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This study aims to ameliorate the contribution capability of doubly-fed induction generator (DFIG) to participate in standalone microgrid operation. The islanded microgrid consists of a solar photovoltaic array for solar energy conversion and battery energy storage in addition to DFIG-based wind energy conversion system. Using a simplified control approach, the study describes multi-mode operation of a DFIG-based AC/DC microgrid using a stator-side solid-state transition switch (SSTS). Using SSTS operation, the DFIG stator can be seamlessly disconnected and reconnected from the point of common coupling without interrupting power to the loads in the microgrid. Additionally, non-ideal AC loads can be handled efficiently without the need for computationally exhaustive approaches to enhance stator voltages and currents. [ABSTRACT FROM AUTHOR]

The effective management of microgrids is important towards transition to sustainable energy paradigm. By optimizing the utilization of different energy sources, such as solar photovoltaic panels and energy storages, it improves the reliability of the grid and develops resiliency in dealing with of challenges of unexpected variations in demand. To this end, the proposed paper presents DeepEMS, a system developed to manage the energy of microgrids through the incorporation of diverse intelligent algorithms. DeepEMS provides dynamic microgrid management through the utilization of Bidirectional Long Short‐Term Memory (BiLSTM) networks, Sliding Linear Programming (SLP), and Random Forest (RF). By implementing these methodologies, DeepEMS can optimize energy consumption throughout the microgrid by dynamically identifying and coordinating the needs of various energy sources. DeepEMS achieves precise multimodal optimization and facilitates integration of storage systems, grid interactions, and renewable energy sources (RES), as demonstrated by simulations and data analytics. DeepEMS presented performance in control, resource allocation, management, and grid utilization. Furthermore, in a comparative analysis with alternative intelligent models including XGBoost, Light GBM, RF, and Decision Trees, DeepEMS consistently demonstrated higher performance as measured by several key performance indicators (KPIs). [ABSTRACT FROM AUTHOR]

Microgrids, featuring distributed generators like solar energy and hybrid energy storage systems, represent a significant step in addressing challenges related to the greenhouse effect and outdated transmission infrastructures. The operation and control of islanded microgrids, particularly in terms of grid voltage and frequency, rely on the synchronization of multiple parallel inverters connected to the distributed generators. However, to determine the necessary grid parameters for effective control, the presence of circulating currents from unbalanced grid voltages arises as a challenge. This situation necessitates the development of a new approach to achieve phase angle locking for grid synchronization, with the aim of maintaining the voltage within acceptable limits in islanded microgrids. This objective is realized through the creation of a microgrid network model, design of an adaptive filter, utilizing the double second-order generalized integrator-phase-locked loop (DSOGI-PLL), for dynamic voltage transformation. The design is evaluated by simulation using MATLAB/Simulink. The primary goal is to investigate the DSOGI-PLL-based droop control and compare its performance with the conventional synchronous reference frame-phaselocked loop (SRF-PLL) control approach. Notably, the DSOGI-PLL successfully eliminates the ripples in phase angle estimation, consequently enhancing the quality of voltage output in the microgrid. [ABSTRACT FROM AUTHOR]

Abstract 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 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 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.

This paper presents a high-gain DC-DC converter for photovoltaic (PV) applications. The proposed high gain DC-DC converter employs two coupled inductors, a voltage-lift capacitor, and two diode-capacitor multiplier cells to achieve a voltage gain of 21.11. The novel hybrid gain extension mechanism was validated by testing a 300 W prototype converter. During experimentation, it yielded an output of 380 V from an 18 V DC input and switched at 50 kHz. Because the switches in the interleaved stage are operated at a duty ratio of 0.5, the input current is ripple-free, and the switch current rating is also reduced. Owing to the location, the voltage stress on the switches was only 10.5% of the output voltage. Due to the adopted gain extension mechanisms, the voltage stress on the diodes was also reduced. The output voltage of the proposed converter is regulated by implementing a closed-loop control. In practice, the converter exhibits a good dynamic response when the line voltage and load current are subjected to step changes. The availability of a common-ground point is an additional advantage. Furthermore, the significant features of the proposed converter were validated by comparing its key parameters with many state-of-the-art converters available in the literature.

Abstract 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 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 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 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 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 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 This research proposes the application of fractional‐order sliding mode control (FOSMC) at the primary controller level to improve the stability of an islanded microgrid by adjusting its voltage and frequency. The control strategies used in the microgrid are performed in two levels (primary and secondary) in the islanded mode. Practically, most previous studies have worked to improve the primary controller. Droop control is one of the most commonly used methods at the primary level and is adopted in this study as well. The sliding mode control (SMC) strategy is normally used to control linear equations. Thus, the non‐linear microgrid equations were transformed into some linear ones using the input‐output feedback linearization technique. Further, a fractional sliding surface was acquainted. The sliding surface and FOSMC were designed to reject system uncertainties and organize the voltage and frequency. Design parameters were chosen using the Lyapunov stability theorem. The validation of the proposed method using Simulink‐MATLAB confirms its effectiveness in enhancing level power sharing, regulating frequency, and maintaining voltage stability across the system.

