Optimal Network Reconfiguration Research Articles

A multi-objective artificial hummingbird algorithm for dynamic optimal volt-var controls for high electric vehicle load penetration in a photovoltaic distribution network

Renewable energy systems (RESs) and electric vehicles (EVs) have become global necessities to meet the sustainable development goals (SDGs). Because they are intermittent and stochastic, these two technologies have caused power grid operation and regulation challenges despite their environmental benefits. A new combined dynamic optimal network reconfiguration (DONR) and capacitor bank switching (CBS) optimization technique reduces solar and multi-type load effects on the distribution network performance. Considering substantial EV load penetrations, ONR and CBS should coordinate to reduce energy loss, improve the voltage profile, and save money during 24 h operation. The artificial hummingbird algorithm (AHA) solves this difficult optimization problem for the first time. An enhanced IEEE 33-bus benchmark test system was used to simulate various instances to evaluate the computational characteristics of the AHA and compare it with other similar algorithms. The obtained results demonstrate the superiority of the combined DONR and CBS for real-time scenarios over any individual approach.

Multi‐objective multi‐period optimal site and size of distributed generation along with network reconfiguration

AbstractWhile extensive research has focused on enhancing distribution networks through either maximizing Distributed Generation (DG) integration or network reconfiguration at specific times, there is a need for further investigation into concurrently optimal network reconfiguration and DG allocation. To reduce the cost of energy delivered, the cost of energy loss, and voltage deviation, this study gives a dynamic multi‐objective network reconfiguration together with siting and sizing of dispatchable and non‐dispatchable DGs. The widely used IEEE 33‐bus and a large‐scale 118‐bus radial test system are employed while considering the time sequence fluctuations in sunlight irradiation and load. To address the pointed‐out challenge of multiperiod optimal DG allocation and reconfiguration while simultaneously decreasing the cost of energy supplied, the cost of energy lost, and the voltage deviation, a novel Multi‐objective Bidirectional co‐evolutionary algorithm (BiCo) is implemented. For better exploration and exploitation, the proposed algorithm integrating the constraint domination principle evolves the population from the feasible and infeasible search space with the help of a novel angle‐based density section. Simulation results demonstrate that the proposed approach outperforms previously published Multi‐objective Evolutionary Algorithms (MOEAs) by discovering a vast collection of uniformly spaced non‐dominated solutions in a single simulation run. Further, a fuzzy set theory is applied to find the best compromise solution among obtained final non‐dominated solutions. The results establish that the Pareto solutions significantly improved the system's voltage profile, with savings of over 22% compared to the baseline case and an exceptional improvement of over 80% in voltage deviation and power loss.

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>

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.

Optimal network reconfiguration and distributed generation integration for power loss minimization and voltage profile enhancement in radial distribution system

Network Reconfiguration with Distributed Generation (DG) integration can significantly minimize the power loss and enhance the voltage profile. This paper presents power loss minimization and voltage profile enhancement measures in radial distribution systems through optimal network reconfiguration and DG integration. Initially, the approach was tested in a standard IEEE 33-Bus system in MATLAB using the Backward/Forward Propagation Power Flow method. Voltage Stability Index (VSI) was used to determine the candidate bus to locate for DG integration. Artificial Bee Colony (ABC) algorithm was used to find the optimal solution. Six different scenarios for network reconfiguration and DG integration were analysed by using the real distribution network of the Nepal Electricity Authority (NEA), Kirtipur Distribution Center. The simulated results were compared with the base case scenario and were validated with similar results from the previous studies. Among them, Scenario VI (simultaneous network reconfiguration and DG integration) gave a most favorable result in terms of power loss minimization and voltage profile enhancement. Test results in this study show that the active power loss has reduced by 72.19% and 57.69% for IEEE 33-Bus system and NEA 63-Bus system respectively. It also showed that ABC algorithm has outperformed other popular metaheuristic algorithms.

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.

Using fuzzy systems for optimal network reconfiguration of a distribution system with electric vehicle charging stations and renewable generation

Using fuzzy systems for optimal network reconfiguration of a distribution system with electric vehicle charging stations and renewable generation

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 Network Reconfiguration via Improved Whale Optimization Approach

Latterly, reduction of power loss in the distribution system is the objective of many researches due to its impact on total costs and voltage profiles. It can be handled by an optimal restructure of the Radial Distribution System (RDS). This article introduces an innovative approach to restructure of RDS by electing the optimal switches combination subject to the system operating constraints, which is Improved Whale Optimization Approach (IWOA). The suggested approach combines exploitation of WOA with exploration of Differential Evolution (DE), and thus it supplies a promising candidate solution. The suggested approach is tested on IEEE 33 and 69 bus RDS. The superiority of the suggested approach compared with other well-known approaches is verified through simulation results by examining total losses, cost and saving. Also, the impact of alterable loading is investigated to prove the effectiveness of the suggested IWOA.

