Network Reconfiguration Algorithm Research Articles

Application of the hippopotamus optimization algorithm for distribution network reconfiguration with distributed generation considering different load models for enhancement of power system performance

Application of the hippopotamus optimization algorithm for distribution network reconfiguration with distributed generation considering different load models for enhancement of power system performance

Oppositional arithmetic optimization algorithm for network reconfiguration and simultaneous placement of DG and capacitor in radial distribution networks

AbstractThe prime objective of this study is the simultaneous network reconfiguration with distributed generation (DG) and capacitor placement in radial distribution networks (RDN) to get the techno and economic benefits for two separate objectives, which are the minimization of actual power loss and annual economic loss as well as a multi objective combining these two single objectives using an oppositional arithmetic optimization algorithm (OAOA). It is an improved version of the currently suggested arithmetic optimization algorithm (AOA) used in the field of engineering for the optimization task. Though the recently developed AOA shows its efficacy in different optimization tasks but to improve the quality of solutions, convergence behavior, and to avoid the local optima, oppositional behavior is added to AOA. The efficacy and exactness of OAOA are tested on three test systems (33‐bus, 69‐bus, and 118‐bus). For the reduction of power loss and annual economic loss as well as the multi objective optimization, two scenarios with different cases are executed using OAOA in RDNs. In scenario 1, the installation of the capacitor (case 1), the installation of unity power factor (UPF) based DG (case 2), and the placement of optimal power factor (OPF) based DG (case 3) have been executed. In scenario 2, allocation of UPF based DG and capacitors simultaneously (case 1), placement of OPF based DG and capacitors simultaneously (case 2) and simultaneous reconfiguration with installation of OPF based DG and capacitor (case 3) has been executed. This recommended OAOA algorithm provides the percentage improvement in real power loss and yearly economic loss for all cases of 33‐bus, and 69‐bus systems (34.28%, 65.50%, 94.43%, 93.26%, 94.89%, and 95.11%), (28.54%, 56.69%, 83.42%, 79.62%, 83.65%, and 83.71%), and (35.51%, 69.16%, 98.10%, 97.52%, 98.22%, and 98.25%), (30.26%, 61.68%, 88.75%, 85.42%, 88.81%, and 88.98%), respectively. The results and comparative study reveal that the OAOA is better than several optimization algorithms in terms of solution quality and good results. This algorithm has a good speed of response and convergence behavior.

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.

Application of Modified Flow Direction Algorithm for Network Reconfiguration with Distributed Generator Installation

Application of Modified Flow Direction Algorithm for Network Reconfiguration with Distributed Generator Installation

Cheetah optimization algorithm for simultaneous optimal network reconfiguration and allocation of DG and DSTATCOM with electric vehicle charging station

The potential of Electric Vehicles (EVs) to decarbonize the transportation industry has attracted a lot of attention in recent years in response to growing environmental concerns. Electric Vehicle Charging Stations (EVCSs) need to be properly located for widespread EV integration. The distribution system is facing additional challenges due to inclusion of EVCS. The adverse impacts of EVCS on the Radial Distribution Network (RDN) may be minimized using Distributed Generations (DGs) or Distribution Static Compensators (DSTATCOMs) or by reconfiguring the network. This paper uses a novel optimization technique to solve the problem of simultaneous optimal placement of EVCS with network reconfiguration and optimal planning (siting and sizing) of DGs and DSTATCOMs. The multiple objective functions are considered in order to minimize the active power losses, the voltage deviation, the investment costs for DGs and DSTATCOMs, and to increase the voltage stability of the system. A novel meta-heuristic Cheetah Optimization Algorithm (COA) is used to solve the optimization problem. To examine the effectiveness of the suggested strategy on 33-bus and 136-bus networks, several scenarios of simultaneous incorporation of EVCS, DG, and DSTATCOM installations with network reconfiguration are taken into consideration. The COA results are also compared to the results of grey wolf optimization and genetic algorithms.

Switching in the Rain: Predictive Wireless x-haul Network Reconfiguration

4G, 5G, and smart city networks often rely on microwave and millimeter-wave x-haul links. A major challenge associated with these high frequency links is their susceptibility to weather conditions. In particular, precipitation may cause severe signal attenuation, which significantly degrades the network performance. In this paper, we develop a Predictive Network Reconfiguration (PNR) framework that uses historical data to predict the future condition of each link and then prepares the network ahead of time for imminent disturbances. The PNR framework has two components: (i) an Attenuation Prediction (AP) mechanism; and (ii) a Multi-Step Network Reconfiguration (MSNR) algorithm. The AP mechanism employs an encoder-decoder Long Short-Term Memory (LSTM) model to predict the sequence of future attenuation levels of each link. The MSNR algorithm leverages these predictions to dynamically optimize routing and admission control decisions aiming to maximize network utilization, while preserving max-min fairness among the nodes using the network (e.g., base-stations) and preventing transient congestion that may be caused by switching routes. We train, validate, and evaluate the PNR framework using a dataset containing over 2 million measurements collected from a real-world city-scale backhaul network. The results show that the framework: (i) predicts attenuation with high accuracy, with an RMSE of less than 0.4 dB for a prediction horizon of 50 seconds; and (ii) can improve the instantaneous network utilization by more than 200% when compared to reactive network reconfiguration algorithms that cannot leverage information about future disturbances. The full paper associated with this abstract can be found at https://doi.org/10.1145/3570616.

