Optimal Reconfiguration Research Articles

The Application of an Improved LESS Dung Beetle Optimization in the Intelligent Topological Reconfiguration of ShipPower Systems

To address the shortcomings of the Dung Beetle Optimization (DBO) algorithm in ship power-system fault reconfiguration, such as low population diversity and an imbalance between global exploration and local exploitation, the authors of this paper propose an improved Dung Beetle Optimization (LESSDBO) algorithm. The improvements include optimizing the initial population using Latin hypercube sampling and an elite population strategy, optimizing parameters with an improved sigmoid activation function, introducing the sine–cosine algorithm (SCA) for position update optimization, and performing multi-population mutation operations based on individual quality. The LESSDBO algorithm was applied to simulate the fault reconfiguration of a ship power system, and it was compared with the traditional DBO, Genetic Algorithm (GA), and Modified Particle Swarm Optimization (MSCPSO) methods. The simulation results showed that LESSDBO outperformed the other algorithms in terms of convergence accuracy, convergence speed, and global search capability. Specifically, in the reconfiguration under Fault 1, LESSDBO achieved optimal convergence in seven iterations, reducing convergence iterations by more than 30% compared with the other algorithms. In the reconfiguration under Fault 2, LESSDBO achieved optimal convergence in eight iterations, reducing convergence iterations by more than 23% compared with the other algorithms. Additionally, in the reconfiguration under Fault Condition 1, LESSDBO achieved a minimum of four switch actions, which is 33% fewer than the other algorithms, on average. In the reconfiguration under Fault Condition 2, LESSDBO achieved a minimum of eight switch actions, which is a 5.9% reduction compared with the other algorithms. Furthermore, LESSDBO obtained the optimal reconfiguration solution in all 50 trials for both Faults 1 and 2, demonstrating a 100% optimal convergence probability and significantly enhancing the reliability and stability of the algorithm. The proposed method effectively overcomes the limitations of the traditional DBO in fault reconfiguration, providing an efficient and stable solution for the intelligent topology reconfiguration of ship power systems.

Optimization of low-thrust periodic reconfiguration in Tianqin orbit

Tianqin is a project scheduled for detecting gravitational waves in space. The project requires high levels of constellation stability. This paper optimizes the Tianqin trajectory over 5 years using low-thrust maneuvers and a ‘3 months on +3 months off’ observation window scheme to improve constellation stability and reduce spacecraft fuel consumption. The result is a series of stable orbit configurations. The problem of fuel optimization is solved efficiently by combining a normalization strategy, a homotopic approach, and switching detection. Additionally, the optimization strategy quantitatively evaluates the fuel consumption required to maintain the stability of the Tianqin orbits. This evaluation provides a valuable reference for the performance design of the low-thrust propulsion system in the spacecraft.

Resilience enhancement program in smart grids by coordinating demand response and optimal reconfiguration during wildfires

Resilience enhancement program in smart grids by coordinating demand response and optimal reconfiguration during wildfires

Network reconfiguration and integration of distributed energy resources in distribution network by novel optimization techniques

Nowadays, electrical load demand in radial distribution networks (RDN) is continuously growing, therefore RDNs are facing some serious challenges like voltage violation and line losses. Since modern RDNs have reconfiguration capabilities, optimal network reconfiguration is preferred over the costly construction of new lines and cables. In addition, the optimal integration of distributed energy resources (DER) helps to decrease above mentioned network challenges. This paper presents an improved version of the radiality maintenance algorithm (IRMA) to solve the optimal reconfiguration problem using a meta-heuristics algorithm. It also proposes two different schemes of algorithms by the blending of a Genetic algorithm (GA) and Teaching learning-based optimization (TLBO) to solve the optimal DER integration problem, where blending enhances the search space of each scheme which helps to find global minima in less iterations and time, while existing algorithms get stuck in local minima with same number of searching agents. The proposed problems are solved by minimizing active power loss by the single objective function and the sum of active power loss, reactive power loss, and voltage deviation index by multi-objective function using the weighted sum method where all objectives are equally dealt with, and their sum is used as single fitness function for the optimization algorithm. Four case studies are simulated using different DER technologies to improve each objective function. The efficiency of the proposed IRMA and two newly developed schemes of optimization algorithms, named GA-TLBO and TLBO-GA, are tested on two IEEE benchmark RDN of 33 and 69 buses. The results validate the efficiency of proposed algorithms that remarkably minimize a single objective from 210.9800 kW in the base case to 58.8768 kW, whereas existing algorithms like GWO and HHO were able to only reduce it to 72.7861 kW and 72.900 kW for IEEE 33 bus RDN, respectively. Similarly, for IEEE 69 bus system, single objective is reduced from base case of 224.9917 kW to 36.2543 kW, while existing algorithms like QOTLBO and QOSIMBO were only able to reduce it to 71.6250 kW and 71 kW, respectively. Furthermore, in multi-objective functions, reactive power losses and voltage deviation are also significantly improved from their base values. After that, the results of improved algorithms are compared with those of existing algorithms in the literature review for a comprehensive evaluation, which proves that proposed algorithm schemes are much more efficient and stable for the proposed problem as well as for standard benchmark optimization functions.