Abstract Microgrids (MGs) have emerged as a promising solution for providing reliable and sustainable electricity, particularly in underserved communities and remote areas. Integrating diverse renewable energy sources into the grid has further emphasized the need for effective management and sophisticated control strategies. This review explores the crucial role of control strategies in optimizing MG operations and ensuring efficient utilization of distributed energy resources, storage systems, networks, and loads. To maximize energy source utilization and overall system performance, various control strategies are implemented, including demand response, energy storage management, data management, and generation‐load management. Employing artificial intelligence (AI) and optimization techniques further enhances these strategies, leading to improved energy management and performance in MGs. The review delves into the control strategies and their architectures, and highlights the significant contributions of AI and emerging technologies in advancing MG energy management.

Abstract A decarbonised future grid should couple technological novelty with innovative market models to efficiently capture the value of grid‐edge decarbonised assets. The transactive energy (TE) concept inverts the centralised grid model by leveraging the evolution of consumers to prosumers to prosumagers. The principal TE market design challenge is transactive control—using market and pricing mechanisms to coordinate autonomous peer interactions, to optimally allocate power and incentivise peers. Peer attraction, incentivisation and retention are all critical for practical TE implementation along three adoption stages, starting from independent peer transactions with the centralised market; to decentralised peer coordination; towards distributed peer‐to‐peer trading. Addressing gaps in related scholarship, the authors investigate the economic positions of distinct peer roles in each adoption stage and two local pricing strategies. Using a real market dataset, trading decisions are simulated over a 1‐year horizon at hourly granularity. Coordinated action achieves better transactive control for the community, with economic superiority over centralised and distributed mechanisms. Distinct peer incentives should equitably align with their contribution to market functionality, such as the value ascribed to prosumagers' flexibility in local pricing and the constrained bargaining power of prosumers in distributed bilateral negotiations.

Abstract This study focuses on improving power system grid performance and efficiency through the integration of distributed energy resources (DERs). The study proposes an artificial intelligence (AI) based effective approach for economic dispatch and load management for three linked microgrids (MGs) that operate in both grid-connected and autonomous modes. A day-ahead scheduling method is suggested to calculate the optimal set points for various energy sources in MGs considering various system constraints for safe operation. In addition, a load management approach that shifts the controllable loads from one interval to another is applied to reduce the operating cost of MG. To handle the optimization challenges of energy scheduling and load shifting such complexity and non-linearity, an advanced meta-heuristic method known as the one-to-one based optimizer (OOBO) is used. Overall, the paper proposes a viable and efficient methodology for economical distribution in linked microgrids, which takes advantage of renewable energy resources and incorporates scheduling optimization via the OOBO algorithm. The proposed energy management strategy enhances the system performance, increases energy efficiency, and reduces the daily operational cost by 1.6% for grid connected mode and by 0.47% for islanded operation mode.

Abstract This article investigated the charge and discharge management structure of electric vehicles (EVs) in intelligent parking lots (IPLs). It seems that with the expansion of renewable energy sources (RESs) as clean energy and investigation of the effects of EVs on the operation and planning of future distribution networks around the way EVs exchange energy with each other and the upstream network operator, RESs such as solar and wind sources, along with hydrogen storage are essential. Therefore, a new stochastic multi‐scenario approach for charge/discharge management of EVs parked in IPL was proposed, in which a newly developed model for the IPL with a hydrogen storage system (HSS) consisting of a fuel cell, an electrolyzer, and a hydrogen storage tank was presented. In the proposed model, the constraints of upstream network and power balance constraints, with the operating constraints related to the IPL, including EVs, renewable energy sources, and the hydrogen storage system, were formulated as the most important objectives of the optimization problem. The optimization algorithm, Competitive Swarm Optimizer (CSO), was formulated for implementation, and its results were compared with those of the PSO and GWO algorithms. The CSO must handle a variety of practical, large‐scale optimization problems. Based on the obtained results, the excess energy purchased from the upstream network or renewable sources was used as hydrogen storage for consumption during peak hours. As expected, the technical constraints and financial goals of the system were met, and the proposed system was evaluated on a 33‐bus network. As the expected benefits will be the most beneficial with the presence of renewable sources, the final profit will be reduced by taking into account uncertainty for charge/discharge management in EVs such as the uncertainty of electric load, market price, wind turbine, and photovoltaic cell sources. Nonetheless, the profit obtained from renewable resources is preferable to losses resulting from the uncertainty of the system, and according to expectation, the performance of the system for managing the optimal charging and discharging of EVs in the IPL will be acceptable with the maximum profit for the grid.

Abstract The issue of generation expansion planning in microgrids has become a challenging issue in electricity industry for two reasons: load growth and uncertainties in renewables' generation. Therefore, this issue is considered here. Here, the modelling of generation expansion planning problem has been developed in a network of microgrids in a decentralized manner, considering normal and contingency conditions. On the other hand, in order to further develop the study considered, decentralized generation expansion planning model of microgrids by considering contingency conditions has been addressed in a cooperative approach to minimize total costs. In developed model, investment decisions are made at the higher level and operational constraints has been considered at lower one. Also, case studies are defined in three different scenarios: islanding operation of microgrids as first scenario and peer‐to‐peer trading of microgrids in non‐cooperative and cooperative approaches as second and third scenarios, respectively. The results of simulations have shown that by facilitating the transactions between microgrids, their total costs are reduced. The costs of the whole set of microgrids in the non‐cooperative scenario are reduced by 9.4% compared to the islanding scenario; and the costs are reduced by 7.5% in the cooperative scenario compared to the non‐cooperative scenario.

