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The most advanced DC shipboard power systems will be based on the wide deployment of controlled converters in a zonal distribution configuration. On one side the power electronics arrangement enables advantages for the onboard functioning (e.g. flexibility, redundancy and adaptability), on the other it can open worrying issues on DC system stability. When perturbing a negative-damped DC power grid, the incoming instability triggers a ship blackout as a consequence. To avoid this catastrophic eventuality, the Power Management System (PMS) is programmed for the smart real-time adaptation of converters bandwidths. The paper presents a Preventive-Resilient (P-R) bandwidths tuning to guarantee the stable functioning, even when assuming the (n−1) eventuality, so the unexpected disconnection of the more stabilizing load. This reconfiguration is an effective measure to constantly ensure a sufficient stability margin, even when low-bandwidth loads are offline. The capability of P-R algorithm is evaluated by a C-HIL test on a digital real-time platform. A Python code acts as a PMS in configuring the bandwidths on emulated converters via the Modbus communication protocol. The combination of a simplified stability assessment method with an optimization algorithm enables a fast bandwidth reconfiguration process without requiring excessive computational effort. These characteristics make the proposed approach suitable for integration into the shipboard Power Management System (PMS), providing central controllers with an online stability assessment and safe load management tool.

The output fluctuation of the high proportion of photovoltaic new energy requires introducing energy storage units for compensation and adjustment, but the voltage stability performance of energy storage port converters under complex working conditions is often not effectively guaranteed. Therefore, this paper proposes an active disturbance rejection voltage stabilization strategy considering observation state correction for energy storage port converters. By deriving the high-precision paradigm of observation deviation, the important structural factors of observation deviation in the system are pointed out, and the ignored high-order terms of deviation are compensated into the observer to achieve accurate and rapid convergence of tracking deviation. Furthermore, through the theoretical analysis of the strategy in four dimensions of stability performance, tracking performance, anti-disturbance performance, and robust performance, the reasons why the strategy can effectively improve the anti-disturbance and robustness of the converter are explained. Finally, the simulation and experimental results jointly demonstrate that the strategy performs better anti-disturbance and robust-ness performance than traditional active disturbance rejection control under various complex working conditions of microgrids, which is of great significance for improving the stability of microgrid operation.

The integration of inverter-interfaced sources into microgrids has led to new protection challenges due to fluctuating fault currents, bidirectional power flow, and low inertia. Traditional protection methods are inadequate for these evolving systems, necessitating the development of novel, fast-acting, and cost-effective strategies that are independent of fault current levels. Machine learning-based protection schemes offer potential for improved performance over traditional methods, but their success is highly dependent on the quality and nature of the input data. Raw fault signal data are often noisy and high-dimensional. Processing it without feature extraction or selection can result in poor performance, longer processing times, higher storage needs, overfitting, and reduced generalizability. The dimensionality reduction step, which encompasses feature extraction and/or feature selection algorithms, is crucial for machine learning-based protection schemes. Many such methods have been proposed in the last decade. This research aims to investigate available techniques for reducing the dimensionality of data used for fault diagnosis in AC microgrids. The research will review and compare different dimensionality reduction methods, highlighting their strengths and weaknesses. It will also explore the most used features and their effectiveness in different scenarios. This article highlights the key gaps in AC microgrid fault diagnosis, which include scalability, real-time performance, and generalization, among many others. It emphasizes that future research should focus on unexplored dimension reduction techniques, hybrid approaches, and improving deep learning interpretability for robust and accurate solutions.

A microgrid is a free-standing electrical network consisting of several energy sources and loads meant for specific applications or localized areas of operation. Power electronic converters are used in a microgrid for interfacing electrical sources and electrical loads of different power and voltage ratings with the standard bus bars of the microgrid. Microgrids use renewable energy sources like the wind and solar photo-voltaic energy source, and the fuel cell-based energy conversion systems. DC to DC converters is inevitable in a microgrid. The Quadratic Boost Converter (QBC) is a high voltage gain DC to DC converter with a single power electronic switch. In this work, the applicability of the Sliding Mode Controller (SMC) for the maximized harvest of energy from a solar photovoltaic system using the QBC is studied. Further, the capability of the QBC, along with an SMC, for the regulation of voltage across the terminals of the load, when the QBC acts as an interface between a load and the bus bar of the microgrid is also investigated. Relevant dynamic equations are derived from fundamental principles. The proposed model is simulated in the MATLAB/Simulink environment and experimentally verified.

The availability and cost of fossil fuels, natural disasters, aging infrastructure, climate change, and rising electricity consumption have affected today’s power grids. One of the most practical solutions for achieving green and reliable energy is the use of microgrids. The stability of microgrids dominated by electronic converters presents several challenges. Among the problems encountered are the absence of physical inertia, delay in detecting islanding, and loss of stability associated with the transition between operating modes and variations of the load power. To overcome these challenges, this study presents a new robust control strategy based on active disturbance rejection control (ADRC). It is suitable for both islanded and connected operation modes with a single control, without an islanding detection algorithm or Phase-Locked Loop (PLL). The effectiveness of the control strategy is demonstrated through simulations and a comparative analysis with conventional droop control. Flexibility of the transition is also ensured. The proposed control strategy is successfully validated using a TI C2000 DSP TMS320F28335 microcontroller.

