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This article discusses a support vector regression-based interval power flow prediction method for distribution networks with distributed generators (DGs) integration. The method aims to address the challenges of uncertain power flow analysis in distribution networks, particularly as the size of the system increases. The proposed method utilizes intervals to describe system uncertainty and employs support vector regression for model training. Simulation results demonstrate that the method exhibits high prediction accuracy, adaptability, and computation efficiency, meeting the requirements for rapid and real-time power flow analysis in distribution networks with DGs integration. The text also discusses the construction of an Interval Power Flow (IPF) prediction model for distribution networks using Support Vector Regression (SVR). The model is a multi-output model that predicts the upper and lower bounds of power flow results, taking into account uncertainties in the distribution system. The model has been shown to have high prediction accuracy and computational efficiency in studies conducted on IEEE 33bw and IEEE 69 cases. The text concludes by stating that the claims expressed in the article are solely those of the authors and do not necessarily represent the views of their affiliated organizations or the publisher. The article titled "Distributionally robust multistage dispatch with discrete recourse of energy storage systems" by Zheng et al. explores the concept of distributionally robust multistage dispatch with discrete recourse of energy storage systems. The authors propose a mathematical model that considers uncertainties in renewable energy generation and electricity demand, and aims to optimize the dispatch of energy storage systems. The study highlights the importance of incorporating uncertainty [Extracted from the article]

This paper investigates the strategic placement of capacitor banks in the distribution network of Gračanica, with a specific focus on the medium-voltage feeder Grades. The primary objective is to optimize voltage profiles, minimize power losses, and enhance the overall performance of the distribution network. The significance of this research lies in its thorough examination of optimal capacitor placement within the medium-voltage (MV) branch of distribution networks, specifically considering the intricate interplay between capacitor banks and MV branch components, underlining the necessity for informed decisions in the context of distributed generators (DG) integration to enhance overall network performance. The study further investigates the impact of integrating DGs on these objectives on capacitor placement in the MV feeder. Employing the DIgSILENT PowerFactory software tool for modelling the MV feeder and utilizing a genetic algorithm for capacitor placement optimization, the study underscores the robustness of this approach in handling various conditions and seeking optimal solutions. Simulation results demonstrate that strategically placing capacitor banks and integrating DGs can significantly improve the voltage profile and reduce power losses within the distribution system. The findings of this research support 2MVA of concentrated DGs at the middle of the line as the most efficient and most economically beneficial situation on the medium-voltage feeder Grades study case and contribute valuable insights, serving as a reference for future studies on optimal capacitor placement. [ABSTRACT FROM AUTHOR]

In this study, a novel method called the Continuous–Discrete Student Psychology-Based Optimization (CD-SPBO) is introduced. The technique is designed to address the simultaneous allocation of renewable distributed generators (DGs) and distribution static compensators (D-STATCOMs). A novel multi-objective function is formed by combining indices concerning the active power loss, voltage profile, voltage stability and cost consisting of investment, penalty for violation of environmental limits, and energy loss, which are combined through analytic hierarchical process. The CD-SPBO algorithm is used for solving the multi-objective allocation problem for non-dispatchable solar PV-DGs, dispatchable biomass DGs, D-STATCOMs, and their combinations. The superiority of the CD-SPBO algorithm was both numerically and statistically established by comparing its results with other state of the art methods such as Gorilla Troop Optimizer, Artificial Humming Bird Optimizer, and Harris Hawk Optimizer for simultaneous allocation of one, two, and three pairs of DGs and D-STATCOMs on 33-bus and 118-bus test systems. Further, the simulation findings involving seven distinct planning scenarios for the allocation of DGs and D-STATCOMs supported the CD-SPBO algorithm's effective execution for simultaneous optimal allocation of dispatchable DGs, non-dispatchable DGs, and D-STATCOMS. The suitability of different planning scenarios for improving the overall performance of the distribution system are analyzed in detail. The research insights may prove beneficial for network planners in determining the best combination of devices to meet their needs. [ABSTRACT FROM AUTHOR]

In distribution networks with distributed generators (DGs), power generation and load demand exhibit increased randomness and volatility, and the line parameters also suffer more frequent fluctuations, which may result in significant state shifts. Existing model-driven methods face challenges in efficiently solving uncertain power flow, especially as the size of the system increases, making it difficult to meet the demand for rapid power flow analysis. To address these issues, this paper proposes an SVR-based interval power flow (IPF) prediction method for distribution networks with DGs integration. The method utilizes intervals to describe system uncertainty and employs Support Vector Regression (SVR) for model training. The input feature vector consists of the intervals of active power generation, load demand, and line parameters, while the output feature vector represents the intervals of voltage or line transmission power. Ultimately, the SVR-based IPF prediction model is established, capturing the linear mapping relationship between input data and output IPF variables. Simulation results demonstrate that the proposed method exhibits high prediction accuracy, strong adaptability, and optimal computation efficiency, meeting the requirements for rapid and real-time power flow analysis while considering the uncertainty in distribution networks with DGs integration.

