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Distribution systems in traditional power systems (PS) constituted of passive elements and the distribution issues were then limited to voltage and thermal constraints, harmonics, overloading and unbalanced loading, reactive power compensation issues, faults and transients, loss minimization and frequency stability problems, to name a few. Contemporary distribution systems are becoming active distributed networks (ADNs) that integrate a substantially increasing amount of distributed energy resources (DERs). DERS include distributed generation (DG) sources, energy storage resources and demand side management (DSM) options. Despite their evidenced great benefits, the large-scale deployment and integration of DERs remain a challenge as they subsequently lead to the network operational and efficiency issues, hampering PS network reliability and stability. This paper carries out a comprehensive literature survey based on the last decade's research on operational challenges reported and focusing on dispatchable and non-dispatchable DGs grid integration, on various demand response (DR) mechanisms and, on battery energy storage system (BESS) charging and discharging challenges, with the aim to pave the way to developing suitable optimization techniques that will solve the coordination of multiple renewable sources, storage systems and DRs to minimize distribution systems' operational issues and thus improve stability and reliability. This paper's findings assist the researchers in the field to conduct further research and to help PS planners and operators decide on appropriate relevant technologies that address challenges inherent to DG grid integration. [ABSTRACT FROM AUTHOR]

Due to increasing load demand and the energy crisis, microgrids (MGs) have attracted more attention. The idea and technology of microgrids (MGs) have undergone significant advancements largely aimed at enabling the automation of distribution systems and enhance the integration of renewable energy sources (RESs). In this paper, the MG is a combined form of various distributed generations (DGs), battery energy storage system (BESS), and plug-in hybrid electric vehicles (PHEVs). A novel approach is introduced to minimize the operational expenses of the grid-connected low-voltage microgrid by leveraging a probabilistic expert optimization technique. This methodology takes into account various factors such as power supply costs, power dissipation, PHEV charging requirements, and the optimal capacity of BESS. Three charging techniques are proposed in this paper to control the PHEVs' charging demand at homes and public charging stations. This study employs a comprehensive model to simulate a whole day, accounting for the stochastic charging requirements of PHEVs, load profiles, RESs outputs, and variations in energy price. To address the inherent nonlinearity of the optimization problem, the energy management within the MG is tackled using the slime mould algorithm, a population-based stochastic optimizer. The primary goal is to minimize operational costs in the microgrid by optimizing the efficient utilization of available resources. [ABSTRACT FROM AUTHOR]

Rapid placement of electric vehicle charging stations (EVCSs) is essential for the transportation industry in response to the growing electric vehicle (EV) fleet. The widespread usage of EVs is an essential strategy for reducing greenhouse gas emissions from traditional vehicles. The focus of this study is the challenge of smoothly integrating Plug-in EV Charging Stations (PEVCS) into distribution networks, especially when distributed photovoltaic (PV) systems are involved. A hybrid Genetic Algorithm and Simulated Annealing method (GA-SAA) are used in the research to strategically find the optimal locations for PEVCS in order to overcome this integration difficulty. This paper investigates PV system situations, presenting the problem as a multicriteria task with two primary objectives: reducing power losses and maintaining acceptable voltage levels. By optimizing the placement of EVCS and balancing their integration with distributed generation, this approach enhances the sustainability and reliability of distribution networks. [ABSTRACT FROM AUTHOR]

More and more distributed generations (DGs), such as wind, PV or battery bank sources, are connected to electric systems or customer loads. However, the locations of these DGs are based on the highest energy that can be potentially harvested for electric power generation. Therefore, these locations create different line impedances based on the distance from the DGs to the loads or the point of common coupling (PCC). This paper presents an adaptive virtual impedance (AVI) in the predictive control scheme in order to ensure power sharing accuracy and voltage stability at the PCC in a microgrid network. The reference voltage from mismatched feeder impedances was modified by utilizing the suggested AVI-based predictive control for creating equal power sharing between the DGs in order to avoid overburdening any individual DG with low-rated power. The AVI strategy used droop control as the input control for generating equal power sharing, while the AVI output was used as the reference voltage for the finite control set–model predictive control (FCS-MPC) for creating a minimum voltage error deviation for the cost function (CF) for the inverter's vector switching pattern in order to improve voltage stability at the PCC. The proposed AVI-based controller was tested using two DG inverter circuits in a decentralized control mode with different values of line impedance and rated power. The performance of the suggested controller was compared via MATLAB/Simulink with that of a controller based on static virtual impedance (SVI) in terms of efficiency of power sharing and voltage stability at the PCC. From the results, it was found that (1) the voltage transient magnitude for the AVI-based controller was reduced within less than 0.02 s, and the voltage at the PCC was maintained with about 0.9% error which is the least as compared with those for the SVI-based controller and (2) equal power sharing between the DGs increased during the change in the load demand when using the AVI-based controller as compared with using the SVI-based controller. The proposed controller was capable of giving more accurate power sharing between the DGs, as well as maintaining the voltage at the PCC, which makes it suitable for the power generation of consumer loads based on DG locations for future energy communities. [ABSTRACT FROM AUTHOR]

The penetration of distributed generation (DG) in the distribution network has become a necessity and a significant solution to improve power grid quality, and solve power losses issue. To reach these targets, integrating these DGs in an optimal placement with an optimal sizing should be investigated and taken into consideration. This paper focuses on obtaining the optimal allocation and size of a photovoltaic (PV) distributed generation (PVDG) in order to reduce the total power losses and enhance voltage and frequency profiles of a modified IEEE 14 node distribution network (Hooshmand in J Appl Sci 8(16):2788–2800, 2008, https://doi.org/10.3923/jas.2008.2788.2800). An objective function is used in this paper aims to reduce grid power losses, and two optimization algorithms are applied to solve this function which are the particle swarm optimization (PSO) and the genetic algorithm (GA). Added to that, two scenarios are discussed in this paper in order to analyze the effects of variable PV penetration level, hourly load consumption profile variation and atmospheric condition change on the sizing optimization resolution. The obtained simulation results prove that the PSO algorithm has better performance compared to the GA in terms of speed convergence, power loss reduction and grid quality improvement (voltage and frequency profiles). Then, it shows that variable load consumption curve and weather condition change can affect not only the determination of the PVDG optimal position and capacity but also grid security. [ABSTRACT FROM AUTHOR]

