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ABSTRACT With the expansion of hybrid AC/DC microgrids (MGs), the AC and DC subgrids should be able to maintain interconnection through interlinking converter (IC) under short‐circuit faults to avoid the system instability caused by power mutation. Conventional IC control methods have given insights into the current restraining between two subgrids but neglect the transient voltage/frequency support requirement, especially under islanded operation mode. This paper proposes a grid‐forming IC control with fault‐ride‐through (FRT) capability. An fn‐Pic‐Vdc control architecture is presented to regulate power exchanges among AC and DC subgrids with solid AC bus voltage supports under the premise of DC voltage stable. Additionally, the Lyapunov large disturbance stability equation is established, and the safe operating boundaries of key parameters are described based on the Takagi–Sugeno (TS) model criterion. It is revealed that within certain range, the proper DC voltage feedback control parameters in IC could enhance the system stability and the power support capability of DC subgrid to the AC side. Finally, comparative case studies are conducted to validate effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

Real‐time acquisition of microgrid (MG) operation data and remote control play a crucial role in the safe and stable operation of MG. A design scheme of monitoring system is proposed for the wind/photovoltaic/energy storage islanded direct current MG. The core controllers used in direct current MG system are programmable logic controller and digital signal processor. The monitoring system mainly adopts Ethernet communication, together with serial port communication mode Recommend Standard 232 (RS232) and Recommended Standard 485 (RS485) communication modes. A heterogeneous networking is built with controllers and data display instruments. Different monitoring interfaces are designed, the important information can be real‐time detected, collected and displayed. The multi‐energy and multi‐load dispatching operation control are realized according to scheduling operation plan. The parameters of the inverter can be adjusted, and the output voltage waveform and sinusoidal pulse width modulation waveform of the inverter can be measured. The communication of the proposed monitoring system is reliable, flexible and expandable, and easy to realize remote operation control. Moreover, the detection and control functions can be extended further. Through experiments, the effective control and monitoring of the designed monitoring system is verified, and the intelligent management is realized. [ABSTRACT FROM AUTHOR]

The shedding of critical distributed energy resources during faults in an islanded microgrid may induce widespread voltage drops, potentially triggering a cascade of reactions leading to the collapse of the entire system. Accurately identifying critical nodes is the key technology to improve the resilience of microgrids. However, multi‐source coupling and the uncertainty in fault‐induced voltage sag can diminish the accuracy of node importance identification. To address this, this paper proposes an adaptive node identification method designed for quick and accurate identification of nodes that cope with various fault scenarios. This method introduces an index for evaluating voltage support capability based on the equivalent voltage drop range. This index adapts to fault uncertainty while integrating electrical parameters with spatial position. Furthermore, a higher‐order transition matrix reconstruction strategy with power propagation characteristics is proposed to reduce the higher‐order complexities arising from remote end faults' current flowing path length. Ultimately, the transition matrix is optimized by integrating it with the PageRank algorithm and highlighting the importance of source nodes. The proposed method is validated by numerical computation and time‐domain simulation results in a benchmark test microgrid, demonstrating its remarkable identification accuracy in a variety of fault scenarios. [ABSTRACT FROM AUTHOR]

Due to the energy storage lifetime effects of the power allocation, there is a large space to improve the economy of the electric‐hydrogen hybrid DC microgrid. This paper provides an optimal control method based on the mutual learn salp swarm algorithm (MLSSA) in real‐time, which aims to enhance the economy and extend the system's service life. In order to realize the economic operation, operation cost and degradation cost of battery and hydrogen system are considered as the objective function first. Then, salp swarm algorithm based on mutual learn strategy is introduced to obtain optimal economy power allocation results in real‐time with higher convergence speed and increased accuracy. In addition, the proposed method also maintains the battery state of charge (SOC) and state of hydrogen charge (SOHC) within a proper range to guarantee the stable operation of the system. Finally, the results including power results, cost analysis and degradation rate analysis of the MATLAB/Simulink show that the proposed method is more economically beneficial than the non‐considering degradation cost strategy. [ABSTRACT FROM AUTHOR]

Typically, in electrical microgrids (MG), the grid-forming type converter (GFRTC) includes a battery pack at its DC bus; thus, the GFRTC is modelled as a voltage source. In contrast with papers reported in recent literature, where the GFRTC with a battery pack is seen as a voltage source, this work proposes a GFRTC which can be used without a battery pack; with this, the GFRTC operates as a smart load to form the voltage at the point of common coupling (PCC); this is possible, as long as the smart load is supported by a grid-following type converter (GFLTC). This battery-less GFRTC proposal ensures a suitable operation of the isolated MG under linear and nonlinear load conditions. Also, this work proposes a control strategy for both GFRTC and GFLTC with a photovoltaic array. The test results showed that the control proposal allows to form a correct PCC voltage, ensuring a suitable operation of the isolated MG; this while the battery pack is: connected, disconnected, and reconnected to the GFRTC DC bus. This GFRTC proposal is very attractive for isolated MGs where the GFRTC battery pack must be replaced or reconfigured because of new demand profiles, without interrupting the isolated MG operation. [ABSTRACT FROM AUTHOR]

This research proposes an intelligent artificial neural network (ANN) controller designed for a micro-grid featuring solar photovoltaic, wind, and Battery Energy Storage (BES) systems, integrated with a shunt voltage source converter. The primary goal of this proposed method is to minimize Total Harmonic Distortion (THD) and ensure a stable Distribution Line Commutation Voltage (DLCV) amidst load variations, achieving a rapid settling period. The STF not only facilitates phase synchronization but also effectively separates harmonic and fundamental components. This research contributes to advancing micro-grid control strategies, particularly in enhancing stability and reducing distortion during variable operating conditions. Based on an analysis of the four test cases, it is evident that the proposed strategy lowered the THDs to 4.63%, 3.45%, 3.05%, and 4.70% for each test case, as well as the PF to a level that was similar. The proposed controller shows better results compared with other controllers like Sliding Mode Controller (SMC) and the Proportional-Integral Controller (PIC). [ABSTRACT FROM AUTHOR]