A new multi-objective-stochastic framework for reconfiguration and wind energy resource allocation in distribution network incorporating improved dandelion optimizer and uncertainty

Improving the reliability and power quality of unbalanced distribution networks is crucial for ensuring consistent and reliable electricity supply. In this research, multi-objective optimization of unbalanced distribution networks reconfiguration integrated with wind turbine allocation (MORWTA) is implemented considering uncertainties of networks load, and also wind power incorporating a stochastic framework. The multi-objective function is defined by the minimization of power loss, voltage sag (VS), total harmonic distortion (THD), voltage unbalance (VU), energy not-supplied (ENS), system average interruption frequency index (SAIFI), system average interruption duration index (SAIDI), and momentary average interruption frequency (MAIFI). A new improved dandelion optimizer (IDO) with adaptive inertia weight is recommended to counteract premature convergence to identify decision variables, including the optimal network configuration through opened switches and the best location and size of wind turbines in the networks. The stochastic problem is modeled using the 2m + 1 point estimate method (PEM) combined with K-means clustering, taking into account the mentioned uncertainties. The proposed stochastic methodology is implemented on three modified 33-bus, and unbalanced 25-, and 37-bus distribution networks. The results demonstrated that the MORWTA enhanced all study objectives in comparison to the base networks. The results also demonstrated that the IDO had superior capability to solve the deterministic- and stochastic-MORWTA in comparison to the conventional DO, grey wolf optimizer (GWO), particle swarm optimization (PSO), and arithmetic optimization algorithm (AOA) in terms of achieving greater objective value. Moreover, the results demonstrated that when the stochastic-MORWTA model is considered, the power loss, VS, THD, VU, ENS, SAIFI, SAIDI, and MAIFI are increased by 18.35%, 9.07%, 10.43%, 12.46%, 11.90%, 9.28%, 12.16% and 14.36%, respectively for 25-bus network, and also these objectives are increased by 12.21%, 10.64%, 12.37%, 9.82%, 14.30%, 12.65%, 12.63% and 13.89%, respectively for 37-bus network compared to the deterministic-MORWTA model, which is related to the defined uncertainty patterns.

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.

A multi-period restoration approach for resilience increase of active distribution networks by considering fault rapid recovery and component repair

As the frequency of extreme weather events continues to rise, there is an urgent need to strengthen the safe and stable operation of active distribution networks (ADNs), and it is of great value to establish highly resilient ADNs to withstand multi-faults caused by extreme weather events. This paper proposes a multi-period restoration approach for the resilience increase of ADNs by considering fault rapid recovery and component repair under typhoon disasters. Firstly, based on the structural reliability theory, the failure rate model of the main components is established, and in light of the system information entropy, the typical fault scenario selection strategy is designed to determine the branches with high fault probability. Then, according to the fault islanding division and network reconfiguration, a fault rapid recovery method is suggested for the ADNs, where the impact of typhoon disasters on the output features of distributed generators (DGs) are taken into account, and meanwhile, the network structure and the output power of the DGs are jointly optimized to minimize the operating cost of the ADNs. Further, a fault component repair model is formulated by adopting the adaptive ant colony algorithm, and a multi-period restoration approach is proposed for the ADNs to fulfill a rolling optimization of the network reconfiguration and fault component repair. The improved IEEE 33-node and IEEE 118-node systems are used for the approach verification, and the results show that the proposed approach can effectively improve the overall load restoration level and increase the component repair efficiency. Following a multi-criteria resilience evaluation system, the proposed approach enables the ADNs to more effectively withstand typhoon disasters, offering a resilience increase of 6.93 % and 32.24 % regarding the 33-node and 118-node systems.