With the emergence of the smart grid, the distribution network is facing various problems, such as power limitations, voltage uncertainty, and many others. Apart from the power sector, the growth of electric vehicles (EVs) is leading to a rising power demand. These problems can potentially lead to blackouts. This paper presents three meta-heuristic techniques: grey wolf optimization (GWO), whale optimization algorithm (WOA), and dandelion optimizer (DO) for optimal allocation (sitting and sizing) of solar photovoltaic (SPV), wind turbine generation (WTG), and electric vehicle charging stations (EVCSs). The aim of implementing these techniques is to optimize allocation of renewable energy distributed generation (RE-DG) for reducing active power losses, reactive power losses, and total voltage deviation, and to improve the voltage stability index in radial distribution networks (RDNs). MATLAB 2022a was used for the simulation of meta-heuristic techniques. The proposed techniques were implemented on IEEE 33-bus RDN for optimal allocation of RE-DGs and EVCSs while considering seasonal variations and uncertainty modeling. The results validate the efficiency of meta-heuristic techniques with a substantial reduction in active power loss, reactive power loss, and an improvement in the voltage profile with optimal allocation across all considered scenarios. [ABSTRACT FROM AUTHOR]

Summary: The potential for distributed generation (DG) to minimize power loss, increase productivity, lower investment costs, and, most significantly, the ability to leverage renewable energy resources such as wind, photovoltaic (PV), and microturbines, which generate power with low greenhouse‐gas emissions, has piqued interest in the power sector. Deploying DG in an unfavorable location can result in a slew of problems, including increased system failures and costs, voltage spikes and swings, and stability and resilience issues. As a result, an optimization or perceptual technique‐based paradigm is needed to identify the optimal configuration of distributed renewable energy generation for a given system that can provide fiscal, environmental, and technological benefits. Several researchers looked at the best possible location for distributed renewable generation based on their needs and goals. The formal theory for this issue, however, remains unsolved. Individual studies on solar PV systems, types of solar PV systems, wind power generation, and pumped hydro storage systems are all covered in detail in this paper. In addition, the study focuses on a variety of optimization techniques and algorithms for providing a better perspective on resolving issues related to the integration and functionality of many renewable resources in an islanded as well as grid‐connected environment. The heuristic algorithms are explained with respective flow charts individually. Finally, a comparative analysis has been carried out in order to understand and validate the usefulness of individual optimization techniques with itself and others. [ABSTRACT FROM AUTHOR]

An active distribution network is an important development trend of the power grid with widespread use of the distributed generation. The reliability of the active distribution network is not negligible due to the uninterruptible power supply. In the paper, the reliability evaluation method of the active distribution network is proposed in detail, based on combining the roulette wheel selection and the sequential Monte Carlo algorithm. The uncertainty of both the distribution generation and the load is taken into consideration based on the power probability distribution and the working state in the presented model. Furthermore, the IEEE-RBTS Bus 6 is used to verify the validity of the proposed method. The result shows that the new energy access improves the availability and the reliability of the active distribution network. [ABSTRACT FROM AUTHOR]

State estimation (SE) is regarded as an essential tool for achieving the secure and efficient operation of distribution networks, and extensive research on SE has been conducted over the past three decades. Nonetheless, the high penetration of distribution generations (DGs) is accompanied by uncertainties and dynamics, and the extensive application of intelligent electronic devices (IEDs) is associated with data processing issues, all of which raise new challenges, and these issues must be taken care of for further development of SE in smart distribution networks. This paper attempts to present a comprehensive literature review of numerous works that address various issues in SE, examining key technical research issues and future perspectives. Hopefully, it will be able to meet the needs for the development of smart distribution networks. [ABSTRACT FROM AUTHOR]

Modern communication networks and digital control techniques are used in a smart grid. The first step is to classify the features of several communication networks and conduct a comparative investigation of the communication networks applicable to the smart grid. The integration of distributed generation has significantly increased as the global energy demand rises, and sustainable energy for electric vehicles and renewable energies worldwide are being pursued. Additional explanations for this surge include environmental concerns, the reforming of the power sector, and the advancing of small-scale electricity generation technologies. Smart monitoring and control of interconnected systems are required to successfully integrate distributed generation into an existing conventional power system. Electric-vehicles-based smart grid technologies are capable of playing this part. Smart grids are crucial to avoid becoming locked in an obsolete energy infrastructure and to draw in new investment sources and build an effective and adaptable grid system. To achieve reliability and high-quality power systems, it is also necessary to apply intelligent grid technologies at the bulk power generation and transmission levels. This paper presents smart grid applicable communication networks and electric vehicles empowering distributed generation systems. Additionally, we address some constraints and challenges and make recommendations that will give proper guidelines for academicians and researchers to resolve the current issues. [ABSTRACT FROM AUTHOR]