Network reconfiguration in the presence of optimally integrated multiple distributed generation units in a radial distribution network

This article presents a hybrid of two metaheuristic techniques, namely the plant growth simulation (PGS) algorithm and particle swarm optimization (PSO), for power loss minimization in a radial distribution network (RDN). The PSO algorithm determines the optimal allocation of multiple distributed generators (DGs), followed by the PGS algorithm for optimal network reconfiguration (NR). Type I and IV DGs were considered. The load flow solutions were obtained using a branch matrix formulation. IEEE 33 and 69 buses were used for performance analysis and the results were compared with previously published results to validate the method in providing solutions even in complex system configurations. The superior results demonstrate that the penetration of multiple DGs along with the NR permits the network to operate successfully under different loading conditions, without violating the constraints.

Distribution network reconfiguration control and protection method based on multi-source data interaction

Distribution network reconfiguration technology is the key technology of distribution network fault treatment and power supply recovery. Given the problem that the traditional distribution network reconfiguration algorithm needs a lot of power flow calculation and low efficiency to find the optimal target, the simulated annealing algorithm is proposed to optimize the computing speed and searching ability of the distribution network reconfiguration algorithm. Firstly, the information interaction technology of distribution network protection and control equipment is studied, and the real-time data rapid peer-to-peer exchange and data and information interaction model is built, which improves the fault detection and timely removal ability of the distribution network. Secondly, the simulated annealing algorithm is used to reduce the number of power flow calculations, reduce the calculation time, improve the calculation speed of network reconstruction problems, and ensure that the optimal or better network topology can be obtained. Finally, the simulation analysis of the IEEE37 node system verifies the effectiveness of the proposed reconfiguration protection method in a complex environment with massive access to distributed energy.

Fast reconfiguration method of low-carbon distribution network based on convolutional neural network

The existing meta-heuristic distribution network reconfiguration (DNR) algorithm has excellent optimization ability through iteration. However, it is difficult to realize the large-scale fast calculation and online real-time response of DNR solution. In order to improve the security and low-carbon economy of distribution network, this paper proposes a fast reconfiguration method of distribution network based on convolutional neural network (CNN). Taking IEEE 33 system and 185 node system as examples, the effectiveness of the proposed method is verified. The reasons why the proposed method can achieve better results are as follows: By mining the historical data of distribution network, the corresponding relationship between load mode (LM) and its optimal topology is established. For a load mode in actual operation, the reconfiguration scheme can be quickly obtained according to the established corresponding relationship. Thus, iterative calculation is avoided and computational efficiency is improved. A multi-branch CNN model is established based on the distribution network structure, and an inception module is introduced into CNN to improve the ability of CNN to extract data features. This model can reduce the dependence on the specific distribution network structure and is easy to expand.

Switching in the Rain: Predictive Wireless x-haul Network Reconfiguration

4G, 5G, and smart city networks often rely on microwave and millimeter-wave x-haul links. A major challenge associated with these high frequency links is their susceptibility to weather conditions. In particular, precipitation may cause severe signal attenuation, which significantly degrades the network performance. In this paper, we develop a Predictive Network Reconfiguration (PNR) framework that uses historical data to predict the future condition of each link and then prepares the network ahead of time for imminent disturbances. The PNR framework has two components: (i) an Attenuation Prediction (AP) mechanism; and (ii) a Multi-Step Network Reconfiguration (MSNR) algorithm. The AP mechanism employs an encoder-decoder Long Short-Term Memory (LSTM) model to predict the sequence of future attenuation levels of each link. The MSNR algorithm leverages these predictions to dynamically optimize routing and admission control decisions aiming to maximize network utilization, while preserving max-min fairness among the nodes using the network (e.g., base-stations) and preventing transient congestion that may be caused by switching routes. We train, validate, and evaluate the PNR framework using a dataset containing over 2 million measurements collected from a real-world city-scale backhaul network. The results show that the framework: (i) predicts attenuation with high accuracy, with an RMSE of less than 0.4 dB for a prediction horizon of 50 seconds; and (ii) can improve the instantaneous network utilization by more than 200% when compared to reactive network reconfiguration algorithms that cannot leverage information about future disturbances.