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

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

Pareto Optimal Reconfiguration Planning and Distributed Parallel Motion Control of Mobile Modular Robots

Pareto Optimal Reconfiguration Planning and Distributed Parallel Motion Control of Mobile Modular Robots

A dynamic reconfiguration model and method for load balancing in the snow-shaped distribution network

The snow-shaped distribution network (SDN) is a cable distribution network composed of eight (or six) 10 kV feeders from four (or three) substations in a regular connection. Compared with the traditional 10 kV distribution network, SDN can support a wide range of load transfer among six or eight feeders. Aiming at the problem of load spatial-temporal unbalanced condition caused by the integration of distributed generators (DGs) and different load types in different feeders, this paper proposes a dynamic reconfiguration strategy for load balancing in SDN considering DGs and energy storage system (ESS). Firstly, the basic structure of SDN is analyzed and the power flow model for its dynamic reconfiguration is developed. Secondly, the dynamic reconfiguration optimization model for load balancing in SDN considering DGs and ESS is proposed to utilize the load transfer capability to mitigate the load unbalanced condition and reduce active power loss. Thirdly, the original non-convex model is converted into a mixed-integer second-order cone programming (MISOCP) model by applying the second-order cone relaxation and the big-M method, which is solved by CPLEX solver. Finally, the effectiveness of the proposed model and method are verified by an actual case in Tianjin and IEEE 33-node system. The analysis results show that the proposed method can significantly alleviate the load unbalanced spatial-temporal distribution and improve the economic efficiency by regulating the operation of SDN including ESS optimization and dynamic reconfiguration.

Optimal reconfiguration, renewable DGs, and energy storage units’ integration in distribution systems considering power generation uncertainty using hybrid GWO-SCA algorithms

ABSTRACT To optimize radial distribution systems, this study suggests the utilization of the Grey Wolf Optimizer (GWO), a hybrid metaheuristic optimization method, combined with the Sine Cosine method (SCA). The primary objective of this work is to enhance the distribution system by determining the most efficient network reconfiguration, sizing, and placement of various distributed energy sources in distribution system. The energy sources considered include capacitors, solar cells, wind turbines, biomass-based distributed generation units, and battery storage units. To achieve this goal, the proposed strategy incorporates the power loss sensitivity technique, which assists in identifying suitable candidate buses and accelerates the resolution process. Moreover, the model considers fluctuations in solar irradiance and wind speed using Weibull and Beta probability distribution functions, compensating for the intermittent nature of renewable energy sources and the variability in demand. To address power fluctuations, voltage surges, significant losses, and inadequate voltage stability challenges, battery energy storage, diesel generators, and dispatchable biomass DGs are employed to mitigate variability and enhance supply continuity. The proposed approach is evaluated and validated by comparing it to existing optimization strategies using IEEE 69-bus and 84-bus RDSs. The results demonstrate that the suggested technique achieves faster convergence to near-optimal solutions. The proposed methodology yields a significant reduction of up to 80% in power losses in the 69-bus system and a 35% reduction in the 84-bus system, signifying higher performance than existing methods.

Hybrid design of optimal reconfiguration and DG sizing and siting using a novel improved salp swarm algorithm

Hybrid design of optimal reconfiguration and DG sizing and siting using a novel improved salp swarm algorithm

Resilience and performance quantification of dynamic reconfiguration

Dynamic reconfiguration is an adaptive resilience mechanism that can help address several system design problems. Adaptation through dynamic reconfiguration can improve quality of service, increase fault-tolerance, help recover from failures, and prevent and recover from cyber attacks. This mechanism acts primarily by reconfiguring one or more of a system’s resources. While system reconfiguration is advantageous, it may bring disadvantages such as performance and availability degradation during reconfiguration intervals. In this work, we quantify the effectiveness of dynamic reconfiguration as a system resilience mechanism and its impact on performance. We define a failure function that captures the effect of dynamic reconfigurations on a system’s resilience to failures and develop metrics that capture the impact of reconfigurations on a system’s execution time and probability of failure. We also derive analytic models that predict the effectiveness of dynamic reconfigurations on execution time and resilience to failures. Several theorems regarding the tradeoff between resilience to failures and performance and availability are presented. Finally, we define an optimization problem, formalized with the help of these theorems, to determine the optimal reconfiguration frequency to meet performance-resilience tradeoffs.

