Search

ABSTRACT In this paper, a family of single‐switch high step‐up DC‐DC converters based on switched‐capacitor (SC) cells and coupled inductor (CL) or built‐in transformer (BIT) is proposed. By replacing one active switch in the 3X‐ladder switched‐capacitor converter (SCC) with the primary side of a CL or BIT whose secondary side is incorporated into a multiplexed current path, the proposed topology integrates the high power‐density feature of a ladder SCC with the voltage‐lift effect of a CL or BIT. In addition, the problems of high current spikes and limited efficiency in the voltage regulation range of the traditional ladder SCC are tackled, making them suitable for distributed generation systems, microgrids, and so forth. For illustration purposes, the performance of the topologies within the family is compared in multiple ways, detailed operation principles and design considerations of one specific converter are given. Finally, experimental results are presented to verify the analytical findings. [ABSTRACT FROM AUTHOR]

Incorporating distributed generation (DG) technology into modern power systems heralds a multitude of technological, economic, and environmental advantages. These encompass enhanced reliability, reduction in power losses, heightened efficiency, low initial investment requirements, abundant availability, and a minimized environmental footprint. However, the rapid detection and disconnection of DG units during islanding events are paramount to circumvent safety hazards and potential equipment damage. Although passive techniques are predominantly utilized for islanding protection due to their minimal systemic interference, their susceptibility to substantial nondetection zones (NDZ) instigates a transition toward more innovative techniques. Addressing this crucial need, the study employs a novel amalgamation of signal processing methodologies and a suite of intelligent classifiers to augment the detection of islanding events in power systems. Concealed features from a variety of signals are extracted by the methodology and utilized as robust inputs to the intelligent classifiers. This empowers these classifiers to make a reliable distinction between islanding events and other types of disturbances. The breadth of the study is expanded to evaluate a range of sophisticated models including gradient boosting, decision tree–based models, and multilayer perceptrons (MLPs). These models are thoroughly tested on a radial distribution system integrated with two DG units and subjected to rigorous simulations and comparative analysis using the DIgSILENT Power Factory software. The findings underscore the efficacy of the proposed method, showcasing a significant improvement over conventional techniques in terms of efficiency and resilience. Above all, the methodology exhibits an exceptional ability to discriminate between islanding events and other system disturbances. Specifically, the islanding detection method demonstrated a detection time of less than one cycle (20 ms), ensuring rapid response. Moreover, the proposed algorithm demonstrated the capability to detect all ranges of power mismatch, including zero‐power mismatch, thereby eliminating the NDZ. These results illuminate the benefits of integrating signal processing methodologies with intelligent classifiers, marking a significant stride forward in the realm of islanding event detection. [ABSTRACT FROM AUTHOR]

Strong uncertainties of distributed renewable generations, loads and real‐time market prices impose higher requirements on the online scheduling for microgrids. Traditional model‐driven methods exist several limitations due to low solving efficiency, difficulty in handling uncertainties, reliance on accurate prediction information, and inability to leverage accumulated historical data. This paper proposes a data‐driven improved imitation learning based approach for online microgrids optimization. First, a mixed integer linear programming model is established to derive offline optimal decisions within the given scenarios, which serve as expert demonstrations to help construct a sample database for imitation learning. Next, a direct imitation learning model based on eXtreme Gradient Boosting (XGBoost) is established to learn the mapping relationship between the system state and the scheduling decision, and the model training is refined by input clustering and output classification. At the input end, the operation scenarios are clustered to form multiple sub‐feature sets to achieve targeted training for different scenarios. At the output end, a binary variable is added to the label set to realize high‐precision decisions of the action of the energy storage system. Numerical case studies demonstrate the performance advantages of the proposed method. [ABSTRACT FROM AUTHOR]

This thesis proposes an improved droop control strategy design based on active disturbance rejection control and LSTM. This strategy uses the droop control method to coordinately control the distributed generation units (DGs) in a microgrid to achieve stable operation of the microgrid system. Linear-Auto Disturbance Rejection Control (LADRC) is introduced and an improved LADRC is designed based on the error principle. A disturbance compensation link is introduced on the basis of traditional LADRC to form ILADRC and a droop control strategy is used. Instead of improving the PD controller in LADRC, an improved droop control strategy is formed, which not only achieves natural decoupling between powers, but also improves the system's immunity and transient operation capabilities. At the same time, in order to achieve adaptive parameter tuning in the improved droop control strategy, this article introduces long short-term memory (LSTM) to form an adaptive improved droop control strategy which further improves the system's immunity and robustness. This article builds a simulation model through the MATLAB/Simulink simulation experiment platform and tests PI control and traditional droop control. The strategy and the improved droop control strategy designed in this thesis are experimentally compared and verified, and simulation analysis and verification are conducted on the two working conditions. The simulation results clearly demonstrate the superiority of the improved droop control strategy over PI control and traditional droop control, indicating that the correctness and reliability under various working conditions are verified. [ABSTRACT FROM AUTHOR]

This paper focuses on the distributed generation (DG) controller of a PV-based microgrid. An independent DG controller (IDGC) is designed for PV applications to improve Maximum-Power Point Tracking (MPPT). The Extreme-Learning Machine (ELM)-based MPPT method exactly estimates the controller's reference input, such as the voltage and current at the MPP. Feedback controls employ linear PI schemes or nonlinear, intricate techniques. Here, the converter controller is an IDGC that is improved by directly measuring the converter duty cycle and PWM index in a single DG PV-based MG. It introduces a fast-learning Extreme-Learning Machine (ELM) using the Moore–Penrose pseudo-inverse technique and online sequential ridge methods for robust control reference (CR) estimation. This approach ensures the stability of the microgrid during PV uncertainties and various operational conditions. The internal DG control approach improves the stability of the microgrid during a three-phase fault at the load bus, partial shading, irradiance changes, islanding operations, and load changes. The model is designed and simulated on the MATLAB/SIMULINK platform, and some of the results are validated on a hardware-in-the-loop (HIL) platform. [ABSTRACT FROM AUTHOR]

For cooperation among distributed generations in a DC microgrid (MG), distributed control is widely applied. However, the delay in distributed communication will result in steady‐state bias and the risk of instability. This paper proposes a novel distributed control for time‐delayed DC MGs to achieve accurate current proportional sharing and weighted average voltage regulation. Firstly, by utilizing an advanced observer based on the PI consensus algorithm, the steady‐state bias problem is addressed. Then, using the passivity theory, stability analysis is conducted to reveal the principle of system instability caused by communication delay. On this basis, to offset the adverse effects of communication delay on the system stability, scattering transformation is introduced in the observer‐based distributed control. Moreover, considering the potential delay from the measurement stage in real‐life scenarios, the sufficient condition of the system stability is concluded by constructing the Lyapunov–Krasovskii functional. Finally, the performance of the proposed control and conclusions of stability analysis are verified by hardware‐in‐loop tests. [ABSTRACT FROM AUTHOR]