Active and reactive powers should be properly shared among sources to avoid instability problems and to improve reliability in inverter-based micro grids. This can be done using a number of control techniques including hierarchical, centralized and decentralized control. Decentralized control techniques offer extra reliability to the system in addition to the plug and play functionality of distributed generators . This control method is conventionally done using a droop controller. However, the droop control method has certain limitations, particularly in terms of the reactive power sharing. The improper reactive power sharing can lead to serious problems when reactive power surges occur, such as severe circulating reactive powers, instability issues and even a complete shutdown of inverters. The aim of this work is to present a new method for a proper sharing of reactive power by utilizing a low-bandwidth communication through power lines. Using these communication signals, the virtual impedances of the inverters are automatically adjusted to achieve accurate reactive power sharing. The method is described and analysed in detail, including stability analysis related to communication loss and delay effects. The same communication through power lines can be used to share additional information within the micro grid to achieve other control tasks such as secondary control. The proposed method is verified by simulations and using a hardware prototype. Results show that the proposed algorithm is able to accurately achieve reactive power sharing while maintaining the expected power quality at load buses.

Abstract As the penetration of distributed generation (DG) systems in the grid is increasing, the challenge of combining large numbers of DGs in the power systems has to be carefully clarified and managed. The control strategy and management concept of the interconnected systems should be flexible and reliable to handle the various types of DGs. This can be suitably met by microgrids. This paper introduces the microgrid structure and elements and states the main objectives that should be achieved by the microgrid controllers and each DG controller in both operation modes (grid‐connected and island mode). It also presents the challenges of having multiple DG units in a microgrid in terms of accurate power control/sharing, voltage and frequency regulation, power management between DGs, different renewable energy sources integration and deployment, seamless mode transfer, and the modeling issues. The centralized and decentralized control techniques as potential solutions have been discussed and compared by highlighting the advantages and disadvantages of each. Furthermore, the recent control techniques for drooped alternating current microgrids and the main proposed solutions and contributions in the literature have been exposed to finally overcome the droop control limitations and obtain a flexible and smart distributed power system.

The worldwide need for electrical energy is increasing, and integrating renewable energy sources (RES) into the power grid will enhance the efficient use of clean energy to fulfill the growing demand for energy. However, the uncertain nature of power from the RES like solar and wind, utility price, and load demand, necessitates accurate forecasting of the uncertain parameters (UP) to improve the reliability of the hybrid microgrid. In this work, optimal energy management (EM) of a hybrid AC-DC microgrid (HMG) is proposed which comprises of two phases, forecasting and scheduling. In the former phase, the uncertainties like day-ahead utility price, electrical demand, and power from the RES are forecasted using the support vector machine (SVM) algorithm and the results are compared with the artificial neural network (ANN) algorithm. In the second phase, the improved Teaching and Learning-Based Optimization (ITLBO) algorithm isused to reduce the generation costs over a 24-h period in a hybrid microgrid. The forecasted uncertain parameters are used as input in the second phase. Power trading occurs between the utility grid and the hybrid microgrid based on load demand and bidding costs, aiming to minimize generation costs. The proposed framework's viability and performance are assessed using IEEE standard test systems. The generating cost, as well as the optimal power dispatch of the HMG, is obtained using the ITLBO algorithm, and the results are compared with different meta-heuristic techniques such as the teaching and learning-based algorithm (TLBO), Ant Lion Optimization algorithm (ALO) and the artificial bee colony algorithm (ABC). The results obtained demonstrate the superiority of the SVM algorithm in forecasting and the ITLBO algorithm over other methods in minimizing operating costs.

The establishment of an electric vehicle charging station (EVCS) infrastructure plays a vital role in fostering the sustainable expansion of the electric vehicle sector. The unplanned placement of EVCS raises various technical and economic issues in the distribution network, and it can lead to increased energy losses in the distribution system. Installing EVCSs and DGs at strategically chosen positions is essential to minimize the impact of EV load on the distribution network. To address these issues, this work has proposed a whale optimization technique (WOT) for optimizing the placement and sizing of EVCS and DG. This novel approach of formulating the multi-objective problem of allocating electric vehicle (EV) charging stations, along with DGs and capacitors simultaneously improves the voltage stability, minimizes active and reactive power losses in the radial distribution system, and requires execution time. A forward–backward power flow method has been carried out for load flow calculations. The efficacy of the proposed approach has been applied on the standard IEEE-33 and 69-bus test systems and the obtained results have been compared with the other latest optimization techniques. The simulation results validate the performance and effectiveness of the Whale Optimization Technique, and it has been analyzed that the optimized sizes and allocation of EVCSs, DGs, and capacitors lead to an extensive reduction in power losses of 81.33% and 80% in the case of active power and 78.46% and 80.26% in the case of reactive power for the IEEE-33 and 69-bus test systems, respectively. [ABSTRACT FROM AUTHOR]