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]

With the integration of a large number of microgrids in the power distribution network operation, economic and strategic challenges arise. To address these challenges, this research provides a comprehensive investigation into the operational, economic, and strategic dynamics of microgrids. Through careful data analysis, the study interprets the complications of microgrid responses to seasonal changes. It also investigates energy consumption patterns and production dynamics. The detailed analysis of microgrid configurations reveals the unique attributes and challenges of PV, wind, and hydropower microgrids. Moreover, the research explains the financial implications of microgrid integration, from setup costs to potential ROI. It is also examining the cooperative relationship between microgrids and conventional grids. Key findings highlight that solar microgrids contribute 3.2% to 5.3%, wind microgrids provide 5.9% to 7.4%, and hydropower microgrids contribute 24.4% of total power. Energy purchase peaks at 850,000 kWh in August and declines to 580,000 kWh in May. 170,000 kWh of energy is sold back to the grid in May. Moreover, the grid energy costs reduced from $0.178 kWh to $0.30 kWh. The total net present cost of the system is achieved at $37,880,023.33. Renewable energy production ranges from 480 kW to 2,300 kW, with a penetration level reaching 160%. [ABSTRACT FROM AUTHOR]

Converters and synchronous-based distributed generators (DGs) play a vital role in developing the microgrid. The integration of a microgrid into the electrical grid provides numerous benefits, including enhanced power system efficiency and reliability. Besides these benefits, excess penetration of the microgrid can lead to various issues, like technical and safety concerns within the power system. One of the biggest challenges is the detection of unintentional islanding. Unintentional islanding can cause damage to protection devices, pose hazards to utility personnel, and degrade network power quality. To overcome the above issues with unintentional islanding. In this paper, the researchers and industries developed various islanding detection schemes to detect unintentional islanding events in active distribution networks quickly and accurately. The islanding detection algorithms are compared based on their effectiveness indices, such as detection times, benefits, drawbacks, power quality issues, and non-detection zones. It was found that passive approaches are more suitable for converters and synchronous generator-based microgrids due to their technical and economic benefits. This research can be helpful for researchers who are looking to select the efficient islanding algorithm for detection islanding in a converter and synchronous generator-based microgrid. [ABSTRACT FROM AUTHOR]

Abstract The global transportation electrification commerce sector is now booming. Stakeholders are paying an increased attention to the integration of electric vehicles and electric buses into the transportation networks. As a result, there is an urgent need to invest in public charging infrastructure, particularly for fast charging facilities. Consequently, and to complete the portfolio of the green environment, these fast-charging stations (FCSs) are designed using 100% of renewable energy sources (RESs). Thus, this paper proposes an optimization model for the techno-economic assessment of FCSs comprising photovoltaic and wind turbines with various energy storage devices (ESDs). In this regard, the FCS performance is evaluated using flywheels and super capacitors due to their high-power density and charging/discharging cycles and rates. Then, optimal sizing of these distributed generators is attained considering diverse technical and economical key performance indicators. Afterwards, the problem gets more sophisticated by investigating the effect of RES’s uncertainties on the selection criterion of the FCS’s components, design and capacity. Eventually, as an effort dedicated to an online energy management approach, a deep learning methodology based on radial basis network (RBN) is implemented, validated, and carried out. In stark contrast to conventional optimization approaches, RBN demonstrates its superiority by obtaining the optimum solutions in a relatively short amount of time.

Abstract The effective management of reactive power plays a vital role in the operation of power systems, impacting voltage stability, power quality, and energy transmission efficiency. Despite its significance, suboptimal reactive power planning (RPP) can lead to voltage instability, increased losses, and grid capacity constraints, posing risks to equipment and system reliability. Rigorous RPP methodologies can mitigate these challenges, resulting in tangible improvements in voltage profiles, system stability, and reduced losses. A comprehensive review of 20 technical articles published between 2020 and 2023 was conducted to compare and synthesize contributions to the field of RPP. The review highlighted the efficacy of strategic RPP approaches in reducing power losses, minimizing equipment malfunctions, and improving power quality, leading to substantial economic benefits—strategic planning approaches and integrating emerging technologies. For instance, examples include renewable energy sources and energy storage systems, which offer promising avenues for enhancing RPP and ensuring stability, reliability, and efficiency in power systems.

Abstract Microgrids play a pivotal role in modern power distribution systems, necessitating precise control methodologies to tackle challenges such as performance instability, especially during islanding operations. This paper introduces an advanced control strategy that employs artificial intelligence, specifically deep neural network (DNN) predictions, to enhance microgrid performance, particularly in an islanding mode where voltage and frequency (VaF) deviations are critical concerns. By utilizing real‐time data and historical trends, the proposed controller accurately forecasts power demand and generation patterns, enabling proactive planning and optimization of efficiency, reliability, and sustainability in microgrid management. One significant aspect of this approach is to establish an intelligent distributed control system that minimizes reliance on communication devices while ensuring that VaF remains within acceptable limits. Moreover, it consolidates the roles of primary and secondary controllers within the microgrid and facilitates the prediction of load changes and load injection processes. This capability significantly reduces microgrid VaF deviations, enhancing system performance through precise power distribution and balanced coordination among distributed generators. Consequently, it ensures the stability and reliability of the system. In summary, the integration of DNN‐based predictive control represents a significant advancement in microgrid management, providing a solution to address performance challenges and optimize operational efficiency, reliability, and sustainability.