Summary: With the characteristics of high efficiency and high power density, the four switch buck–boost (FSBB) converter is widely used in electric vehicles, direct current microgrids, and so forth. However, how to achieve smooth switching between different modes of FSBB has always been a hot issue in the theoretical and engineering fields. In this paper, considering the dynamic performance and output voltage or inductance current deviation, a control algorithm with integrated natural trajectory method is designed to realize the switching of different modes of FSBB converter. The proposed control algorithm is based on the theory of large signal natural locus and average natural locus. The normalized phase locus of inductance current and output voltage divides the phase plane into five regions and performs large signal or average switching operations in different regions. Theoretical analysis shows that the proposed control algorithm can achieve the balance between the dynamic performance and output voltage or inductance current deviation of FSBB converter in different mode switching. Finally, the performance of the proposed control algorithm is verified by experiments results. [ABSTRACT FROM AUTHOR]

Summary: In order to meet the efficient and convenient usage requirements for bidirectional DC‐DC converters in some fields such as DC microgrids and vehicle power conversion systems, a bidirectional resonant CLLC converter combining three‐level characteristic and bipolar DC structure is used and analyzed in this paper. A three‐level topology is adopted in the resonant CLLC converter, which can achieve stable and wider range voltage regulation through the frequency adjustment combined with duty cycle and phase shift modulation, and can also increase the flexibility of control strategy. The application of bipolar DC structure enables this resonant CLLC converter to have multi‐port on one side, and the method of using two interleaved inductors combined with synchronous rectification enables the output voltage of the converter to maintain a certain balance. In this paper, the voltage gains and running states of the three‐level regulation method are analyzed, and then these operation modes and the bipolar DC voltage balance effect of the converter under synchronous rectification control are verified by the experiment results. [ABSTRACT FROM AUTHOR]

This paper presents a quasi-resonant, isolated, high-gain, Y-source, DC/DC converter for a DC microgrid. Here, quasi-resonance is obtained by the LC tank during the shoot through period, resulting in natural commutation at the turn-off instant of the MOSFETs. The LC tank is formed by an additional parallel capacitor $({C_p})$ (C p) and transformer leakage inductance. The high voltage gain is obtained by including an isolation transformer with lower turns ratio, coupled inductor and voltage doubler. Here, frequency is modulated to regulate the output voltage with ON period $({T_{on}})$ (T on ) constant. This converter's zero current switching feature allows it to operate at a higher switching frequency and thus enhances the power density and efficiency. This converter's continuous input current feature is an added advantage. The proposed converter inherits all the benefits of traditional impedance source converters. Finally, a 250 W hardware prototype is developed, and its performance is tested in the laboratory. [ABSTRACT FROM AUTHOR]

In this paper, a single-switch hybrid dual diode-capacitor (HDDC) boost converter with less stress over all devices for high voltage gain applications is proposed. It combines a voltage boost cell with two back-to-back diode-capacitor cells for providing high voltage gain. The current spikes across the switching devices, occurring due to the diode-capacitor circuit, are effectively truncated by an inductor that is used at the input side. With a single inductor and a single MOSFET, the proposed HDDC converter provides continuous input current, a common ground (C.g) structure and keeps the device voltage stress (V stress ) and current stress under check. This allows the use of lower-rating devices and is helpful in restricting switching losses, thus improving the comprehensive efficiency of the converter. For integrating RES with micro-grid, the proposed HDDC converter provides all the desirable features. A MATLAB/Simulink model is employed for testing purposes of the proposed HDDC. Additionally, a hardware prototype of the HDDC, with a power rating of 280 W and voltage output of 200 V, is subjected to laboratory testing at a frequency of 33 kHz. The findings from both the simulation and hardware testing are then compared to validate the performance of the proposed HDDC. At near-rated load, the converter operates at an efficiency of around 95.4%. [ABSTRACT FROM AUTHOR]

Direct current (DC) microgrid has recently gained potential interest since it supports easy integration of distributed generators (DGs) and energy storage devices (ESDs). However, most DGs and ESDs are integrated into the DC bus with the power electronic converter/inverter. Thus, controlling large-scale power electronic-based generators, loads, and ESDs becomes a challenging task. This paper introduces a new control strategy for the DC microgrid to regulate the bus voltage and power sharing among the DGs, ESDs, resistive loads, and constant power loads (CPLs). Each battery's state of charge (SoC) is utilized in the double control loop to determine the charging/discharging speed. Besides, an additional fractional-order proportional-integral voltage compensation loop is introduced to regulate the DC bus voltage. The SoC-based inner loops assist in the automatic balancing of battery charges, while the compensation loop minimizes the DC bus voltage deviation. A detailed small-signal model is derived to design the controller parameter. The optimization approach is applied to the developed model with the objective of reducing the integral square error of the step response. The DC microgrid system with the designed controller is tested with the simulation study. The results depict that the bus voltage deviation is reduced by 63.27% while the SoC balancing is faster for both charging and discharging modes. [ABSTRACT FROM AUTHOR]

Microgrids are designed to utilize renewable energy resources (RER) that are revolutionary choices in reducing the environmental effect while producing electricity. The RER intermittency poses technical and economic challenges for the microgrid systems that can be overcome by utilizing the full potential of hybrid energy storage systems (HESS). A microgrid comprising of a solar photovoltaic panel, wind turbine, lead-acid battery, electrolyzer, fuel cell, and hydrogen (H 2 ) tank is considered for techno-economic feasibility and environmental impact assessment on a grid integration scenario. Mathematical functions are utilized to model the components for estimating annual hourly renewable generation and energy storage behavior. The load consumption model for 50 homes is generated using Gaussian distribution to incorporate the uncertainty. Optimal sizing of the microgrid components is determined using the particle swarm optimization (PSO) algorithm to minimize the upfront installation cost and levelized cost of energy (LCOE). Different energy storage penetration scenarios, e.g., 25%, 50%, 75%, and 100% for the microgrid system, are considered where 100% penetration level stands for maintaining the load demand using the available resources without depending on the grid energy supply. The lowest LCOE is found between 0.06 $/kWh and 0.11 $/kWh, and the highest annual GHG is reduced to half compared to the grid emission. GHG is imposed around 62.14 (tCO 2 e/yr) - 73.57 (tCO 2 e/yr) for Madrid and Seville, respectively. [ABSTRACT FROM AUTHOR]

