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As one of the important components of a ship, the ship's integrated power system is an important safeguard for ships. In order to improve the service life of the ship's power grid, the power system should be able to realize rapid reconstruction to ensure continuous power supply of important loads when the ship is attacked or fails suddenly. Therefore, it is of vital importance to study the reconfiguration technology of the ship's integrated power system to ensure that it can quickly and stably cope with all kinds of emergencies in order to guarantee the safe and reliable navigation of the ship. This paper takes the ship's ring power system as the research object and sets up the maximum recovery load and the minimum number of switching operations. The load is divided uniformly and the generator efficiency is balanced for the reconstruction of comprehensive function. It also sets up the system capacity, topology, and branch current limitations of the constraints to establish a mathematical model. The load branch correlation matrix method is used for branch capacity calculation and generator efficiency equalization calculation, and the load backup power supply path matrix is added on the basis of the matrix to judge the connectivity of some loads before reconfiguration. In this paper, for the network reconfiguration of the ship circular power system, which is a discrete nonlinear problem with multiple objectives, multiple time periods, and multiple constraints, we choose to use the particle swarm algorithm, which is suitable for global optimization, with a simple structure and fewer parameters; improve the particle swarm algorithm using the swarm exchange strategy by setting up two main and auxiliary swarms for global and local search; and exchange some of the particles with the golden ratio in order to keep the diversity of the populations. The simulation results of the network reconfiguration of the ship power system show that the improved algorithm can solve the power system network reconfiguration problem more effectively and provide a feasible reconfiguration scheme in a shorter time compared with the chaotic genetic algorithm under the same fault case test, and it also proves that the use of the swarm exchange particle swarm algorithm greatly improves the performance of reconfiguring the power grid of the ship. [ABSTRACT FROM AUTHOR]

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Because of the low voltage level and high current in the distribution system, as well as the high ohmic resistance of the conductors, the majority of power system losses are attributed to the distribution system. As a result, it is necessary to address the issue of reducing distribution system losses. To reduce losses in distribution systems, a wide variety of methods and algorithms have been proposed and are still being developed. The allocation of capacitors in a distribution system is one of the earliest and most effective methods for reducing system losses. With the growth of distributed generations and the diversity of their productive power, the use of capacitors in distribution systems has decreased slightly. Distributed generation resource allocation has replaced this method. Conversely, the reconfiguration of distribution systems has proven to be one of the easiest and cheapest methods for reducing distribution system losses in the past half‐century. Distribution system reconfiguration has been the subject of many studies, each with different objectives. Many studies have also reconfigured the distribution system on a daily and hourly basis. A novel method for dynamic distribution network reconfiguration is presented in this study, which utilizes capacitors and distributed generation resources simultaneously. This method divides 24 h into several periods instead of considering real‐time and hourly data. By reconfiguring and allocating capacitors and distributed generation resources during these periods, the voltage profile is improved and losses are reduced. An IEEE 33 bus network is used to test the simulations. A genetic algorithm was used to reduce losses and improve the voltage profile using MATLAB software. Based on the simulation results, this model provides better results than previous approaches. [ABSTRACT FROM AUTHOR]

In response to global energy, environment, and climate concerns, distributed photovoltaic (PV) power generation has seen rapid growth. However, the intermittent and uncertain nature of PVs can cause voltage fluctuations in distribution systems, threatening their stability. To address this challenge, this paper proposes an active distribution network voltage optimization method, of which the main contribution is the development of a comprehensive voltage optimization strategy that integrates day-ahead prediction and real-time adjustment, significantly enhancing the stability and efficiency of distribution networks with high PV penetration. In the day-ahead prediction stage, the forecast scenarios of load and PV output guide network reconfiguration for improved voltage distribution. In the real-time operation stage, flexible regulation of PV and energy storage systems is used to adjust power outputs, further optimizing voltage quality. Simulations on the IEEE 33-bus system show that the method effectively improves voltage distribution, enhances renewable energy consumption, and ensures the safe, economic operation of the distribution system. [ABSTRACT FROM AUTHOR]

In view of the complexity and importance of nuclear research reactor (NRR) installations, it is imperative to uphold high standards of reliability and quality in the electricity being supplied to them. In this paper, the performance of low-voltage (LV) distribution feeders integrated with NRRs is improved in terms of reduced distribution loss, improved voltage profile, and reduced greenhouse gas (GHG) emissions by determining the optimal location and size of photovoltaic (PV) systems. In the second stage, the power quality of the feeder is optimized by reducing the total harmonic distortion (THD) by optimally allocating D-STATCOM units. In the third and fourth stages, the reliability and resilience aspects of the feeder are optimized using optimal network reconfiguration (ONR) and by integrating an energy storage system (ESS). To solve the non-linear complex optimization problems at all these stages, an efficient meta-heuristic Chernobyl disaster optimizer (CDO) is proposed. Simulations are performed on a modified IEEE 33-bus feeder considering the non-linear characteristics of NRRs, variability of the feeder loading profile, and PV variability. The study reveals that the proposed methodology can significantly improve the service requirements of NRRs for attaining sustainable research activities. [ABSTRACT FROM AUTHOR]