Abstract There is a change in the traditional power system structure as a result of the increased incorporation of microgrids (MGs) into the grid. Multi‐area MGs will emerge as a result, and issues related to them will need to be addressed. Load frequency control (LFC) is a challenge in such structures, which are more complicated due to variations in demand and the stochastic characteristics of renewable energy sources. This paper presents a cascade fuzzy active disturbance rejection control technique to deal with the LFC problem. In order to tune different parameters of controllers, a newly developed heuristic algorithm called the Gazelle optimization algorithm (GOA) is also employed. Moreover, due to the fact that multi‐area MGs are regarded as cyber‐physical systems (CPSs), a relatively new concern for LFC problems is their resilience to cyberattacks such as false data injection (FDI) and denial of service (DoS) attacks. Therefore, this research also presents a novel machine learning approach called parallel attack resilience detection system (PARDS) to deal with the LFC problem in the presence of cyberattacks. The efficiency of the proposed strategy is investigated under different scenarios, such as non‐linearities in the power system or server cyberattacks.

Abstract This paper introduces static and dynamic competitive optimal control of DC/DC converters in DC microgrids based on game theory considering mutual effects. Using static and dynamic game theories, the conditions for competitive optimal control of DC/DC converters in Nash equilibrium with static and dynamic states are determined. Also, to reduce the complexity in the design of competitive optimal control of DC/DC converters due to the bilinear dynamic model of the system, generalized objective functions are considered. The competitive optimal controls using the Lyapunov and Riccati equations are determined to track the system trajectory to Nash equilibrium. Also, the numerical and experimental results are presented and compared in several cases. The results show that the competitive optimal control of the DC/DC converters in the DC microgrids can effectively improve the performance of the system.

Globally, effective and efficient healthcare is critical to the wellbeing and standard of living of any society. Unfortunately, several distant communities far from the national grid do not have access to reliable power supply, owing to economic, environmental, and technical challenges. Furthermore, unreliable, unavailable, and uneconomical power supply to these communities contributes significantly to the delivery of substandard or absence of qualitative healthcare services, resulting in higher mortality rates and associated difficulty in attracting qualified healthcare workers to the affected communities. Given these circumstances, this paper aims to conduct a comprehensive review of the status of renewable energy available to rural healthcare clinics around the globe, emphasizing its potential, analysis, procedures, modeling techniques, and case studies. In this light, several renewable energy modeling techniques were reviewed to examine the optimum power supply to the referenced healthcare centers in remote communities. To this end, analytical techniques and standard indices for reliable power supply to the isolated healthcare centers are suggested. Specifically, different battery storage systems that are suitable for rural healthcare systems are examined, and the most economical and realistic procedure for the maintenance of microgrid power systems for sustainable healthcare delivery is defined. Finally, this paper will serve as a valuable resource for policymakers, researchers, and experts in rural power supply to remote healthcare centers globally.

Renewable energy sources (RESs) are considered a crucial energy transformation to reduce carbon emissions, so more RESs are being integrated into contemporary power systems. Power electronic converters are extensively utilized to connect power grids with renewable generators to manage the fluctuations and unpredictability of these renewable energy sources. This paper introduces a multi-terminal multi-function inverter (MT-MF) designed for a battery energy storage system (BESS) to maintain the frequency stability of a hybrid microgrid (MG). The MG comprises a photovoltaic generation system, a diesel generator, BESS, and two loads: one constant load and the other variable, fed through a medium-voltage radial feeding system. An introduced approach involves utilizing a model predictive control controlled virtual synchronous generator (MPC-VSG) for BESS. This method offers inertia support during transient states and improves the dynamic characteristics of system frequency. In addition, it enables the connection of multiple batteries, provides individualized control for each, and supports the injection of reactive power into the MG. The required power from the BESS is shared between the two batteries using the low pass filter technique. The simulation outcomes affirm the proposed control strategy’s effectiveness and underscore the MT-MF inverter approach’s potential in integrating extensive RESs. This paper also explores how the proposed technique outperforms other methods in improving frequency stability.

Abstract This work develops a dual-layer energy management (DLEM) model for a microgrid (MG) consisting of a community, distributed energy resources (DERs), and a grid. It ensures the participation of all these energy entities of MG in the market and their interaction with each other. The first layer performs the scheduling operation of the community with the goal of minimizing its net-billing cost and sends the obtained schedule to the DER operator and grid. Further, the second layer formulates a power scheduling algorithm (PSA) to minimize the net-operating cost of DERs and takes into account the load demand requested by the community operator (COR). This PSA aims to achieve optimal operation of MG by considering solar PV power, requested demand, per unit grid energy prices, and state of charge of the battery energy storage system of the DER layer. Moreover, to study the impact of electric vehicles (EVs) load programs on DLEM, the advanced probabilistic EV load profile model is developed considering practical and uncertain events. The EV load is modelled for grid to vehicle mode, and a new mode of EV operation is introduced, i.e., vehicle to grid with EV demand response strategy (V2G_DRS) mode. The solar PV and load demand data are obtained from the MG setup installed and buildings present at the university campus. However, a scenario reduction technique is used to deal with the uncertainties of the obtained data. In order to evaluate the efficacy of the developed DLEM, its results are compared to previously reported energy management models. The results reveal that DLEM is superior to the existing models as it decreases the net-billing cost of COR by 13% and increases the profit of the DER operator by 17%. Further, it is found that for the highest EV penetration, i.e., 30 EVs, the V2G_DRS mode of EV operation reduces the total energy imported by COR by 11.39% and the net-billing cost of COR by 7.88%. Therefore, it can be concluded that the proposed model with the introduced V2G_DRS mode of EV makes the operation of all the entities of MG more economical and sustainable.