Generation planning in the power system has been a complex and challenging multi-objective optimization problem. Numerous methodologies have been developed and applied to solve this problem. Still, researchers look forward to further improved methods that can further enhance the outcomes for this problem. This article suggests a hybrid FPA-PPSO scheme that combines the flower pollination algorithm (FPA) and phasor particle swarm optimization (PPSO) algorithm to solve this problem. The suggested FPA-PPSO scheme includes a high search efficiency, balanced local and global search capabilities, etc. which can help in more efficient planning of the generation in a microgrid. To analyze the supremacy of the suggested FPA-PPSO scheme over FPA and PPSO methods, a comparative study using twenty-three benchmark functions is presented. From the analysis conducted from the Friedman test, the suggested method gets a rank of 1.59 which is better compared to PPSO and FPA which get ranks of 2.61 and 3.04 for the twenty-three benchmark functions. Further, the suggested method is tested using four test cases to show its effectiveness in solving the scheduling problem of a microgrid considering renewable energy sources, energy storage systems, and demand response management. The obtained results conclude that the suggested methods show promising performance in planning the generating unit. Using the DRM scheme further helps to efficiently schedule the generation as the utilities save up to $ 25545 more when DRM planning is considered. [ABSTRACT FROM AUTHOR]

This paper introduces a cutting-edge deep learning-based model aimed at enhancing the short-term performance of microgrids by simultaneously minimizing operational costs and emissions in the presence of distributed energy resources. The primary focus of this research is to harness the potential of demand response programs (DRPs), which actively engage a diverse range of consumers to mitigate uncertainties associated with renewable energy sources (RES). To facilitate an effective demand response, this study presents a novel incentive-based payment strategy packaged as a pricing offer. This approach incentivizes consumers to actively participate in DRPs, thereby contributing to overall microgrid optimization. The research conducts a comprehensive comparative analysis by evaluating the operational costs and emissions under scenarios with and without the integration of DRPs. The problem is formulated as a challenging mixed-integer nonlinear programming problem, demanding a robust optimization technique for resolution. In this regard, the multi-objective particle swarm optimization algorithm is employed to efficiently address this intricate problem. To showcase the efficacy and proficiency of the proposed methodology, a real-world smart microgrid case study is chosen as a representative example. The obtained results demonstrate that the integration of deep learning-based demand response with the incentive-based pricing offer leads to significant improvements in microgrid performance, emphasizing its potential to revolutionize sustainable and cost-effective energy management in modern power systems. Key numerical results demonstrate the efficacy of our approach. In the case study, the implementation of our demand response strategy results in a cost reduction of 12.5% and a decrease in carbon emissions of 14.3% compared to baseline scenarios without DR integration. Furthermore, the optimization model shows a notable increase in RES utilization by 22.7%, which significantly reduces reliance on fossil fuel-based generation. [ABSTRACT FROM AUTHOR]

This article presents an experimental study that evaluated droop control strategies in DC microgrids with parallel-connected converters. In a decentralized control scheme, it is critical to ensure voltage regulation and load sharing in each converter to maintain a stable operation. Two scenarios are considered: the first involves two converters operating in parallel as voltage mode control, a conventional method discussed in the literature. In the second scenario, a less commonly used method is presented, in which one converter operates in voltage mode control and another operates in current mode control. The proposed decentralized control method is experimentally validated in a DC microgrid using parallel-connected lithium-ion batteries and converters. Load sharing results are examined under conditions with equal droop coefficients, demonstrating equivalent outcomes for specific load steps in both scenarios. However, in the case of different droop coefficients, the alternative method proves to be highly satisfactory, particularly for a broader range of load variations. The results confirm the efficacy of the control method in load sharing and voltage regulation among each converter, as well as the equivalence of control between both scenarios. [ABSTRACT FROM AUTHOR]

Economic dispatch (ED) is one of the key problem in power systems. ED tends to minimize the fuel/operating cost by optimal sizing of conventional generators (CG). Greenhouse/toxic gas emission is one of the major problem associated with the CG. Emission dispatch (EMD) deals with the reduction of greenhouse/toxic gas emissions by the optimal output of generators. The multi-objective economic emission dispatch (MOEED) problem has been formulated by considering both fuel cost and emission objectives. The main objective is optimization of fuel cost and environmental emissions from the CG in a compromised way. In this paper, CONOPT solver in General Algebraic modeling system (GAMS) has been proposed to find the the optimal solutions for ED, EMD, and MOEED problems of a microgrid. The microgrid consists of a wind turbine generator (WTG), a photovoltaic (PV) module, three CGs, and a battery energy storage system (BESS) option. The proposed algorithm has been implemented in four case studies, including all energy sources, without WTG, without PV module, and without renewable energy sources (RES). To establish the effectiveness of the proposed algorithm, it has been compared with various algorithms. The comparison result shows that proposed algorithm is more effective, novel, and powerful. Finally, result shows the effectiveness of proposed approach to optimize the objective function for all aforementioned case studies and the CONOPT solver in GAMS outperformed all the approaches in comparison. The impact of BESS on the operating/fuel cost of the microgrid has also been presented for ED. Paradigm is changing in terms of demand response in μG. Demand flexibility (DF) model has also been established with consumers demand variation in optimization process. Result with DF shows the reduction in cost and better management from demand side. [ABSTRACT FROM AUTHOR]

In recent years, there has been a growing interest in incorporating microgrids into existing electrical power networks to reduce reliance on conventional grids. This is further due to the numerous benefits of microgrids, most notably the ability to operate in either autonomous or grid-connected mode, making microgrids a highly versatile structure to incorporate intermittent production and energy storage. However, despite the many benefits, it is difficult to present resilience protection for DC bus bar to ensure the efficient operation of a DC microgrid, as there is a non-zero crossing phenomenon of DC fault current, while zero crossing phenomenon of an AC fault. Additionally, the DC bus bar has both an AC side with rectified DC and a DC line current, creating protection concerns for microgrids. To address these issues, this article proposes a novel fast fault detection scheme for DC microgrids with multiple renewable energy sources, energy storage, and loads. The scheme incorporates current and voltage sensing through current and voltage transformers, along with advanced digital relays, to detect and isolate faults on the DC microgrid. By analyzing rectified AC and DC line data, the scheme employs a differential current-based protection strategy to safeguard the DC bus bar. Validated through comprehensive fault simulations in PSS/SINCAL, the method proves effective in maintaining DC bus voltage stability and enhancing microgrid flexibility. [ABSTRACT FROM AUTHOR]

