Optimal Network Reconfiguration Research Articles

Iterative relaxation solution for AC optimal transmission network reconfiguration considering bus splitting

Optimal transmission network reconfiguration is an important means to improve the economy and reliability of power grid. However, the traditional optimal transmission switching only considers the transmission line switching strategy and some approximate power flow models are adopted to make it tractable, such as linearisation or convex relaxation ones. In this article, first, a comprehensive network reconfiguration model considering both the line switching strategy and the bus splitting strategy inside substations is established. This optimal network reconfiguration incorporating AC power flow equations is an intractable mixed integer non-linear programming problem. The model is first relaxed to a mixed integer second-order cone programming to get the initial solution and an extended feasibility recovery algorithm is developed to obtain the solution satisfying the AC power flow equations. Numerical tests show that the proposed method can reduce the total generation cost and guarantee the system security while the baseline methods cannot obtain feasible solution in most cases.

Optimal Network Reconfiguration with Distributed Generation and Electric Vehicle Charging Stations

This paper proposes an optimal network reconfiguration (ONR) by integrating the renewable energy (RE) based distributed generation (DG) resources, i.e., wind and solar photovoltaic (PV) modules, and electric vehicle charging stations (EVCS). The uncertainties related to renewable energy sources (RESs) are handled by using probability analysis. In this work, wind uncertainty is handled by using Weibull probability density function (PDF), and solar PV uncertainty is modeled by using Beta PDF. This paper also models the load of EVCSs. The ONR is a tool to operate distribution systems (DSs) at optimum cost/loss. In the literature, most of the ONR problems are solved as single objective type. This neccessiate the development of multi-objective based ONR problem and solved using the multi-objective algorithms by considering multiple objectives. Therefore in this paper, total cost of operation and power losses are considered as two objectives functions. The single objective-based ONR is solved using crow search algorithm (CSA) and multi-objective-based ONR is solved using multi-objective-based CSA. As the DS is unbalanced, the power flow for the unbalanced system will include the three-phase transformer. The ONR problem has been solved by considering 17 bus unbalanced and balanced DSs.

Optimal Network Reconfiguration in Presence of Renewable Distributed Generation Using Evolutionary Algorithm

Utilization of new technologies and people lifestyle has greatly affected the world’s electricity market. This demands to design innovative renewable energy systems for efficient use of green energy. In terms of greenhouse gas emissions, electricity from traditional energy supplies has become particularly harmful for the world. To decrease the reliance on fossil fuels, it is need of time to enhance the renewable energy integration in the conventional energy systems. Renewable DGs integration in existing energy systems is not a simple task. To overcome challenges caused by enhanced penetration of renewable energy systems in existing networks, adaptation of smart techniques is essential. DGs Optimal size and selection of their suitable location for integration is crucial for cost effective power delivery to the consumers without compromising the quality of power. This paper presents impartial performance management by optimal network reconfiguration in parallel with renewable DGs and selecting suitable size for reducing active power losses, pollutant gas emissions and costs of annual operation. For analysis of active power losses, Fuzzy and SPEA2 based algorithms are used in MATLAB with IEEE BUS14 acting as load bus. While the cost of power generation and pollutant gases emissions are estimated using HOMER Pro software.

Dynamic optimal network reconfiguration under photovoltaic generation and electric vehicle fleet load variability using self-adaptive butterfly optimization algorithm

Abstract Currently, electrical distribution networks (EDNs) have used modern technologies to operate and serve many types of consumers such as renewable energy, energy storage systems, electric vehicles, and demand response programs. Due to the variability and unpredictability of these technologies, all these technologies have brought various challenges to the operation and control of EDNs. In this case, in order to operate effectively, it is inevitable that effective power redistribution is required in the entire network. In this paper, a multi-objective based dynamic optimal network reconfiguration (DONR) problem is formulated using power loss and voltage deviation index considering the hourly variation of load, photovoltaic (PV) power, and electric vehicle (EV) fleet load in the network. This paper introduces recently introduced meta-heuristic butterfly optimization algorithm (BOA) and it’s improve variant of self-adaptive method (SABOA) for solving the DONR problem. The simulation study of IEEE 33-bus EDN under different conditions has proved the effectiveness of DONR, and its adoptability for real-time applications. In addition, by comparing different performance indicators (such as mean, standard deviation, variance, and average calculation time) of 50 independently run simulations, the efficiency of SABOA can be evaluated compared with other heuristic methods (HMs). Comparative studies show that SABOA is better than PSO, TLBO, CSA and FPA in the frequent occurrence of global optimal values.