Multi-Timescale Coordinated Control With Optimal Network Reconfiguration Using Battery Storage System in Smart Distribution Grids

This paper focuses on the global optimality of the relaxed solutions of a multi-timescale co-optimization problem. The proposed co-optimization framework involves the multi-timescale co-optimization of distribution feeder reconfiguration with the optimal dispatch of traditional voltage regulating devices and utility-scale distributed energy resources. The on-load tap changer (OLTC) is scheduled on an hourly basis while the potential of fast-acting battery energy storage system and photovoltaic inverters is exploited by dispatching them on a 20-min basis. The optimal switching plan is computed on a daily basis. The proposed multi-timescale co-optimization model is formulated as a mixed-integer second-order cone program to achieve global optimum. The objective is to reduce power losses and improve load balancing among feeders. The proposed co-optimization framework satisfies the grid security constraints by employing the accurate DistFlow branch equations and exact linearization of the OLTC model. To ensure radiality, the limitation of widely used spanning tree constraints is addressed by combining them with single-commodity flow constraints. The simulation results demonstrate the feasibility (hence global optimality) of the relaxed solutions computed by the co-optimization framework.

Reliability enhancement and power loss reduction in medium voltage distribution feeders using modified jellyfish optimization

The reliability of electrical systems is a critical operational characteristic for distribution companies. On the other hand, they pay great attention on reducing losses in primary and secondary distribution lines. In this article, a modified Jellyfish Search (MJFS) algorithm is presented for handling Optimal Network reconfiguration (ONR) in Medium Voltage Distribution Feeders (MVDFs). An ONR formulation is proposed for reliability enhancement and power loss reduction in a multi-objective model where three reliability measures are incorporated which are total energy not supplied (TENS), system average interruption unavailability index (SAIUI) and system average interruption frequency index (SAIFI). The proposed MJFS expands on the JFS searching benefits by introducing a quasi-oppositional-based learning and social neighborhood strategies. Also, a proposed bus-line feeding matrix (BLFM) is incorporated to develop the MJFS. Through the BLFM, each distribution line is simulated as an outage and the regarding distribution nodes which are not supplied is estimated. The simulation results are performed on a large-scale IEEE MVFD of 137-bus which derive significant improvements of the proposed MJFS over the standard JFS algorithm. Considering the proposed multi-objective model, the proposed MJFS achieves great reduction in power losses, TENS, SAIUI and SAIFI with 44.42, 30.57, 30.78 and 23.92 %, respectively compared to the initial case. Also, versus the standard JFS, grey wolf optimizer, tuna swarm optimization, equilibrium optimizer and tunicate swarm optimization, the proposed MJFS acquires great reduction in the standard deviation with improvement of 2.5, 92.51, 10.67, 91.53 and 96.51%, respectively.

Multi-Objective Framework for Optimal Placement of Distributed Generations and Switches in Reconfigurable Distribution Networks: An Improved Particle Swarm Optimization Approach

Distribution network operators and planners face a significant challenge in optimizing planning and scheduling strategies to enhance distribution network efficiency. Using improved particle swarm optimization (IPSO), this paper presents an effective method for improving distribution system performance by concurrently deploying remote-controlled sectionalized switches, distributed generation (DG), and optimal network reconfiguration. The proposed optimization problem’s main objectives are to reduce switch costs, maximize reliability, reduce power losses, and enhance voltage profiles. An analytical reliability evaluation is proposed for DG-enhanced reconfigurable distribution systems, considering both switching-only and repairs and switching interruptions. The problem is formulated in the form of a mixed integer nonlinear programming problem, which is known as an NP-hard problem. To solve the problem effectively while improving conventional particle swarm optimization (PSO) exploration and exploitation capabilities, a novel chaotic inertia weight and crossover operation mechanism is developed here. It is demonstrated that IPSO can be applied to both single- and multi-objective optimization problems, where distribution systems’ optimization strategies are considered sequentially and simultaneously. Furthermore, IPSO’s effectiveness is validated and evaluated against well-known state-of-the-art metaheuristic techniques for optimizing IEEE 69-node distribution systems.