The renewable energy mobile offshore platform, which adopts the combined power supply of renewable energy and energy storage, is an important carrier for the development and utilization of marine resources. The randomness of renewable energy generation has a more prominent effect on the bus voltage stability and transient voltage deviation of the power system with small capacity and low inertia. Considering the operation and maintenance characteristics of the offshore platform, a virtual inertia control method for small‐ and medium‐sized wind turbines is proposed. Firstly, by analyzing the characteristics of the renewable energy microgrid of the unattended offshore platform, considering the operating environment with high average wind speed at sea, the mechanical inertia in the wind turbine is selected as the energy source of virtual inertia. The structure of the wind power generation unit is analyzed, and small signal modeling is carried out. A virtual inertia control method based on power droop is proposed, and the rotational inertia and the damping coefficient are obtained from the characteristics of transient and steady‐state analysis of the system. Finally, the DC microgrid experiment platform of the offshore platform is constructed, and it is verified that the proposed method makes full use of the characteristics of the offshore platform to enhance system inertia and improve the operational stability of the offshore platform DC microgrid system. [ABSTRACT FROM AUTHOR]

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]

Multiple microgrid (MG) distribution systems are facing challenges owing to variations in the operational statuses of the individual MGs, which experience voltage and current fluctuations during transient interconnections. The impedances of the interconnecting lines further exacerbate the unevenness of power distribution among the MGs, hence threatening the operational stability of the system. To achieve flexible and seamless interconnections between multiple MGs, we fully analyzed the interconnected structures and operation modes of the MGs; then, we designed a transient switching control method based on investigation of the transient interconnection processes to ensure smooth transition of the MGs. Additionally, to balance the power distribution among the interconnected MGs, a voltage–current-based coordinated power control strategy was synthesized using advanced synchronized fixed-frequency technology. Simulation case studies were conducted, and the results indicate that the proposed coordinated power control scheme effectively facilitated instantaneous interconnections between the isolated regions, thereby avoiding voltage disturbances and current surges. Furthermore, it efficiently equalized and distributed the output power from the distributed energy sources, thereby enhancing the operational flexibilities and stabilities of the MGs and distribution system. [ABSTRACT FROM AUTHOR]

Unbalanced voltage and harmonics are major challenges in a microgrid. Single‐ and two‐phase loads and short circuit can make unbalanced voltage. Besides, nonlinear loads create harmonic components in the grid. Thus, compensating both of the unbalanced voltage and harmonic distortion is necessary. Otherwise, they could cause low power quality, resonance, and stability issues. The present paper suggests a combination of distribution static synchronous compensator (DSTATCOM) and shunt active power filter (SAPF) to address the unbalanced voltage and harmonic pollution in hybrid grid‐connected microgrid. A four‐wire distributed static synchronous compensator has been adopted to deal with the negative‐ and zero‐sequence components of the microgrid, extracting these components by using second‐order generalized integrator (SOGI). To solve the harmonic issue, a SAPF is presented to dynamic compensation of grid current and voltage harmonic components in addition to DSTATCOM. The SAPF relies on estimating the fundamental frequency positive‐sequence component (FFPSC) of the load current by applying the second‐order sequence filter (SOSF). Since combining the reference signals of voltage balancer and harmonic eliminating at one controller is impossible, two independent devices are presented. Although both of the equipment have a same conception, their control and operation are different. So, simultaneous incorporation of distributed static synchronous compensator and SAPF for mitigating the unbalancing and distortion in the grid‐connected microgrid based on hybrid solar and wind power is the work's novelty. Some merits of this scheme include simple structure, fast real‐time controller, low computational burden, and more effective operation. Robustness and effectiveness of such very simple scheme are evaluated by simulation in MATLAB/Simulink environment. Simulation report demonstrates that the total harmonic distortion (THD) of grid voltage and current decrease well under 1.44% and 2.33%, respectively. Meanwhile, the voltage unbalance factor (VUF) of negative and zero sequence of grid voltage reduces to 1.1% and 0%, respectively. [ABSTRACT FROM AUTHOR]

This paper presents a new structure of a high step‐up DC–DC converter. The proposed converter has one input and two outputs with different voltage levels, suitable for applications that require multiple voltage levels such as photovoltaic systems, electric vehicles and DC microgrids. The proposed converter is expandable to more outputs and has a higher voltage conversion ratio compared to similar converters. The operational modes of the proposed converter are investigated and the conversion ratio of the converter is obtained. Additionally, small‐signal equations of the converter are derived, and using state‐space matrices, transfer functions of the converter are obtained and the controller is designed. Also, the proposed converter is compared with similar converters and power losses equations of the converter are derived. To investigate the converter's performance, simulations are done using MATLAB/Simulink software. Moreover, to validate the theoretical analysis, a laboratory prototype is implemented. The obtained results confirm the operation of the proposed converter. [ABSTRACT FROM AUTHOR]

This paper emphasizes the significance of implementing an effective control system to enhance the performance of a three-stage DC–DC buck converter (TDDC). Herein, we present the development of an observer controller (OC) for TDDC, where average modeling of the DC–DC converter was employed alongside an observer algorithm for the OC. The primary focus is on the adoption of an observation topology aimed at enhancing system responsiveness under various conditions, including variable input voltages, reference voltage changes, load fluctuations, integration with photovoltaics, and battery charging. This approach ensures improved stability and performance, addressing the dynamic challenges inherent in such systems. A comparative analysis was conducted on two distinct control topologies for the proposed TDDC system, namely proportional–integral (PI) and advanced OC. Simulink software was used to model and simulate the proposed system. The results demonstrate lower rising and settling times of the TDDC for the OC and PI implementations, respectively. Additionally, the system with the OC eliminates 20% of the overshoot, while the system with OC minimizes the output voltage oscillations from 13% to 2% compared to the PI system. The OC was integrated into the TDDC to enhance system stability and improve the control loops, particularly the third loop. These improvements make the TDDC with OC suitable for application to renewable energy systems, microgrids, telecommunication networks, and electric vehicles, where precise control and stability are essential. [ABSTRACT FROM AUTHOR]