The research focuses on incorporating microgrids into engineering curricula for achieving voltage stability in today's power systems. This helps to meet the increasing demand for engineers to integrate distributed power generation and renewable energy sources. Some limitations of the current literature include the absence of models outlining approaches to microgrid education and limited insight into teaching strategies for electrical power systems. The research used a quantitative methodology to survey 100 engineering students enrolled in a microgrid modeling class to achieve the study's objectives. The data analysis involved machine learning models such as Random Forest, Gradient Boosting, K-Means, hierarchical clustering, and regression models. The major findings identified exam score as the most significant determiner of student performance (weight ≈ 0.40). Based on the clustering analysis, it was found that microgrid systems can be grouped into four operational states. It was also seen that linear regression models were highly accurate and better than other highly complex models, like Decision Tree, with a model accuracy of R2 ≈ 0.4. One of the study's major strengths is the potential impact of the proposed framework for integrating microgrids into engineering education on the professional training of engineers. This framework, based on theoretical knowledge and practical experience as well as on developing advanced analytical skills, can significantly enhance the professional training of engineers to deal with the complexities of contemporary power systems, including microgrids and sustainable energy progress. [ABSTRACT FROM AUTHOR]

A key issue in the transition to decarbonized energy sources with constant power sources is inaccurate current sharing and voltage quality. The main reason which causes these issues is the inevitable system impedance mismatches. This study addresses this issue by proposing an enhanced power‐sharing strategy that regulates adjustable resistance and compensates for voltage drops across the line impedance at the output of distributed generation converters. The control approach involves analyzing the mathematical relationship between system impedance mismatches and voltage drops in DC residential systems. Utilizing low‐computation proportional‐integral controllers, the secondary current sharing and load voltage controllers regulate converter outputs to compensate for mismatched resistance and voltage, ensuring proper current sharing and load voltage quality across various load conditions. Through the implementation of the proposed scheme incorporating adjustable resistance and voltage shifting terms, both the current‐sharing error and load voltage deviation are gradually reduced to negligible values, while maintaining stable performance in steady‐state conditions, without the need for prior knowledge of system parameters. Simulations and experimental studies are both conducted to demonstrate the effectiveness of the proposed control scheme. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

The development of microgrid automation depends on information and communication technologies, which are vulnerable to cyber-attacks. Recent advancements in MGs enhance power systems' efficacy and reliability, but cybersecurity remains a significant concern, especially with false data injection attacks (FDIAs) posing serious threats. FDIAs can compromise measurement devices and tamper with State Estimation (SE), risking the seamless operation of MGs. To address this, this paper proposes an efficient Iterative Free Detection of False Data (IFDFD) scheme for detecting FDIAs in microgrid state estimation. The IFDFD scheme uses complex Micro Phasor Measurement Unit (μPMU) measurements and computes nodal power injections to detect FDIAs. Furthermore, the proposed scheme integrates an S-Estimator to eliminate noise errors caused by environmental factors and the component lifespan, making IFDFD robust against sophisticated attackers. The proposed IFDFD scheme has been tested and validated on the modified IEEE 14 bus test system, integrating Distributed Generations (DGs). False data was injected into the measurements to test the scheme's effectiveness. The efficacy of proposed IFDFD scheme has been validated by comparing it to existing method of FDIAs. The obtained result clearly validates the efficacy of the proposed IFDFD scheme. • Addresses cybersecurity concerns in MG automation dependent on Information and Communication Technology. • Proposed IFDFD scheme detects FDIAs using μPMU measurements and nodal power injections. • IFDFD integrates S-Estimator to eliminate noise errors from environmental factors. [ABSTRACT FROM AUTHOR]

Ensuring the secure and cost-effective operation of smart power microgrids has become a significant concern for managers and operators due to the escalating damage caused by natural phenomena and cyber-attacks. This paper presents a novel framework focused on the dynamic reconfiguration of multi-microgrids to enhance system's security index, including stability, reliability, and operation costs. The framework incorporates distributed generation (DG) to address cyber-attacks that can lead to line outages or generation failures within the network. Additionally, this work considers the uncertainties and accessibility factors of power networks through a modified point prediction method, which was previously overlooked. To achieve the secure and cost-effective operation of smart power multi-microgrids, an optimization framework is developed as a multi-objective problem, where the states of switches and DG serve as independent parameters, while the dependent parameters consist of the operation cost and techno-security indexes. The multi-objective problem employs deep learning (DL) techniques, specifically based on long short-term memory (LSTM) and prediction intervals, to effectively detect false data injection attacks (FDIAs) on advanced metering infrastructures (AMIs). By incorporating a modified point prediction method, LSTM-based deep learning, and consideration of technical indexes and FDIA cyber-attacks, this framework aims to advance the security and reliability of smart power multi-microgrids. The effectiveness of this method was validated on a network of 118 buses. The results of the proposed approach demonstrate remarkable improvements over PSO, MOGA, ICA, and HHO algorithms in both technical and economic indicators. [ABSTRACT FROM AUTHOR]

In alternating current (AC) microgrids, the prevalent approach for controlling the power distribution between generators and loads is droop control. This decentralized technique ensures accurate power sharing; however, its utility is restricted by significant drawbacks. Notably, in scenarios involving dissimilar power sources, mismatched impedance lines, or meshed microgrids, conventional droop control fails to ensure effective reactive power sharing among inverters, often leading to notable circulating currents. Hence, the primary objective of this paper is twofold: firstly, to examine limitations inherent to conventional droop control; secondly, to introduce a robust power-sharing methodology for AC microgrids. This novel approach is specifically designed to achieve consistent sharing of active and reactive power across meshed topology microgrids. The technique considers the presence of distributed power loads and the dynamic nature of the topology. Despite the attainment of satisfactory active and reactive power sharing, deviations in voltage and frequency occasionally manifest. To address this issue, a supplementary control mechanism is proposed as a third phase. This secondary control method focuses on reinstating the microgrid's voltage and frequency to rated values, all while upholding the precision of power sharing. The efficacy of this multi-stage methodology is rigorously validated through simulations using MATLAB/Simulink and practical experimentations. [ABSTRACT FROM AUTHOR]