• I wish to submit an original research article entitled "Performance of a New Hybrid Approach for Detection of Islanding for Inverter-Based DGs" for consideration by Engineering Science and Technology, an, Elsevier. I certify that this work is unique and has never been published before, nor is it being considered for publication anywhere. • At present scenario renewable energy sources need to satisfy the load demand.In this Islanding identification is critical for inverter-based DGs to operate safely and reliably. Generally Islanding detection is possible by either active or passive method but in this paper, hybrid method follows the combination of both active and passive methods. For an inverter-based grid integrated DG system, this hybrid method allows for quick detection of islanding possible by minimizing the Non-Detection Zone (NDZ). The hybrid method combines the active Sandia Frequency Shift (SFS) with the passive reactive power variation technique. Adaptive tweaking of the gain parameter Ksfsutilising the Adaptive Particle Swarm Optimization (APSO) technique yields the improved active SFS method. The proposed hybrid method produces a significant reduction of NDZ and suitable for a more accurate detection of islanding condition. The effect of NDZ for the proposed hybrid method has been derived analytically and the system performance has been assessed, under various power mismatch conditions for a DG-grid interface. The overall system performance has been found to be superior under the presence of constant power control strategy, the effect of frequency and voltage variations at the common coupling point during islanding have also been considered. Finally, the performance of the proposed method under the IEEE standard 1547 islanding test condition has been investigated for different load factors and load variations. The simulation results presented for a three-phase grid connected 120 KVA DG, using MATLAB presents the capability of the proposed method. This paper proposes a new hybrid method for islanding detection and minimizes the Non-Detection Zone (NDZ), for an inverter-based grid-connected distributed generation (DG) system. The proposed hybrid method is an integration of the modified active Sandia Frequency Shift (SFS) method and the passive reactive power variation technique. The modified active SFS method is obtained by adaptive tuning of the gain parameter K sfs , using the Adaptive Particle Swarm Optimization (APSO) technique. The proposed hybrid method produces a significant reduction of NDZ and is suitable for more accurate detection of islanding condition. The effect of NDZ for the proposed hybrid method has been derived analytically and the system performance has been assessed, under various power mismatch conditions for a DG-grid interface. The overall system performance has been found to be superior under the presence of constant power control strategy. The effect of frequency and voltage variations at the common coupling point during islanding have also been considered. Finally, the performance of the proposed method under the IEEE standard 1547 islanding test condition has been investigated for different load factors and load variations. [ABSTRACT FROM AUTHOR]

Phase-locked loop (PLL) detects the total harmonic distortion variation by considering the fundamental component of current and voltage. The conventional PLL harmonic monitoring method considers only the fundamental harmonic content to decide the islanding. This paper proposes technique by introducing modified synchronous reference frame PLL to detect island condition with support of harmonic variation at point of common coupling. The proposed method can detect state of island using fundamental 3rd, 5th, and 7th harmonic content to decide the islanding condition. Three different islanding conditions and five different non-islanding conditions were taken into consideration for analysing the proposed method. The proposed method was compared with the conventional PLL to evaluate the effectiveness. The simulation results were tested and validated with experiment results, and the proposed method is recommended as another effective alternative for islanding detection. Also the proposed method has been tested for multiple distributed generation sets connected to the grid for testing of effectiveness of technique. [ABSTRACT FROM AUTHOR]

Due to Immense employment of electronics equipment/gadgets quality of power has turned into essential and important factor. The conventional devices or equipment is not sufficient to increase the power quality. The UPQC is the modern method to improving the quality of power & furthermore it attends simultaneously voltage deformity and current deformity. In this paper new FACTs (flexible Alternating current Transmission) circuit termed as UPQC (unified power quality conditioner) is designed and developed to recompose the voltage and current imperfections. The two different controlling method are implemented. The sag as well as swells of voltage along with current harmonic compensation are shown. The micro-grids are interconnection of renewable resources available at distribution end. Micro -grids can be regulated in three different forms, they are grid connected mode, autonomous mode & micro generation mode .The battery system is conceited to DC-link for supplying real power in the micro-grid. The new controlling method is employed for the integration and control of quality in distribution generation based micro-grid system. [ABSTRACT FROM AUTHOR]

This paper investigates the harmonic interactions of current controllers in multiparalleled grid-connected inverters. Potential harmonic instability phenomenon, which features oscillations above the fundamental frequency, is evaluated by the impedance-based stability criterion. The possible reasons of the destabilized inverter with different types of grid impedances are explained by the passivity-based analysis and impedance-based stability criterion. Thereafter, case studies are provided, which shows how the nonpassive grid connected inverters interact with the varying grid impedance due to load changes or by the new connections of other grid-connected inverters. Time domain simulations in the PSCAD/EMTDC environment and experimental results show the corresponding harmonic interaction problems may exist in nowadays power system and the impedance-based stability analysis can be an effective tool to assess those problems. [ABSTRACT FROM PUBLISHER]

Demand response acts as an effectual tool to better balance electricity demand and supply in the smart grid. The response to prices and incentives is described as "changes on electricity usage from conventional consumption patterns to end‐use customers." In this paper, a hybrid approach is proposed for Electric Vehicle Based Grid connected to the distribution generation (DG). The proposed system is joined performance of student psychology optimization algorithm (SPOA) and AdaBoost algorithm, thus it known as SPOA2B approach. The major aspire of this study is "to minimize the peak power cutoff, voltage regulation, spin reserve for making the optimization mode ideally convex, and accurate second‐order conic relaxations." Additionally, the SPOA2B method is a proficient deal by standard business solvers with allocation models, and then acquires least annualized costs. In this study, the electric vehicle charging station (EVCS) is linked to DG, and then it gives charging service to electric vehicles (EVs). When the EV appears at the charging station, it informs the EVCS operator of their own energy requirement with anticipated departure time. Every EVCS can capture the information of all EVs using an SPOA2B technique. By then, the proposed system is performed on MATLAB/Simulink site and the efficiency is compared with other processes. The number of iteration, like, 100, 250, 500, and 1000, the simulation time of the proposed technique is 14.8, 29.2, 69.9, and 77.1 s, respectively. [ABSTRACT FROM AUTHOR]