Optimizing the target grid framework during network reconfiguration is crucial for the recovery of the power system. Existing methods for assessing node importance and optimizing the target grid framework often overlook the impact of natural disasters， expected recovery benefits of nodes （ERBN）， and the necessity for expanded concurrent recovery zones. To address these challenges， a network reconfiguration method for power systems is proposed. First， the ERBN under disaster conditions are comprehensively evaluated， considering the recovery of critical loads， expanded available power， and the capacity for incremental recovery. The importance of both generating and non-generating nodes is unified based on their expected recovery benefits， taking into account the available recovery power margin. Then， a method for multi-time-point recoverable state simulation based on time sequence Monte Carlo simulation is proposed under disaster conditions to assess the importance of nodes and lines in the simulated grid. The grid framework is optimized based on this importance， transforming grid connectivity and loop network constraints into penalty functions that are incorporated into the fitness function. This approach eliminates the need for optimization adjustments to unreasonable grid frameworks， thereby improving the efficiency of the algorithm. Finally， the effectiveness of the proposed method is validated through case studies.

In view of the complexity and importance of nuclear research reactor (NRR) installations, it is imperative to uphold high standards of reliability and quality in the electricity being supplied to them. In this paper, the performance of low-voltage (LV) distribution feeders integrated with NRRs is improved in terms of reduced distribution loss, improved voltage profile, and reduced greenhouse gas (GHG) emissions by determining the optimal location and size of photovoltaic (PV) systems. In the second stage, the power quality of the feeder is optimized by reducing the total harmonic distortion (THD) by optimally allocating D-STATCOM units. In the third and fourth stages, the reliability and resilience aspects of the feeder are optimized using optimal network reconfiguration (ONR) and by integrating an energy storage system (ESS). To solve the non-linear complex optimization problems at all these stages, an efficient meta-heuristic Chernobyl disaster optimizer (CDO) is proposed. Simulations are performed on a modified IEEE 33-bus feeder considering the non-linear characteristics of NRRs, variability of the feeder loading profile, and PV variability. The study reveals that the proposed methodology can significantly improve the service requirements of NRRs for attaining sustainable research activities.

Network reconfiguration is a very economical technique that can improve electrical system performance. The development of semiconductor electrical equipment technology to meet human needs and work, known as nonlinear loads, has had a negative impact in the form of the spread of harmonic distortion which can accelerate the aging process and even damage equipment. In this paper, the effect of the optimization results of network reconfiguration techniques on the Sungguminasa 165-bus Executive Unit Service or Unit Layanan Pelanggan (ULP) electrical system is contaminated with harmonic distortion due to the use of nonlinear loads. This technique was optimized using the particle swarm optimization (PSO) method with a multi objective function in the form of minimizing %THDv and total losses with several limitations. Simulation results from the optimization process of several study cases are shown by activating the five tie switches from the network reconfiguration process on the Sungguminasa 165-bus ULP system which is able to improve power quality by reducing the average %THDv by 3.89% and total losses by 8.19%. [ABSTRACT FROM AUTHOR]

Minimization of power losses in distribution network (DN) is one of the vast areas of research, where researchers are performing different techniques and methods to reduce the losses. In recent times, network reconfiguration (Nr) has gained more significance in DN to reduce the losses due to its ease of implementation. Most of the work carried on Nr has usually assumed only single hour load model, which didn't vary with time. But in practical DN, load varies during the day. The tie line switching configuration obtained with single hour load model (which doesn't vary with time) is implemented in practical DN, it induces high power losses, reduces the reliability, loadability and voltage profile of the network. Most of the researchers have used single hour load model with Nr and not considered variation in load with Nr. In this study, variation in load is considered with twenty-four hour load. An investigation has been performed to find the losses obtained with variation in load for twenty-four hours. However, changing the topological of the network without disturbing the radiality is a complex non differentiable optimization problem. Nr implemented with twenty-four hour load has more complex computation as compared with single hour load. A quantum-inspired evolutionary algorithm, i.e., adaptive quantum-inspired evolutionary algorithm (AQiEA), is used to solve this complex optimization problem. In this study, two test cases are created in which the first case, uses four different scenarios to find the effect of power losses incurred in the system after opening the switches on single hour load and twenty-four hour load model. An effort has been made for optimal tie line switching configuration for twenty-four hour load instead of single hour load. In addition, an attempt has been made to maximize the economic benefits in DN with Nr on twenty-four hour load. Switching operation cost is considered to study the effect of frequent Nr on the life span of switches. Second case is used to test the efficacy of the proposed algorithm as compared with other techniques. The performance of AQiEA is tested on two IEEE standard test bus systems. Wilcoxon signed-rank test is also used to demonstrate the effectiveness of AQiEA. [ABSTRACT FROM AUTHOR]

As global climate change intensifies, extreme weather events are becoming more frequent, with ice disasters posing an increasingly significant threat to the stable operation of power distribution networks. Particularly during power outages for de-icing, multiple power islands may form within a distribution area, increasing the complexity of grid operations. Existing research has not fully considered the comprehensive coordination of stable operation of these power islands and de-icing maintenance schedules. Therefore, for the potential multi-island operation of distribution networks caused by freezing disasters, this paper first establishes a dynamic island partitioning model based on distribution network reconfiguration technology. Secondly, based on the characteristics of the de-icing phase, a de-icing maintenance schedule model is established. Finally, dispatch optimization of the distribution network is coordinated with the line de-icing maintenance schedule. By adjusting the de-icing strategies and network structure, the aim is to minimize the risk of load loss. The relevant case analysis indicates that the collaborative optimization model established in this paper helps power distribution networks to reduce their economic losses when facing adverse weather conditions. [ABSTRACT FROM AUTHOR]