In power systems, nonlinear loads cause harmonic distortion, adversely affecting sensitive equipment such as induction motors, power electronics, and variable-speed drives. This paper presents a novel control strategy that integrates with existing hierarchical control systems to mitigate voltage imbalances and harmonic disturbances in AC-islanded microgrids. The proposed method utilizes selective harmonic order filtering through multiple second-order generalized integrators (MSOGI) to extract negative, positive, and harmonic order components. The distributed generation (DG) unit control mechanism is designed to immediately correct voltage imbalances and harmonic disruptions, distributing the compensatory load evenly to rectify real and reactive power imbalances and harmonic disturbances. The microgrid's control architecture primarily includes droop controllers for real and reactive power of positive sequences, voltage and current regulation inner control loops, an additional loop for correcting imbalances and harmonics, and secondary controllers to maintain voltage magnitude and frequency at nominal levels, ensuring high-quality voltage at the point of common coupling (PCC). The effectiveness of this approach is demonstrated through simulation results on the MATLAB/Simulink platform, proving its ability to effectively mitigate voltage imbalances and harmonic issues with the total harmonic of voltage reduced to approximately THDv = 0.5% and voltage unbalance factor (VUF) within approximately 0.1%. [ABSTRACT FROM AUTHOR]

The penetration of renewable energy resources for distribution networks can significantly impact the security of power supply systems. To improve the penetration efficiency, a hierarchical optimization planning for a distributed generation (DG) network using the improved sand cat swarm optimization (ISCSO) is proposed to determine the optimal location and capacity of DG into the distribution network. Firstly, the sensor is used to collect data, and the ISCSO algorithm is used to construct a complex nonlinear DG planning problem, thus reducing the impact of DG uncertainty. Second, a DG hierarchical planning model is established to select the optimal location and capacity of DG access to the distribution network. Finally, in the classical test system, the effectiveness of the proposed model is verified by setting up multiple cases. The test results confirm that the total annual cost and power loss of the DG system can be reduced by 13.1 and 40.5%, respectively. [ABSTRACT FROM AUTHOR]

The control strategy of a distributed photovoltaic (PV) power generation system within a microgrid consists of an inner-loop controller and an outer-loop controller. The inner-loop controller is divided into two types, namely, the maximum power point tracking (MPPT) control strategy and DC bus voltage support strategy. Switching between these two control strategies results in issues such as DC bus overvoltage, system oscillations, or even PV system failure. An improved droop control strategy with a novel inner-loop controller is proposed, incorporating an output power derivative regulator. The control system unifies MPPT and DC bus voltage support strategy without switching the controller structure. A simulation model is built to validate the effectiveness of the proposed control strategy, and the results show that the ripple of DC bus voltage decreases by more than 60%. [ABSTRACT FROM AUTHOR]

Regulating voltage and current signals in microgrids (MG) is essential to ensure stability, optimize power quality, support grid integration, enhance operational efficiency, and promote safety within the system. This paper introduces a novel Linear Matrix Inequalities (LMI)-based approach aimed at regulating voltage and current signals within microgrids through the utilization of sliding mode control. The MG under examination in this paper is composed of a voltage source inverter (VSI) for DC to AC voltage conversion, a filter to ensure sinusoidal signal quality, and an array of loads, including those with uncertain characteristics. The objective of this study is to regulate the output voltage and current in a short period of time in the presence of diverse loads. By promptly adjusting voltage and current levels, the microgrid can effectively accommodate fluctuations in demand and maintain optimal performance under changing conditions. The presented controller consists of two parts: a state feedback gain calculated from the LMI and a sliding mode-based controller to maintain system stability. This controller is intended to reject disturbances, track reference signals, and minimize steady-state errors in a limited time. The satisfactory performance of the microgrid will have a significant impact on various parameters, such as frequency, active power, reactive power, and power factor. Simulating the voltage source inventor and presenting numerical results demonstrate the effectiveness of the proposed controller to provide high robustness against uncertainty and nonlinear loads while maintaining system stability. [ABSTRACT FROM AUTHOR]

This article presents a detailed analysis of a previously introduced boost converter based on cascaded capacitor connections. The analyzed converter maintains the same voltage gain as its conventional double-boost converter counterpart. The main advantage of the redesigned converter is that it requires smaller capacitors, rated to a lower voltage, due to the cascaded connection. This makes it suitable for various applications, including distributed generation and microgrids. This paper conducts the mathematical modeling of the converter with large signal models and the equilibrium operation in continuous conduction mode, employed to implement a control scheme. Even though the individual capacitor voltage ripple reaches high values, for voltage gains above five, the output voltage ripple is lower compared with that of other converters. The analysis also includes a comparative study with the conventional double dual boost converter. Simulations assess the proposed topology's ability to effectively minimize the switching ripple in the output voltage, even with smaller capacitors. Finally, experimental results are also provided to confirm the functionality of the proposed converters under various operational conditions, which confirm its suitability for low-voltage generator applications. [ABSTRACT FROM AUTHOR]

Aiming at the consumption problems caused by the high proportion of renewable energy being connected to the distribution network, it also aims to improve the power supply reliability of the power system and reduce the operating costs of the power system. This paper proposes a two-stage planning method for distributed generation and energy storage systems that considers the hierarchical partitioning of source-storage-load. Firstly, an electrical distance structural index that comprehensively considers active power output and reactive power output is proposed to divide the distributed generation voltage regulation domain and determine the access location and number of distributed power sources. Secondly, a two-stage planning is carried out based on the zoning results. In the phase 1 distribution network-zoning optimization layer, the network loss is minimized so that the node voltage in the area does not exceed the limit, and the distributed generation configuration results are initially determined; in phase 2, the partition-node optimization layer is planned with the goal of economic optimization, and the distance-based improved ant lion algorithm is used to solve the problem to obtain the optimal distributed generation and energy storage system configuration. Finally, the IEEE33 node system was used for simulation. The results showed that the voltage quality was significantly improved after optimization, and the overall revenue increased by about 20.6%, verifying the effectiveness of the two-stage planning. [ABSTRACT FROM AUTHOR]

In isolated operation, DC microgrids require multiple distributed energy storage units (DESUs) to accommodate the variability of distributed generation (DG). The traditional control strategy has the problem of uneven allocation of load current when the line impedance is not matched. As the state-of-charge (SOC) balancing proceeds, the SOC difference gradually decreases, leading to a gradual decrease in the balancing rate. Thus, an improved SOC droop control strategy is introduced in this paper, which uses a combination of power and exponential functions to improve the virtual impedance responsiveness to SOC changes and introduces an adaptive acceleration factor to improve the slow SOC balancing problem. We construct a sparse communication network to achieve information exchange between DESU neighboring units. A global optimization controller employing the consistency algorithm is designed to mitigate the impact of line impedance mismatch on SOC balancing and current allocation. This approach uses a single controller to restore DC bus voltage, effectively reducing control connections and alleviating the communication burden on the system. Lastly, a simulation model of the DC microgrid is developed using MATLAB/Simulink R2021b. The results confirm that the proposed control strategy achieves rapid SOC balancing and the precise allocation of load currents in various complex operational scenarios. [ABSTRACT FROM AUTHOR]