The population is speeding up and the demands for electrical energy are clearly increasing, this growth in load leads to higher power loss and Voltage drop. This paper is focused on a method to decrease the power losses and voltage profile improvement. The first suggested technique binary particle swarm optimization (BPSO) is utilized for solving the problem of the power loss minimization in network distribution. This work based on optimum position and sizing of the distribution generation (DG) units, shunt capacitor (SC) with network reconfiguration is applied to show the improvement of the network distribution efficiency. The MATLAB programming part and software package MATPOWER7 are used to simulate 69-bus and 33-bus test system with three different cases of loads and different number of DG and SC. The result showed a positive impact on system efficiency in comparison with other previous studies. This paper showed that increase of DG and capacitor does not usually give the best result although the increase of system cost, maintenance, and the units' distance for gas supplying. [ABSTRACT FROM AUTHOR]

• Traditionally, assessment of static voltage stability for distribution power networks with distributed generation (DG) utilizes the continuous power flow (CPF) method by calculating load margin index (LMI) at various load levels, which suffers from slow calculation speed and low accuracy when power flow calculation becomes ill-conditioned. • To overcome this issue, this paper presents a novel holomorphic embedding load flow method (HELM)-based approach that calculates voltage stability indices for various types of nodes in distribution network. • Different orders of sensitivity information are calculated using the HELM method, and the criteria for assessing voltage stability are derived, which are used as constraints to optimize the location and capacity of DG. • Such formulation of the optimization problem does not need to calculate the maximum load margin using CPF that can be effectively solved using the Genetic Algorithm and easily adaptable to various operating conditions of DG. • The effectiveness of the proposed approach is verified on IEEE 33-node and 69-node distribution power network model. • Results show that considering the HELM VSI, the location and capacity of DG in the distribution network can be better optimized. Traditionally, assessment of static voltage stability for distribution power networks with distributed generation (DG) utilizes the continuous power flow (CPF) method by calculating load margin index (LMI) at various load levels, which suffers from slow calculation speed and low accuracy when power flow calculation becomes ill-conditioned. To overcome this issue, this paper presents a novel holomorphic embedding load flow method (HELM)-based approach that calculates voltage stability indices for various types of nodes in a distribution network. Different orders of sensitivity information are calculated using the HELM method, and the criteria for assessing voltage stability are derived, which are used as constraints to optimize the location and capacity of DG. Such formulation of the optimization problem does not need to calculate the maximum load margin using CPF. Moreover, it can be effectively solved using the Genetic Algorithm and easily adaptable to various operating conditions of DG, which improve the calculation speed of the stability index. The effectiveness of the proposed approach is verified on IEEE 33-node and IEEE 69-node distribution power network models. Results show that considering the HELM VSI, the location, and capacity of DG in the distribution network can be better optimized. For the IEEE 33-node model, the total cost is reduced by approximately 2.33%, the network loss is reduced by 1.2%, and the HELM voltage stability criteria index is increased by about 4.23%; while for the IEEE 69-node system, the total cost is reduced by about 2.98%. [ABSTRACT FROM AUTHOR]

The climate conditions can affect the accuracy of photovoltaic (PV) system performance models that can be used for monitoring and diagnostic purposes. In this paper, an improved energetic physical model has been considered to estimate the power production of PV systems. Specifically, in this model, special attention has been devoted to the modeling of optical losses (IAM losses) and to the inverter efficiency under low-power conditions. A comparative analysis of the proposed model applied to two PV plants installed respectively in the north of Denmark (Aalborg) and in the south of Italy (Agrigento) has been performed. The different ambient, operating and installation conditions allow to understand how these factors impact the precision and effectiveness of such model. The experimental results show that the proposed model allows us to have a good estimation of AC power in terms of Relative Mean Bias Error, normalized Root-Mean-Square Error, and the Correlation Coefficient. Further in order to have the possibility to analyze and compare the performance of the studied PV plants with others, the efficiency of the systems has been estimated by both Performance Ratio and Corrected Performance Ratio. [ABSTRACT FROM AUTHOR]

In this paper a practical power system planning and control strategy based on a new adaptive sine cosine arithmetic optimization algorithm (ASC_AOA) is proposed to enhance the technical performances of radial distribution (RD) systems. The main objective considered in this study is to optimize the location and the size of shunt compensators based STATCOM devices and multi distributed generation to reduce the total power losses, and to maximize the loading margin stability of practical RD systems. As well confirmed in many recent researches, the success of metaheuristic algorithms is related to the interactivity between intensification and diversification stages. In this study, an adaptive process based on sine and cosine functions is incorporated within the standard AOA to guide the search process toward the best solution. The particularity of the proposed variant (ASC_AOA) has been validated on many benchmark functions, and also applied for optimal automation control of multi DGs and shunt compensators based STATCOM Controllers to enhance the performances of various types of RD systems, such as the 33-bus, the 69-bus, and 85-bus. Obtained results are compared to many recent optimization methods. It is confirmed that the proposed variant achieves the best solution in all of the cases studies elaborated. The proposed variant seems to be a competitive technique and an alternative tool for solving various combinatorial planning and control problems of modern RD systems. [ABSTRACT FROM AUTHOR]

Increasing the short circuit current due to the penetration of distributed generations (DGs) in various voltage levels and meshed topology is a basic problem in power systems. Using fault current limiter (FCL) is an efficient approach to mitigate the exceeded short circuit levels. In this paper, a new approach is presented for multiple FCLs locating to decrease short circuit levels in meshed networks with several subsystems and multi-level voltages. Modified hybrid genetic algorithm (GA) and sensitivity analysis (SA) are used to determine the type, number, location, and voltage level of FCLs. Also, an effective sensitivity index is proposed, which can reduce the search space for optimal allocation. This method suggests the optimal allocation with the least investment cost in multi-level voltages networks according to the FCL costs. The proposed method is evaluated in the IEEE 30-bus, 57-bus, and 300-bus test systems. Numerical results indicate the accuracy and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