Aiming at the frequency instability caused by insufficient energy in microgrids and the low willingness of grid source and load storage to participate in optimization, a microgrid source and load storage energy minimization method based on an improved competitive deep Q network algorithm and digital twin is proposed. We have constructed a basic framework structure for the coordinated operation of source grid load and energy storage, and analyzed the modules on the power supply side, grid side, load side, and energy storage side. Under the improved competitive deep Q network algorithm, modifications were made to the energy storage of microgrid loads. Based on the processing results, the objective function for optimizing microgrid source load energy storage is constructed using digital twin technology, and the optimization of the objective function is achieved to solve the optimization objective function for microgrid source load energy storage and complete the optimization of microgrid source load energy storage. The experimental results show that this method can control the distortion rate within 5.12%, with frequency fluctuations around 50.0 Hz, and relatively good MSE, MAE, and R2 values. This method can effectively control frequency fluctuations and has a good effect on optimizing energy storage for microgrid power sources and loads. [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]

ABSTRACT This paper gives a detailed study for the design and implementation of an energy management system (EMS) for a hybrid renewable microgrid system using real‐time software. Microgrids, with their ability to integrate renewable energy sources, face challenges in maintaining stability and reliability. The implemented EMS aimed to maximize the renewable energy sources utilization, including PV and wind power, in conjunction with a battery energy storage system. The objectives of this research included the implementation of an EMS that ensures a reliable and stable operation between the microgrid system and the main grid including the control of charge and discharge of the battery using Typhoon Hardware‐in‐the‐Loop (HIL) software. The simulation results and case studies demonstrated the effectiveness and performance of the developed EMS in managing a hybrid renewable microgrid system. The results also demonstrated that the time of charging was maximized by utilizing a higher power. By doing so, the battery was fully charged in a shorter timeframe. The battery state of charge (SOC) was maintained between the fixed values (20% and 100%) as stated by the algorithm. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper presents a family of multiport Z‐source converters with minimal number of switches and passive components. The paper focuses on the development of a novel converter and its methodology for selecting the topology of common ground/noncommon ground Z‐source DC–DC single‐input dual‐output configurations, aimed at higher power density, enhancing operational efficiency, size, cost, and centralized power management across multiple ports. In comparison to conventional Z‐source single input single output converters, this design achieves high‐voltage conversion rates while concurrently delivering two distinct outputs with and without common ground, rendering it particularly advantageous for electric vehicle and isolated microgrid applications with renewable energy integration. The proposed converters undergo comprehensive analysis and comparison across various potential configurations. Specifically, configurations yielding equivalent output voltages are prioritized, facilitating their utility in multilevel inverter applications. The assessment encompasses the determination of maximum efficiency; voltage stresses on capacitors, diodes, and switches; inductor currents; and ripples under diverse loads and duty cycles for all configurations. Experimental results are provided to showcase the validity and capabilities of these converters. As research efforts on converter topologies continue to grow, it is expected that this paper will enhance comprehension of multiport Z‐source converter topologies ideas for numerous applications. [ABSTRACT FROM AUTHOR]

Recently, many high-gain topologies have been derived. However, there is a need for a high-gain converter with fault-tolerant features. In this paper, a fault-tolerant reconfigurable quadratic boost converter is proposed for DC microgrid application. In this novel topology, 2-level redundancy is achieved by addressing the fault in the switch and capacitors. The operation of the converter in normal operating conditions and reconfiguration mode is discussed. The derived topology can achieve the same voltage gain even in the reconfiguration mode. The proposed topology exhibits better performance in the reconfiguration state. The voltage stress across the input and output capacitor is reduced in that state. The reliability analysis of capacitors in both states is carried out and compared with the aid of the reliability handbook. Finally, the topology operation in a normal and reconfiguration state is validated by building a 1-kW hardware setup. The results show that the quadratic boost converter in a reconfiguration state operates without altering the voltage gain of the converter and with reduced voltage stress across the capacitors. [ABSTRACT FROM AUTHOR]

A DC microgrid with renewable energy sources can achieve reduced current ripple, higher efficiency, faster dynamics, high voltage gain, and less operational stress by interfacing with an interleaved boost converter (IBC). The stability of an IBC linked to a DC microgrid supplying a constant power load (CPL) can be imperceptibly guaranteed by a conventional controller. A tightly regulated CPL with nonlinear and negative incremental impedance characteristics will lead to stability issues. Uncertainties such as load and line variations will further affect the stability of the system. A nonlinear passivity-based control algorithm requires more attention than a traditional controller to achieve the stability of power converters. This article explains the Brayton–Moser (BM) passivity-based controller (PBC) for a 2-level interleaved boost converter (IBC) interfaced DC microgrid with CPL. The suggested controller can achieve high signal stability by injecting a series-connected virtual impedance. The stability of the proposed controller has been assessed using the Lyapunov stability approach. A BM passivity-based controller for a 2-level IBC with CPL has been derived and investigated under various operating modes using MATLAB and Simulink. It was also observed that the proposed system achieves at least 2 % improvement in efficiency and 50 % reduction in current ripple. To evaluate the performance of BM Passivity-based controller, a comparative analysis was performed between the suggested controller and the traditional PI controller, which is also included in this paper. [ABSTRACT FROM AUTHOR]

Distributed cooperative control strategies for DC microgrids have been rapidly evolving in recent years. However, introducing a cyber layer to enhance robustness, scalability, and reliability also exposes the system to potential cyber‐attacks. The damage inflicted by such attacks on the system performance can be catastrophic, reaching a point where it may devastate the system normal operation. By using model reference adaptive control (MRAC), this article proposes a resilient approach that does not require an accurate model of the system and despite the uncertainties for detecting false data injections into the reference DC voltage and simultaneously mitigating their adverse effects on the system stability and performance. The proposed technique employs an observer to detect possible false data injections in an online manner. By emulating the behavior of an ideal reference model, the MRAC ensures adaptive adjustments of the control parameters over time to mitigate the negative effects of potential attacks effectively and despite non‐idealities such as measurement noise, parameter variations, and environmental changes in DC microgrids, the MRAC effectively manages false data injection attacks. Simulation studies are conducted using diverse scenarios involving a three‐node DC microgrid to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