Optimal feeder reconfiguration in distributed generation environment under time-varying loading condition

The development and planning of optimal network reconfiguration strategies for electrical networks is greatly improved with proper application of graph theory techniques. This paper investigates the application of Kruskal's maximal spanning tree algorithm in finding the optimal radial networks for different loading scenarios from an interconnected meshed electrical network integrated with distributed generation (DG). The work is done with an objective to assess the prowess of Kruskal's algorithm to compute, obtain or derive an optimal radial network (optimal maximal spanning tree) that gives improved voltage stability and highest loss minimization from among all the possible radial networks obtainable from the DG-integrated mesh network for different time-varying loading scenarios. The proposed technique has been demonstrated on a multiple test systems considering time-varying load levels to investigate the performance and effectiveness of the suggested method. For interconnected electrical networks with the presence of distributed generation, it was found that application of Kruskal's algorithm quickly computes optimal radial configurations that gives the least amount of power losses and better voltage stability even under varying load conditions.Article HighlightsInvestigated network reconfiguration strategies for electrical networks with the presence of Distributed Generation for time-varying loading conditions.Investigated the application of graph theory techniques in electrical networks for developing and planning reconfiguration strategies.Applied Kruskal’s maximal spanning tree algorithm to obtain the optimal radial electrical networks for different loading scenarios from DG-integrated meshed electrical network.

A method based on cuckoo search for optimal network reconfiguration

The optimal reconfiguration of the distribution network doesn’t require any cost and becomes an effective technique for reducing power loss. So this paper proposes an efficient cuckoo search algorithm for this problem. First, the mapping relationship between the cuckoo search algorithm and the distribution network is established; secondly, the algorithm generates the initial population based on the set of feasible solutions; finally, algorithm updates the nest position by the Lévy flight and nest parasitic behavior. A variable domain search algorithm is introduced to improve for the shortcomings of the cuckoo search algorithm in the process of solving the distribution network reconfiguration problem. Finally, it is verified that the algorithm has the ability to quickly solve the optimal solution by the IEEE33 model simulation analysis.

Comprehensive optimization of distributed generation considering network reconstruction based on Archimedes optimization algorithm

To mitigate the effect of the distributed generation (DG) connection on the voltage profile and power loss, the DG optimal configuration and network reconfiguration are used in distribution automation. In this paper, a new optimization method named Archimedes Optimization Algorithm (AOA) is proposed, unifying the DG optimal configuration and network reconfiguration. The DG locations and capacities are coded by integral and real numbers simultaneously in the proposed method. The integer mesh-network coding method is integrated into the new method to solve the network reconfiguration problem. The new method is compared with the Improved Genetic Algorithm (IGA) as well as the Particle Swarm Optimization (PSO). Results demonstrate that the proposed method has advantages in improving the voltage profile and reducing the power loss.

Multi-objective based Optimal Network Reconfiguration using Crow Search Algorithm

This paper presents an optimal network reconfiguration (ONR)/feeder reconfiguration (FRC) approach by considering the total operating cost and system power losses minimizations as objectives. The ONR/FRC is a feasible approach for the enhancement of system performance in distribution systems (DSs). FRC alters the topological structure of feeders by changing the close/open status of the tie and sectionalizing switches in the system. Apart from the power received from the main grid, this paper considers the power from distributed generation (DG) sources such as wind energy generators (WEGs), solar photovoltaic (PV) units, and battery energy storage (BES) units. The proposed multi-objective-based ONR/FRC problem has been solved by using the multi-objective crow search algorithm (MO-CSA). The proposed methodology has been implemented on two (14 bus and 17 bus) distribution systems with three feeders.

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.

Optimal network reconfiguration and DG allocation using adaptive modified whale optimization algorithm considering probabilistic load flow

The optimal DG allocation (ODGA) and network reconfiguration (NR) are significant tools to improve voltage stability and minimize power losses in a distribution network. In a real power distribution network loads are random variables, thus ODGA and NR processes are incorporated using a probabilistic load flow (PLF). The reactive power capability of DGs has a significant impact on the network parameters which can be studied using PLF. In the present work, an ODGA and NR processes have been incorporated to improve the voltage stability and loss profile of the distribution system considering probabilistic loads and DGs which are operated at varying pfs. The optimization problem has been solved using an adaptive modified whale optimization algorithm (A-MWOA). A novel technique based on depth first search algorithm has been employed to check radiality constraints. The effectiveness of proposed technique has been tested on two standard distribution networks.