Recently, the voltage stability and control of distribution networks become challenges due to the large line impedance, load variations and particularly the presence of distributed generation. This paper presents a coordinated voltage control scheme of distribution systems with distributed generation based on on-load tap changer and shunt capacitors. The problem is to determine the optimal operation of voltage regulation devices to minimize a multi-objective function including power losses, voltage deviations and operation stresses while subject to the allowable voltage ranges, line capacity and switching stresses, etc. The problem is formulated and solved by a modified particle swarm optimization algorithm to treat the large-scale and high nonlinearity property. The proposed scheme is applied to a typical 48-bus distribution network in Vietnam. The result of simulation shows that the voltage profile can be improved while the power loss of distribution systems can be reduced significantly. [ABSTRACT FROM AUTHOR]

In this paper is proposed a reconfiguration methodology with the presence of Distributed Generation (DG), aimed at achieving the minimum power loss, minimum number of switching operation and minimum deviation of bus voltage while satisfying all constraints using improved shuffled frog leaping algorithm (ISFLA).The performance of the proposed method is examined on 33 and 69 bus IEEE test distribution systems. The ISFLA performance is evaluated with the well-known algorithm including of harmony search algorithm (HSA), refined genetic algorithm (GRA), particle swarm optimization (PSO), differential evolutionary (DE) and conventional SFLA. Simulation results showed that the total power loss and voltage bus minimum in primary distribution network can be reduced significantly. Also the results in different scenarios are showed that the simultaneous reconfiguration and DG placement method is better in less losses and also in more minimum voltage. Moreover, the ISFLA superiority is proved in comparison with the HAS, GRA, PSO, DE, and SFLA in view of more convergence speed and accuracy and also converges in less number iteration. Also, the performance of the proposed method is favorable compared to previous studies. [ABSTRACT FROM AUTHOR]

The incorporation of renewable energy microgrids brings along several new protection coordination challenges due to the new and stochastic behaviour of power flow and fault currents distribution. An optimal coordination scheme is a potential solution to develop an efficient protection system to handle the microgrid protection challenges. In this paper, new optimal Over Current (OC) relays coordination schemes have been developed using nonstandard tripping characteristics for a power network connected to renewable energy resources. The International Electrotechnical Commission (IEC) microgrid and IEEE-9 bus systems have been used as benchmark networks to test and evaluate the coordination schemes. The proposed OC relays coordination approach delivers a fast and more reliable performance under different OC faults scenarios compared to traditional approaches. In addition, to improve and evaluate the performance of the proposed coordination approach, four modern and novel metaheuristic optimization algorithms are developed and employed to solve the OC relay coordination problem, namely: Modified Particle Swarm Optimization (MPSO), Teaching Learning (TL), Grey Wolf Optimizer (GWO) and Moth-Flame Optimization Algorithm (MFO). In this paper, the modern metaheuristic algorithms have been employed to handle the impact of renewable energy on the grid, and enhance the sensitivity and selectivity of the protection system. The test cases, consider the impact of integrating the different levels of renewable energy resources (with a capacity increment of 25% and 50%) in the microgrid on the OC relays protection performance by using nonstandard and standard tripping characteristics. In addition, a comparison analysis for the modern metaheuristic algorithms with Particle Swarm Optimization (PSO) algorithm as a common and standard technique in solving coordination problems under different fault scenarios considering also the higher impedance faults are introduced. The results in all cases showed that the proposed optimal nonstandard approach successfully reduced the overall tripping time and improve the performance of the protection system in terms of sensitivity and selectivity.

This paper presents an approach for islanding detection in distributed generation systems (DGs) using fast discrete s-transform (FDST) algorithm and bidirectional extreme learning machine (BELM) classifier. The system undertaken for this study comprises of two different kinds of DGs such as hydro turbine governor system and wind turbine generator. The analysis starts with extracting the non-stationary negative sequence voltage and current signals at DG end and then the instantaneous amplitude and frequency information are extracted through FDST algorithm. From this information, different distinguishing features are computed such as energy and standard deviation of amplitude to track the islanded event from different non-islanded events. The obtained features are examined through an extreme learning machine classifier to discriminate islanding and non-islanding events, under various operating conditions of distribution system. The accuracy of the proposed method is compared with other recently published techniques by various researchers to justify its improved performance for islanding detection. [ABSTRACT FROM AUTHOR]

This paper proposes a coordinated active–reactive power optimization model for an active distribution network with energy storage systems, where the active and reactive resources are handled simultaneously. The model aims to minimize the power losses, the operation cost, and the voltage deviation of the distribution network. In particular, the reactive power capabilities of distributed generators and energy storage systems are fully utilized to minimize power losses and improve voltage profiles. The uncertainties pertaining to the forecasted values of renewable energy sources are modelled by scenario-based stochastic programming. The second-order cone programming relaxation method is used to deal with the nonlinear power flow constraints and transform the original mixed integer nonlinear programming problem into a tractable mixed integer second-order cone programming model, thus the difficulty of problem solving is significantly reduced. The 33-bus and 69-bus distribution networks are used to demonstrate the effectiveness of the proposed approach. Simulation results show that the proposed coordinated optimization approach helps improve the economic operation for active distribution network while improving the system security significantly. [ABSTRACT FROM AUTHOR]

Distributed generation (DG) is small-scale energy generation which is closely installed to a load area. It is widely preferred in distribution networks due to its potential solution for loss mitigation and voltage profile improvement. The purposes of this research paper are to find the optimal location and size of the DG in a distribution system. The Newton-Raphson power flow method is used to solve the power flow in the system. In this paper, the overall active power loss minimization in the system is the objective function. The optimal location and sizing of the DG can be obtained by using, respectively, the fast voltage stability index and whale optimization algorithm which is the most recent metaheuristic optimization algorithm based on the natural hunting behaviour of the humpback whale. Moreover, the proposed algorithm is evaluated through 15 and 33 buses of the radial distribution networks. Furthermore, 3 kinds of the DG contribution are considered to compare the efficiency and performance. The results show the effectiveness and great performance of the proposed method in determining the optimal size and location of the DG in distribution systems. [ABSTRACT FROM AUTHOR]

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Abstract Rapid placement of electric vehicle charging stations (EVCSs) is essential for the transportation industry in response to the growing electric vehicle (EV) fleet. The widespread usage of EVs is an essential strategy for reducing greenhouse gas emissions from traditional vehicles. The focus of this study is the challenge of smoothly integrating Plug-in EV Charging Stations (PEVCS) into distribution networks, especially when distributed photovoltaic (PV) systems are involved. A hybrid Genetic Algorithm and Simulated Annealing method (GA-SAA) are used in the research to strategically find the optimal locations for PEVCS in order to overcome this integration difficulty. This paper investigates PV system situations, presenting the problem as a multicriteria task with two primary objectives: reducing power losses and maintaining acceptable voltage levels. By optimizing the placement of EVCS and balancing their integration with distributed generation, this approach enhances the sustainability and reliability of distribution networks.