As the penetration of distributed generation (DG) systems in the grid is increasing, the challenge of combining large numbers of DGs in the power systems has to be carefully clarified and managed. The control strategy and management concept of the interconnected systems should be flexible and reliable to handle the various types of DGs. This can be suitably met by microgrids. This paper introduces the microgrid structure and elements and states the main objectives that should be achieved by the microgrid controllers and each DG controller in both operation modes (grid‐connected and island mode). It also presents the challenges of having multiple DG units in a microgrid in terms of accurate power control/sharing, voltage and frequency regulation, power management between DGs, different renewable energy sources integration and deployment, seamless mode transfer, and the modeling issues. The centralized and decentralized control techniques as potential solutions have been discussed and compared by highlighting the advantages and disadvantages of each. Furthermore, the recent control techniques for drooped alternating current microgrids and the main proposed solutions and contributions in the literature have been exposed to finally overcome the droop control limitations and obtain a flexible and smart distributed power system. [ABSTRACT FROM AUTHOR]

An essential building block of today's smart grids is the integration of various renewable power generators into the system. Renewable distributed generators (DGs) provide environment friendly energy at a low cost. In this work, a distributed economic dispatch (ED) system is considered for renewable DGs to address the scalability challenge and reduce single points of failure compared to a centralised ED system. A consensus control mechanism is devised to achieve the incremental cost (IC) consensus among the DGs and utilise a self-triggered control (STC) mechanism to reduce the communication of the distributed DG system. A lossy communication channel between local DGs is considered in this work, which can arise from power lines or congestion on noisy wireless communication channels. A regression-based estimation approach is proposed to account for lost or corrupted data. The proposed method is compared with a centralised ED system and periodic communication. The proposed STC approach achieves the same level of performance as periodic communication while using half the communication resources. [ABSTRACT FROM AUTHOR]

This paper proposes a tube model predictive control method considering uncertainties of renewable energy generation in the low-voltage distribution network. For relatively low dependence on communication networks, a decentralized voltage control framework is urgently needed to be developed for voltage regulation in the low-voltage distribution network. Opposed to the traditional voltage control techniques, a novel decentralized tube model predictive voltage control (TMPC) strategy is proposed. First, this strategy entails the formulation of a discrete model encompassing distributed photovoltaic units and energy storage systems intertwined with the distribution grid model based on voltage sensitivity. Then, considering the worst case of voltage deviations caused by uncertain and stochastic power fluctuation, we specifically discuss a robust positively invariant to determine the control section of controllers. Further, the strategy employs tube model predictive controllers encompassing nominal and auxiliary controllers in distribution areas. The nominal controller generates a central path for different voltage operating points of each distribution area, and the auxiliary controller makes all trajectories lie in a bounded neighborhood called a tube. The effectiveness of the proposed control strategy is investigated on a test system containing renewable energy resources.

This study combines the metaheuristic algorithm Transient Search Optimization (TSO) with the Levy flight distribution to find the optimal proportional-integral (PI) controllers for robust operation of islanded microgrids. The first step is to use the response surface methodology (RSM) to empirically express the multi-objective function. This function includes the transient variations of the terminal voltages of the microgrids. To demonstrate the efficacy of the hybrid Levyflight and TSO (LTSO), a benchmark microgrid system undergoes rigorous testing under different operational scenarios: i) transitioning the system into autonomous mode by disconnecting from the main grid; ii) adapting to varying load conditions while isolated; and iii) responding to a 3-phase fault while operating in islanded mode. Numerous simulations are run to verify the suggested methodology, employing conventional data extracted from the PSCAD/EMTDC software. The study’s findings are further reinforced through a comparative analysis with established optimization techniques such as the least mean and the square root of exponential approaches, the enhanced block-sparse adaptive Bayesian algorithm, the adaptive-width generalized correntropy diffusion algorithm, the sunflower optimization algorithm, the Coot bird metaheuristic optimizer, and particle swarm optimization. The results collectively underscore the superiority of the LTSO algorithm in enhancing the transient response of the terminal voltages of islanded microgrids, thereby offering a promising avenue for optimizing the control and stability of such systems.

Abstract Background The transition towards renewable energy sources has become an imperative step to mitigate climate change, reduce carbon emissions and improve energy security and economic prosperity in a sustainable manner. Maximizing the cost effectiveness of electric power generation is crucial to making renewable energy sources viable and attractive options for clean energy production. The strategic allocation of wind, hydro and solar power systems is essential to achieving this goal. This paper attempts to demonstrate how the cost effectiveness of electrical power system could be maximized through the integration of wind, solar and hydropower systems and comparison at different penetration levels of 0, 25, 50, 75 and 100% on cost effectiveness of electric power generation. The different generator technologies were designed based on their electrical output attributions. Results The cost of electric generation for the integration of each generator at the various buses were calculated at different penetration level for fair comparison. The results indicate that the minimum money loss for the integration of solar power was $743.90 at bus 4 and at 50% penetration level, the minimum money loss for the integration of wind power was $999.00 at bus 4 and at 25% penetration level while the minimum amount loss for the integration of hydropower was $546.50 at bus 4 and at 75% penetration level. Conclusions The magnitude to which the integration of the different generator affects the cost effectiveness of power production hinges on the type of generator, the penetration level and the location of the generator in the grid.