Optimal Reconfiguration of Distribution Network in Presence of D-STATCOM and Photovoltaic Array using a Metaheuristic Algorithm

In this paper, grasshopper optimization algorithm (GOA) a novel meta-heuristic optimization algorithm is used to solve the network reconfiguration problem in presence of distribution static compensator (D-STATCOM) and photovoltaic (PV) arrays in a distribution system. Here, D-STATCOM acts as distribution flexible ac transmission (D-FACT) device and PV arrays as decentralized or distributed generation (DG). The main purpose of the present research includes power loss minimization and voltage profile (VP) enhancement in radial distribution systems under different loading conditions. The proposed GOA is based on swarming behavior of grasshoppers in nature. The proposed GOA is validated using the standard 33, 69 and 118 – bus test systems. The simulation results proved that the optimal network reconfiguration in presence of D-STATCOM units and PV arrays leads to significant reduction in power loss and enhancement in VP. The results obtained by the proposed GOA are compared with base value and found that the optimal network reconfiguration in presence of D-STATCOM and PV arrays is more beneficial than individual objective optimization. Also, the proposed GOA is more accurate, efficient and reliable in finding optimal solution when compared to existing modified flower pollination algorithm (MFPA), firework algorithm (FWA), fuzzy-based ant colony optimization (ACO) and genetic algorithm (GA).

Ɛ‐constraint multiobjective approach for optimal network reconfiguration and optimal allocation of DGs in radial distribution systems using the butterfly optimizer

In recent days, the optimal allocation of distributed generators (DGs) problem in the distribution system caught several reader's attention to improve the system efficiency, to meet the future load growth. In this article, a multiobjective approach is proposed to determine the optimal locations for DGs, optimal DGs sizes, optimal power factors of DGs in the presence of optimal network reconfiguration to maximize the maximum loadability (λmax), minimize the active power loss and maximize the loading margin factor (λv) of the system. Butterfly Optimization (BO) algorithm is implemented to optimize the desired objectives. The Ɛ-constraint method is used for multiobjective optimization, spanning tree technique is used for checking the radiality of the system and modified repetitive power flow using radial load flow algorithm is developed for finding theλmax, λv.The proposed approach is tested on 33 bus and 69 bus radial distribution test systems. Four scenarios are considered to achieve the desired objectives and each scenario consists of two cases: optimal allocation of DGs in the initial configured network, optimal allocation of DGs in the optimal reconfigured network. Results suggest that optimal allocation of DGs in scenario 4 gives a better improvement in maximum system loadability, loading margin factor, and active power loss reduction of the system. Obtained results compared with the suitable methods and algorithms that are available in the literature.

A New Affine Arithmetic-Based Optimal Network Reconfiguration to Minimize Losses in a Distribution System Considering Uncertainty Using Binary Particle Swarm Optimization

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

Network reconfiguration and DG output including real time optimal switching sequence for system improvement

ABSTRACT This research proposes an optimal switching sequence path method in order to minimise power losses during the network switching operation. Apart from this contribution, simultaneous optimal network reconfiguration and optimal DG output generation were also presented. The proposed methodology involves (1) optimal radial network reconfiguration and DG output simultaneously and (2) real time optimal sequence of switching operations required to convert the network from the original configuration to the optimal configuration obtained from (1). The proposed method is applied to reduce power losses and improve the overall voltage profile of the system. The chosen optimisation techniques include (EP), (PSO), (GSA), and (FA). To assess the capabilities of the proposed method, simulations using MATLAB were carried out on IEEE 33-bus and IEEE 69-bus radial distribution networks. The obtained results demonstrate the effectiveness of the proposed strategy to determine the sequence path of switching operations, as well as the optimal network configuration and optimal generation output of DG units. For more validation, real-time analysis test using PSCAD/EMTDC software is used to check the voltage stability of the system during switching process. The test proved that the optimal switching sequence does not cause over voltage or under voltage when implemented in real-time conditions.