This paper deals with the establishment of the optimal size and placement of a distributed generation (DG) unit in the different buses of a low voltage radial distribution network applying the U-shape curve fitting method with the criterion of system power loss minimization. For modeling and simulation of a test network, the NEPLAN software is used, obtaining information about power flow analysis, voltage level in buses and power loss in branches of network. The results show that the U-shape curve fitting method could be applied for large number of buses in radial networks. [ABSTRACT FROM AUTHOR]

Distribution systems in traditional power systems (PS) constituted of passive elements and the distribution issues were then limited to voltage and thermal constraints, harmonics, overloading and unbalanced loading, reactive power compensation issues, faults and transients, loss minimization and frequency stability problems, to name a few. Contemporary distribution systems are becoming active distributed networks (ADNs) that integrate a substantially increasing amount of distributed energy resources (DERs). DERS include distributed generation (DG) sources, energy storage resources and demand side management (DSM) options. Despite their evidenced great benefits, the large-scale deployment and integration of DERs remain a challenge as they subsequently lead to the network operational and efficiency issues, hampering PS network reliability and stability. This paper carries out a comprehensive literature survey based on the last decade’s research on operational challenges reported and focusing on dispatchable and non-dispatchable DGs grid integration, on various demand response (DR) mechanisms and, on battery energy storage system (BESS) charging and discharging challenges, with the aim to pave the way to developing suitable optimization techniques that will solve the coordination of multiple renewable sources, storage systems and DRs to minimize distribution systems’ operational issues and thus improve stability and reliability. This paper’s findings assist the researchers in the field to conduct further research and to help PS planners and operators decide on appropriate relevant technologies that address challenges inherent to DG grid integration.

This paper presents a multi-objective formulation for optimal sizing of renewable energy resources in distribution systems in order to maximize net present worth of system. The implemented technique is based on Genetic Algorithm (GA) and weight method that employed to obtain the best compromise between these costs. This study presents an optimized design of Micro Grid (MG) in a distribution system including various sources like, photovoltaic array, wind turbine and battery bank for standalone mode and with multiple DG units under pool electricity market in grid-connected mode. An objective function to maximize of total net present worth is presented. The optimization algorithm is applied to a typical LV network operating under pool market policy. [ABSTRACT FROM AUTHOR]

This paper presents optimal operation of hybrid fuel cell/diesel generator/battery as known "Micro-grid" in pool market under various power pricing strategies such as uniform pricing and pay ad bid pricing. The present paper focuses on the comparison of two different strategy rules applied in a centralized wholesale power market: uniform pricing and pay-as-bid. The market consists of an Independent System Operator (ISO) and Dispersed Energy Resources as DER in Micro-grid (MG) and Main Utility Grid (UG) as Power Generators (Players) who submit their offers to the ISO in the form of curves. The ISO purchases energy from the Independent Power Producers (IPPs) in order to cover the electricity demand. The power producers do not know the costs, the offers and the payoffs of their competitors and therefore they use an adaptive learning tool to compensate their lack of knowledge trying to maximize their revenue. The effect of the power pricing strategy on electricity price, in a environment of market consisting of power producers with similar attributes, i.e. cost, capacity range, adaptation and reaction capability, is analyzed for both two pricing strategy systems. This paper presents a multi-objective formulation for optimal sizing of micro grid (MG) in distribution systems, under two strategy of pool power market in order to maximize net present worth (NPW). The implemented technique is based on Genetic Algorithm (GA) and weight method that employed to obtain the best compromise between these costs. Simulation performed on test distribution system to demonstrate the effectiveness of the proposed procedure. [ABSTRACT FROM AUTHOR]

• An innovative protection approach for OCR coordination in microgrid system. • A new Optimal protection scheme using new nonstandard current-voltage characteristics. • Minimizing the total tripping time of OCRs in microgrid system. Due to the high penetration of renewable energy sources in the electrical power network, tripping time and ideal overcurrent coordination are currently among the key power system protection problems. In this paper, a new adaptive and optimal coordination approach for the Over Current Relays (OCRs) in a power network coupled to a Photovoltaic System (PV) has been devised employing nonstandard voltage-current tripping characteristics. This research presents and proves the novelty of the proposed adaptive optimal coordination method based on Genetic Algorithm (GA) to achieve the minimum total tripping time compared to conventional and nonstandard current characteristics used in the literature. The proposed adaptive optimal coordination strategy uses the nonstandard voltage-current tripping characteristics to minimize the operational time of OCRs in an effort to increase the sensitivity and reliability of the protective system. Results of the proposed optimal coordination scheme are compared to conventional characteristics and nonstandard current characteristics in the literature using a case study involving an IEEE-9 and IEEE-33 bus systems connected to two PV plants. This case study was developed and presented using industrial software (ETAP). Utilizing various fault and power network model scenarios, the suggested nonstandard voltage-current OCR coordination method and optimization algorithm are assessed. The findings in this paper show that the suggested nonstandard voltage-current optimal nonstandard scheme successfully decreases the total running time of OCRs and enhances relay sensitivity under minimum and maximum fault scenarios. The proposed adaptive optimal coordination approach reduces the overall operational time of all OCRs compared to the conventional and current characteristics of the literature by 57.9% and 54.2%, respectively. [ABSTRACT FROM AUTHOR]