Distribution companies (DISCOs) aim to maximize their annual profits by performing the optimal planning of distributed generators (DGs) or energy storage systems (ESSs) in the deregulated electricity markets. Some previous studies have focused on the simultaneous planning of DGs and ESSs for DISCO profit maximization but have rarely considered the reactive powers of DGs and ESSs. In addition, the optimization methods used for solving this problem are either traditional or outdated, which may not yield superior results. To address these issues, this paper simultaneously performs the optimal planning of DGs and ESSs in distribution networks for DISCO profit maximization. The utilized model not only takes into account the revenues of trading active and reactive powers but also addresses the active and reactive powers of DGs and ESSs. To solve the optimization problem, a new hybrid evolutionary algorithm (EA) called the oppositional social engineering differential evolution with Lévy flights (OSEDE/LFs) is proposed. The OSEDE/LFs is applied to optimize the planning model using the 30-Bus and IEEE 69-Bus networks as test systems. The results of the two case studies are compared with several other EAs. The results confirm the significance of the planning model in achieving higher profits and demonstrate the effectiveness of the proposed approach when compared with other EAs. [ABSTRACT FROM AUTHOR]

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Failure of islanding detection techniques leads to improper operation of DGs, risk for working personnel, loss of control over voltage and frequency, uncoordinated protection, poor power quality, etc. Therefore, the islanding event (IE) should be identified, and its detection algorithm should be incorporated according to the standard guidelines. Through this paper, a communication-assisted passive islanding detection technique (IDT) is proposed based on an index, which is the ratio of multiplications of superimposed negative sequence voltages and currents of DG and grid sides. Proposed ID is implemented in the modified IEEE-13 bus system through the micro phasor measurement unit. The calculated index is compared with a set threshold to discriminate between the IEs and non-islanding events (NIEs). The calculation of threshold is not very complex as compared to other schemes. The efficacy of the proposed IDT is tested under various system conditions, such as zero power mismatch (ZPM), wide range of active power mismatch, and reactive power mismatch. DG disconnection and unbalanced load test cases are also performed under ZPM condition. Similarly, different NIEs like capacitor switching, load switching, nonlinear load and different fault cases are verified through the proposed IDT. Real-time implementation of proposed IDT is conducted using opal-RT. [ABSTRACT FROM AUTHOR]

This article aims to provide a comprehensive review of control strategies for AC microgrids (MG) and presents a confidently designed hierarchical control approach divided into different levels. These levels are specifically designed to perform functions based on the MG's mode of operation, such as grid-connected or islanded mode. The primary control ensures frequency (f) and voltage (V) stability, whereas the secondary control adjusts their values to their references and the tertiary control efficiently manages the power of distributed generators (DGs) in a cost-effective manner. The article extensively discusses centralized, decentralized, and distributed strategies for each control level, highlighting their differences, advantages, disadvantages, and areas of application. Finally, the usefulness of different control strategies at different levels is demonstrated through practical examples. [ABSTRACT FROM AUTHOR]

The paper optimizes the placement of soft open points (SOPs) devices, shunt capacitor banks (SCBs), and distributed generators (DGs) in the IEEE 69-node distribution power grid for reducing the power loss of a single hour and total energy losses of one year. EO is proven to be more effective than previous methods and three other applied algorithms, including the Coot optimization algorithm (COOT), Modified weight inertia factor and modified acceleration coefficients-based particle swarm optimization (CFPSO), and Tunicate swarm algorithm (TSA). So, EO is applied for the last case considering one SOPs, one wind turbine (WT), two solar photovoltaic systems (PVs), and two SCBs over one year with twelve months and 24 h each month. The study reaches the smallest power loss compared to previous studies in the first case with one SOPs device. The results from the second to the fourth cases indicate that the power grid needs the placement of SCBs and DGs first and SOPs devices to reach the lowest power loss. Case 5 indicates that the hybrid system with one WT and two PVs suffers higher power losses than the base system at hours with high generation from renewable sources; however, integrating the SOPs and SCBs into the hybrid system can reach smaller losses than the base system at these hours. Thus, using SOPs and SCBs in integrated distribution power grids with renewable energies can greatly benefit energy loss reduction.

A distribution system's network reconfiguration is the process of altering the open/closed status of sectionalizing and tie switches to change the topological structure of distribution feeders. For the last two decades, numerous heuristic search evolutionary algorithms have been used to tackle the problem of network reconfiguration for time-varying loads, which is a very difficult and highly non-linear efficiency challenge. This research aims to offer an ideal solution for addressing network reconfiguration difficulties in terms of a system for power distribution, to decrease energy losses, and increase the voltage profile. A hybrid Genetic Archimedes optimization technique (GAAOA) has also been developed to size and allocate three types of DGs, wind turbine, fuel cell and PV considering load variation. This approach is quite useful and may be used in many situations. This technique is evaluated for loss reduction and voltage profile on a typical 33-bus radial distribution system and a 69-bus radial distribution system. The system has been simulated using MATLAB software. The findings suggest that this approach is effective and acceptable for real-time usage.

