Reconfiguration Of Radial Distribution Networks Research Articles

Hybrid Genetic Algorithm With k-Nearest Neighbors for Radial Distribution Network Reconfiguration

Hybrid Genetic Algorithm With k-Nearest Neighbors for Radial Distribution Network Reconfiguration

Self-healing radial distribution network reconfiguration based on deep reinforcement learning

Distribution network reconfiguration (DNR) has long been used to minimize losses in normal conditions and restore out-of-service areas in fault situations. However, the integration of renewable resources into power systems has made operating strategies more challenging, requiring fast and responsive approaches to handle abrupt interruptions. Traditional DNR methods rely on mathematical programming, heuristic and meta-heuristic methods that cannot generalize unseen generation and load profiles, and may require significant computational time to converge, posing an overrun risk for real-time execution. Recently, deep reinforcement learning (DRL) has been shown to be effective in optimizing DNR in normal conditions with calculation times of only a few milliseconds. However, previously proposed dynamic DNR approaches are not able to self-heal and restore in fault conditions, where timely restoration is critical for customer satisfaction. To address this gap, we propose a restorative dynamic distribution network reconfiguration (RDDNR) framework that enables continuous reconfiguration after faults, enabling the fast restoration of out-of-service areas. Our proposed framework was tested on two balanced test feeders and demonstrated competitiveness regardless of operating states. By leveraging RDDNR, distribution system operators can effectively restore service in a timely manner, improving overall grid resilience and customer satisfaction.

Optimal reconfiguration of distribution systems considering reliability: Introducing long-term memory component AEO algorithm

This article introduces a modified version of the Artificial Ecosystem Optimization (AEO) algorithm, called Long-term Memory Component AEO (LMAEO), for optimizing the reconfiguration of radial distribution networks. The LMAEO algorithm incorporates a long-term memory component, enabling individuals in the population to make decisions based on past experiences. This integration of long-term memory allows the algorithm to explore a wider range of potential solutions during the optimization process, potentially leading to improved performance and better exploration of the solution space. To verify the effectiveness and superiority of the LMAEO technique, it is compared with the conventional AEO algorithm and other well-known algorithms using seven benchmark functions. The proposed LMAEO algorithm successfully addresses the reconfiguration of distribution systems considering reliability for the modified 12-bus, 33-bus and 69-bus IEEE test systems. Leveraging the strengths of AEO and the long-term memory component, the LMAEO algorithm achieves efficient solutions for this problem. To assess the performance of the proposed LMAEO, a comparison is made with the original AEO algorithm. The results demonstrate that the LMAEO technique surpasses the AEO optimizer in terms of optimal reconfiguration of distribution systems jointly considering reliability, system losses and voltage deviations.

Modified GRA method for probabilistic hesitant fuzzy MAGDM and application to network health performance evaluation of radial distribution system

Radial distribution system is an important link connecting power supply and users, and its power supply reliability is directly related to users. Radial distribution network reconfiguration can transform the network structure by changing the switching state of the distribution network lines, and achieve the goals of reducing network operational losses, improving power quality, and power supply reliability while meeting various constraints such as radial operation, power supply and demand balance, capacity, and voltage. Radial distribution systems have the characteristics of multiple components and complex structures. How to quickly and accurately evaluate the health performance of radial distribution systems and find an optimal solution for network reconfiguration are important issues in distribution network analysis. The network health performance evaluation of radial distribution system is classical multiple attributes group decision making (MAGDM). The probabilistic hesitancy fuzzy sets (PHFSs) are used as a tool for characterizing uncertain information during the network health performance evaluation of radial distribution system. In this paper, we extend the classical grey relational analysis (GRA) method to the probabilistic hesitancy fuzzy MAGDM with unknown weight information. Firstly, the basic concept, comparative formula and Hamming distance of PHFSs are briefly introduced. Then, the definition of the score values is employed to compute the attribute weights based on the information entropy method. Then, probabilistic hesitancy fuzzy GRA (PHF-GRA) method is built for MAGDM under PHFSs. Finally, a practical case study for network health performance evaluation of radial distribution system is designed to validate the proposed method and some comparative studies are also designed to verify the applicability.