The droop control strategy is popularly employed in DC microgrids. However, its virtual resistance will cause voltage deviation and reduce transient response. The DOB‐based method is proven to improve transient response in literature. However, it is analyzed in this study that this method will negatively influence the current sharing when employed in droop control. A weakened disturbance observation (WDOB) is proposed in this work to improve the drawbacks. To employ the proposed method, the equivalent models of the droop controller and the physical system are separately established, and several transformations are conducted. An auxiliary compensation is added and the current loop is considered as a whole to be transmitted into the control plant, making the traditional DOB method successfully adopted. It is obvious that the dynamic performance is improved, but it disabled virtual resistance in the steady‐state. And the current sharing cannot be achieved in a multi‐converter parallel system. The reason for this problem is analyzed from the control process and transfer function, and the WDOB solution is finally proposed. Through the proposed method, both aims of improving dynamic response and current sharing can be achieved, and the steady voltage deviation is much less than that of the traditional droop controller. [ABSTRACT FROM AUTHOR]

Hybrid microgrid (HMG) control strategies reveal several critical research gaps that must be addressed. Current approaches often fall short in efficiency and reliability due to the inherent complexities of transitioning between grid-connected mode (GCM) and islanded mode (IM), resulting in increased losses and operational difficulties. Additionally, managing overloads, especially in IM, poses significant challenges for maintaining stable voltage and frequency regulation. This paper seeks to address these issues by introducing a novel universal control structure (UCS) for HMGs, which includes a frequency compensation unit (FCU) and a three-stage bidirectional AC/DC droop. It presents a streamlined yet robust solution for handling overloads, maintaining power balance, and ensuring precise voltage and frequency regulation while promising smooth maneuver in both GCM and IM. The proposed UCS provides flexible and reliable operation, allowing for the versatile placement of DC and AC sources and loads. This will reduce power transformation stages thereby lowering cost and increasing efficiency of system. Through experimental validation, this study demonstrates the effectiveness of the proposed control in addressing these critical research gaps and advancing the field of HMG control strategies. [ABSTRACT FROM AUTHOR]

In the multi‐paralleled converter microgrid system, the traditional hierarchical control strategy can eliminate the bus voltage amplitude and frequency deviation from the rated value. However, isolated AC microgrids may face extreme scenarios such as communication delays and interruptions in data transmission due to the use of low‐bandwidth communication (LBC) lines. Additionally, the inconsistent line impedance of each distributed generation (DG) unit may result in the inaccurate division of reactive power in multi‐paralleled converter systems, thus affecting system stability. To address these issues, this paper presents a distributed coordinated control strategy for isolated AC microgrids based on the consensus algorithm. The proposed strategy first replaces LBC lines with a filter to alleviate the effects of communication delays. A small‐signal model is established in its state space, and stability of the microgrid system under the proposed control strategy is verified through eigenvalue analysis. Furthermore, based on the above theoretical analysis, a consensus algorithm is introduced, and a distributed control strategy for isolated AC microgrids based on the consensus algorithm is proposed to solve the issue of inaccurate equalization of the system's reactive power. Finally, factors that influence the dynamic convergence performance of the consensus algorithm are analyzed through simulation in PLECS software. Also, the maximum tolerable communication delays of the microgrid system under different communication topologies are also compared, and the system's robustness is evaluated under the condition of sudden communication interruption in a DG unit and sudden weather variation. These analyses confirmed the robustness of the proposed strategy against communication delays, output power fluctuation, and communication interruptions. [ABSTRACT FROM AUTHOR]

When False Data Injection (FDI) attacks inject false data into the controllers of a microgrid, it can lead the controllers to make decisions based on inaccurate information, ultimately resulting in system collapse. This study presents a novel approach to dynamically adjust output voltage in Campus Microgrids (CMG). Initially, we use feedback linearization techniques to address the inherent nonlinear complexities in Distributed Generation (DG) dynamics. Moreover, during voltage regulation, controllers and sensors may be susceptible to FDI attacks. To address this, an elastic follower observer is introduced to mitigate the impact of false information injection attacks on voltage coordination, thus ensuring the robustness and stability of the system. Notably, our method reduces dependence on global information and fault boundaries within the communication network, thereby enhancing system elasticity and efficiency. In conclusion, through comprehensive simulation experiments, we evaluate the effectiveness and performance of our proposed strategy. The simulation results validate the success of our approach in voltage regulation and its efficiency in fault management. These findings bear significant implications for the advancement of voltage regulation strategies in distributed generation systems. [ABSTRACT FROM AUTHOR]

Grid‐forming inverters are used for voltage regulation and frequency control in autonomous hybrid microgrids and multi‐microgrid systems by imitating synchronous generators. However, in microgrids with weak grids including low inertia levels and small X/R ratios, these inverters interact with each other, and as a result low‐frequency oscillations (LFO) arise. LFO impacts the frequency stability of multi‐microgrid systems. Nevertheless, LFO can be mitigated by the load‐frequency control system, which serves as a secondary control mechanism. However, the presence of wind turbines and photovoltaic systems in hybrid microgrids adds complexity to the operation of the load‐frequency control due to the uncertainty associated with these renewable energy resources, and various controllers have been employed. This paper proposes a novel approach to enhance the performance of the load‐frequency control system and suppress LFO. The presented technique reduces the complexity of the hybrid microgrid structure by reducing the number of controllers. The model predictive control (MPC) technique is utilized for load‐frequency control and the weight parameters of the MPC are determined using the rain optimization algorithm. The proposed method demonstrates improved dynamic response, reduced overshoot and undershoot responses, decreased controller complexity, and effective LFO suppression. The simulation results verify the effectiveness of the method. [ABSTRACT FROM AUTHOR]

This study developed a model to compensate for both active and passive power in an electric microgrid with an optimal power flow to allow a detailed analysis of its power flow, which encourages the integration of renewable energy sources (RESs). Mixture integer linear programming (MILP) is proposed, with distributed generation (DG) integrated into the existing bars. It is developed in the GAMS tool to minimize operating costs in the microgrid as an objective function, to guarantee reliability, and quality in the microgrid. The algorithm calculates an optimal solution that meets all requirements for reliability and quality in the microgrid. Finally, a comparison of data between the two systems without and with DG was performed, showing an improvement in voltage profiles, reduced active power losses in the lines, a better power factor (PF), and improved angular behavior; that also adheres to the restrictions set forth at a minimum operating cost. A nine-bar model has been used to model the problem by IEEE standards. [ABSTRACT FROM AUTHOR]