This study proposes an innovative and improved random weighted particle swarm optimization algorithm with the goal of effectively resolving the static reconfiguration issue of PV distribution networks. The algorithm, tailored to suit the unique characteristics of distributed PV distribution grids, not only enhances the search's efficiency and precision, but also successfully avoids the algorithm's premature convergence by incorporating the Tabu search approach. This significantly augments the comprehensive capability for global search and local optimization. Simulation is utilized to authenticate the algorithm's efficacy, and a comprehensive comparison, such as search speed and accuracy, is conducted with the particle swarm algorithm, the improved particle swarm algorithm, and the stochastic weighted particle swarm algorithm, all of which are based on the IEEE 33-node distribution network model with distributed photovoltaic. The comparison results demonstrate that the upgraded algorithm exhibits remarkable performance in terms of search efficiency, global search aptitude, and accuracy, far surpassing the other comparison algorithms. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper introduces a new algorithm designed to address the challenge of distribution network reconfiguration, employing the tabu search metaheuristic in conjunction with the voltage drop concept. Distinguishing itself from existing methods, our proposed approach not only utilizes voltage drop for obtaining the initial solution but also introduces a novel technique for generating a candidate solution neighborhood. This method leverages both randomness and voltage drop, ensuring a smooth and steady descent during algorithm execution. The primary goal of our algorithm is to minimize active power losses within distribution networks. To validate its effectiveness, the proposed method underwent testing on three commonly referenced distribution systems: the 33-Bus, 69-Bus, and 94-Bus systems, widely acknowledged in the literature. A pivotal aspect of our work involves the synergy of the tabu search algorithm with a combination of both random and deterministic methods for generating neighbors. This strategic amalgamation plays a crucial role, enabling rapid execution while consistently yielding high-quality solutions. Additionally, the adoption of the electric distance method for generating the initial solution adds significant value, offering a commendable solution with minimal computational effort. Comparative assessments against other algorithms documented in the literature underscore the superior efficiency of our proposed algorithm. [ABSTRACT FROM AUTHOR]

A continuous trend of growing load demand in the present-day power distribution networks (PDNs) often forces these networks to operate on the verge of their loadability limits. Under such a stressed condition, the network reconfiguration (a proven effective method) needs to be executed efficiently in the presence of distributed generators (DGs) so as to enhance the stability limit along with other objectives. The complexity grows even more when parameters like loadability and voltage stability need to be evaluated under critical loading conditions. For all the above reasons, the proper arrangement of the line data for different open switch combinations satisfying the system operational constraints is a significant challenge for the execution of load flow for effective planning. To address this issue, a new framework is proposed using a graph theory integrated sweep-based radial load flow (RLF) technique with an inbuilt mechanism of feasibility (radiality and connectivity) checking. Unlike other conventional RLF techniques, this approach does not require any sequential node and branch numbering and it has the ability to handle topological changes with constant annual load growth. The superiority of the suggested approach is validated by considering a 69-bus test distribution system for four different cases of DG and network reconfiguration (NR) under four different load conditions. The comparative analysis of various parameters including active power loss reduction, loadability maximization, voltage stability maximization, and minimum system voltage under distinct load conditions are presented which can be a part of futuristic distribution system planning and can successfully avoid system voltage collapse. [ABSTRACT FROM AUTHOR]

This work presents a stochastic two-stage mixed-integer nonlinear programming (MINLP) optimization model for the long-term planning of a distribution system (DS) to improve renewable energy integration over a ten-year period. The outer-stage problem simultaneously minimizes the long-term expected planning costs, power losses, and voltage deviations by determining the optimal sizing and placement of renewable energy resources (RESs), such as solar photovoltaic distributed generators (PV-DGS), wind-DGs, and battery energy storage systems (BESSs). In contrast, the inner-stage problem emphasizes the reduction of hourly operational expenses, power losses, and voltage deviations through the identification of optimal scheduling for demand response programs (DRPs) and network reconfiguration (NR). The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is utilized to address the outer-stage optimization problem. Multi-Objective Particle Swarm Optimization (MOPSO) is employed to address the inner-stage issue. In both phases, the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) is utilized at the conclusion of each iteration to identify the ideal solution from a collection of non-dominated solutions. Monte Carlo simulation (MCS) is utilized to model the system’s unknown factors, including solar radiation, wind speed, load demand, and energy pricing. Subsequently, the backward reduction algorithm (BRA) is employed to streamline the resulting scenarios into a more feasible and representative subset, therefore mitigating excessive computational effort. The suggested model is validated utilizing the IEEE 33-bus DS developed in MATLAB R2023b. Simulation outcomes from various case studies indicate that incorporating optimal DRP and NR scheduling into a hybrid system of RESs and BESSs enhances renewable energy penetration by 17.39% compared to the case utilizing just BESSs. Moreover, the established model, featuring a wind-DG/PV-DG/BESS/DRP/NR configuration, achieves significant improvements in all objective functions, including a 31.14% reduction in total system cost, a 61.67% decrease in power loss, and a 58.11% improvement in voltage deviation, compared to the base case.