With the growth of the load in the electricity networks, sufficient investment in the generation and lines expansion should be made in order to provide the energy needed by consumers with the lowest possible investment and operation costs. This issue is especially important in distribution networks, which are faced with the uncertainties of renewable energy generation and the development of microgrids and related issues. In this article, the planning of generation and lines expansion has been modeled with the aim of minimizing the total costs of microgrids, based on the cooperative approach. For this purpose, a bi‐level model has been developed; on the upper level, microgrids make investment decisions with a cooperative approach, and a constrained stochastic formulation has been developed with considering operational uncertainties on the lower level. Also, in this article, in order to ensure the supply of critical loads in island conditions, the self‐sufficiency index is defined. Three case studies have been considered to ensure the effectiveness of the developed model. In case 1, each microgrid will be able to supply its load only by generating of its units and purchasing from the retail market. In case 2, the possibility of trading with other microgrids in a non‐cooperative approach will also be available to the microgrids operators, and in case 3, microgrids can exchange energy with other microgrids in a cooperative manner. The simulation results showed that due to the possibility of using nearby microgrid resources, the cost of microgrid load supply in case 2 was reduced by 4.84% compared to case 1. Also, this cost in case 3 was reduced by 5.23% and 0.38%, respectively, compared to cases 1 and 2, due to the use of a cooperative manner in microgrid load providing. [ABSTRACT FROM AUTHOR]

To improve the stability of the inverter‐based microgrid (MG), this paper employs a novel data‐driven based method to coordinately adjust control parameters of inverters in a fast local manner. During the design process, an offline eigenvalue based optimization problem that is used to calculate the optimal control parameters under various operating conditions is first constructed. In order to reduce reliance on full system information, a feature selection algorithm is utilized to extract the most relevant local measurements that influence the adjustment of each control parameter. Then, regarding local measurements as input variables and optimal control parameters as output variables, based on northern goshawk optimization (NGO) and long short‐term memory (LSTM) network, a novel deep learning algorithm is proposed to train the local parameter adjustment model (LPAM) by learning the mapping relationship between them. During the application, to guarantee the stability of MG all the time, a security region based shielding mechanism is developed, where the improper control parameter adjustment will be replaced by a safe one. The case study indicates that the proposed algorithm has better mapping accuracy than traditional LSTM neural networks and also faster calculation speed than the traditional offline optimization‐based method. The effectiveness and advantages of the proposed method are demonstrated in a modified 9‐bus MG. [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]

To address the issues of node interaction power overrun and high carbon emissions that may arise during distributed optimization in multi‐energy parks (MEPs), this paper proposes a distributed low‐carbon and economic operation method for multi‐energy parks based on a cloud platform that considers network transmission capacity.The proposed method achieves maximun profit by designing a two layer collaborative architecture for distributed optimization operations. At the top level, cloud platform services are utilized to build a model for checking network transport capacity and carbon emission quotas, optimizing network node over‐limit inspection. The bottom layer constructs a distributed optimization model for multi‐energy complementation in each park, taking into account the information privacy and individual interests of each multi‐energy park. An improved alternating direction multiplier method (ADMM) is proposed to effectively solve the two‐layer framework. The case studies show that the distributed optimization method under cloud platform services proposed in this paper can achieve maximum revenue for integrated energy service provider while ensuring the safe operation of multi‐energy parks, and reasonably allocate the benefits of collaborative operation among various parks while promoting carbon emission reduction. [ABSTRACT FROM AUTHOR]

Considered as basic structures of next‐generation energy system, environment‐friendly and flexible microgrid (MG) systems are potential solutions to address integration issues of stochastic renewable energy sources. Adaptable energy management approaches provide the possibility to construct effective and various energy interaction. The purpose of this paper is to present a problem‐oriented review of energy management in MG systems. This paper first comprehensively reviews recent research studies on MG, particularly in multi‐microgrid (MMG). Then, this paper proposes a concept of energy utilization model for energy management, which includes a discussion of modern concepts including MG, MMG along with picogrid, nanogrid and virtual power plant. And a synthetic energy management framework including stability, touch, efficiency, evenness, and resilience dimensions is proposed and formulated based on energy utilization mode. Then energy management system is illustrated from the perspectives of system function, management architecture, operation logic and data analysis, and further, a systematic four‐layer hierarchical architecture of management systems for whole MG and MMG systems is proposed. Moreover, key technologies in energy management are summarized and reviewed from the aspects of control, communication, prediction, optimization, and evaluation. Last, eight main prospects on the future trend of energy management in MG and MMG are also presented. [ABSTRACT FROM AUTHOR]

As the penetration of distributed renewable energy continues to increase in distribution networks, the traditional scheduling model that the inertia and primary frequency support of distribution networks are completely dependent on the transmission grid will place enormous regulatory pressure on the transmission grid and hinder the active regulation capabilities of distribution networks. To address this issue, this paper proposes a coordinated optimization method for distribution networks and microgrid clusters considering frequency constraints. First, the confidence interval of disturbances was determined based on historical forecast deviation data. On this basis, a second-order cone programming model for distribution networks with embedded frequency security constraints was established. Then, microgrids performed economic dispatch considering the reserves requirement to provide inertia and primary frequency support, and a stochastic optimization model with conditional value-at-risk was built to address uncertainties. Finally, a cooperative game between the distribution network and microgrid clusters was established, determining the reserve capacity provided by each microgrid and the corresponding prices through Nash bargaining. The model was further transformed into two sub-problems, which were solved in a distributed manner using the ADMM algorithm. The effectiveness of the proposed method in enhancing the operational security and economic efficiency of the distribution networks is validated through simulation analysis. [ABSTRACT FROM AUTHOR]

