm in Multiobjective Perspective

This article presents a novel improved Elitism oppositional Jaya algorithm for the optimal power distribution network reconfiguration and optimal sizing, siting of multiple distributed generations (DGs) with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</i> , PQV buses. Here, PQV bus is a voltage-specified PQ type bus controlled by the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</i> bus. In this proposed planning model, the basic Jaya algorithm is modified by incorporating variable inertia weight, vicinity searching capability, elitism property, and blended with an opposition learning-based strategy. This establishes an effective approach between global search and local search in quest of an optimal solution. A multiobjective framework is developed to optimize technical and economic objectives simultaneously. Optimal weights are allocated to each objective using an improved analytical hierarchy process method. Novel indices are proposed to identify the optimal locations of DGs also considering critical loads. During reconfiguration, the line data sequence is rearranged by a smart graph theory mechanism. Furthermore, different schemes have been studied by the suggested method on IEEE 33 and 69 bus distribution test systems. The findings of the study by using the proposed algorithm in comparison with particle swarm optimization (PSO) and enhanced leader PSO show its effectiveness and validity.

Butterfly optimizer assisted Max\u2013Min based multi-objective approach for optimal connection of DGs and optimal network reconfiguration of distribution networks

Currently, the electrical distribution system is experiencing challenges such as low system efficiency due to substantial real power losses, a poor voltage profile, and inadequate system loadability as a result of the tremendous increase in system load demand. Therefore, distribution system operators are searching for ways to improve system efficiency and loadability. Distributed Generation technology has attracted a lot of researchers’ interest in recent days because of its enormous technological advantages in dealing with the aforementioned issues. This work presents a Max–Min based multi-objective optimization approach for optimal connection of distributed generators (OCDG) in the presence of optimal distribution network reconfiguration (ODNR) to enhance the system loadability (lambda_{{{text{max}}}}) and to reduce real power loss. Two scenarios are taken to achieve the proposed objectives. Scenario-1 deals with the enhancement of loss mitigation & system loadability. In scenario-2, to extract maximum benefits with less amount of real power injection by DGs into the system, DGs real power injection is taken as one of the objectives. Under each scenario, three cases are investigated. Case 1 and case 2 deal with single-objective optimization, whereas case 3 deals with multi-objective optimization. The butterfly Optimization (BO) technique is implemented for the optimization of proposed objectives. The proposed method is tested on 33 bus, 69 bus radial distribution test systems. To test the potential of the BO algorithm, the outcomes are contrasted with the suitable results that are accessible in the literature. From the outcomes, it was observed that real power loss of the system is reduced to (75–89)%, loadability enhanced to (94–121)% with the injection of 64% KVA by DGs into 33 & 69 bus systems.

A Mixed-Integer Linear Programming Model for the Simultaneous Optimal Distribution Network Reconfiguration and Optimal Placement of Distributed Generation

Distributed generation (DG) aims to generate part of the required electrical energy on a small scale closer to the places of consumption. Integration of DG into an existing electric distribution network (EDN) has technical, economic, and environmental benefits. DG placement is typically determined by investors and local conditions such as the availability of energy resources, space, and licenses, among other factors. When the location of DG is not a decision of the distribution network operator (DNO), the simultaneous integration of distribution network reconfiguration (DNR) and DG placement can maximize the benefits of DG and mitigate eventual negative impacts. DNR consists of altering the EDN topology to improve its performance while maintaining the radiality of the network. DNR and optimal placement of DG (OPDG) are challenging optimization problems since they involve integer and continuous variables subject to nonlinear constraints and a nonlinear objective function. Due to their nonlinear and nonconvex nature, most approaches to solve these problems resort to metaheuristic techniques. The main drawbacks of such methodologies lie in the fact that they are not guaranteed to reach an optimal solution, and most of them require the fine-tuning of several parameters. This paper recasts the nonlinear DNR and OPGD problems into linear equivalents to obtain a mixed-integer linear programming (MILP) model that guarantees global optimal solutions. Several tests were carried out on benchmark EDNs evidencing the applicability and effectiveness of the proposed approach. It was found that when no DG units are considered, the proposed model can find the same results reported in the specialized literature but in less computational time; furthermore, the inclusion of DG units along with DNR usually allows the model to find better solutions than those previously reported in the specialized literature.

A Quasioppositional-Chaotic Symbiotic Organisms Search Algorithm for Distribution Network Reconfiguration with Distributed Generations

This study suggests an enhanced metaheuristic method based on the Symbiotic Organisms Search (SOS) algorithm, namely, Quasioppositional Chaotic Symbiotic Organisms Search (QOCSOS). It aims to optimize the network configuration simultaneously and allocate distributed generation (DG) subject to the minimum real power loss in radial distribution networks (RDNs). The suggested method is developed by integrating the Quasiopposition-Based Learning (QOBL) as well as Chaotic Local Search (CLS) approaches into the original SOS algorithm to obtain better global search capacity. The proposed QOCSOS algorithm is tested on 33-, 69-, and 119-bus RDNs to verify its effectiveness. The findings demonstrate that the suggested QOCSOS technique outperformed the original SOS and provided higher-quality alternatives than many other methods studied. Accordingly, the proposed QOCSOS algorithm is favourable in adapting to the DG placement problems and optimal distribution network reconfiguration.

On the optimal reconfiguration of radial AC distribution networks using an MINLP formulation: A GAMS-based approach

This paper deals with the problem of the optimal reconfiguration of medium voltage distribution networks by proposing a mixed-integer nonlinear programming (MINLP) model. This optimization model has as objective function the minimization of the total power losses in all the branches of the network constrained by active and reactive power balance equations, voltage regulation bounds and device capabilities, among others. The proposed MINLP formulation works with branch-to-node incidence that allows representing the active and reactive power flow in branches as a function of the real and imaginary parts of the voltages and currents. The solution of the MINLP model is reached through the general algebraic modeling system widely know as GAMS package by presenting it in a tutorial form. This software allows implementing in compact form the proposed model and solve it via branch and bound methods. Two test feeders composed by 5 and 14 nodes permits demonstrating the fidelity of the proposed MINLP model regarding power losses minimization when compared with literature reports.

Optimal Operation of Automated Distribution Networks Based-MRFO Algorithm

Nowadays, distribution utilities expend large investments on Distributed System Automation (DSA) based on smart secondary substations at load, capacitor, and distributed generator points with installed automatic sectionalizing switches on their branches. This article addresses the optimal control and operation of distribution systems that minimize the wasted energy and introducing quantitative and qualitative power services to meet consumers’ satisfaction. Simultaneous allocations of Distributed Generators (DGs) and Capacitor Banks (CBs) are handled at peak loading condition. Then, the DSA is optimally activated for optimal Distribution Network Reconfiguration (DNR), optimal DGs commitment, and optimal CBs switching for losses minimization in coordination with different loading conditions. Practical daily load variation is applied to simulate the dynamic operation of automated distribution systems. For achieving these targets, the Manta Ray Foraging Optimization Algorithm (MRFOA) is adopted. MRFOA is an effective and simple structure optimizer that emulates three various individual manta rays foraging organizations. The capability of the MRFOA is applied to the IEEE 33-bus, 69-bus and practical distribution network of 84-bus due to the Taiwan Power Company (TPC). A comparison with recent techniques has been conducted to prove the effectiveness of MRFOA. The accomplished results demonstrate that the proposed MRFOA has great effectiveness and robustness among other optimization techniques.