This paper investigates a novel distributed optimization scheme for the economic dispatch problem of distributed generators (DGs) connected via directed communication links. The optimization is carried out using incremental cost (IC) consensus under the constraint of supply–demand balance while considering the capacity and ramp rate limits of the DGs. The optimality analysis has been provided via a variant of the conventional Lyapunov function for an additional term, addressing both consensus of ICs and convergence of supply–demand mismatch on directed graphs. In contrast with the previous work, the proposed algorithm is not susceptible to initial conditions of DGs, which makes it more resilient to communication failure and delay despite working under directed communication topologies. Extra limitations of capacity and ramp rate limits of DGs are taken into account by the algorithm while avoiding any supply mismatch. The simulation tests are carried on an IEEE benchmark to verify the efficacy of the proposed technique. The results indicate that the proposed technique is superior to existing traditional and distributed methods in various performance aspects. [ABSTRACT FROM AUTHOR]

Renewable energy sources (RESs) are increasingly used to meet the world's growing electrical needs, especially for the economic benefits and environmental problems associated with fossil fuel use. Small-scale renewable energy sources, controllable loads, energy storage devices, and other nonrenewable sources are effectively integrated to form a virtual power plant (VPP). Uncertainty in forecasting renewable energy generation due to the intermittent nature of renewable energy sources is one of the biggest challenges in VPPs. Power generation by RESs changes with the day of the week, season, location, climate, and resource availability. Also, load demand and utility price vary with time and need to be forecasted for proper energy management of VPPs. However, the dispatching and planning of VPPs are significantly impacted by the volatile nature of RESs, load demand, and utility price. Predicting these uncertainties with high accuracy is essential to balance the electrical power generation and the load demand. In this article, a decision tree (DT) algorithm is proposed, to predict the uncertainty parameters, such as the day-ahead power from the RES, load demand, and utility prices of VPPs. The efficiency of the proposed model and the predicted results are compared with other complex models, such as the artificial neural network (ANN) and auto-regressive integrated moving average (ARIMA) algorithms. Root-mean square error (RMSE), mean square error (MSE), coefficient of determination (R2), and mean absolute error (MAE) are the statistical metrics used to evaluate the accuracy of the prediction. One-year meteorological data of the Chennai zone in India is considered for predicting the uncertainty parameters. IEEE 16-bus and 33-bus test systems are used to validate the forecasting model. It is evident from the results that the proposed DT algorithm can predict the uncertainty parameters more accurately and use lesser time than the ANN and ARIMA algorithms. [ABSTRACT FROM AUTHOR]

Recently, distributed cooperative control has been popularly used in the secondary control of microgrids due to its reliability and flexibility. However, traditional distributed control schemes based on fixed‐cycle communication usually result in a waste of communication resources. Thus, this paper proposes an event‐triggered distributed control strategy that can significantly reduce the requirements of communication. In addition, compared with existing schemes, the proposed algorithm can achieve the secondary control goal in a directed graph, which can be applied to more scenarios, especially for weaker communication conditions. The stability of the strategy is investigated with the Lyapunov method. The simulation results verify the validity and effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

Distributed generators (DGs) have a high penetration rate in distribution networks (DNs). Understanding their impact on a DN is essential for achieving optimal power flow (OPF). Various DG models, such as stochastic and forecasting models, have been established and are used for OPF. While conventional OPF aims to minimize operational costs or power loss, the "Dual-Carbon" target has led to the inclusion of carbon emission reduction objectives. Additionally, state-of-the-art optimization techniques such as machine learning (ML) are being employed for OPF. However, most current research focuses on optimization methods rather than the problem formulation of the OPF. The purpose of this paper is to provide a comprehensive understanding of the OPF problem and to propose potential solutions. By delving into the problem formulation and different optimization techniques, selecting appropriate solutions for real-world OPF problems becomes easier. Furthermore, this paper provides a comprehensive overview of prospective advancements and conducts a comparative analysis of the diverse methodologies employed in the field of optimal power flow (OPF). While mathematical methods provide accurate solutions, their complexity may pose challenges. On the other hand, heuristic algorithms exhibit robustness but may not ensure global optimality. Additionally, machine learning techniques exhibit proficiency in processing extensive datasets, yet they necessitate substantial data and may have limited interpretability. Finally, this paper concludes by presenting prospects for future research directions in OPF, including expanding upon the uncertain nature of DGs, the integration of power markets, and distributed optimization. The main objective of this review is to provide a comprehensive understanding of the impact of DGs in DN on OPF. The article aims to explore the problem formulation of OPF and to propose potential solutions. By gaining in-depth insight into the problem formulation and different optimization techniques, optimal and sustainable power flow in a distribution network can be achieved, leading to a more efficient, reliable, and cost-effective power system. This offers tremendous benefits to both researchers and practitioners seeking to optimize power system operations. [ABSTRACT FROM AUTHOR]

Abstract Recently, distributed cooperative control has been popularly used in the secondary control of microgrids due to its reliability and flexibility. However, traditional distributed control schemes based on fixed‐cycle communication usually result in a waste of communication resources. Thus, this paper proposes an event‐triggered distributed control strategy that can significantly reduce the requirements of communication. In addition, compared with existing schemes, the proposed algorithm can achieve the secondary control goal in a directed graph, which can be applied to more scenarios, especially for weaker communication conditions. The stability of the strategy is investigated with the Lyapunov method. The simulation results verify the validity and effectiveness of the proposed scheme.