Environmentally sustainable metropolitan environments are characterized by their ability to effectively produce and distribute power while reducing their impact on the environment. Smart homes are essential in smart cities since they enhance sustainability and efficiency in urban settings. A key advantage of smart homes is their capacity to diminish energy use and carbon emissions. This is accomplished by optimizing energy consumption in home appliances, which is customized to fulfill the individual requirements and preferences of consumers. However, there is still a need for further academic research to investigate and improve the functioning of intelligent residential homes in microgrids. To efficiently manage microgrids, it is crucial to gather and analyze large amounts of electrical data related to power production from microgrid sources and energy consumption of the loads. This study examines the use of Non-Intrusive Load Monitoring (NILM) methods to monitor electrical parameters of different loads in microgrids. The research focuses on the application of affordable smart meters that are equipped with Internet of Things (IoT) capabilities. An empirical study showcases the possibility of collecting significant data on microgrid operation via the deployment of an operational microgrid that integrates a hybrid wind-solar power source with a variety of home appliances.

Abstract The energy management (EM) solution of the multi-microgrids (MMGs) is a crucial task to provide more flexibility, reliability, and economic benefits. However, the energy management (EM) of the MMGs became a complex and strenuous task with high penetration of renewable energy resources due to the stochastic nature of these resources along with the load fluctuations. In this regard, this paper aims to solve the EM problem of the MMGs with the optimal inclusion of photovoltaic (PV) systems, wind turbines (WTs), and biomass systems. In this regard, this paper proposed an enhanced Jellyfish Search Optimizer (EJSO) for solving the EM of MMGs for the 85-bus MMGS system to minimize the total cost, and the system performance improvement concurrently. The proposed algorithm is based on the Weibull Flight Motion (WFM) and the Fitness Distance Balance (FDB) mechanisms to tackle the stagnation problem of the conventional JSO technique. The performance of the EJSO is tested on standard and CEC 2019 benchmark functions and the obtained results are compared to optimization techniques. As per the obtained results, EJSO is a powerful method for solving the EM compared to other optimization method like Sand Cat Swarm Optimization (SCSO), Dandelion Optimizer (DO), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), and the standard Jellyfish Search Optimizer (JSO). The obtained results reveal that the EM solution by the suggested EJSO can reduce the cost by 44.75% while the system voltage profile and stability are enhanced by 40.8% and 10.56%, respectively.

To ensure the continuous supply of power in remote areas utilizing renewable energy resources is significant. Hence, in this research, an effective energy management method for a small-scale hybrid wind-solar-battery-Ultra-capacitor-based microgrid is proposed. Hybrid Energy Storage System (HESS) has been presented in conjunction with wind and solar energy conversion technologies characterized by coupling of power electronic converters, neural networks, optimization, battery, controllers, and ultra-capacitor storage systems to attain the intended performance. The power balance is regulated via an energy management system by considering the fluctuation in load demand and renewable energy power generation. Further, the voltage controller utilizes the deep Siamese neural network (deep SNN) that effectively carries out energy management among the hybrid renewable energy sources. The smelling-based hunting optimization assists in optimal parameter tuning and training of the deep SNN for enhancing the HESS’s efficiency. In addition, the microgrid operates independently and offers a testing area for different energy management systems and testing scenarios. The proposed small-scale microgrid, which is based on renewable energy, can serve as a significant testing area for methods utilized in smart grid applications. The proposed SBHO model’s efficiency is determined by varying the voltage, current, and power of the wind, solar, battery, and ultra-capacitor measurements. The current capacity of the battery reaches −11.06A and the battery voltage reaches 259.831 V in 0.82 s. The DC load measurement utilizing the SBHO approach obtained a DC bus voltage of 357.11V, a load current of 3.348 A within 0.82 s, and in DC power load attained the 1195.58 W within 0.82 s. The battery’s SOC by applying the smelling-based hunting optimization is gradually increased to 50.008% in 0.82s.In terms of the PV measurement, the PV current, PV voltage, and PV power are obtained as 4.238A,241.08V, and 1021.64W for the SBHO approach which surpasses other competent techniques. The ultra-capacitor current ranges from 35A to 40A with reduced heavy discharge of SOC from 98% obtained on evaluating the performance. The output power of wind using a boost converter remains 3000 W with few harmonics. [ABSTRACT FROM AUTHOR]