Recently, various distributed energy resources are significantly integrated into the modern power systems. This introduction of distributed energy resource‐rich systems can cause various power quality issues, due to their uncertainties and capacity variations. Therefore, it is crucial to establish energy balance between generation and demand to improve power system's reliability and stability and to minimize energy costs without sacrificing customers' comfort or utility. In this regard, power system flexibility concept is highlighted as a robust and cost‐effective energy management system, especially on the demand side, to provide consumers' demands with an acceptable level of power quality. Accordingly, here, a comprehensive review of recent developments in the power system flexibility and demand‐side management strategies and demand response programs are provided to include mainly classifications, estimation methods, distributed energy resource modelling approaches, infrastructure requirements, and applications. In addition, current research topics for applying power system flexibility solutions and demand‐side management strategies based on modern power system operation are deliberated. Also, prominent challenges, research trends, and future perspectives are discussed. Finally, this review article aims to be an appropriate reference for comprehensive research trends in the power system flexibility concept in general and in demand‐side management strategies and demand response programs, specifically. [ABSTRACT FROM AUTHOR]

Summary: Maintaining proper current sharing among distributed generation (DG) units while regulating the output voltage is a challenging control issue in DC microgrids. The conventional droop control is widely used at the primary level of control to tackle this issue, but this method faces some limitations in voltage control and current sharing. To address this problem, this paper presents a new distributed secondary control method that determines the resistance of transmission lines by injecting an external pulse to the reference voltage of each converter and measuring output voltage and current changes. The proposed secondary control signal, denoted as Ui, enables simultaneous proportional current sharing and voltage restoration in the DC microgrid by correcting the droop coefficients based on the determined resistances. In order to ensure the stability of the proposed method, the small signal stability analysis of the closed‐loop system is conducted, considering various parameter changes. In contrast to similar methods, the proposed method introduces a new hybrid technique of voltage setpoint shifting and droop parameter adjustment that achieves precise proportional current sharing and voltage restoration, even when faced with varying resistive loads and constant power loads (CPLs). Additionally, the proposed method demonstrates outstanding characteristics, such as high convergence speed, the ability to determine transmission line resistances instead of assuming them to be known, and robustness in plug‐and‐play scenarios and communication system failures. The simulation results in MATLAB and experimental tests confirm the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The advent of distributed generation has brought with it a plethora of challenges for the nascent power systems that are being deployed on a large scale. Firstly, the majority of power electronic converters connected to the grid are in current source mode, which results in a lack of inertia and an inability to provide effective inertia or achieve damping support during fluctuations in grid frequency. Secondly, the issue of power quality, caused by the presence of harmonics, is becoming increasingly severe. This is particularly problematic in microgrids or systems with high line impedance, where harmonics can be amplified, thereby further compromising the stability of the power system. To address the deficiency in system inertia, numerous scholars are currently utilizing grid-forming (GFM) technology to achieve virtual inertia. In order to address the issue of system harmonics, it is possible to install active power filter (APF) devices at the point of common coupling (PCC), which serve to mitigate the effects of harmonics. This paper puts forth a proposal for the implementation of an APF with virtual inertia, based on PR + RC composite control. This composite control mechanism serves to enhance the harmonic suppression capabilities of the APF. The introduction of a frequency droop enables the capacitor voltage amplitude to be adjusted during fluctuations in system frequency, thereby achieving virtual inertia and providing active support for system frequency. The experimental results demonstrate that this strategy not only reduces the total harmonic distortion (THD) by 13% in comparison to PI control, indicating excellent harmonic suppression performance, but also allows the system to be inert, achieving positive results in suppressing frequency fluctuations during transients. [ABSTRACT FROM AUTHOR]

This paper demonstrates an enhancement of power quality for a photovoltaic (PV) system connected to the grid with a hybrid energy storage system (HESS). The proposed system utilizes a ramp-rate control (RRC) strategy to limit severe fluctuations in the PV power output. Battery storage is integrated to store surplus energy generated by the PV system and is used for continuous power application. A high-power density device, known as a supercapacitor (SC), is employed to mitigate transient fluctuations in the battery. The proposed system facilitates smooth PV power generation, stabilizes the DC bus voltage (VDC), and eliminates source current harmonics induced by non-linear loads. The Shunt Active Power Filter (SAPF) discussed in this paper serves two primary purposes. Firstly, it acts as a reactive power buffer, smoothing out fluctuations and reducing current harmonic distortions. Secondly, it enables active power injection into the grid, utilizing a specific renewable solar PV source. The efficiency of the modeled compensation system is demonstrated by the sinusoidal shape of the current and the compensation of reactive power (RPC). The targeted system showcases the effectiveness of the current setup by exhibiting low total harmonic distortion (THD). The multifunctional features of the proposed system were implemented using the MATLAB/Simulink software, and the results were validated using an OP5700 Hardware-in-the-Loop (HIL) test bench. This integration of distributed power generation capabilities not only enhances the overall power quality but also improves the efficient utilization of renewable energy resources (RESs). [ABSTRACT FROM AUTHOR]

With the increasing use of renewable energy sources and decentralized power systems, certain challenges have emerged in meeting consumers' electrical energy demands. The intermittent nature of renewable energy generation means that it cannot always align with consumers' needs, resulting in periods of excess energy production when it is not required. To bridge this gap between production and consumption, energy storage systems are necessary. This paper defines the work of an isolated microgrid, which consists of renewable sources (wind and PV) for energy production, households with electric vehicles as consumers, and a combined storage system. This storage system is made from batteries, hydrogen storage, and a control system that defines the best use of the storage. Stored energy is utilized through fuel cells to generate electricity for consumption when renewable sources cannot meet the demand. This paper presents the principles of electrolysis and models of individual elements within such a system, as well as the definition and principle of control of the system functionality based on rules and conditions. The proposed control system aims to increase the energy storage lifecycle by deciding when and how to utilize which type of storage and define a self-sufficient microgrid based on renewable sources of production. An economic analysis of the storage part of the system was carried out in which the levelized cost of energy stored and the NPC of the storage systems are calculated. A simulation of the system's operation is conducted using one-hour measurements of wind turbines, solar panels, and household consumption in Serbia. To analyze the system's behavior, a one-week time horizon is considered. [ABSTRACT FROM AUTHOR]

Today the integration of renewable energy production technologies into power systems brings a new challenge in terms of optimal usage of renewable. It is known that due to the discontinuous nature of renewable energy, the challenge can be associated with the presence of unwanted voltage fluctuations and power losses in power grids. This study performs optimization of the calculation of hosting capacity to determine the maximum amount of renewable energy that can be further expanded. This can be possible by energy storage deployment on the same power grids. The battery storages are expected to have power injection and absorption associated with discharging and charging of energy storages, respectively, to solve voltage fluctuations and power disturbances. Thus, to enhance the probability of distributed generation penetration, it is necessary to have optimal allocation and size during the renewable energy penetration in low-voltage distribution networks. Global optimization as a genetic algorithm (GA) method is used during hosting capacity assessment. The results show that using GA-based optimization, total cost and loss can be reduced by 38.8 and 73%, respectively. The practical results are verified by the IEEE-33 bus system. [ABSTRACT FROM AUTHOR]