For the microgrids to operate securely, distributed generators must be able to interact with one another without experiencing any communication delays or noise. To develop a more effective economic dispatch strategy, this research focuses on noise's effect on the performance of an islanded microgrid. Three different strategies, Lagrange, firefly, and artificial bee colony algorithms, are compared for optimal solutions of economic dispatch. Their performance is compared based on stability during noise interference and faster response time with and without the virtual synchronous generator-STATCOM strategy. The virtual synchronous generator is used as a voltage source to regulate active power and reactive power with the grid. A STATCOM controller is introduced in the system for reactive power compensation. Reactive power compensation is the process of controlling reactive power to enhance the efficiency of alternating current power systems. By boosting the active power, reactive power compensation in the transmission system will increase the stability of microgrids. The voltage, output power, power factor, and phase angle of the microgrid benefit from the stability provided by the controller. As a result, the performance and resilience of the microgrid is improved. [ABSTRACT FROM AUTHOR]

The proper operation of microgrids depends on Economic Dispatch. It satisfies all requirements while lowering the microgrids' overall operating and generation costs. Since distributed generators constitute a large portion of microgrids, seamless communication between generators is essential. While guaranteeing a reliable microgrid operation, this should be achieved with the fewest losses as possible. The distributed generator technology introduces noise into the system by design. To find the best economic dispatch strategy, noise was considered in this research as a limitation in grid-connected microgrids. The microgrid's performance was improved, and the proposed technique also showed increased resilience. A virtual synchronous generator (VSG) control approach is proposed with a noiseless consensus-based algorithm to improve the power quality of microgrids. Voltage and frequency regulation modules are the foundation of the VSG paradigm. The synchronous generator's second-order equation (hidden-pole configuration) was also used to represent the voltage of the stator and rotor motion. This study compared changes in power, frequency, and voltage for the microgrid by utilizing the described control approach using MATLAB. According to the findings, this method aids in controlling load and noise variations and offers distributed generators an efficient control strategy. [ABSTRACT FROM AUTHOR]

Integrating distributed generations (DGs) into the radial distribution system (RDS) are becoming more crucial to capture the benefits of these DGs. However, the non-optimal integration of renewable DGs and shunt capacitors may lead to several operational challenges in distribution systems, including high energy losses, poor voltage quality, reverse power flow, and lower voltage stability. Therefore, in this paper, the multi-objective optimization problem is expressed with precisely selected three conflicting goals, incorporating the reduction in both power loss and voltage deviation and improvement of voltage stability. A new index for voltage deviation called root mean square voltage is suggested. The proposed multi-objective problems are addressed using two freshly metaheuristic techniques for optimal sitting and sizing multiple SCs and renewable DGs with unity and optimally power factors into RDS, presuming several voltage-dependent load models. These optimization techniques are the multi-objective thermal exchange optimization (MOTEO) and the multi-objective Lichtenberg algorithm (MOLA), which are regarded as being physics-inspired techniques. The MOLA is inspired by the physical phenomena of lightning storms and Lichtenberg figures (LF), while the MOTEO is developed based on the concept of Newtonian cooling law. The MOLA as a hybrid algorithm differs from many in the literature since it combines the population and trajectory-based search approaches. Further, the developed methodology is implemented on the IEEE 69-bus distribution network during several optimization scenarios, such as bi- and tri-objective problems. The fetched simulation outcomes confirmed the superiority of the MOTEO algorithm in achieving accurate non-dominated solutions with fewer outliers and standard deviation among all studied metrics. [ABSTRACT FROM AUTHOR]

Electric Vehicles (EVs) are gaining popularity due to their low maintenance, better performance and zero carbon emission. To expand their adoption, Electric Vehicles Charging Stations (EVCS) must be integrated with the distribution system constructively to charge EVs. This study suggests an RAO-3 based on the fuzzy classification technique for the optimum EVCS, Distributed Generations (DGs), and Shunt Capacitors (SCs) sizing and positioning for 69 bus radial distribution systems with network reconfiguration. The proposed method has the following advantages (i) lower active power loss, (ii) enhanced voltage profiles, (iii) improved power factor at the substation, and (iv) optimum distribution of EVs at charging stations. Characteristic curves of Li-Ion battery charging are utilised for load flow analysis to build EV battery charging loads models. The proposed simultaneous fuzzy multi-objective study with a reconfigured network can handle the optimal number of EVs in EVCS and maintain the substation power factor at the required level, yielding an impressive distribution system performance. For example, the minimum active power loss of 18.0884 kW is achieved with a minimum voltage enhanced to 0.9905 p.u., maintaining the bus voltages at their permissible limit. The numerical results indicate that using the RAO-3 algorithm, the simultaneous technique with system reconfiguration is computationally efficient and scalable, outperforming the two-stage methodology and the method without system reconfiguration. [ABSTRACT FROM AUTHOR]