Application of Optimal Network Reconfiguration for Loss Minimization and Voltage Profile Enhancement of Distribution System Using Heap-Based Optimizer

A distribution system’s network reconfiguration (NR) is the process of changing the status of the switches to change the topology of the feeders. Using optimal NR at various network load levels, this research proposed an economical way for improving the voltage profile and reducing power loss in distribution systems. A new metaheuristic Heap-based optimizer based on the corporate rank hierarchy is used to solve the optimization problem in order to find the optimal radial distribution network. The objective functions used in this article include voltage profile improvement, power loss reduction, reduction of operation cost, and minimization of gas emissions. This novel technique provides accurate results while avoiding the issue of premature convergence and requires fewer control parameter settings. The proposed method is evaluated at three different load levels on a standard IEEE 33-bus test system, and the simulation results are compared to those of other optimization methods published in the literature, demonstrating that the proposed method improves the system voltage profile while minimizing losses and power costs, establishing that this technique is effective in finding the best result. The Wilcoxon and Friedman tests are also used to verify the effectiveness of the proposed technique.

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.

Postfault optimal islanding of smart grids using a reinforcement learning approach

AbstractThis paper presents a method for optimal reconfiguration of smart grids following the occurrence of short circuit faults. Due to restoration delays, the aim of the proposed approach is to save the maximum possible number of loads by forming stable islands and serving loads with Distributed Energy Resources (DERs). The islanding plan aims to prevent island instability and to help DERs continue supplying the maximum number of loads by the optimal network reconfiguration. Fault isolation is carried out by the protection system and the proposed procedure is commenced right after the fault isolation by controlling the condition of the network remote‐controlled switches. The proposed islanding plan is a novel method by this paper in the management of the postfault conditions of smart grids. Furthermore, a Q‐learning reinforcement approach is presented as the optimization tool because of its great capability and fast response for the determination of optimal reconfiguration. Numerous simulation tests for various fault locations on a 6‐bus and a 33‐bus test networks show the effectiveness of the proposed method in the improvement of postfault network reliability and sustainability.

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.

Simultaneous Optimal Network Reconfiguration and Allocation of Four Different Distributed Generation Types in Radial Distribution Networks Using a Graph Theory-Based MPSO Algorithm

Simultaneous Optimal Network Reconfiguration and Allocation of Four Different Distributed Generation Types in Radial Distribution Networks Using a Graph Theory-Based MPSO Algorithm

Multicriteria analysis for optimal reconfiguration of a distribution network in case of failures

This work presents a methodology for the optimal reconfiguration of a distribution network in the presence of a failure, through a multicriteria optimization algorithm. For this purpose, the optimal network reconfiguration alternative is verified in the IEEE 33 bus test system and 123 bus test system. The variables analyzed within the multicriteria decision matrix include the total interruption time per nominal kVA installed (TITK), mean frequency of interruption per nominal kVA installed (MFIK), reset time for reconfiguration, energy not supplied, total losses in the lines of the system and operation and maintenance costs. The result enables selecting the best scenario based on analyzing every decision criterion; the multicriteria decision algorithm is developed in the Matlab environment. Subsequently, the winning reconfiguration alternatives are validated through simulations in Cymdist for different failure scenarios. In the analysis of results, metrics are presented that enable us to observe a significant improvement in the typical problems that occur in an electric system.

A Novel Scalable Reconfiguration Model for the Postdisaster Network Connectivity of Resilient Power Distribution Systems.

The resilient operation of power distribution networks requires efficient optimization models to enable situational awareness. One of the pivotal tools to enhance resilience is a network reconfiguration to ensure secure and reliable energy delivery while minimizing the number of disconnected loads in outage conditions. Power outages are caused by natural hazards, e.g., hurricanes, or system malfunction, e.g., line failure due to aging. In this paper, we first propose a distribution-network optimal power flow formulation (DOPF) and define a new resilience evaluation indicator, the demand satisfaction rate (DSR). DSR is the rate of satisfied load demand in the reconfigured network over the load demand satisfied in the DOPF. Then, we propose a novel model to efficiently find the optimal network reconfiguration by deploying sectionalizing switches during line outages that maximize resilience indicators. Moreover, we analyze a multiobjective scenario to maximize the DSR and minimize the number of utilized sectionalizing switches, which provides an efficient reconfiguration model preventing additional costs associated with closing unutilized sectionalizing switches. We tested our model on a virtually generated 33-bus distribution network and a real 234-bus power distribution network, demonstrating how using the sectionalizing switches can increase power accessibility in outage conditions.

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.