The expansion planning of distribution network is based on the implementation information fusion management of distributed generation in distribution network. In order to improve the management and scheduling capability of distributed generation, an implementation information management platform for distributed generation in distribution network based on information integration is proposed, and a comprehensive optimization model for distributed generation and energy storage of distribution network is constructed. The load change from the planning base year to the level year is simulated by Monte Carlo Method; According to the installed capacity of distributed generation, Monte Carlo Method is used to simulate the output of distributed generation, and the planning model of township distribution network expansion is established to meet all power supply needs. The line loss calculation is based on the simulation results of load and distributed generation output. The corresponding steady state model for all kinds of distributed generations is established, and the type of interface connected to the power grid should be confirmed. The information integration method is used to optimize the design of the information management platform for distributed generation in the distribution network. The test shows that the designed implementation information management platform for distributed generation in distribution network can accurately realize the information fusion and management of distributed generation, solve the simultaneity problem caused by the fluctuation and interval of load data and distributed generation output on power flow calculation, and integrate distribution network planning with distributed generation planning. [ABSTRACT FROM AUTHOR]

High dependability, efficiency and low carbon emissions are just a few of the many potential environmental advantages of distributed generation (DG), which is used widely. Accurate islanding detection and quick DG disconnection were crucial to avoid safety concerns and equipment damage brought on by the island mode actions of DGs. Several researchers concentrate on island detection and load scheduling separately. The proposed work focused on islanding detection and load shedding during an island condition. A sophisticated, intelligent mode detection controller detected the system circumstance, and an intelligent shedding controlled the optimal load shedding. The proposed model used a standard IEEE 30-bus system with voltage and current parameters sensed by sensors and given inputs to the intelligent mode detection controller. If the island condition was predicted to move on load scheduling or normal condition was predicted, electricity continued to the utility grid and fulfilled the consumer's required power. The load shedding working process includes system design, data collection and the creation of efficient load scheduling. Maintain steady voltage stability margin throughout that time to complete that priority-based shedding depending on the power generation and its accompanying load restriction. The proposed method was tested for two operating modes, namely detection of islanding modes and load shedding. The proposed approach provides better results in both modes and maintains voltage stability across the entire time period. The proposed method offered a better accuracy of 99% for islanding detection mode when the results were contrasted with those from several other existing methods to validate the performance. As a result, it can be demonstrated that the suggested approach offers better islanding detection performance as well as load shedding with a constant voltage stability margin. [ABSTRACT FROM AUTHOR]

The fixed time control problem for the secondary voltage and frequency of islanded AC microgrids is studied. Based on the multi-agent consensus method, an adaptive fuzzy fixed-time secondary voltage controller considering state constraints and the secondary frequency controller based on control barrier function are proposed. In multi-agent consensus control, each distributed generation is regarded as a nonlinear agent, and the agents communicate with each other through a sparse network. In designing the voltage controller, the adaptive fuzzy estimation of the unknown variables after feedback linearization is used to improve the adaptive ability of the controller and a new sliding mode surface is introduced to make the voltage converge in fixed time. Considering the problem of system state constraints, the barrier Lyapunov function and the control barrier function are used to design the voltage and frequency controllers respectively, so that the system state is within the preset constraint range. The sharing of active power and reactive power is also considered. A proof about fixed time convergence and stability is given. Under the environment of MATLAB/SimPowerSystem, the effectiveness of the proposed controller is verified by the simulation about the load variations and large disturbance of microgrids. [ABSTRACT FROM AUTHOR]

Recently, there has been a huge advancement in renewable energy integration in power systems. Power converters with grid-forming or grid-following topologies are typically employed to link these decentralized power sources to the grid. However, because distributed generation has less inertia than synchronous generators, their use of renewable energy sources threatens the electrical grid's reliability. Suitable control approaches for ensuring frequency and voltage stability in the grid-connected form of operation are established in this study, which offers dynamic, seamless power switching in the islanded mode of operation. In this research, effective Phase Locked Loop (PLL) techniques for grid-forming (GFM) and grid-following (GFL) converters are designed to achieve a smooth transition from grid-tied to islanded mode of operation. In this work, PLL configurations are implemented while considering the active and reactive power, frequency, voltage, and current parameters of the system, and ensuring voltage and frequency stability. The simulation results in a microgrid network that ensures a smooth transition of power transfer while switching between modes of operation, and supports the voltage and frequency stability of the system. [ABSTRACT FROM AUTHOR]

Interconnecting multiple microgrids (MGs) to form a MG cluster (MGC) is significant to improve the MG power supply reliability and increase the new energy accommodation capacity. However, for the traditional MGC in complex operational conditions, the following technologies need to be further researched, such as mutual power support among MGs, voltage support and power quality improvement and so on. Focusing on the above problems, a novel virtual synchronous port control (VSPC) strategy for MGC is proposed. Firstly, a multi‐port soft open point (SOP) is used to interconnect each sub‐MG in the MGC, to achieve a better flexible interconnection among sub‐MGs. Secondly, on this basis, the VSPC strategy is proposed. By adopting the DC voltage control of multi‐port SOP and the virtual synchronisation control of interconnecting ports, multiple voltage source converters (VSCs) are cluster controlled to provide the sub‐MGs with superordinate ports with virtual synchronisation characteristics, which ensures the dynamic balance of the power inside the MGC and achieves better voltage and frequency support capability. Finally, the example results verify the feasibility and effectiveness of the proposed VSPC strategy. [ABSTRACT FROM AUTHOR]

Over the past few decades, many universities have turned to using microgrid systems because of their dependability, security, flexibility, and less reliance on the primary grid. Microgrids on campuses face challenges in the instability of power production due to meteorological conditions, as the output of renewable sources such as solar and wind power relies entirely on the weather and determining the optimal size of microgrids. Therefore, this paper comprehensively reviews the university campuses' microgrids. Some renewable energy sources, such as geothermal (GE), wind turbine (WT), and photovoltaic (PV), are compared in terms of installation costs, availability, weather conditions, efficiency, environmental impact, and maintenance. Furthermore, a description of microgrid systems and their components, including distributed generation (DG), energy storage system (ESS), and microgrid load, is presented. As a result, the most common optimization models for analyzing the performance of campus microgrids are discussed. Hybrid microgrid system configurations are introduced and compared to find the optimal configuration in terms of energy production and flexibility. Therefore, configuration A (Hybrid PV- grid-connected) is the most common configuration compared to the others due to its simplicity and free-charge operation. [ABSTRACT FROM AUTHOR]