Distributed Generation Units (DGs) are an effective solution to the power loss problem of the distribution network, which increases at peak demand. Also, it helps to meet consumer demands and needs. Many optimization algorithms have been applied to achieve the optimal location and size of DG units. Although this technique reduced active power loss, it has several problems, such as complexity, accuracy, and premature convergence to a local optimum. This work aims to improve the performance of the distribution network by considering the minimization of active and reactive power loss, reducing the voltage deviation, and finding the solution in a shorter time. Therefore, an improved Particle Swarm Optimization (PSO) algorithm is suggested to solve the problem of convergence at a local optimum. The proposed algorithm considers DG units that produce only active power. Two test cases are used to validate the performance of our proposed technique: an IEEE network with 33 nodes and another with 69 nodes. [ABSTRACT FROM AUTHOR]

Installation of distribution generation (DG) in the distribution system gains many technical benefits. To obtain more benefits, the location and size of DG must be selected with the appropriate values. This paper presents a method for optimizing location and size of DG in the distribution system based on enhanced sunflower optimization (ESFO) to minimize power loss of the system. In which, based on the operational mechanisms of the original sunflower optimization (SFO), a mutation technique is added for updating the best plant. The calculated results on the 33 nodes test system have shown that ESFO has proficiency for determining the best location and size of DG with higher quality than SFO. The compared results with the previous methods have also shown that ESFO outperforms to other methods in term of power loss reduction. As a result, ESFO is a reliable approach for the DG optimization problem. [ABSTRACT FROM AUTHOR]

Control of integrated utility grid plays important role, as renewable energy sources are rapidly increasing with installation of different Distributed Generation (DG) systems. Issues arising during the operation like interruption, disorder and uncertainty of network to be resolve. One of the situations is island state, and require to secure human beings and converter devices those are working in integrated energy sources environment. Fuzzy sets are to be identifying this state and these are updating with device control parameters of network like voltage, frequency. This paper presents a k-nearest neighbour Machine Learning (k n n − M L) to obtain Upper Limit (UL) Lower Limit (LL) of parameters for continuous fuzzy based state estimation. Also proposed k n n − M L helps to overcome existing challenges and motivate to better recognition of integrated network state. Outcomes of the paper, to update device control at every Abnormal Mode (AM) of operation and as when system parameter changes at different conditional operating modes. Non Detection Zone (NDZ) is also effectively reduced as per with knn parameter in both [LL, UL] mismatch cases. Mismatch cases of 50% to lower value of 10% is also successful tested with proposed method and a proper trip signal has been generated even at lower mismatch conditions. Effective outcome has been obtained with k n n − M L soft computing algorithm. • Soft computing fuzzy set is used to identify Islanding detection. • knn machine learning trained fuzzy set from integrated energy system data variation. • Proper detection for low power mismatch of system and unwanted detection is avoided. [ABSTRACT FROM AUTHOR]

The paper considers using renewable wind energy for electricity generating. The system is characterized by high reliability and ecological purity. The authors briefly present the main methodological principles of choosing the parameters of the considered wind-hydro power system. [ABSTRACT FROM AUTHOR]

In this paper a decentralized reactive power control scheme is proposed to optimally control the switched capacitor in the system in order to minimize system losses and maintain acceptable voltage profile. The proposed technique is based on placing a remote terminal unit (RTUs) at each DG and each at line capacitor. These RTUs being coordinated together through communication protocols form a multiagent system. Novel decentralized algorithm is proposed to estimate the voltage profile change as a result of injecting reactive power at the capacitor bus. Simulation results are presented to show the validity and the effectiveness of the proposed technique. [ABSTRACT FROM PUBLISHER]

A distributed power generating system can run in an off-grid or off grid mode and can be powered by either sustainable or nonrenewable energy sources. The widespread use of renewable energy sources, like grid-connected solar systems, presents distribution networks with additional technological difficulties, such as accidental islanding. Utility employees who are islanding may not be aware that a circuit is still energized, which might be harmful. On islanding detection in gird-connected PV systems, several proposed solutions were considered. In order to examine their current limits, this study aims to develop an islanding detection system for grid connected solar systems under various fault conditions using intelligent detection method (IDM). To use and monitor the many grid-connected PV system characteristics in this context, numerous sensors and controllers were proposed. The controller will carry out the islanding detection based on the sensor data's categorization of defect. The circuit breaker will do the islanding, and an intelligent technique has been proposed to identify the islanding condition under different fault condition. For real-time realization, a 1 kW grid-connected solar system had been used, and an artificial neural network (ANN) was employed as a smart approach to identify the islanding mode under various fault conditions. [ABSTRACT FROM AUTHOR]

In this paper, a comprehensive algorithm using modified invasive weed optimization is introduced for allocating distribution generation (DG) sources along with considering demand response (DR). Three aspects such as technical, economic and environmental are taken to account to define the optimized size and location for DG or DR. In addition, a new voltage fitness function is proposed for better improvisation of voltage profile. The study is done on 30-bus IEEE transmission system and to examine the proposed algorithm, three other optimization algorithms such as GA, PSO and DE are used. The simulation is carried out in MATLAB which shows excellent performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes an effective strategy to overcome the impacts on coordination among protection devices due to distributed generator (DG) integration. Increased fault current magnitude and changes in power ow directions are the major impacts imposed by DGs on a typical distribution system. The recloser-fuse coordination is much in uenced upon the integration of DGs. The proposed approach presents the rehabilitation of recloser-fuse coordination for post-DG integration effects using the directional properties of a recloser. The simulation results show that coordination among protection devices can be regained using fast operation of the recloser to design a fuse saving scheme in the scenario of temporary fault occurrence. The designed scheme also works satisfactorily for the isolation of a permanently faulted section of the feeder. This technique is verified by simulation results performed on a real 11-kV radial distribution feeder for different fault locations and DG sizes. [ABSTRACT FROM AUTHOR]