Optimal network reconfiguration for power loss minimization and voltage profile enhancement in distribution systems

This paper presents an optimal method for optimizing network reconfiguration (NR) problems in a power distribution system (PDS) for the purpose of power loss reduction and voltage profile (VP) improvement. Furthermore, a modified algorithm was presented to address this problem in order to provide a more efficient PDS. Various works which used NR to improve VP and reduce power loss were discussed and summarized in detail. In particular, a modified Selective particle swarm optimization (SPSO) method was used for NR in existing networks considering different loading conditions. The main objective of this study is to minimize real power losses and enhance VP of a distribution system using the proposed SPSO method. The SPSO method was programmed in MATLAB R2016b software and tested using IEEE 33-bus radial distribution system (RDS). The obtained test results show that the real power was enhanced by 99.341%, 97.289%, and 95.389% for the light, normal, and heavy load conditions, respectively. Also, the minimum voltage level in the worst case was significantly enhanced from 0.8841 p.u. to 0.9510 p.u. Towards the end, a comparative analysis of the proposed SPSO with existing methods for distribution network reconfiguration (DNR) is presented. The comparative results show that the proposed SPSO was found to be more efficient in reducing voltage deviation (VD) and power losses in the system.

Equivalent Network Technique for Feeder Reconfiguration Considering Harmonics Generating From PV Solar Sources in the Distribution System

This paper proposes a methodology to determine the optimal distribution network reconfiguration connected with distributed generation (DG) units to improve the reliability of the system. The aim of the methodology is to reduce harmonics in the distribution system injected by the PV sources and choose the optimal feeder configuration. In the process, to avoid the complexity of the system, equivalent network concept is used. To keep the voltage harmonics distortions in limits specified by IEEE-519 standards, a strategy is adopted for the calculation of maximum allowable harmonic currents and PV capacity that can be injected into the network. A test system of two feeders is taken to validate the suggested method, and the obtained results proved that the proposed method is suitable in real-time application. MATLAB programming is used for finding harmonic current, and power system simulation tool MiPower™ is used for modeling the network and for verifying the result.

Optimal Network Reconfiguration to Reduce Power Loss Using an Initial Searching Point for Continuous Genetic Algorithm

In this paper, an effective method to determine an initial searching point (ISP) of the network reconfiguration (NR) problem for power loss reduction is proposed for improving the efficiency of the continuous genetic algorithm (CGA) to the NR problem. The idea of the method is to close each initial open switch in turn and solve power flow for the distribution system with the presence of a closed loop to choose a switch with the smallest current in the closed loop for opening. If the radial topology constraint of the distribution system is satisfied, the switch opened is considered as a control variable of the ISP. Then, ISP is attached to the initial population of CGA. The calculated results from the different distribution systems show that the proposed CGA using ISP could reach the optimal radial topology with better successful rate and obtained solution quality than the method based on CGA using the initial population generated randomly and the method based on CGA using the initial radial configuration attached to the initial population. As a result, CGA using ISP can be a favorable method for finding a more effective radial topology in operating distribution systems.

Multi-objective Distribution Network Reconfiguration Optimization Considering Space Allocation of Electric Vehicles

This paper explores the space allocation method and fuzzy C cluster processing method of electric vehicles, establishes a multi-objective distribution network model, solves the calculation process of the model, and verifies the feasibility of this research through simulation analysis method, thus providing help for orderly charging of electric vehicles and reducing line loss.

Optimal Reconfiguration of Distribution Networks Using Hybrid Heuristic-Genetic Algorithm

This paper describes the algorithm for optimal distribution network reconfiguration using the combination of a heuristic approach and genetic algorithms. Although similar approaches have been developed so far, they usually had issues with poor convergence rate and long computational time, and were often applicable only to the small scale distribution networks. Unlike these approaches, the algorithm described in this paper brings a number of uniqueness and improvements that allow its application to the distribution networks of real size with a high degree of topology complexity. The optimal distribution network reconfiguration is formulated for the two different objective functions: minimization of total power/energy losses and minimization of network loading index. In doing so, the algorithm maintains the radial structure of the distribution network through the entire process and assures the fulfilment of various physical and operational network constraints. With a few minor modifications in the heuristic part of the algorithm, it can be adapted to the problem of determining the distribution network optimal structure in order to equalize the network voltage profile. The proposed algorithm was applied to a variety of standard distribution network test cases, and the results show the high quality and accuracy of the proposed approach, together with a remarkably short execution time.

Optimal Network Reconfiguration and DG Integration in Power Distribution Systems Using Enhanced Water Cycle Algorithm

Optimal Network Reconfiguration and DG Integration in Power Distribution Systems Using Enhanced Water Cycle Algorithm