An improved equilibrium optimiser-based algorithm for dynamic network reconfiguration and renewable DG allocation under time-varying load and generation

This paper presents an improved equilibrium optimiser (IEO)-based method for dynamic hourly network reconfiguration (NR) with optimal allocation of dispatchable and non-dispatchable renewable distributed generator (DG) units in radial power distribution networks. In this work, solar- and wind-based DGs are considered non-dispatchable DG, while biomass DGs are considered dispatchable DG. The output of biomass DGs is adjusted according to the 24-hour load demand curve and that of solar and wind DG follow their hourly power generation. For this purpose, a multi-objective function is developed with consideration of hourly power loss minimisation and profit maximisation without violating the system’s operational constraints. The viability and efficacy of the proposed method are tested on IEEE 33-bus and 69-bus distribution systems for five distinct scenarios of network reconfiguration and renewable DG allocation. The analysis shows that the simultaneous NR and DG have a more substantial impact than considering the cases separately.

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.

Resource Fragmentation-Aware Embedding in Dynamic Network Virtualization Environments

In network virtualization environments, with random arrival and departure of virtual network requests, there exist some resources (i.e., link resources and node resources) isolated from others in substrate networks. This phenomenon is referred to as resource fragmentation. In this paper, we attempt to improve the resource utilization efficiency by avoiding resource fragmentation in substrate networks. First and most importantly, we define a new metric called resource fragmentation degree (RFD) to quantitatively measure the status of resource fragmentation at substrate nodes and links. The basic idea of RFD is that the resource availability of a node (or a link) is determined by the residual link and node resources around the node (or the link). Based on the definition of RFD, we formulate the virtual network embedding (VNE) problem as a mixed integer programming problem with consideration of the cost of resource fragmentation. Then, an online VNE algorithm with consideration of RFD (VNERFD) is proposed to solve the problem, which is performed according to the current resource status of substrate networks and virtual network requests. To reduce accumulated fragmented resources produced by dynamic arrival and departure of virtual network requests, a heuristic virtual network reconfiguration algorithm based on RFD (VNR-RFD) is proposed. Simulation results show that VNE-RFD and VNR-RFD can effectively reduce fragmented resources and thus embed more virtual networks into substrate networks.

Reliability improvement and loss reduction in radial distribution system with network reconfiguration algorithms using loss sensitivity factor

&lt;p&gt;Studies on load flow in electrical distribution system have always been an area of interest for research from the previous few years. Various approaches and techniques are brought into light for load flow studies within the system and simulation tools are being used to work out on varied characteristics of system. This study concentrates on these approaches and the improvements made to the already existing techniques considering time and the algorithms complexity. Also, the paper explains the network reconfiguration (NR) techniques considered in reconfiguring radial distribution network (RDN) to reduce power losses in distribution system and delivers an approach to how various network reconfiguration techniques support loss reduction and improvement of reliability in the electrical distribution network.&lt;/p&gt;

Plug-and-Play Network Reconfiguration Algorithms to Maintain Regularity and Low Network Reconfiguration Needs

In this paper we propose algorithms to connect and disconnect nodes to/from an existing graph, which are able to maintain the regularity of a graph while requiring minimal reconfiguration of the network. Further, simulations suggest that the algorithms maintain a minimum bound on the algebraic connectivity of the graph. These properties of the algorithms suit their use in plug-and-play networks.

An improved corona-virus herd immunity optimizer algorithm for network reconfiguration based on fuzzy multi-criteria approach

Reconfiguration of the distribution network to determine its optimal configuration is a technical and low-cost method that can improve different characteristics of the network based on multi-criteria optimization. In this paper reconfiguration of unbalanced distribution networks is presented with the objective of power loss minimization, voltage unbalance minimization, voltage sag improvement, and minimizing energy not supplied by the customers based on fuzzy multi-criteria approach (FMCA) using new improved corona-virus herd immunity optimizer algorithm (ICHIOA). The voltage unbalances and voltage sag is power quality criteria and also the ENS refers to the reliability index. Conventional CHIOA is inspired based on herd immunity against COVID-19 disease via social distancing and is improved using nonlinearly decreasing inertia weight strategy for global and local exploration improvement. The methodology is implemented as single and multi-objective optimization on 33 and 69 bus IEEE standard networks. Moreover, the performance of the ICHIOA in problem-solving is compared with some well-known algorithms such as particle swarm optimization (PSO), grey wolf optimizer (GWO), moth flame optimizer (MFO), ant lion optimizer (ALO), bat algorithm (BA) and also conventional CHIOA. The simulation results based on the FMCA showed that all criteria are improved with reconfiguration due to compromising between them while in single-objective optimization, some criteria may be weakened. Also, the obtained results confirmed the superiority of the ICHIOA in comparison with the other algorithms in achieving better criteria with lower convergence tolerance and more convergence accuracy. Moreover, the results cleared that the ICHIOA based on FMCA is capable to determine the best network configuration optimally to improve the power loss, voltage sag, voltage unbalance, and ENS in different loading conditions.