The energy storage adjustment strategy of source and load storage in a DC microgrid is very important to the economic benefits of a power grid. Therefore, a multi-timescale energy storage optimization method for direct current (DC) microgrid source-load storage based on a virtual bus voltage control is studied. It uses a virtual damping compensation strategy to control the stability of virtual bus voltage and establishes a virtual energy storage model by combining different types of distributed capability units. The design of an optimization process for upper-level daily energy storage has the objective function of maximizing the economic benefits of microgrids to cope with unplanned fluctuations in power. A real-time energy-adjustment scheme for the lower level is introduced, and a real-time energy-adjustment scheme based on virtual energy storage for the short-term partition of the source-load storage is designed to improve the reliability of microgrid operations. The experiment shows that, in response to the constant amplitude oscillation of the power grid after a sudden increase in power, this method introduces a virtual damping compensation strategy at 20 s, which can stabilize the virtual bus voltage. From 00:00 to 09:00, the battery power remains at around 4 MW, and from 12:00 to 21:00, the battery exits the discharge state. The economic benefits from applying this method are significantly higher than before. This method can effectively adjust the source-load energy storage in real time. During peak electricity price periods, the SOC value of supercapacitors is below 0.4, and during normal electricity price periods, the SOC value of supercapacitors can reach up to 1.0, which can make the state of the charge value of supercapacitors meet economic requirements. [ABSTRACT FROM AUTHOR]

An increase in renewable energy generation in the microgrid can cause voltage oscillation problems. To address this issue, an equivalent circuit of the microgrid was established, including a synchronous generator, grid-connected inverter, and constant power load. Then, the impact of different renewable energy generation ratios, different direct current (DC) voltage loops, and phase-locked loop control bandwidths of the grid-connected inverter on microgrid stability were analyzed. The results indicate that an increase in the renewable energy generation ratio leads to a decrease in the stability margin of the microgrid. A microgrid stability improvement method involving the parallel connection of a virtual resistor with the grid-connected inverter was proposed. The resistance value of the virtual resistor was obtained through an adaptive algorithm. This method ensures the stable operation of the microgrid under different renewable energy generation ratios. [ABSTRACT FROM AUTHOR]

This article proposes a multi‐objective and multi‐stage low‐carbon planning approach for park integrated energy systems (PIES) considering the impacts of random outages from the superior electrical grid. This approach incorporates optimal multi‐stage construction sequencing and stepped carbon emission trading to leverage the economic and low‐carbon benefits of long‐term planning. First, the islanding modes of PIES are described using four random variables: island type, duration, start time, and typical day of occurrence, from which islanding scenarios are generated based on scenario tree. Next, a multi‐objective planning model that considers both economics and reliability is constructed, with the objectives of minimizing the total lifecycle planning cost and the expected economic loss during islanding. The improved Normalized Normal Constraint (NNC) method is proposed to solve the multi‐objective planning problem. Then, the fuzzy membership function is used to determine the optimal compromise solution, resulting in a planning scheme that balances economic efficiency and supply reliability. Finally, simulations indicate that, at the cost of a slight increase in planning expenses, the proposed model significantly reduces the loss costs under islanding modes compared with single‐objective economic‐focused planning. Additionally, the improved NNC method can achieve a more uniform Pareto frontier compared with the conventional NNC method. [ABSTRACT FROM AUTHOR]

Microgrids require efficient energy management systems to optimize the operation of microgrid sources and achieve economic efficiency. Bi-level energy management model is proposed in this paper to minimize the operational cost of a grid-tied microgrid under load variations and uncertainties in renewable sources while satisfying the various technical constraints. The first level is day ahead scheduling of generation units based on day ahead forecasting of renewable energy sources and load demand. In this paper, a recent meta-heuristic algorithm called Coronavirus Herd Immunity Optimizer (CHIO) is used to solve the problem of day-ahead scheduling of batteries, which is a complex constrained non-linear optimization problem, while the Lagrange multiplier method is used to determine the set-point of the Diesel Generator (DG). The second level of the proposed EMS is rescheduling and updating the set-points of sources in real-time according to the actual solar irradiance, wind speed, load, and grid tariff. In this paper, a novel real-time strategy is proposed to keep the economic operation during real-time under uncertainties. The obtained results show that the CHIO-based bi-level EMS demonstrates an optimal economic operation for a grid-connected microgrid in real-time when there are uncertainties in weather, utility tariffs, and load forecasts. [ABSTRACT FROM AUTHOR]

Clean energy production is now the main challenge for sustainable future which can be addressed by introducing renewable energy sources. Fuel cells are critical in providing sustainable energy solutions by enabling efficient energy conversion in microgrids. To enhance the performance of fuel cell-based microgrids, advanced controllers and inverters are necessary to manage power quality and stability. One of the key benefits of this fuel cell is the separation of energy conversion and management, which allows independent optimization for each function of microgrid's performance, cost, or other installation considerations. The ability to individually control of each component of a microgrid can significantly impact various applications positively. Beyond their technological and economic advantages, fuel cells are highly sensitivity to microgrid's characteristics such as current quality and harmonics which creates difficulties for their design and management. Therefore, the entire microgrid must be managed by an effective energy management strategy and integration of advanced inverter topologies. This study aims to improve fuel cell efficiency and power quality within the microgrid through an advanced energy management (AEM) strategy. The proposed AEM structure is developed focusing on two key components: a self-regulated controller (SRC) and a switched capacitor multilevel inverter (SCMLI). These elements work together to mitigate the disturbances and enhance the power quality of the system. The methodology includes simulation and hardware-in-the-loop (HIL) testing under non-linear load conditions. Initial grid-side current total harmonic distortion (THD) of 28.38% was reduced to 2.19% through the proposed system. An impact analysis is also presented to demonstrates that reducing current harmonics and voltage imbalance have improved the efficiency of fuel cell to 50.18% and fuel utilization to 98.05%. The findings show that the proposed scheme significantly reduces current harmonics and improves the overall performance benchmark of the microgrid and fuel cell. • An advanced energy management (AEM) is proposed for fuel cell-based microgrid. • A self-regulated controller (SRC) is utilized to reduce computational burden. • Power quality is improved utilizing a 7-level switched capacitor multilevel inverter. • Fuel cell's performance is improved due to improved power quality of the system. [ABSTRACT FROM AUTHOR]