Multifactorial evolutionary algorithm for optimal reconfiguration capability of distribution networks

Most existing studies on distribution network reconfiguration (DNRC) are predominantly based on a fixed initial topology and optimize switch operations to achieve various objectives, without considering the flexible allocation of backup lines. This paper proposes a novel problem of optimizing the reconfiguration capability of distribution networks (DNs) by considering the expansion of power lines under resource constraints. The reconfiguration capability reflects the ability of a DN to respond to uncertainties and disturbances by adjusting its topology. A novel metric based on the number of spanning trees is proposed to quantify the reconfiguration capability of a DN. Moreover, an optimization model is formulated to maximize the reconfiguration capability of a DN subject to resource constraints on line expansions. To solve this model efficiently, a multifactorial evolutionary algorithm (MFEA) is developed, which can optimize multiple tasks with different expansion line quantities simultaneously by exploiting knowledge transfer across tasks. The proposed metric, model, and algorithm are validated on two case studies using the IEEE 33-bus and 70-bus test systems, and the results show their effectiveness and superiority over existing methods.

Synergistic Integration of EVs and Renewable DGs in Distribution Micro-Grids

This paper proposes a multi-objective optimization framework for safe, reliable, and economic integration of electric vehicles (EVs) and renewable distributed generators (DGs) in distribution micro-grids. EV and DG coordination optimization with the use of vehicle-to-grid (V2G) technology along with system reconfiguration optimization is developed to provide collective revenues and address integrational complications that may occur by additional system loading due to EV charging and EV-DG energy exchanges. A Genetic Algorithm (GA) optimizes the EV charging/discharging in synergies with renewable DGs to maximize benefits that can be captured by their collaborative participation in electricity market and through renewable energy arbitrage. The developed EV charging/discharging optimization is implemented in a real 134-bus distribution network and is evaluated for its potential operational implications, namely, increased system losses. A system reconfiguration is then proposed to reduce the system losses by optimizing the flow of power through switching on/off the connections within the micro-grid and/or with other distribution systems. Simulation results demonstrate the efficiency of the proposed method in not only providing collective revenues, but also in enhancing the system operation by reducing the losses of the distribution grid. The collective benefits proposed by the developed optimization and validated by the simulation results facilitate transitioning to clean and eco-friendly sources of energy for generation and transportation, which in turn leads to more sustainable development of societies and communities.

Hybridization of Improved Northern Goshawk Optimization and Line Loadability Index for Reconfiguration Considering Solar and Electric Vehicles

Hybridization of Improved Northern Goshawk Optimization and Line Loadability Index for Reconfiguration Considering Solar and Electric Vehicles

English

English

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

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

Resilience Measure and Formation Reconfiguration Optimization for Multi-UAV Systems

Resilience Measure and Formation Reconfiguration Optimization for Multi-UAV Systems

Analysis of large scale distribution network using Whale Optimization Algorithm

In this study, we use a loop matrix to describe the reorganisation of the RDN's formulation. Calculation time is increased when an optimum reorganisation is determined analytically. More network buses means more time to calculate. Therefore, a technique of optimisation is required to determine the best reorganisation of the radial distribution system. The optimum reorganisation aims to reduce network losses to a minimum and the voltage profile is enhanced. Loop matrices are used to describe the re-formulation of the radial distribution system. The analytical technique of identifying optimum reconfiguration involves additional calculation time. The computational complexity grows as the system's bus count rises. Therefore, a search for the best possible radial distribution system reconfiguration necessitates an optimization technique. The ideal reconfiguration focuses mostly on reducing the system's overall loss. The genetic algorithm (GA), particle swarm optimization (PSO), and whale optimization algorithm (WOA) are the optimization methods used in this paper. Two test systems consisting of 119 buses, and 135 buses are used to evaluate the effectiveness of various optimization strategies. The outcomes are then compared with one another.