This article provides a comprehensive review of optimal relay coordination (ORC) in distribution networks (DNs) that include distributed generators (DGs). The integration of DGs into DNs has become a real challenge for power system protection, as the power flow changes from unidirectional to bidirectional, which complicates the relay settings. The introduction of DGs in DNs requires changes and modifications in the protective schemes to maintain proper operation, reliability, stability, and security of the system. This paper focuses on the impacts of DGs penetration into DNs, including the effects on protective scheme coordination. Various expressions for characterizing the overcurrent (OC) coordination problem, as well as related solution attempts, are discussed. Several optimization strategies and techniques are suggested by scientists to deal with coordination optimization problems aiming to achieve less computation time and better accuracy. All these efforts ultimately aim to define optimal relay settings to achieve ORC by generating the optimal setting of cascading relative OC relays. This comprehensive review provides a broad overview of the contributions of scholars in recent publications in this field, with more than 210 articles reviewed and analyzed. It is a valuable resource for other researchers in the same field who aim to tackle ORC problems in their future endeavors. [ABSTRACT FROM AUTHOR]

The proliferation of distributed energy resources in distribution systems has given rise to a new concept known as Microgrids (MGs). The effective control of MGs is a crucial aspect that needs to be prioritized before undertaking any implementation procedure. This article provides a comprehensive overview of hierarchical control methods that ensure efficient and robust control for MGs. Specifically, it focuses on the secondary controller approaches (centralized, distributed, and decentralized control) and examines their primary strengths and weaknesses. The techniques are thoroughly discussed, deliberated, and compared to facilitate a better understanding. According to functionality, the hierarchical-based control scheme is allocated into three levels: primary, secondary, and tertiary. For secondary control level, the MG communication structures permit the usage of various control methods that provided the significance of the secondary controller for consistent and reliable MG performance and the deficiency of an inclusive recommendation for scholars. Also, it gives a review of the literature on present important matters related to MG secondary control approaches in relation to the challenges of communication systems. The problem of the secondary level control is deliberated with an emphasis on challenges like delays. Further, at the secondary layer, the distributed control techniques for reducing communication system utilization and then reducing communication system delays are conferred. Furthermore, the benefits and limitations of various control structures, such as centralized, decentralized, and distributed are also discusses in this study. Later a comparative analysis of entire control approaches, the best methods of control according to the author’s perspective are also discussed.

The main objective in microgrids is keeping their frequency and voltage close to the rated values, and ensuring a precise power sharing of their distributed generators (DGs). This can be challenging in a meshed microgrid due to the tight coupling between active and reactive power resulting from its various power line impedances. Centralized control strategies are widely used to address these issues. But, the increase in DGs number in meshed microgrids rises the communication system cost due to the high number of data to be acquired and processed. Most of existing distributed control strategies in literature are unable to treat simultaneously power sharing as well as voltage and frequency restoration. In this paper a distributed consensus-based control is proposed for accurate active and reactive power sharing as well as frequency and voltage restoration in meshed reconfigurable islanded microgrids. The controllers for primary and secondary control are locally adjusted and do not require knowledge of the microgrid structure. The efficiency of the proposed strategies is verified with large load variations, microgrid topology change and various communication delays by simulation and validated by real-time Hardware in The Loop tests.

The efficiency of distribution networks is hugely affected by active and reactive power flows in distribution electric power systems. Currently, distributed generators (DGs) of energy are extensively applied to minimize power loss and improve voltage deviancies on power distribution systems. The best position and volume of DGs produce better power outcomes. This work prepares a new hybrid SSA–GWO metaheuristic optimization algorithm that combines the salp swarm algorithm (SSA) and the gray wolf optimizer (GWO) algorithm. The SSA–GWO algorithm ensures generating the best size and site of one and multi-DGs on the radial distribution network to decrease real power losses (RPL) (kW) on lines and resolve voltage deviancies. Our novel algorithm is executed on IEEE 123-bus radial distribution test systems. The results confirm the success of the suggested hybrid SSA–GWO algorithm compared with implementing the SSA and GWO individually. Through the proposed SSA–GWO algorithm, the study decreases the RPL and improves the voltage profile on distribution networks with multiple DGs units.