A Simple and Fast Heuristic Approach for the Reconfiguration of Radial Distribution Networks

This letter proposes a two-stage heuristic approach to reconfigure a radial distribution network in the least possible computational time. All the network switches are closed in the initial stage of the operation, and a sequential switch opening strategy is suggested to obtain a near-optimal radial configuration. In the subsequent stage, a pair of switches is selected from the radial network to exchange its ON/OFF status. A mathematical model, using bus injection to branch current matrix, is developed to choose a pair of switches with an objective to reduce the power loss in the network after each exchange operation. The proposed approach is tested on 33-bus, 69-bus, 84-bus, 136-bus, and 417-bus distribution networks, and the results confirm that the suggested methodology helps in achieving the best configuration of the radial distribution networks in a significantly low run-time.

Network Reconfiguration for Power Loss Reduction on Distribution feeder Using Particle Swarm Optimisation: Case study of 11kv feeder in Ilorin, Nigeria

The distribution system is the largest segment of the electrical power system and the final stage in delivering electricity to consumers. It experiences power losses which could be technical or non-technical due to its radial configuration and connected components. Technical losses occur as energy dissipation in the form of heat in the system components, which is unavoidable. It affects the efficiency of the system and increases operational costs. Therefore, the reduction is essential for adequate power supply and improvement in utility revenue generation. This study focuses on reducing power losses on 11 kV Taiwo distribution feeder in Ilorin using the Network reconfiguration approach. The feeder was modelled, and its power losses were evaluated based on the Backward Forward Sweep method suitable for the radial network. The reconfigured network was optimised using the Particle Swarm Optimisation technique. The study shows that the values of feeder real power losses before and after reconfiguration are 176.883 kW and 121.972 kW, respectively.Further improvement was, however, achieved when PSO was used for the reconfiguration, as the power loss value stands at 98.465 kW. This is 44.33 % reduction compared to the initial power loss value and 19.273 % after reconfiguration. Furthermore, the values of the reactive power losses before and after reconfiguration are 9.474 kVar and 6.527 kVar, respectively, amounting to a 31.11 % reduction. However, PSO reduces the value to 3.101 kVar, which is 67.27 % of the initial value. Thus, radial distribution network reconfiguration using PSO has proven to be a robust method for reducing power loss in the distribution network.

Allocation of renewable resources with radial distribution network reconfiguration using improved salp swarm algorithm

This paper presents an optimal and simultaneous allocation of the photovoltaic panel (PV) and wind turbine (WT) with the reconfiguration of radial distribution networks to reduce power losses cost and improve reliability. Determining the optimal optimization variables are very important to obtain the maximum benefits of renewable resource allocation and network reconfiguration, i.e. achieving the lowest losses and reliability cost. Problem optimization variables are the location, size and power factor of PV and WT as well as network tie-line switches states. Operational constraints include renewable sources size and radiality constraints. The optimization is conducted using an improved salp swarm algorithm (ISSA). Operators of the differential evolutionary (DE) algorithm are used to enhance the performance of conventional salp swarm algorithm (SSA) to prevent trapping in local optima and to increase the convergence speed in achieving the global optimal solution. The methodology is implemented on IEEE 33 and 69 bus distribution networks in various scenarios of renewable sources allocation and reconfiguration and simulation results including the cost of losses, cost of reliability improvement and net saving is presented before and after the optimization. The simulation results show that the ISSA is easily able to find the location and size of the renewable units optimally as well as the best network configuration in different scenarios. The results clear that optimal and simultaneous allocation of WTs and reconfiguration using ISSA has the lowest losses and reliability improvement cost, more net saving and better network performance than the other scenarios. The superiority of the ISSA is also confirmed in solving the problem in comparison with conventional SSA and also the previous studies.