A modified marine predators optimization algorithm for simultaneous network reconfiguration and distributed generator allocation in distribution systems under different loading conditions

A modified marine predators optimizer (MMPO) is proposed for simultaneous distribution network reconfiguration (DNR) associated with the allocation of distributed generators (DGs). In the MMPO, the predator’s strategies are merged to consider the possibilities for variation in the environmental and climatic circumstances. The suggested MMPO is contrasted with the standard marine predators optimizer (MPO) and genetic, harmony search, fireworks, firefly and improved sine–cosine optimizers. The proposed MMPO is validated on single and multiple objectives using 33- and 69-bus distribution systems at light, nominal and heavy loading levels. The results obtained by the proposed MMPO are compared with those obtained by the original MPO and other optimizers. The achieved simulation outputs reveal a great improvement over the standard MPO and demonstrate the superiority of the proposed MMPO for simultaneous DNR and DG allocation.

Improved Particle Swarm Optimization Algorithm in Power System Network Reconfiguration

With the rapid development of the social economy, the rapid development of all social circles places higher demands on the electricity industry. As a fundamental industry supporting the salvation of the national economy, society, and human life, the electricity industry will face a significant improvement and the restructuring of the network as an important part of the power system should also be optimised. This paper first introduces the development history of swarm intelligence algorithm and related research work at home and abroad. Secondly, it puts forward the importance of particle swarm optimization algorithm for power system network reconfiguration and expounds the basic principle, essential characteristics, and basic model of the particle swarm optimization algorithm. This paper completes the work of improving PSO through the common improved methods of PSO and the introduction of mutation operation and tent mapping. In the experimental simulation part, the improved particle swarm optimization algorithm is used to simulate the 10-machine 39-bus simulation system in IEEE, and the experimental data are compared with the chaos genetic algorithm and particle swarm optimization discrete algorithm. Through the experimental data, we can know that the improved particle swarm optimization algorithm has the least number of actions in switching times, only 4 times, and the chaos genetic algorithm and discrete particle swarm optimization algorithm are 5 times; compared with the other two algorithms, the improved particle swarm optimization algorithm has the fastest convergence speed and the highest convergence accuracy. The improved particle swarm optimization algorithm proposed in this paper provides an excellent solution for power system network reconfiguration and has important research significance for power system subsequent optimization and particle swarm optimization algorithm improvement.

A Nondominated Sorting Stochastic Fractal Search Algorithm for Multiobjective Distribution Network Reconfiguration with Distributed Generations

This paper aims to propose a new multiobjective algorithm for multiobjective distributed network reconfiguration (DNR) with the placements of distributed generation (DG) in radial distribution networks (RDNs). The new proposed algorithm, called the nondominated sorting stochastic fractal search (NSSFS), is a new multiobjective version of the original SFS algorithm. NSSFS incorporated fast nondominated sorting strategies, crowding distance computation, and selection mechanism into SFS to find and maintain the best nondominated solutions. The proposed NSSFS algorithm was tested with eight multiobjective benchmark test functions to validate its performance. The NSSFS was then implemented to define the optimal network configuration, positions, and sizes of DG units in the RDNs, where real power loss, voltage profile, and voltage stability index were optimized simultaneously. The implementation of multiobjective DNR-DG (MODNR-DG) significantly enhanced the performance of the system. Based on the comparison outcomes, the NSSFS algorithm obtained better solution quality than other multiobjective techniques, proving the effectiveness of NSSFS in dealing with the MODNR-DG problem.

ZigBee wireless smart plug network with RSSI multi‐lateration‐based proximity estimation and parallelised machine learning capabilities for demand response

This study explores how wireless ZigBee technology may be applied to automation of electric loads in residential and commercial spaces, allowing to participate in demand response initiatives. The authors discuss development of a custom smart plug with sensing, wireless communication, and electric load actuation capabilities along with several innovative upgrades. There are many commercially available smart plugs that contain multiple sensors and relays. However, very few provide the ability to effectively estimate the proximity between modules or the ability to perform robust system-wide optimisation. The authors propose two innovative smart plug eco-system improvements. One is the use of a received signal strength indicator (RSSI) multi-lateration-based method to estimate the relative proximities of modules. The RSSI values for almost all transmission paths within the ZigBee network are acquired via the authors' forced network reconfiguration algorithm, addressing the limitations of RSSI observation within a star structure. A second innovation is the development of a parallelised neural network training method for application to load automation. The authors use a k-means clustering algorithm to divide training data into subsets such that training may be parallelised.