ABSTRACTRenewable generation plays an important part in today's power system. With the inclusion of the inverter interfaced renewable energy sources (RESs) into a microgrid, the total system inertia decreases and it leads to increased frequency deviation in presence of a disturbance. This paper proposes a cascaded Integral Minus Tilt Integral Derivative with Filter (I–TDN)‐Proportional‐Integral (PI), [(I‐TDN)‐PI] controller for frequency stabilization of a hybrid microgrid in presence of electric vehicles (EV). The microgrid model includes reheated thermal power plant with high degree of non‐linear system such as inverter based RESs like photovoltaic and wind generation systems. A diesel engine generator is incorporated for load frequency control during perturbation in the system frequency. Virtual inertia controller (VIC) with inverter based energy storage system (ESS) is commonly used to improve system inertia and frequency stability of the microgrid. In addition to the ESS, this paper proposes inclusion of the EVs in this VIC. The optimal gains of the proposed cascaded I‐TDN‐PI controller are determined using Mountain Gazelle Optimizer (MGO), a modern metaheuristic optimization algorithm. Sensitivity of the proposed controller is investigated in presence of system nonlinearities, load perturbations, time delay, system parameter variation and RES power fluctuations. The simulation results justify the robustness of the proposed control structure for frequency stabilization of the microgrid. [ABSTRACT FROM AUTHOR]

This paper introduces a distributed control approach for optimizing the operation of islanded DC microgrids (µGs), accounting for cyber delays and network failures. Initially, an integrated network emulation platform is utilized to calculate realistic cyber delays for islanded DC µGs employing IEC 61850 communication standard. To address the instability issues arising from cyber delays, the Artstein-Kwon-Pearson reduction technique is employed to convert the system with cyber delays into one without delays. The Subsequently, a consensus-based generation-cost optimization algorithm is designed for maintaining the economic operation of the µG. This algorithm efficiently equalizes the incremental costs (ICs) of all distributed generators (DGs) in a fully distributed manner, while adhering to the DGs capacity limits. Additionally, a fully distributed voltage regulation control is developed to stabilize µG average voltage, ensuring the balance between generation and demand. The efficacy of the formulated control technique is substantiated through diverse case studies, thereby underscoring its superior operational performance. [ABSTRACT FROM AUTHOR]

This article comprehensively reviews strategies for optimal microgrid planning, focusing on integrating renewable energy sources. The study explores heuristic, mathematical, and hybrid methods for microgrid sizing and optimization-based energy management approaches, addressing the need for detailed energy planning and seamless integration between these stages. Key findings emphasize the importance of optimal sizing to minimize costs and reduce carbon dioxide (CO2) emissions while ensuring system reliability. In a pedagogical manner, this review highlights the integrated methodologies that simultaneously address sizing and energy management and the potential of emerging technologies, such as smart grids and electric vehicles, to enhance energy efficiency and sustainability. This study outlines the importance of accurate load modeling and carefully selecting models for renewable energy sources and energy storage systems, including degradation models, to achieve long-term operational efficiency and sustainability in microgrid design and operation. Future research should focus on developing multi-objective optimization techniques and incorporating cutting-edge technologies for improved microgrid planning and operation. [ABSTRACT FROM AUTHOR]

Networked microgrids (NMGs) enhance the resilience of power systems by enabling mutual support among microgrids via dynamic boundaries. While previous research has optimized the locations of mobile energy storage (MES) devices, the critical aspect of MES capacity sizing has been largely neglected, despite its direct impact on costs. This paper introduces a two-stage optimization framework for MES sizing, pre-positioning, and re-allocation within NMGs. In the first stage, the capacity sizing and pre-positioning of MES devices are optimized before a natural disaster. In the second stage, the re-allocation and active power output of MES devices are adjusted post-disaster, with boundary switches operated based on the damage scenarios. The framework restores unserved loads by either forming isolated microgrids using MES or re-establishing connections between microgrids via smart switches. The proposed framework is modeled mathematically and solved using a customized progressive hedging algorithm. Extensive experiments on modified IEEE 33-node and 69-node systems demonstrate the model's effectiveness and applicability in improving system resilience. [ABSTRACT FROM AUTHOR]

The transient stability control for disturbances in microgrids based on a lithium-ion battery–supercapacitor hybrid energy storage system (HESS) is a challenging problem, which not only involves needing to maintain stability under a dynamic load and changing external conditions but also involves dealing with the energy exchange between the battery and the supercapacitor, the dynamic change of the charging and discharging process and other factors. This paper focuses on the bus voltage control of HESS under load mutations and system uncertainty disturbances. A BP Neural Network-based Active Disturbance Rejection Controller (BP-ADRC) is proposed within the traditional voltage-current dual-loop control framework, leveraging uncertainty quantification. Firstly, system uncertainties are quantified using system-identification tools based on measurable information. Subsequently, an Extended State Observer (ESO) is designed to estimate the total system disturbance based on the quantified information. Thirdly, an adaptive BP Neural Network-based Active Disturbance Rejection Controller is studied to achieve transient stability control of disturbances. Robust controllers, PID controllers and second-order linear Active Disturbance Rejection Controllers are employed as benchmark strategies to design simulation experiments. Simulation results indicate that, compared to other benchmark strategies, the BP-ADRC controller based on uncertainty quantification exhibits superior tracking and disturbance-rejection performance in transient stability control within microgrids of hybrid energy storage systems. [ABSTRACT FROM AUTHOR]

The increased integration of renewable energy sources (RESs), such as photovoltaic and wind turbine systems, in microgrids poses significant challenges due to fluctuating weather conditions and load demands. To address these challenges, this study introduces an innovative approach that combines Unscented Transformation (UT) with the Enhanced Cheetah Optimization Algorithm (ECOA) for optimal microgrid management. UT, a robust statistical technique, models nonlinear uncertainties effectively by leveraging sigma points, facilitating accurate decision-making despite variable renewable generation and load conditions. The ECOA, inspired by the adaptive hunting behaviors of cheetahs, is enhanced with stochastic leaps, adaptive chase mechanisms, and cooperative strategies to prevent premature convergence, enabling improved exploration and optimization for unbalanced three-phase distribution networks. This integrated UT-ECOA approach enables simultaneous optimization of continuous and discrete decision variables in the microgrid, efficiently handling uncertainty within RESs and load demands. Results demonstrate that the proposed model significantly improves microgrid performance, achieving a 10% reduction in voltage deviation, a 10.63% decrease in power losses, and an 83.32% reduction in operational costs, especially when demand response (DR) is implemented. These findings validate the model's efficacy in enhancing microgrid reliability and efficiency, positioning it as a viable solution for optimized performance under uncertain renewable inputs. [ABSTRACT FROM AUTHOR]