High penetration of distributed generations (DGs) into a distribution network (DN) could not only enhance the power-system reliability indexes but also reduce power losses. The use of DGs is effective for service restoration plans of the DN to minimize outage customers and power loss. However, the presence of DGs could lead to the protection miscoordination of overcurrent relays/reclosers due to fault isolation and service restoration processes. In light of this assumption, a fault detection, location, isolation, and service restoration (FDLISR) method is proposed to apply to both rural and urban distribution networks with DGs such that the comparative results of this FDLISR method are analyzed and discussed to demonstrate which type of the distribution network has a high risk of protection miscoordination of overcurrent relays/reclosers from service restoration scenarios. The faulted-section isolation and the distribution-network service restoration can be performed by solving a two-constraints-based objective function and seven appropriate performance indices (PI). By using the E-Terra Dispatching Operator Training Simulator (E-DOTS), simulation results of the FDLISR method applied for both the 22 kV rural-type and urban-type distribution networks are presented and compared to demonstrate its effectiveness by showing the possibility of protection miscoordination and the consideration of power losses for each service restoration plan. Consequently, the computation time of the FDLISR method proposed for the DN is within one-and-half minutes. Moreover, two performance indices of power loss and protection miscoordination, LOSS-PI and PRO-PI respectively, can highly impact the priority rank of possible service restoration plans for the rural DN while only the PRO-PI affects multiple service-restoration plans for the urban DG-contained DN.

With the high penetration of renewable energy integrated into distribution networks, distributed generators (DGs) are encouraged to participate in market competition with a “peer-to-peer” (P2P) strategy. The operation strategy of the distribution network is increasingly characterized by weak centralization and decentralization. The traditional centralized operation strategy of the distribution network is difficult to ensure the efficiency and effectiveness of terminal energy consumption calculation and equipment regulation, which increases the complexity of distributed energy interactive operation cost calculation and subsequent planning scheme evaluation. Consequently, to enhance the penetration of DGs in distribution networks and ensure their economic operation, the distribution network planning calls for market-oriented integration reform and theoretical innovation. Hence, this paper investigates whether P2P strategy has a profitable and attractive point for power companies if non-utility-owned DGs are integrated into distribution networks. An investment planning scheme of the distribution network is presented to improve the profit of the power company. Network usage charges (NUC) is introduced to provide charge service for both parties of power transaction and optimize the distribution network planning and operation strategy. The profit of power companies is maximized by NUC while the economic benefit of non-utility-owned DGs is also taken into account. The effectiveness and availability of non-utility-owned DGs planning with P2P strategy on investment planning profit are illustrated by the numerical case studies.

The uncertain power flow analysis is essential to assessing the operating states of distribution networks under various uncertainties. The power flow calculations considering the single uncertainty have been well developed. However, different types of uncertainties may coexist in power systems. In order to comprehensively assess the impacts of mixed uncertainties, this paper proposes a surrogate-assisted point estimate method to solve the hybrid probabilistic and interval power flow. By taking advantage of the point estimate method and high-fidelity surrogate model, the proposed method can accurately obtain the relevant results of power flow calculation with high efficiency. The numerical studies in IEEE 33-bus and 141-bus systems verify the accuracy and efficiency of the proposed method by comparing it with other well-known methods.

Zero Trust Network Architecture implementation is gaining popularity in the ICT sector to defend and deter cyber threats or cyber crime. Considering the architecture’s ability, it is unlikely that a single compromised endpoint in the zero-trust networks could spread laterally, infecting the whole network.This provides us with the ability to adopt the architecture in the energy sector. The popularity of the distributed generators helps us beself-reliant on the power and enables a consumer to supply the power to the grid. The pool of these small generators constitutes a virtual power plant. With this arrangement, its network is also exposed to security challenges. Protecting these physical systems, data protection, and information privacy becomes critical. Motivated by this convergence, this paper aims to present a comprehensive Zero trust security architecture to help reduce the risks and help secure the physical systems and ensure privacy and data protection.

In this article, a new implementation of Artificial Ecosystem Optimizer (AEO) technique is developed for distributed generators (DGs) and capacitors allocation considering the Reconfiguration of Power Distribution Systems (RPDS). The AEO is inspired from three energy transfer mechanisms involving production, consumption, and decomposition in an ecosystem. In the production mechanism, the production operator allows AEO to produce a new individual randomly, whereas the search space exploration can be improved as illustrated in the consumption mechanism and exploitation can be performed in the decomposition. A practical case study of 59-bus Cairo distribution system in Egypt is simulated with different loading percentages. For optimizing the performance of that practical network, the AEO algorithm is employed for different scenarios. Besides, the results obtained by recent optimization techniques which are Jellyfish Search Optimizer (JFS), Supply Demand Optimizer (SDO), Crow Search Optimizer (CSO), Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO) and Whale Optimization Algorithm (WOA) are compared with the developed AEO. The simulation results demonstrate the efficacies and superiority of the AEO compared to the others. It surpasses the other algorithms in terms of obtaining the best, mean, worst, and standard deviations. After optimal RPDS and DGs placements, the power losses are decreased by 78.4, 77.84 and 71.4% at low, nominal and high levels, respectively. However, the best scenario with its application prospects is mentioned after optimal RPDS, DGs, and capacitors where the power losses are decreased by 68.8, 85.87 and 89.91% at low, nominal and high levels, respectively.