Combining green warehousing with wind-solar-storage systems can enhance economic power consumption, energy saving, and emission reduction in green warehousing. To achieve efficient and stable operation of the wind-solar-storage microgrid, this paper proposes an optimal microgrid scheduling strategy based on the Improved Sparrow Algorithm (ISSA). Firstly, a comprehensive benefit model is established based on the economic and environmental benefits of microgrid daily operation. Then, an innovative improved sparrow search algorithm is proposed, which aims to improve the global search and local search capability of the microgrid scheduling problem by introducing improvements such as Logistic-Circle chaotic mapping, Bottle Sea Sheath swarm optimization algorithm, dynamic inertia weights, water wave dynamic factor, and Cauchy-Gaussian variational strategy. Finally, the microgrid optimal scheduling model is solved by the improved sparrow search algorithm and compared with other algorithms. In this paper, Matlab 2016b is used for simulation, and the simulation results show that the ISSA algorithm outperforms other algorithms in terms of solution stability and optimization search capability. Under three modes of operation, ISSA improves the microgrid operation revenue by 6.29%, 5.98%, and 6.31% at least. Therefore, the optimal scheduling scheme obtained based on ISSA improves the daily operating total revenue and the system operation stability of the microgrid. [ABSTRACT FROM AUTHOR]

This article discusses the need for a multi-grade pricing strategy for emergency power supply services provided by mobile energy storage systems. The authors argue that the existing uniform pricing model fails to meet the differentiated demands of customers and undermines their enthusiasm for purchasing emergency supply services. They propose a hierarchical trading framework and a bi-level pricing optimization model based on a Stackelberg game to obtain tiered prices of emergency power supply and increase the revenue of mobile energy storage while reducing the cost for customers. The article provides insights and discussions on this pricing strategy, considering power supply capacity, duration, and response time as metrics for differentiating power supply levels. The document also suggests future research directions, such as integrating real-time scheduling of mobile energy storage into the pricing model. The research was supported by the Guangdong Basic and Applied Basic Research Foundation and the Scientific Research Startup Fund for Shenzhen High-Caliber Personnel. Additionally, there is a list of references cited in an article or research paper, which includes various sources related to the topic of energy storage systems, such as studies on optimal configuration and operation methods, pricing strategies, and the use of mobile energy storage in distribution networks. The document provides a range of perspectives and approaches to the subject, allowing library patrons to explore different aspects of energy storage for their research. [Extracted from the article]

Incorporating PEM electrolyzers (PEMElz) into microgrid systems presents a promising avenue for utilizing surplus renewable energy to produce hydrogen while meeting the system operation requirements. The accurate emulation of the behavior of PEMElz in such systems requires a detailed representation of their electrochemical and thermal characteristics. Yet modeling these characteristics optimally poses a challenge due to their inherent complexity, characterized by multivariate, non-linear, and multi-modal characteristics. To address this challenge, this paper proposes an advanced approach to model PEMElz by introducing general forms for the integrated electrochemical and thermal model based on a set of optimally identified parameters. The introduced general forms are generated using an n-dimensional polynomial fitting process for a set of optimally identified parameters, which are determined using the Partial Reinforcement Optimizer (PRO) under various temperature and pressure levels. These general forms provide a reliable PEMElz model to adapt to any temperature and pressure operating conditions. The proposed approach's effectiveness in capturing the static characteristics of PEMElz is verified by analyzing polarization and thermal curves across multiple operating conditions. Furthermore, the proposed general forms are implemented in Matlab/Simulink to emulate the PEMElz behavior when it is incorporated into a microgrid dominated by high penetration levels of variable generation resources such as solar photovoltaic (PV). This demonstration highlights the direct relevance and tangible impact of the proposed general-formulated model based on the optimal identified parameters approach in various operating scenarios. • Identified optimal parameters for a combined electrochemical and thermal PEMElz. • Tailored partial reinforcement optimizer (PRO) for PEMElz parameter identification. • Deriving general forms for the PEMElz model parameters. • Investigated proposed PEMElz in microgrid system. • The general forms are a computationally effective tool reflecting PEMElz performance. [ABSTRACT FROM AUTHOR]

The main causes of frequency instability or oscillations in islanded microgrids are unstable load and varying power output from distributed generating units (DGUs). An important challenge for islanded microgrid systems powered by renewable energy is maintaining frequency stability. To address this issue, a proportional integral derivative (PID) controller is designed in this article. Firstly, islanded microgrid model is constructed by incorporating various DGUs and flywheel energy storage system (FESS). Further, considering first order transfer function of FESS and DGUs, a linearized transfer function is obtained. This transfer function is further approximated into first order plus time delay (FOPTD) form to design PID control strategy, which is efficient and easy to analyze. PID parameters are evaluated using the Chien-Hrones-Reswick (CHR) method for set point tracking and load disturbance rejection for 0% and 20% overshoot. The CHR method for load disturbance rejection for 20% overshoot emerges as the preferred choice over other discussed tuning methods. The effectiveness of the discussed method is demonstrated through frequency analysis and transient responses and also validated through real time simulations. Moreover, tabulated data presenting tuning parameters, time domain specifications and comparative frequency plots, support the validity of the proposed tuning method for PID control design of the presented islanded model. [ABSTRACT FROM AUTHOR]