This paper proposes an optimal planning technique for placing the multiple renewable energy (RE) based distributed generators (DGs), Distribution Static Compensators (DSTATCOMs), and electric vehicle charging stations (EVCSs) in the radial distribution network (RDN) considering the related uncertainties. This approach gives optimal placement and sizes for DGs and DSTATCOMs as well as a number of electric vehicles (EVs) that can be charged at the EVCSs by considering the network reconfiguration (NR). The optimal allocation of EVCSs fulfills the power demand from EVs at various locations and minimizes the negative impact on the power network. The RE-based DGs considered for this work are solar photovoltaic (PV) and wind. The uncertainties related to RE-based DGs and EVCSs have been modeled by using the probabilistic-based two-point estimate method (2PEM). The best locations and sizes are identified by optimizing the individual objectives that is active power losses and voltage stability index (VSI) using the teaching learning based optimization (TLBO) algorithm. Then both objectives are optimized by using the non-dominated sorting-based TLBO algorithm. Furthermore, the optimal planning approach is implemented on IEEE 33 and 69 bus test systems to demonstrate the suitability, practicality, and efficiency of the proposed optimal planning strategy. The obtained results reveal that the proposed technique is beneficial for determining the optimal locations for DGs, DSTATCOMs, and EVCSs without affecting the grid stability. The proposed planning approach can search better network structure with reduced power losses and voltage deviation, enhanced voltage profile, and improved voltage stability.

This research emphasizes a meta-heuristic modified ant lion optimizer (MALO) optimization approach for the simultaneous utilization of DSTATCOM devices and distributed photovoltaic (PV) sources with network reconfiguration in a radial power distribution scheme. In a radial power distribution network with network reconfiguration, the majority of the research is based on constant power model analysis. However, it is noticed that load models have a substantial impact on the distributed PV sources and the DSTATCOM device's optimal size and position. The effect of the constant power (CP) and polynomial (ZIP) with load growth load models for the simultaneous insertion of distributed PV sources and DSTATCOM devices with network reconfiguration is examined in this research work for power system planning. The penetration levels of distributed PV sources considered for the investigation are 25%, 50%, 75%, and 100%. The principal objective of this research is to reduce network total power losses, enhance the voltage magnitude profile at all buses, and reduce the overall operating cost while adhering to equality and inequality constraints. The proposed algorithm is verified on 118-node test systems. The investigation is carried out for planning network upgrading to a high-voltage distribution system (HVDS) on 317 nodes in the rural Bangalore Electricity Supply Company Limited (BESCOM) radial distribution scheme. The simulated results obtained with this method are validated with the BAT algorithm and techniques available in the literature. It is observed that in the IEEE 118-bus system, via the simultaneous placement and sizing of PV sources considering a 25% penetration level and DSTATCOM devices during network reconfiguration, the total power loss reduction is 41.47% and 42.98% for the constant power model and ZIP with the load growth model. For the 317-bus system, the total power loss reduction observed for 11 kV is 49.77% and 59.34% for the constant power model and ZIP model with load growth. Similarly, for the 22 kV system, the power loss reduction observed is 51.69% and 55.75% for the constant power model and ZIP with the load growth model. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Significant power losses and a subpar voltage profile compromise the performance of radial distribution networks. The radial distribution system's performance can be upgraded by utilizing different techniques. The paper thoroughly analyzes two least expensive techniques--network reconfiguration and capacitor placement--for diminishing power losses and upgrading the radial distribution system's voltage profile, which boosts the system's performance. The methodology involves formulating an objective function that minimizes the overall real power loss in a radial distribution network while subject to equality and inequality requirements, which are addressed by dingo optimization algorithm for each technique. A standard IEEE 33-bus network was utilized as a test case, and simulations were performed utilizingMATLAB/SIMULINK package. The results demonstrate that network reconfiguration outperformed capacitor placement technique, with total real power loss diminishing by 36.76% versus 34.44% for capacitor placement technique. Further, the minimum voltage magnitude is compared with capacitor placement technique, which was 0.9393 pu; the least voltage magnitude compared with network reconfiguration technique was 0.9824 pu. Therefore, network reconfiguration is a smart choice for improving the efficiency of radial distribution networks. [ABSTRACT FROM AUTHOR]

In order to drive electric vehicle adoption and bolster grid stability, the incorporation of battery swapping stations (BSSs) into the power grid is imperative. Conversely, network reconfiguration plays a crucial role in optimizing energy exchange within the power network, ensuring its economical and safe operation. Therefore, this study proposes an optimal planning method for battery swapping stations that integrates dynamic power distribution network reconfiguration while addressing technical aspects of the grid. The proposed method aims to concurrently optimize the placement and capacity of battery swapping stations, along with power distribution network reconfiguration, to enhance grid reliability and efficiency. The optimization model accounts for various factors including power quality, technical considerations, grid limitations, and operational expenses. A multi-objective optimization framework is devised to simultaneously reduce system losses, improve voltage stability, and mitigate environmental impacts of the power distribution network incorporating DG units. Case studies are conducted to illustrate the efficacy of the proposed approach in enhancing overall grid performance while accommodating the integration of battery swapping stations. The findings underscore the significance of considering technical factors and grid reconfiguration in battery swapping station planning to achieve optimal system operation and maximize benefits for electric vehicle users and grid operators alike. [ABSTRACT FROM AUTHOR]