Utilities apply a fuse‐saving strategy during auto‐reclosing on transient faults. Integration of distribution generation (DG) into distribution networks (DNs) challenges this strategy as the fault current contribution from the DG may lead to the loss of recloser–fuse coordination. This study proposes a relaying scheme to be applied on microprocessor‐based reclosers for fuse saving under transient fault conditions. A relay operating characteristic is defined which utilises voltage and current magnitudes at the recloser location. The voltage term in the relay characteristic compensates for the reclosing delay resulting from the DG fault contribution. The main features of the relaying scheme are use of local measurements, i.e. no communication link is required, and its independence of number and location of DG. The proposed relaying scheme is validated by simulation study on a practical 20 kV Iranian DN. It is shown that the new scheme maintains proper recloser–fuse coordination for different fault conditions and DG configurations. Moreover, the maximum DG penetration in different locations is obtained whilst the recloser–fuse coordination is upheld. It is shown in a comparative study that the proposed relaying scheme can maintain the coordination for higher DG penetrations than a recent adaptive method reported in the literature. [ABSTRACT FROM AUTHOR]

Distributed generation (DG) refers to small generating plants that usually develop green energy and are located close to the load buses. Thus, reducing active as well as reactive power losses, enhancing stability and reliability, and many other benefits arise in the case of a suitable selection in terms of the location and the size of the DGs, especially in smart cities. In this work, a new nature-inspired algorithm called Garra Rufa optimization is selected to determine the optimal DG allocation. The new metaheuristic algorithm stimulates the massage fish activity during finding food using MATLAB software. In addition, three indexes which are apparently powered loss compounds and voltage profile, are considered to estimate the effectiveness of the proposed method. To validate the proposed algorithm, the IEEE 30 and 14 bus standard test systems were employed. Moreover, five cases of DGs number are tested for both standards to provide a set of complex cases. The results significantly show the high performance of the proposed method especially in highly complex cases compared to particle swarm optimization (PSO) algorithm and genetic algorithm (GA). The DG allocation, using the proposed method, reduces the active power losses of the IEEE-14 bus system up to 236.7873%, by assuming 5DGs compared to the active power losses without DG. Furthermore, the GRO increases the maximum voltage stability index of the IEEE-30 bus system by 857% in case of the 4DGs, whereas GA rises the reactive power of 5DGs to benefit the IEEE-14 bus system by 195.1%. [ABSTRACT FROM AUTHOR]

Widely used distribution generations (DGs) and energy storage systems (ESSs) enable a distribution system to have a more flexible fault reconfiguration capability. In order to enhance the service reliability and the benefit of distribution networks with DGs and ESSs, this paper proposes a novel distribution system reconfiguration (DSR) model including DGs and ESSs. Meanwhile, the impact of sectionalizing switches and tie switches on reliability is considered. The concept of "boundary switch" is introduced for quantifying the customer interruption duration. The DSR model is presented to minimize the sum of the customer interruption cost, the operation cost of switches, and the depreciation cost of DGs and ESSs. Furthermore, the proposed model is converted into a mixed-integer linear programming, which can be efficiently solved by commercial solvers. Finally, the validity and efficiency of the proposed DSR model are verified by a modified IEEE 33-bus system and a modified PG&E69-bus network. The obtained results indicate the advantages of DGs and ESSs in reducing outage time, and suggest that the types and locations of SSs have great effects on the resulting benefit of DGs and ESSs. [ABSTRACT FROM AUTHOR]

The current practice of Distributed Generation (DG) disconnection for every fault in distribution systems has an adverse effect on utility and stable power trading when the penetration level of DGs is high. That is, in the process of fault detecting and Circuit Breaker (CB) reclosing when a temporary fault occurs, DGs should be disconnected from the Point of Common Coupling (PCC) before CB reclosing. Then all DGs should wait at least 5 minutes after restoration for reconnection and cannot supply the pre-bid power in power market during that period. To solve this problem, this paper proposes a control method that can keep operating without disconnection of DG. This control method is verified through modeling and simulation by the PSCAD/EMTDC software package for distribution systems with DGs based on PCS (Power Conditioning Systems) and CB reclosing protection. [ABSTRACT FROM AUTHOR]

The uncertainty inherent in power load forecasts represents a major factor in the mismatches between supply and demand in renewables-rich electricity networks, which consequently increases the energy bills and curtailed generation. As the transition to a power grid founded on the so-called grid-of-grids becomes more evident, the need for distributed control algorithms capable of handling computationally challenging problems in the energy sector does so as well. In this light, the consensus-based distributed algorithm has recently been shown to provide an effective platform for solving the complex energy management problem in microgrids. More specifically, in a microgrid context, the consensus-based distributed algorithm requires reliable information exchange with customers to achieve convergence. However, packet losses remain an important issue, which can potentially result in the failure of the overall system. In this setting, this paper introduces a novel method to effectively characterize such packet losses during information exchange between the customers and the microgrid operator, whilst solving the microgrid scheduling optimization problem for a multi-agent-based microgrid. More specifically, the proposed framework leverages the virulence optimization algorithm and the earth-worm optimization algorithm to optimally shift the energy consumption during peak periods to lower-priced off-peak hours. The effectiveness of the proposed method in minimizing the overall active power mismatches in the presence of packet losses has also been demonstrated based on benchmarking the results against the business-as-usual iterative scheduling algorithm. Also, the robustness of the overall meta-heuristic- and multi-agent-based method in producing optimal results is confirmed based on comparing the results obtained by several well-established meta-heuristic optimization algorithms, including the binary particle swarm optimization, the genetic algorithm, and the cuckoo search optimization.