With the rapid development of renewable energy and the increasing maturity of energy storage technology, microgrids are quickly becoming popular worldwide. The stochastic scheduling problem of microgrids can increase operational costs and resource wastage. In order to reduce operational costs and optimize resource utilization efficiency, the real-time scheduling of microgrids becomes particularly important. After collecting extensive data, reinforcement learning (RL) can provide good strategies. However, it cannot make quick and rational decisions in different environments. As a method with generalization ability, meta-learning can compensate for this deficiency. Therefore, this paper introduces a microgrid scheduling strategy based on RL and meta-learning. This method can quickly adapt to different environments with a small amount of training data, enabling rapid energy scheduling policy generation in the early stages of microgrid operation. This paper first establishes a microgrid model, including components such as energy storage, load, and distributed generation (DG). Then, we use a meta-reinforcement learning framework to train the initial scheduling strategy, considering the various operational constraints of the microgrid. The experimental results show that the MAML-based RL strategy has advantages in improving energy utilization and reducing operational costs in the early stages of microgrid operation. This research provides a new intelligent solution for microgrids' efficient, stable, and economical operation in their initial stages. [ABSTRACT FROM AUTHOR]

With the development of distributed generation and power electronics technology, DC microgrids have made certain progress with their advantages of simple structure, economic efficiency, and easy control. Considering factors such as the geographical location of some residential areas, microgrids with fuel cells that can operate independently are also receiving increasing attention. In this paper, an online control method named virtual hydrogen consumption is proposed based on an independent DC microgrid which solves the problem of electro‐hydrogen conversion successfully. By solving the overall consumption function, the analytical solution is further obtained to realize the online control. Based on the results of RT‐LAB platform, the proposed method can maintain the output of fuel cell system in its high efficiency range with a lower volatility. [ABSTRACT FROM AUTHOR]

This article studies the effect of new optimization techniques with the aim of finding the best solution using a fuzzy approach for operating an independent microgrid. The power flow technique is used to model and solve the problem of optimal active and reactive power control of distributed generation units (DGs) in an island microgrid (MG), while taking into account the improvement of the voltage profile and the voltage stability index by regulating the droop characteristics of DG sources. The suggested algorithms, Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Harmony Search (HS), Hybrid HS-GA, and Teaching-Learning Based Optimization, solve the optimization issue (TLBO). The numerical results of optimization algorithms are compared in terms of best solution, number of iterations, and convergence rate. Regarding multi-objective nature of the problem, the non-dominated sorting method is used to obtain the Pareto front for all algorithms. Finally, the best solution is selected from Pareto solutions using proposed fuzzy approach. Simulation results on the standard IEEE 33-bus network in MATLAB show that the TLBO algorithm has higher efficiency, and faster convergence rate compared to other methods in this study. [ABSTRACT FROM AUTHOR]

In a microgrid, load power should be properly shared among multiple distributed generation (DG) units, not only for fundamental power but also for negative sequence and harmonic power. In this paper, the operation of a microgrid under imbalance and nonlinear load conditions is studied, and a consensus algorithm-based distributed control strategy is proposed for the microgrid power allocation, frequency, and voltage restoration. First of all, the output current of DG unit is decomposed by second-order generalized integrator (SOGI) modules to obtain the fundamental power and harmonic power through the power calculation formula. Then, state values of DG units, such as local power, frequency, and voltage, are transmitted on a sparse communication network. Under the action of a consensus algorithm, the real power of DG units is allocated following the equal increment principle; the reactive power, imbalance, and harmonic power are allocated according to the capacities of DG units; and the frequency of the microgrid and the voltage at the point of common coupling (PCC) are rated. In the consensus-based strategy, DG units only communicate with their neighbor units; thus, the "plug and play" function is reserved. Compared with the centralized control strategy, the proposed strategy with a distributed consensus protocol can simplify the maintenance and possible expansions of the system, making the microgrid more flexible. Moreover, as the structure of the detailed network is not required, it is easy to apply in practice. Simulation and experiment results are presented to verify the proposed method. [ABSTRACT FROM AUTHOR]

This paper introduces a novel and improved double-resistor damped double-tuned passive power filter (DR-DDTF), designed using multi-objective optimization algorithms to mitigate harmonics and increase the hosting capacity of distribution systems with distributed energy resources. Although four different topologies of single-resistor damped double-tuned filters (DDTFs) have been studied before in the literature, the effectiveness of two different DR-DDTF configurations has not been examined. This work redresses this gap by demonstrating that via comprehensive simulations on two power systems, DR-DDTF provides better harmonic suppression and resonance mitigation than single-resistor alternatives. When it comes to optimizing the DR-DDTF for maximum hosting capacity and minimum system active power losses, the multi-objective artificial hummingbird outperformed six other algorithms in the benchmark. To allow for higher penetration of distributed generation without requiring grid upgrades, this newly developed harmonic mitigation filter provides a good alternative. [ABSTRACT FROM AUTHOR]

The growing demand for energy, driven by rapid economic development, necessitates higher electricity consumption. However, conventional energy systems relying on fossil fuels present environmental challenges, prompting a shift towards renewable energy sources. In Kosovo, coal-fired power plants dominate electricity production, highlighting the need for cleaner alternatives. Worldwide efforts are underway to increase the efficiency of photovoltaic systems using sustainable materials, essential for ecological and human health. Solar and wind energy are emerging as sustainable alternatives to traditional fossil fuels. However, global concerns about energy security and environmental sustainability are driving countries to prioritize renewable energy development. In Kosovo, the integration of renewable energy sources, such as wind and solar energy, is progressing rapidly. However, challenges such as voltage stability and power losses need to be addressed. Distributed generation offers a solution by increasing energy reliability and reducing greenhouse gas emissions. Further research is needed to assess the technical, economic, and environmental implications of integrating renewable resources into Kosovo's energy system, focusing on power quality, system reliability, and voltage stability. The research focused on the eastern region of the country, operating at the 110 kV substation level. Challenges in energy quality arise due to the lack of 400 kV supply and the continuous increase in energy consumption, especially in the Gjilan area. This paper investigated integrating renewable energy, especially wind and solar sources, into the medium- and long-term plans at the Gjilan 5 substation to enhance energy quality in the area. Successful integration requires detailed analysis of energy flows, considering the impact of photovoltaics (PVs) on distribution system operation and stability. To simulate and analyze the effects of renewables on the transmission system, voltage profile, and power losses, a case study was conducted using ETAP software. The simulation results present a comparison between scenarios before and after integrating renewable systems to improve energy quality in the identified area. [ABSTRACT FROM AUTHOR]