Adaptive particle swarm optimization approach to simultaneous reconfiguration and shunt capacitor allocation in radial distribution network

Simultaneous radial distribution network reconfiguration (RDNR) and shunt capacitor allocation (SCA) is one of the compensation techniques that are used for getting an improved radial structure with reduced real power loss and enhanced voltage stability. This study presents a novel adaptive particle swarm optimisation (APSO) technique for the simultaneous RDNR and SCA, which is a complex and nonlinear optimisation problem. Unlike the conventional particle swarm optimization (PSO) technique in which an initial population of particles is randomly generated, the fundamental loop concept is used to populate the search space of APSO with the candidate branches for each tie switch (open branch) in the loop. The candidate branches are preselected with the graph theory. This is done to mitigate infeasible configurations in the optimization process and also to ensure that the conditions for radiality of the network are satisfied. The effectiveness of the proposed APSO technique for simultaneous RDNR and SCA is demonstrated on the standard IEEE 33-bus and Nigerian Ayepe 34-bus RDNs using six event cases. The efficacy of the proposed APSO technique is further validated with the comparison of the observed simulation results with the reported results of similar work implemented with established algorithms like improved binary particle swarm optimization (IBPSO), modified pollinated flower algorithm (MFPA) and mixed integer linear programming (MILP). The result of the comparative study reveals that the proposed APSO technique outperforms the selected algorithms in most of the considered event cases.

Artificial Electric Field Algorithm-Pattern Search for Many-Criteria Networks Reconfiguration Considering Power Quality and Energy Not Supplied

Considering different objectives and using powerful optimization methods in the distribution networks reconfiguration by accurately achieving the best network configuration can further improve network performance. In this paper, reconfiguration of radial distribution networks is performed to minimize the power loss, voltage sag, voltage unbalance, and energy not supplied (ENS) of customers using a new intelligent artificial electric field algorithm-pattern search (AEFAPS) method based on the many-criteria optimization approach. The voltage sag and voltage unbalance are defined as power quality indices and the ENS is the reliability index. In this study, the pattern search (PS) algorithm enhances the artificial electric field algorithm’s (AEFA) flexibility search both globally and locally. AEFAPS is applied to determine the decision variables as open switches of the networks considering the objective function and operational constraints. The proposed methodology based on AEFAPS is performed on an unbalanced 33-bus IEEE standard network and a real unbalanced 13-bus network. The reconfiguration problem is implemented in single-criterion and many-criteria optimization approaches to evaluate the proposed methodology’s effectiveness using different algorithms. The single-criterion results demonstrated that some power quality indices might be out of range, while all indices are within the permitted range in the many-criteria optimization approach, proving the effectiveness of the proposed many-criteria reconfiguration with logical compromise between different objectives. The results show that AEFAPS identified the network configuration optimally and different objectives are improved considerably compared to the base network. The results confirmed the superior capability of AEFAPS to obtain better objective values and lower values of losses, voltage sag, voltage unbalance, and ENS compared with conventional AEFA, particle swarm optimization (PSO), and grey wolf optimizer (GWO). Moreover, the better performance of AEFAPS is proved in solving the reconfiguration problem compared with previous studies.

Performance enhancement of radial distribution system using simultaneous network reconfiguration and switched capacitor bank placement

This paper presents simultaneous radial distribution network reconfiguration and switched capacitor bank allocation to enhance the performance of distribution systems. The paper primarily targets minimizing active and reactive power losses and improving the voltage profile of all buses. In the paper, network re-structuring and switched capacitor bank integration are performed to optimally allocate and size switchable capacitor banks and to select the optimal structure of the network. Additionally, a modified particle swarm optimization (MPSO) algorithm is incorporated for both network reconfiguration and optimal capacitor bank allocation, considering different loading condition scenarios such as light, normal and heavy load. The simulation results show that the voltage deviation and active and reactive power losses are substantially reduced for all loading scenarios when the proposed method is applied. This shows that MPSO algorithm is effective in maintaining the bus voltage profile within the allowable threshold value and in significantly minimizing the corresponding power losses. Furthermore, the performance comparison of MPSO and conventional particle swarm optimization in terms of voltage profile and active and reactive power loss has been conducted, and it has been found that the proposed method (MPSO) is more effective to enhance the performance of radial distribution systems.