Most of the previous SOC equalization methods for microgrid energy storage target DC microgrids and use centralized control structures, while in recent years many researchers have begun to focus on a decentralized, communication-based implementation of distributed control structures. In this paper, based on the existing research, we use the multi-agent system (MAS) structure and dynamic consistency algorithm (DCA) to realize the estimation of the SOC mean value of microgrid energy storage obtained by each agent in the system. For the problems of the iterative convergence that exists in the distributed control, such as being too slow, too frequent communication, stability, etc., we optimally select the parameters of the consistency iterative summation to improve convergence. In addition, we use the event-driven method to further reduce the unnecessary communication frequency. Finally, a numerical simulation model of the AC microgrid is established by Matlab to verify the effectiveness of the method, which reduces the communication volume by about 50% while maintaining the effect of the control strategy. [ABSTRACT FROM AUTHOR]

Accelerating the green transformation of the power system is the inevitable path of the energy revolution; the increasing installed capacity of new energy and the penetration rate of electricity, uncertainty regarding new energy output, and the rising proportion of distributed power supply access have led to the threat against the safe and stable operation of the current power system. With the increasing uncertainty on both sides of power supply and demand, the microgrid (MG) is needed to effectually aggregate, coordinate, and optimize resources, such as adjustable resources, distributed power supply, and distributed energy storage in a certain area on the demand side. Therefore, in this paper, the uncertainty of wind power and PV is first dealt with by Latin hypercube sampling (LHS). Secondly, differentiated resources in the MG region can be divided into adjustable resources, distributed power supply, and energy storage. Adjustable resources are classified according to demand response characteristics. At the same time, the MG operating cost and carbon trading mechanism (CTM) are comprehensively considered. Finally, a low-carbon economy optimal scheduling strategy with the lowest total cost as the optimization goal is formed. Then, in order to verify the effectiveness of the proposed algorithm, three different scenarios are established for comparison. The total operating cost of the proposed algorithm is reduced by about 30%, and the total amount of carbon trading in 24 h can reach nearly 600 kg, bringing economic and social benefits to the MG. [ABSTRACT FROM AUTHOR]

In the DC microgrid system, the bidirectional DC/DC converter is one of the most important components; thus, research on its control strategy has attracted widespread attention. Firstly, the single bidirectional DC/DC converter based on a sliding mode (variable structure) control (SMC) strategy exhibits an inherent contradiction between the reaching time and the chattering phenomenon. In order to address this problem, an SMC strategy based on the improved exponential reaching law was designed. This control strategy modifies the constant-speed reaching term and introduces the system state variable to indicate the chattering level, which not only improves the dynamic performance of the bidirectional DC/DC converter but also suppresses the chattering problem. Secondly, the bidirectional DC/DC converter group based on the traditional droop control strategy exhibits an inherent contradiction between load power allocation and bus voltage stabilization. In order to address this problem, an improved droop control strategy that takes the line impedance characteristics into account is proposed. This control strategy modifies the traditional droop control strategy by introducing virtual resistance and uses DC bus voltage information to replace the line impedance value. This ensures the accuracy of power allocation and stability of the DC bus voltage simultaneously. Finally, the stability of each designed strategy is verified individually. The combination of the two control strategies is applied to a group of bidirectional DC/DC converters group to conduct a semi-physical simulation experiment, and the results verify that the proposed control strategies are effective and feasible. [ABSTRACT FROM AUTHOR]

The shared energy storage device acts as an energy hub between multiple microgrids to better play the complementary characteristics of the microgrid power cycle. In this paper, the cooperative operation process of shared energy storage participating in multiple island microgrid systems is researched, and the two-stage research on multi-microgrid operation mode and shared energy storage optimization service cost is focused on. In the first stage, the output of each subject is determined with the goal of profit optimization and optimal energy storage capacity, and the modified grey wolf algorithm is used to solve the problem. In the second stage, the income distribution problem is transformed into a negotiation bargaining process. The island microgrid and the shared energy storage are the two sides of the game. Combined with the non-cooperative game theory, the alternating direction multiplier method is used to reduce the shared energy storage service cost. The simulation results show that shared energy storage can optimize the allocation of multi-party resources by flexibly adjusting the control mode, improving the efficiency of resource utilization while improving the consumption of renewable energy, meeting the power demand of all parties, and realizing the sharing of energy storage resources. Simulation results show that compared with the traditional PSO algorithm, the iterative times of the GWO algorithm proposed in this paper are reduced by 35.62%, and the calculation time is shortened by 34.34%. Compared with the common GWO algorithm, the number of iterations is reduced by 18.97%, and the calculation time is shortened by 22.31%. [ABSTRACT FROM AUTHOR]

In traditional power grids, the unidirectional flow of energy and information has led to a decrease in efficiency. To address this issue, the concept of microgrids with bidirectional flow and independent power sources has been introduced. The components of a microgrid utilize various IoT protocols such as OPC-UA, MQTT, and DDS to implement bidirectional communication, enabling seamless network communication among different elements within the microgrid. Technological innovation, however, has simultaneously given rise to security issues in the communication system of microgrids. The use of IoT protocols creates vulnerabilities that malicious hackers may exploit to eavesdrop on data or attempt unauthorized control of microgrid devices. Therefore, monitoring and controlling security vulnerabilities is essential to prevent intrusion threats and enhance cyber resilience in the stable and efficient operation of microgrid systems. In this study, we propose an RBAC-based security approach on top of DDS protocols in microgrid systems. The proposed approach allocates roles to users or devices and grants various permissions for access control. DDS subscribers request access to topics and publishers request access to evaluations from the role repository using XACML. The overall implementation model is designed for the publisher to receive XACML transmitted from the repository and perform policy decision making and enforcement. By applying these methods, security vulnerabilities in communication between IoT devices can be reduced, and cyber resilience can be enhanced. [ABSTRACT FROM AUTHOR]