The deeper penetration of renewables in the energy mix is an intense requirement in order to reduce global carbon dioxide emissions. In addition, new technologies are being developed, such as electric mobility and Distributed Generation (DG) in urban areas. However, the unpredictable fluctuations in energy generation from roof-installed PVs and the switching operation of their inverters greatly aggravate the already-present grid quality problems. In this paper, the Smart Boiler (SB) concept for grid quality improvement is presented. Furthermore, its experimental verification is implemented on a flexible testbed that accurately emulates several realistic scenarios for the low voltage distribution grid, under complex operating conditions. The proposed low-cost electronic kit, which contains a converter of fairly simple topology and requires connection to the internet, is used to upgrade conventional domestic boilers to smart devices. The SB automatically regulate the local reactive power flow, helping to stabilize the voltage level and suppress the grid current harmonic content, with both services provided in a matter of seconds. The higher the active power consumed and the denser the SB cluster, the wider the beneficial impact on the affected network area. While this service is provided, excess energy generated by PVs is temporarily stored as heat in the boiler tanks, given the users' hot water consumption habits. The whole application, as a powerful demand-side management tool, proves beneficial for both the network operator and the end-user, especially when self-consumption is desirable in order to achieve a Nearly Zero Energy Building. [ABSTRACT FROM AUTHOR]

With the advancement of distributed power generation technology and the deepening of the low-carbon transformation of energy structure, a high proportion of renewable energy has become an inevitable trend in future energy systems, especially for microgrids. However, the volatility and uncertainty associated with renewable energy pose significant challenges to the secure and stable operation of power systems, necessitating the exploration of the flexible regulation of resources. Energy storage, as a crucial flexible resource characterized by technological diversity and a variety of regulation capabilities, has been extensively studied and applied. Nonetheless, the high investment costs and limited returns of energy storage technology, coupled with the ambiguous utility in different scenarios under the current electricity market's framework, complicate its broader application. To thoroughly analyze the utility of energy storage in facilitating flexible adjustments in microgrids, this study developed a composite weight-TODIM (an acronym in Portuguese for interactive and multi-criteria decision making) model for assessing the utility of energy storage that incorporates heterogeneity in the risk preferences. This model enabled a comparative analysis of the utility of energy storage technology across multiple scenarios, taking the risk preferences of decision-makers into account, thereby providing strategic insights for the application of multi-temporal energy storage in microgrids. The feasibility and effectiveness of the model were validated through a case study analysis. [ABSTRACT FROM AUTHOR]

This paper proposes a novel stabilizing control method aimed at overcoming the instability challenges posed by the negative incremental resistance characteristics of a constant power load (CPL) within an autonomous microgrid (MG). The proposed stabilization technique integrates a power derivative-integral term with conventional droop control, strategically applied to enhance the MG's dynamic stability in the presence of CPL. The considered MG model, encompassing three inverter-based distributed generations (DGs), a constant impedance load (CIL), and CPL, is meticulously developed and simulated using MATLAB environment. Phase Locked Loop (PLL) is employed to synchronize the CPL with MG. The proposed controller is a key highlight, featuring optimally designed and tuned controller parameters for all DGs, CPL, and PLL. Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are employed to address the challenges linked to the laborious tuning process of controllers. The simulation results, which include scenarios involving three-phase faults and step changes, provide compelling evidence of the proposed controller's superior performance compared to conventional droop scheme. Furthermore, a comparative analysis is conducted to affirm and quantify the enhancements achieved through the proposed modified droop PSO-based MG controller concerning transient response. The results obtained emphasize the effectiveness of the proposed approach in simultaneously minimizing both overshoot and settling time. In comparison with the conventional controller, the proposed controller demonstrates a substantial decrease in percentage overshoot for the active power of DG3 and DC voltage of the CPL, with values of 93.89 and 99.9%, respectively. The corresponding improvements in settling time are notable, showcasing reductions of 83.11% for the active power of DG3 and 66.1% for the DC voltage of the CPL. When compared to the GA-based controller, the proposed controller exhibits significant percentage overshoot reductions for and DC voltage of the CPL, achieving 79.42 and 99.8%, respectively. Additionally, the settling time records noteworthy improvements, with reductions of 76.19% for the active power of DG3 and 57.57% for the DC voltage of the CPL. To further validate the real-world applicability and effectiveness of the proposed method, a real-time digital simulator (RTDS) is employed. The RTDS experiments results confirm the proposed scheme's ability to enhance MG stability, substantiating the simulation findings. This holistic approach, encompassing theoretical modeling, simulation studies, and real-time validation, establishes the proposed stabilizing control method as a promising and effective solution for mitigating instability issues associated with CPL in autonomous MGs. [ABSTRACT FROM AUTHOR]

Microgrids with large‐scale photovoltaic systems constitute a large part of distributed renewable generation in many grids around the world. Managing the performance of such microgrids and especially their interaction with the main power grid is a challenging task, because it requires the control of renewable resources. This paper presents a comprehensive overview of DC‐DC converter structures used in microgrids and presents a new classification for converters. This paper also provides an overview of the control techniques of DC‐DC converters in DC microgrids and the advantages and disadvantages of the control methods are discussed. In connection with the increasing penetration of distributed generation sources (DGS) and renewable sources in power systems and their power management has been raised as a major concern and methods for power management have been investigated in this paper. Finally, a DC microgrid system, which includes a solar system, wind turbine, and battery, is simulated in MATLAB/Simulink software and its performance is analyzed. [ABSTRACT FROM AUTHOR]

The B4 topology is an interesting alternative to the conventional B6 inverter due to its reduced number of parts and lower cost. Although it has been widely used in the past, especially in low-power motor drive applications, its application as a grid-connected inverter is an open area of research. In this regard, this paper analyses the feasibility of the B4 inverter topology for grid-connected applications. A versatile 7 kW inverter prototype, which may be configured as B4 and B6, was built, allowing for a comprehensive evaluation of the performance of both topologies. Through an analytical study and experimental tests, the performance of the B4 and B6 topologies was comparatively evaluated in terms of efficiency, total harmonic distortion of line currents, current unbalance, cost, and mean time between failures. The study was carried out in the context of microgrid systems, highlighting their role in the integration of renewable energy and distributed generation. [ABSTRACT FROM AUTHOR]

DC microgrids have emerged as a promising solution to provide reliable and efficient power for various applications. However, similar to any power system, DC microgrids are prone to faults that can disrupt their performance. Accordingly, the lack of publication of sufficient standards and guidelines for the protection of DC microgrids makes it necessary to develop protection methods in these networks. Therefore, the purpose of this paper is to create a new fault detection method in islanded DC microgrids. In this method, the current signal samples are entered into a chaotic state, and using the feature of sensitivity to the initial conditions of this method, it accurately identifies the fault. In this case, the signal undergoes a very large chang during the fault, which is easily visible compared to the normal state. It should be noted that, unlike other methods, in the proposed method in this paper, only one measurement unit is used in the DC bus for sampling signals. Therefore, there is no need to use communication links in the proposed method. The proposed method has been implemented using MATLAB/Simulink software on a sample DC microgrid. The results show that the proposed method is capable to detect pole-to-pole and pole-to-ground faults on the microgrids and also faults on the distributed generations and electrical vehicles. Also, results prove that this method is resistant to the operational uncertainty of distributed generations, electrical vehicles, and the destructive effects of noise on the sampled signals. [ABSTRACT FROM AUTHOR]