This study provides an overview of distributed generation (DG) and shunt capacitor banks (SCB) integration in radial distribution networks (RDN). The integration of DG and SCB (IDG-SCB) supplies active and reactive power to the network to fulfill the network's increasing power demand, improve power quality, and increase network stability and security. This work also takes into consideration network reconfiguration (N-Rec). The N-Rec is rearranging RDN topology or structure to accomplish particular goals including load balancing, loss decreasing, regulating voltage and enhanced reliability. Reconfiguring the RDN makes sure that no single line is overloaded while others are left unused by dividing loads across its branches. Through load balancing, the system's overall capacity is maximized and the chance of overloading and related losses is decreased. In addition, N-Rec establishes channels for two-way power flow and maximizes the network's ability to manage fluctuating generation patterns; reconfiguration makes it easier to integrate IDG-SCBs into the RDN. The purpose of this work is to conduct a review of IDG-SCB with N-Rec in the last five years. The focus of this survey is on the allocation of DG and SCBs, their sizes both continuous and discrete. The optimization methods are categorized based on whether they are single-objective, multi-objective, or hybrid, and then further classified based on the objective functions applied in the method, constraints subjected to optimization, and the RDN that has been tested in the method. Additionally, a statistical analysis is conducted to analyze the most frequently used methods, objective functions, constraints, and RDNs in the field. Finally, As a case study, an analysis was conducted to propose the results of the most objective function used, i.e. power loss, in the most frequently used RDN, which are IEEE 33 and 69-bus RDN. The analysis considered IDG-SCB with N-Rec. The results show the effectiveness of IDG-SCB with N-Rec in improving power distribution system resilience and efficiency, as evidenced by the significant reduction in power loss of up to 97.95% that can be achieved by its implementation. [ABSTRACT FROM AUTHOR]

This article used an improved version of the Flow Direction Algorithm (FDA) with acceleration coefficients named Modified Flow Direction Algorithm (MFDA) for simultaneous Optimal Network Reconfiguration (ONR) and Optimal Distribution Generation's (ODG) installation to reduce the Active Power Losses (APL), maximize the Voltage Profile (VP) and stability of the Distribution System (DS). FDA is motivated by the simulation of the direction of water flow into a drainage basin at a lower elevation area. The neighboring flow and its slope also affect the direction of the water flow; its slope was obtained using the D8 method. FDA has a low convergence rate, stuck with local optima. This article proposed acceleration coefficients to acquire the proper balance between exploration and exploitation, accelerate the global convergence rate, and avoid getting stuck with local optima. The efficiency of the MFDA is evaluated based on its performance on 16 standard benchmark functions and is also used to solve the simultaneous ONR and ODG installation. Two DSs consisting of 33 and 69 bus test systems are considered to solve the problem. There are three methodologies to reduce the APL and maximize the VP and stability: ONR, ODG, and simultaneous ONR and ODG installation. This article uses four case studies with different load levels to show the proposed method's reliability. The analysis shows a better way to minimize the power loss by solving simultaneous ONR and ODG installation rather than only optimizing reconfiguration or installation of DG. It has been discovered that the results achieved are superior to those produced using the ISCA, FWA, HSA, etc. So, the MFDA shown can be a potentially useful way to solve 16 standard benchmark functions and simultaneous ONR and ODG installation. [ABSTRACT FROM AUTHOR]

This study presents a unique method for network reconfiguration for Searching Maximum Loading Capacity Radial Network from an interconnected meshed network using continuation power flow. The network reconfiguration outcomes are also evaluated for various graph theory approaches presented in the literature. The combined graph theory and machine learning methods are used to determine the best sectional switch position. The system's radiality is verified using graph theory. The recommended technique is tested on a 30-node mesh network with 41 sectional switches. The results demonstrate that the distribution system can manage a higher connected load using the suggested Bayesian-Ridge approach while lowering total active and reactive power losses. [ABSTRACT FROM AUTHOR]

Distribution systems are facing challenges in serving lifeline loads after extreme events. Network reconfiguration is a traditional and practical method for power supply restoration, which has strong but inflexible power transfer capabilities influenced by network topology. Multiple failures of utility power under extreme events will further limit the efficiency of network reconfiguration. Electric buses (EBs) can be utilized to achieve power supply considering their discharging capabilities as mobile storage devices. However, the mobility of EBs and the influences of transport systems must be carefully considered to enhance the resilience of distribution systems. Reconfiguration and EBs are complementary in terms of recovery capabilities and location flexibility, and more important loads can be recovered by the coordination between EBs and network reconfiguration. This paper proposes a coordinated restoration method for EBs and reconfigurations considering the influences of transport systems. The post-disaster restoration problem is formulated as a bi-level model, in which the network topology is optimized in the upper-level aiming at maximizing restoration loads through the main grid and EBs, while the traffic paths of all EBs are optimized with the goal of maximizing the restoration loads by the EBs in the lower-level considering time consumption and energy consumption during movement. The PSO and a genetic algorithm are used to solve the proposed bi-level optimization problem. Simulation studies are performed to verify the superiority of the proposed method.