The stability of global economies relies heavily on power systems (PS) that have sufficient operating reserves. When these reserves are insufficient, power systems become vulnerable to issues such as load shedding or complete blackouts. Maintaining grid stability becomes even more challenging with a high penetration of renewable energy sources (RES). However, RES, connected through power electronic devices, offer significant potential as ancillary service (AS) sources. Renewable energy-based microgrids (MG), which aggregate various RES resources and have substantial load control potential, further enhance the capability of AS provision from RES. The presence of diverse AS resources raises the question of how to dispatch ancillary service signals optimally to all resources. Most of the previous research work related to AS allocation relied on single-bus MG models. This paper proposes a detailed MG model for the optimal dispatching of ASs among the resources using Virtual Load, along with an optimization procedure to achieve the best results. The model incorporates voltage profiles and power losses for AS dispatching, and a comparative analysis is conducted to quantify the significance of grid modeling. The model and proposed procedure are tested using the CIGRE microgrid benchmark model. The results indicate that detailed modeling of MG can impact the results by 11%, compared to single-bus modeling, which qualifies detailed MG modeling for all future research work and shows the impact that modeling can have on technical and economic indicators of MG operation. [ABSTRACT FROM AUTHOR]

The deployment of DC microgrids presents an excellent opportunity to enhance energy efficiency in buildings. Among other components, DC-DC converters play a crucial role in ensuring the interface between the microgrid and its energy generation, storage, and consumption components. However, the reliability of these energy conversion solutions remains somewhat limited. Adopting strategies for accurate monitoring and diagnostics of the DC-DC converter topologies that best suit each equipment's constraints is, therefore, of critical relevance. Solutions available in the literature concerning fault diagnostics on DC-DC converters do not consider the application of such converters in the household and tertiary sector environments and associated constraints—cost effectiveness, robustness against parameter uncertainty of the converter model, and obviation of the need for historical data. On this basis, this paper presents a simple and effective fault diagnostic strategy, based on a time-domain analysis of the second-order derivative of the converter input current. Its implementation is straightforward and can be integrated into the pre-installed converter control unit. The unique features of the fault diagnostic algorithm show good results for a broad range of operating points, along with insensitivity against load transients and supply voltage fluctuations. [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]

In this study, we address the challenge of detecting and mitigating cyber attacks in the distributed cooperative control of islanded AC microgrids, with a particular focus on detecting False Data Injection Attacks (FDIAs), a significant threat to the Smart Grid (SG). The SG integrates traditional power systems with communication networks, creating a complex system with numerous vulnerable links, making it a prime target for cyber attacks. These attacks can lead to the disclosure of private data, control network failures, and even blackouts. Unlike machine learning-based approaches that require extensive datasets and mathematical models dependent on accurate system modeling, our method is free from such dependencies. To enhance the microgrid's resilience against these threats, we propose a resilient control algorithm by introducing a novel trustworthiness parameter into the traditional cooperative control algorithm. Our method evaluates the trustworthiness of distributed energy resources (DERs) based on their voltage measurements and exchanged information, using Kullback-Leibler (KL) divergence to dynamically adjust control actions. We validated our approach through simulations on both the IEEE-34 bus feeder system with eight DERs and a larger microgrid with twenty-two DERs. The results demonstrated a detection accuracy of around 100%, with millisecond range mitigation time, ensuring rapid system recovery. Additionally, our method improved system stability by up to almost 100% under attack scenarios, showcasing its effectiveness in promptly detecting attacks and maintaining system resilience. These findings highlight the potential of our approach to enhance the security and stability of microgrid systems in the face of cyber threats. [ABSTRACT FROM AUTHOR]

The increasing demand for reliable and sustainable electricity has driven the development of microgrids (MGs) as a solution for decentralized energy distribution. This study reviews advancements in MG planning and optimization for renewable energy integration, using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses methodology to analyze peer-reviewed articles from 2013 to 2024. The key findings highlight the integration of emerging technologies, like artificial intelligence, the Internet of Things, and advanced energy storage systems, which enhance MG efficiency, reliability, and resilience. Advanced modeling and simulation techniques, such as stochastic optimization and genetic algorithms, are crucial for managing renewable energy variability. Lithium-ion and redox flow battery innovations improve energy density, safety, and recyclability. Real-time simulations, hardware-in-the-loop testing, and dynamic power electronic converters boost operational efficiency and stability. AI and machine learning optimize real-time MG operations, enhancing predictive analysis and fault tolerance. Despite these advancements, challenges remain, including integrating new technologies, improving simulation accuracy, enhancing energy storage sustainability, ensuring system resilience, and conducting comprehensive economic assessments. Further research and innovation are needed to realize MGs' potential in global energy sustainability fully. [ABSTRACT FROM AUTHOR]