Optimal reconfiguration of radial distribution networks using improved genetic algorithm

Reconfiguration of an electrical power radial distribution network is aimed at finding a radial operating structure that minimizes the system active power loss, enhancing the system voltage profile via reducing the active power losses whilst satisfying operating constraints is one of the most important measures of improving the operational performance of a distribution system. In this paper, an efficient approach to solving the problem of reconfiguration considering active power loss, total voltage deviation for a typical distribution network. The method developed is based on improved genetic algorithm to determine the optimal location of tie and sectionalizing switches, with a view to yield an optimal performance for the network. The reconfiguration model was implemented using MATLAB R2016a simulation environment. The effectiveness and validity of the proposed model was tested on a 16, 33 and 69 IEEE-Bus standard systems for distribution network. The results show that with a reduction of 8.86%, 31.15% and 53.53% in active power loss as compared to the initial configuration, whilst a total voltage deviation of 0.0284p.u., 0.0622p.u and 0.0517p.u for 16, 33 and 69 IEEE Bus respectively.Keywords: System reconfiguration, Switching state, Power loss, Genetic algorithm, Distribution system

Radial distribution network reconfiguration for power losses reduction using a modified particle swarm optimisation

Abstract: Recently, losses reduction gained a great deal of attention in distribution system due to low-voltage level and the high-current passing through the lines, pushing the distribution utilities to improve their profit margins on one hand by reducing the unnecessary operational cost, and improving their delivered power quality on the other hand by maintaining the system reliability, and the continuity of supply for varying load demand. Load balancing, voltage regulation, network reconfiguration and others are different techniques used to reduce the losses. This study addresses the distribution network reconfiguration to minimise the network losses. A new modified form of particle swarm optimisation (PSO) is used to identify the optimal configuration of distribution network effectively. The difference between the modified PSO (MPSO) algorithms and the typical one is the filtered random selective search space for initial position, which is proposed to accelerate the algorithm for reaching the optimum solution. The suggested MPSO is tested via 33 and 69 IEEE networks. A benchmark comparison has been conducted to prove the effectiveness of MPSO compared with previous optimisation techniques.

A comprehensive review on power distribution network reconfiguration

Reconfiguration of radial distribution networks is becoming a viable solution for improving the performance of distribution networks. Configurations may be varied with manual or automatic switching operations so that all of the loads are supplied and reduce power loss, increase system security, and enhance power quality. Reconfiguration also relieves the overloading of the network components. The change in the network configuration is performed by opening sectionalizing (normally closed) and closing tie (normally open) switches of the network. These switchings are performed in such a way that the radiality of the network is maintained and all of the loads are energized. Several researchers have attempted to solve the power distribution network reconfiguration problem using various techniques. This paper presents a comprehensive survey on network reconfiguration to bring out a clear idea for future research.

Reconfiguration of Distribution Network with Distributed Energy Resources Integration Using PSO Algorithm

This paper presents optimal reconfiguration of radial distribution network with integration of distributed energy resources (DER) using improved particle swarm optimization (PSO) algorithm. The benefits of DER integration in distribution system are reducing power losses, improving voltage profiles and load factors, eliminating system upgrades, and reducing environmental impacts. However, the presence of DER could also cause technical problems in voltage quality and system protection. Reconfiguration of distribution network is aimed to minimize power loss and to improve voltage quality in order to enhance the distribution system performance. In this study, reconfiguration method is based on an improved PSO. The method has been tested in a 60-bus Bantul distribution network of Yogyakarta Special Region province, Indonesia. The simulation results show the importance of reconfiguring the network for enhancing the distribution system performance in the presence of DER.

Reconfiguration of Distribution Networks based on Fuzzy Multiobjective Approach by Considering Loads of Different Types

The paper presents an algorithm for reconfiguration of radial distribution networks based on fuzzy multiobjective approach by considering different types of loads. Multiple objectives are considered to minimize the real power loss, deviation of nodes voltage and improvement of voltage stability of the network while subject to a radial network structure, in which all loads must be energized. These objectives are modelled with fuzzy sets to evaluate their imprecise nature and one can provide his or her anticipated value of each objective. These multiple objectives are integrated into an objective function through appropriate weighting factors which is maximized for each tie switch operation. Heuristic rules are also incorporated in the algorithm for minimizing the number of tie switch operations. The effectiveness of the proposed method is demonstrated through one example.