This paper proposes a voltage source converter (VSC) -based AC-DC hybrid distribution system (HDS) resilient model to mitigate power outages caused by wildfires. Before a wildfire happens, the public-safety power shutoff (PSPS) strategy is applied to actively cut some vulnerable lines which may easily cause wildfires, and reinforce some lines that are connected to critical loads. To mitigate load shedding caused by active line disconnection in the PSPS strategy, network reconfiguration is applied before the wildfire occurrence. During the restoration period, repair crews (RCs) repair faulted lines, and network reconfiguration is also taken into consideration in the recovery strategy to pick up critical loads. Since there exists possible errors in the wildfire prediction, several different scenarios of wildfire occurrence have been taken into consideration, leading to the proposition of a stochastic multi-period resilient model for the VSC-based AC-DC HDS. To accelerate the computational performance, a progressive hedging algorithm has been applied to solve the stochastic model which can be written as a mixed-integer linear program. The proposed model is verified on a 106-bus AC-DC HDS under wildfire conditions, and the result shows the proposed model not only can improve the system resilience but also accelerate computational speed.

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.

As one of the important components of a ship, the ship’s integrated power system is an important safeguard for ships. In order to improve the service life of the ship’s power grid, the power system should be able to realize rapid reconstruction to ensure continuous power supply of important loads when the ship is attacked or fails suddenly. Therefore, it is of vital importance to study the reconfiguration technology of the ship’s integrated power system to ensure that it can quickly and stably cope with all kinds of emergencies in order to guarantee the safe and reliable navigation of the ship. This paper takes the ship’s ring power system as the research object and sets up the maximum recovery load and the minimum number of switching operations. The load is divided uniformly and the generator efficiency is balanced for the reconstruction of comprehensive function. It also sets up the system capacity, topology, and branch current limitations of the constraints to establish a mathematical model. The load branch correlation matrix method is used for branch capacity calculation and generator efficiency equalization calculation, and the load backup power supply path matrix is added on the basis of the matrix to judge the connectivity of some loads before reconfiguration. In this paper, for the network reconfiguration of the ship circular power system, which is a discrete nonlinear problem with multiple objectives, multiple time periods, and multiple constraints, we choose to use the particle swarm algorithm, which is suitable for global optimization, with a simple structure and fewer parameters; improve the particle swarm algorithm using the swarm exchange strategy by setting up two main and auxiliary swarms for global and local search; and exchange some of the particles with the golden ratio in order to keep the diversity of the populations. The simulation results of the network reconfiguration of the ship power system show that the improved algorithm can solve the power system network reconfiguration problem more effectively and provide a feasible reconfiguration scheme in a shorter time compared with the chaotic genetic algorithm under the same fault case test, and it also proves that the use of the swarm exchange particle swarm algorithm greatly improves the performance of reconfiguring the power grid of the ship.

In response to global energy, environment, and climate concerns, distributed photovoltaic (PV) power generation has seen rapid growth. However, the intermittent and uncertain nature of PVs can cause voltage fluctuations in distribution systems, threatening their stability. To address this challenge, this paper proposes an active distribution network voltage optimization method, of which the main contribution is the development of a comprehensive voltage optimization strategy that integrates day-ahead prediction and real-time adjustment, significantly enhancing the stability and efficiency of distribution networks with high PV penetration. In the day-ahead prediction stage, the forecast scenarios of load and PV output guide network reconfiguration for improved voltage distribution. In the real-time operation stage, flexible regulation of PV and energy storage systems is used to adjust power outputs, further optimizing voltage quality. Simulations on the IEEE 33-bus system show that the method effectively improves voltage distribution, enhances renewable energy consumption, and ensures the safe, economic operation of the distribution system.

This paper presents the development of a generic active distribution network (ADN) operation simulation framework that incorporates selected swarm optimization algorithms (SOAs) for the purpose of reducing CO2 emissions and line loss minimization through network reconfiguration (NR). The framework has been implemented in the ADN of Taipower. Network data, provided by the Distribution Mapping Management System and Distribution Dispatch Control Center (DDCC) of Taipower, were converted into an OpenDSS script to create ADN models. The SOA is integrated into the framework and utilized to determine the statuses of both four-way and two-way switches in the planning and operating stages, in accordance with the proposed multi-objective function and operational constraints. The weightings for these decisions can be customized by distribution operators to meet their specific requirements. In this paper, the weighting for line loss reduction is set to one for minimizing CO2 emissions. The numerical results demonstrate that the proposed ADN framework can recommend a feeder switching scheme to distribution operators, aiming to balance feeder loading and minimize the neutral line current. Finally, this approach leads to reduced line losses and minimizes CO2 emissions. In contrast to relying solely on historical operational experience, this generic ADN reconfiguration framework offers a systematic approach that can significantly contribute to reducing CO2 emissions and enhancing the operational efficiency of ADNs. [ABSTRACT FROM AUTHOR]

Distribution networks transmit the electrical power generated by generation systems to consumers. The distribution network poses a significant challenge in a power system, as it incurs the highest losses due to its low voltage and high current characteristics. Therefore, addressing the issue of reducing distribution network losses is crucial. Over the past 46 years, distribution network reconfiguration (DNR) has been extensively studied as a combinatorial optimization problem. DNR is one of the most intensively investigated topics, accompanied by new challenges. This study focuses on the optimal reconfiguration of the distribution network and the allocation of shunt capacitors to reduce losses and enhance voltage profiles. The cultural algorithm (CA) and the cuckoo search algorithm (CSA) were compared for solving network reconfiguration and capacitor allocation. The cultural algorithm (CA) and the cuckoo search algorithm (CSA) were compared for solving network reconfiguration and capacitor allocation by using direct load flow to minimize power loss and voltage deviation. By comparing the performance of these two algorithms and examining their advantages and disadvantages, the appropriate algorithm can be selected for future studies. The performance of the simulations was verified using MATLAB software on IEEE 33-bus and IEEE 69-bus systems. [ABSTRACT FROM AUTHOR]