Optimal Reconfiguration for Supply Restoration With Informed A$^{\ast}$ Search

Reconfiguration of radial distribution networks is the basis of supply restoration after faults and of load balancing and loss minimization. The ability to automatically reconfigure the network quickly and efficiently is a key feature of autonomous and self-healing networks, an important part of the future vision of smart grids. We address the reconfiguration problem for outage recovery, where the cost of the switching actions dominates the overall cost: when the network reverts to its normal configuration relatively quickly, the electricity loss and the load imbalance in a temporary suboptimal configuration are of minor importance. Finding optimal feeder configurations under most optimality criteria is a difficult optimization problem. All known complete optimal algorithms require an exponential time in the network size in the worst case, and cannot be guaranteed to scale up to arbitrarily large networks. Hence most works on reconfiguration use heuristic approaches that can deliver solutions but cannot guarantee optimality. These approaches include local search, such as tabu search, and evolutionary algorithms. We propose using optimal informed search algorithms in the A family, introduce admissible heuristics for reconfiguration, and demonstrate empirically the efficiency of our approach. Combining A with admissible cost lower bounds guarantees that reconfiguration plans are optimal in terms of switching action costs.

Application of Ant Colony Optimization and Harmony Search Algorithms to Reconfiguration of Radial Distribution Networks with Distributed Generations

Application of Ant Colony Optimization and Harmony Search Algorithms to Reconfiguration of Radial Distribution Networks with Distributed Generations

Distribution Network Reconfiguration Using Genetic Algorithms With Sequential Encoding: Subtractive and Additive Approaches

This paper deals with radial distribution network reconfiguration for loss and switching mitigation. Its main contribution is the presentation of a genetic algorithm (GA) with two network encodings, capable of representing only radial connected solutions without demanding a planar topology or any specific genetic operator. The code was named sequential because the evaluation of the <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$i$</tex></formula> th gene depends on the information forthcoming from all the previous genes. In addition to a full description of proposed techniques with examples, the paper presents a survey of existing network codifications including their search space and chromosomes for a 33-bus example system. In order to validate the proposed algorithms, these and other two techniques in the state of the art are applied to reconfigure three sample systems which have been broadly published in the technical literature. Performance is assessed by using the results of implemented representations and the reconfiguration literature.

Loss allocation to consumers before and after reconfiguration of radial distribution networks

This paper presents allocation of power losses to consumers connected to radial distribution network before and after network reconfiguration in a deregulated environment. Loss allocation is made in a quadratic way, which is based on identifying the real and imaginary parts of current in each branch and losses are allocated to consumers. Comparison of loss allocation after multi-objective approach based distribution network reconfiguration is made with those before reconfiguration. For network reconfiguration, multiple objectives are considered for minimization of system real power loss, deviations of nodes voltage, branch current constraint violation and transformer loading imbalance and they are integrated into an objective function through appropriate weighting factors which is minimized for each tie-switch operation. The effectiveness of the proposed approach is demonstrated through an example.

A new computational approach for optimal reconfiguration of radial distribution systems using a fuzzy controlled genetic algorithm

PurposeThis research paper reports a novel genetic algorithm (GA)‐based approach for reconfiguration of radial distribution networks for real loss minimization and power quality improvement.Design/methodology/approachA fuzzy controlled GA has been used for efficient reconfiguration of radial distribution systems for loss minimization and power quality improvement. The special features of the proposed algorithm are: an improved chromosome coding/decoding for network representation so as to preserve the radial property without islanding any load after reconfiguration and an efficient convergence characteristics attributed to fuzzy controlled mutation.FindingsThe proposed network reconfiguration algorithm is very much effective in arriving at the global optimal solution (minimum loss network structure) because of efficient search of the solution space. Also, no invalid chromosomes are generated in the genetic evolution because of appropriate coding/decoding. The algorithm is found to be very much suitable for real time implementations.Research limitations/implicationsThis research paper provides the power distribution engineers with a computationally efficient approach for optimal operation of distribution systems.Practical implicationsThe algorithm proposed in this paper is computationally much faster compared to most of the present day mathematical programming approaches for distribution system operation. This makes it very much attractive for online implementations in any radial distribution network.Originality/valueThis paper has proposed a novel chromosome coding/decoding technique for radial distribution system and a fuzzy logic‐based mutation probability controller for efficient search of global solution space to be used in GA‐based optimal operation of radial distribution systems.