Rapid electric vehicle (EV) adoption has increased the need for public charging infrastructure. Public charging station (PCS) placement and size must be determined to enable efficient and sustainable charging services. This work proposes a novel method for optimal design and allocation of hybrid photovoltaic systems (HPVs) and PCSs in reconfigurable feeders. A novel LFHBA combines the strengths of the levy flight (LF) exploration technique and the honey badger algorithm (HBA) to solve the multi-objective function focused on distribution loss reduction, improve voltage profiles, and improve reliability index. To test the suggested technique, modified IEEE 69-bus distribution feeders are simulated with varied EV load penetrations. LFHBA is compared to basic HBA, butterfly optimization algorithm (BOA), pelican optimization algorithm (POA), pathfinder algorithm (PFA) in computing performance. The comparison analysis shows that LFHBA has lower target values and greater convergence. Reconfigurable feeder topology permits distribution network design changes, improving system dependability and minimizing power losses. In comparison to base case, EV penetration causes to raise the losses by 26.65%, by optimal allocation of PCSs alone causes to reduce the losses by 38.82%, optimal allocation of PVs and ONR causes reduce losses by 92.19%, whereas, simultaneous allocation of PCSs, and HPVs results to reduce losses by 96.47%. This study emphasizes the need of optimizing PCS placement and capacity with HPVs for efficient and sustainable charging services. As shown by its greater performance over other optimization methods, the LFHBA algorithm helps achieve these goals. Reconfigurable feeders and renewable energy sources improve system dependability and power losses, increasing EV charging infrastructure. [ABSTRACT FROM AUTHOR]

Emergency load shedding (ELS) is a vital measure for power systems to manage extreme events, ensuring the safety, stability, and economic operation of the grid. The integration of distributed energy resources and controllable devices in modern power systems has bolstered grid flexibility. Consequently, developing precise load shedding strategies to balance economic and security goals has emerged as a prominent subject in power system optimization. However, existing methods exhibit inadequacies, including overlooking practical operability, privacy concerns, and a lack of adaptability to response time requirements. To address these gaps, this paper introduces a precise ELS approach for distributed networks with a focus on response time needs. Contributions encompass designing load shedding processes for various response times, integrating demand response, and partitioning networks for optimized load shedding. Through validation using standard test cases, the proposed approach effectively utilizes response time and demand-side resources for precise ELS control in distribution networks. It accommodates different scenarios, offering a robust solution for rapid and accurate load shedding during emergencies. [ABSTRACT FROM AUTHOR]

Voltage instability and power losses is a key problem in the power system that increases the cost of operation of electric utilities and later increases the cost of electricity. One approach to solving the issue is to reconfigure the distribution network. By using a FACTS device, such as SVC (Static VAR Compensator) in a reconfigured network the loss can be reduced, and the voltage stability margin can be preserved. The primary challenge is locating the ideal system position for SVC connections. In this study, the machine learning technique is utilized to forecast the FVSI (Fast Voltage Stability Index) values of the lines for the preferred network placement of SVC. The ML algorithms are trained and predicated using the estimated FVSI values for various network reconfigurations. The proposed work has been tested using the IEEE 14 bus system. [ABSTRACT FROM AUTHOR]

To achieve the flexible operation of the active distribution network (ADN), an optimal control method for the ADN considering the soft open point (SOP) and thermostatically controlled loads under distributed photovoltaic (PV) access is proposed. First, the mathematical model of the SOP and thermostatically controlled loads are constructed based on the flexibility of the SOP and the thermal storage characteristic of thermostatically controlled loads, respectively. Then, the mathematical model of theADNconsidering the SOP and thermostatically controlled loads are further constructed. The optimal control of the ADN is realized by adjusting the transmission power of the SOP and the switching state of the electric water heater. Finally, the optimal operation analysis of the SOP and thermostatically controlled loads under various control methods is carried out, and the impact of the SOP and thermostatically controlled loads on the economic and secure operation of the ADN is further investigated. The results show that the proposed method taps the demand response potential of thermostatically controlled loads while satisfying the water consumption habits of different customers, and utilizes the network reconfiguration with the SOP to further improve the security and economy of the ADN operation. [ABSTRACT FROM AUTHOR]

This study focuses on mitigating power losses within unbalanced radial distribution networks by employing transformer modeling and network reconfiguration. The process commences with load flow analysis, utilizing a simplified three-phase load flow method tailored for unbalanced radial distribution networks (URDNs) featuring voltage-dependent loads. Using vector data and fundamental electric circuit analysis, the algorithm efficiently resolves voltage magnitude equations, conserving memory resources, and accurately identifies buses and branches downstream from a designated bus. This method circumvents the repetitive identification issues inherent in conventional forward-backward sweep approaches. The proposed methodology demonstrates robust convergence when applied to URDNs with realistic resistance/reactance ratios and has been rigorously tested on 19-bus and 25-bus unbalanced radial distribution networks. Evaluation criteria encompass CPU execution time and iteration benchmarks. Leveraging empirical formulas, this study achieves optimal designs characterized by improved voltage profiles and reduced power losses. An asymmetric power flow program is employed to compare bus voltages and system power losses, facilitating informed switch operation decisions and allowing for the elimination of feeder sectionalizing switch actions. This approach streamlines CPU processing time by eliminating switching procedures and has been successfully validated using 19-bus and 25-bus URDN samples. This work distinguishes itself through its efficiency, necessitating fewer switching operations when compared to existing methodologies.

This paper introduces a new algorithm designed to address the challenge of distribution network reconfiguration, employing the tabu search metaheuristic in conjunction with the voltage drop concept. Distinguishing itself from existing methods, our proposed approach not only utilizes voltage drop for obtaining the initial solution but also introduces a novel technique for generating a candidate solution neighborhood. This method leverages both randomness and voltage drop, ensuring a smooth and steady descent during algorithm execution. The primary goal of our algorithm is to minimize active power losses within distribution networks. To validate its effectiveness, the proposed method underwent testing on three commonly referenced distribution systems: the 33-Bus, 69-Bus, and 94-Bus systems, widely acknowledged in the literature. A pivotal aspect of our work involves the synergy of the tabu search algorithm with a combination of both random and deterministic methods for generating neighbors. This strategic amalgamation plays a crucial role, enabling rapid execution while consistently yielding high-quality solutions. Additionally, the adoption of the electric distance method for generating the initial solution adds significant value, offering a commendable solution with minimal computational effort. Comparative assessments against other algorithms documented in the literature underscore the superior efficiency of our proposed algorithm.

To achieve the flexible operation of the active distribution network (ADN), an optimal control method for the ADN considering the soft open point (SOP) and thermostatically controlled loads under distributed photovoltaic (PV) access is proposed. First, the mathematical model of the SOP and thermostatically controlled loads are constructed based on the flexibility of the SOP and the thermal storage characteristic of thermostatically controlled loads, respectively. Then, the mathematical model of the ADN considering the SOP and thermostatically controlled loads are further constructed. The optimal control of the ADN is realized by adjusting the transmission power of the SOP and the switching state of the electric water heater. Finally, the optimal operation analysis of the SOP and thermostatically controlled loads under various control methods is carried out, and the impact of the SOP and thermostatically controlled loads on the economic and secure operation of the ADN is further investigated. The results show that the proposed method taps the demand response potential of thermostatically controlled loads while satisfying the water consumption habits of different customers, and utilizes the network reconfiguration with the SOP to further improve the security and economy of the ADN operation.

The active distribution network (ADN) integrated optimization is the optimization of the system using three optimization techniques: reconfiguration, reactive power optimization and optimal configuration of distributed generation. This study establishes an optimization model of the renewable energy ADN and proposes a novel multi-objective bald eagle search algorithm to optimize the model. This study integrates three techniques of network reconfiguration, reactive power optimization and optimal configuration of distributed power sources to establish a comprehensive optimization model of ADN, which aims to reduce ADN energy losses, improve active distribution network power quality and economic efficiency. The contributions are as follows: (1) a multi-objective bald eagle search algorithm is proposed to deal with the complex nonlinear ADN integrated optimization problem, and its superiority is verified; (2) an ADN integrated optimization model is established; and (3) multiple scenarios are established in the IEEE33 node system for comparison and validation to verify the effectiveness of the proposed model. The results show that the proposed model can effectively improve the ADN operation with power loss, node voltage deviation and system cost reduced by 86.34%, 83.12% and 30.27%. The proposed algorism improves the power quality of ADN and promotes economic production and sustainable energy development. [ABSTRACT FROM AUTHOR]

Electrical Power quality in distribution systems is crucial to both utility and consumers simultaneously. The main issue that affects the quality factor of electrical distribution systems is phase load imbalance. Phase imbalance is a major issue in distribution networks in Pakistan, India, the United States, China, and other nations and regions. The distribution system in Pakistan is normally a three-phase, four-wire system, whereas our residential and commercial loads are often single-phase, resulting in an unbalanced system. These unbalanced circumstances in the system result in single-phasing, overloading, and overheating situations, and the return of current to neutral, as well as increased power system investment and operational expenses. In this paper, several methodologies for phase balance are being studied. After researching several techniques of phase balancing and building on that methodology, a simulation prototype is developed, and different unbalanced situations are studied. To analyze unbalanced conditions in practical mode, a hardware prototype is developed on the basis of simulation. Examined some unbalanced loads on the simulation prototype and then on the hardware prototype and achieved the best possible load balancing on phases. [ABSTRACT FROM AUTHOR]

The random output of renewable energy and the disorderly grid connection of electric vehicles (EV) will pose challenges to the safe and stable operation of the power system. In order to ensure the reliability and symmetry of the microgrid operation, this paper proposes a microgrid optimization scheduling strategy considering the access of EVs. Firstly, in order to reduce the impact of random access to EVs on power system operation, a schedulable model of an EV cluster is constructed based on the Minkowski sum. Then, based on the wavelet neural network (WNN), the renewable energy output is predicted to reduce the influence of its output fluctuation on the operation of the power system. Considering the operation constraints of each unit in the microgrid, the network active power loss and node voltage deviation are taken as the optimization objectives, and the established microgrid model is equivalently transformed via second-order cone relaxation to improve its solution efficiency. Based on network reconfiguration and flexible load participation in demand response, the economy and reliability of system operation are improved. Finally, the feasibility and effectiveness of the proposed method are verified based on the simulation examples. [ABSTRACT FROM AUTHOR]

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