This study focuses on detecting Power Quality Disturbance Events (PQDE) in microgrids integrated with a Solar Energy Conversion System (SECS). The research proposes a novel signal reduction technique called Clark Transformed Modal, which reduces three-phase voltage to a single unit signal, optimizing memory utilization and reducing computational load during feature extraction. A total of 16 features are extracted from the proposed modal signal by performing multi-resolution analysis through Maximum Overlap Discrete Wavelet Transform (MODWT). Various disturbances, including sag, swell, transients, notches, and flicker, are intentionally simulated in a PV-grid tied MATLAB/Simulink model to obtain a dataset of 10800 samples. Further, the dataset is randomly divided into training-testing subsets to verify the classification ability of a novel ensemble classifier called subspace weighted k-nearest Neighbor (SWKNN). In addition to that the optimum mother wavelet (dmay) is identified to even further boost the classifier performance. The results demonstrate the superior classification capabilities of the proposed MODWT-SWKNN classifier in terms of various performance metrics like precision, recall and F1-score. It also outperformed when compared with several competitive PQ classification models based on PV-integrated systems both under ideal and noisy conditions. Additionally, the disturbance detection system is validated in an OPAL-RT real-time environment to demonstrate its efficiency in terms of detection time. The accuracy of detection is found to be 99.74% in ideal case and fall back to no more than 3% regulation i.e., 97.28% even in dense noise of 20dB with as low as 8 WKNN subspaces. Further, average detection time with 500 trails is found to be 0.0285 seconds. The efficacy of the proposed PQ detection algorithm is also tested in a large PV integrated IEEE 13-bus system.

This paper proposes the mixed sensitivity constraint H $\infty $ based virtual inertia controller (VIC) design for load frequency control (LFC) of islanded AC microgrid (MG). Owing to the increasing integration of renewable energy sources (RESs) in the MG, the power electronic converters required to interface RESs into grid, does not support and maintain required inertia for MG reliable operation. So, system frequency deviates from its nominal value. Virtual synchronous generator (VSG) concept has been introduced to support system inertia in the inverter-based MG. This work introduces the robust control technique based on H $\infty $ approach for design of VIC for the frequency regulation of MG. The mixed-sensitivity concept is utilized for H $\infty $ control-based VIC design. The proposed design aims to strike a balance between achieving high performance in terms of control objectives, such as frequency regulation, while ensuring robust stability in the face of uncertainties and disturbances. The effectiveness and robustness of the suggested VIC for frequency control are assessed under communication time delay, step load demand, parametric uncertainty, and various generation scenarios. Further, the efficacy of the suggested control design is verified in comparison to the existing control approach.

In a MicroGrid (MG) equipped with a Battery Energy Storage System (BESS), an Energy Management System (EMS) plays a crucial role in predictive controlling BESS operations for optimal power flow among uncertainties from renewable energy resources and heavy loads, such as solar photovoltaic systems and electric vehicles, respectively. State-of-the-art EMS designs have integrated Deep Reinforcement Learning (DRL) for EMS development and Probabilistic Power Flow (PPF) for preventing the violation of power system constraints while accounting for all uncertainties. However, using PPF to handle uncertainties alongside training a single-agent DRL provides the optimal solution for addressing all uncertain scenarios, but not the best solution for each scenario. Moreover, employing a single-agent DRL yields a low performance in predictive controlling of BESS operations. To address these challenges, a multi-agent DRL based on Deep Deterministic Policy Gradient (DDPG) is proposed. This method divides the roles of each agent for predicting 24-hour-ahead actions in BESS control based on changing 24-hour-ahead MG behavior every hour. Furthermore, MG parameters are randomly sampled to retain MG uncertainties instead of relying on PPF for uncertainty mitigation. Consequently, multi-agent DDPG with random sampling can directly learn from the MG environment and provide the best solution for each scenario. Simulation results demonstrate that the proposed method can reduce training computational time by 92.84%, provide a higher value of the summed mean of 24-hours-ahead reward by 1.50% to 28.37%, and achieve a lower mean daily total related cost by 9.22% compared to applying the state-of-the-art method.

This study presents a comprehensive bibliometric analysis of grid-connected microgrid (MG) scheduling controller techniques. An extensive search was done in the Scopus database using preset parameters to extract articles relating to the MG scheduling controller. The selection of the most cited paper involved careful keyword filtering on grid-connected MG scheduling controllers over the period from 2013 to 2024. Within the timeframe, a total of 115 top-cited articles were extracted, focusing on the scheduling controller algorithms applied to the grid-connected MG system. These highly cited articles originated from a diverse source, encompassing 49 distinct journals, spanning 28 different regions, and representing the publications of 7 distinct publishers. This paper seeks to identify and analyze the highly referenced published articles in the relevant area to yield an in-depth analysis of advanced controllers and optimization strategies in MG energy management systems. The key challenges such as power electronic interface, quality, controller, safety and optimization were also highlighted to provide the clearest insight on the recent MG development. Valuable recommendations for future research directions are also provided, aimed at promoting the sustainable growth of MGs. A substantial total of 63.56% articles were published based on simulation while 18.6%, 13.95% and 3.87% of total articles were published on the experimental setup, critical analysis and review-based study. Therefore, it can be inferred that ongoing research and development efforts continually seek to improve the sustainability of MG systems within the electric power sector. The bibliometric analysis was employed to identify pivotal research publications concerning grid-connected MG scheduling controller technique. This analysis aimed to delineate the multidisciplinary nature, illustrate trends, and outline areas warranting further research in the field. Thus, to ensure an effective, economical, reliable, and sustainable power supply, this analysis will broaden the scope and offer context for the development of MG scheduling controller integrated grid systems.

Microgrids are fundamental elements in modern energy systems. Among the various microgrid components, the Energy Storage System (ESS) plays a pivotal role in ensuring system reliability, but its high cost and inevitable degradation over time pose significant challenges. Many current studies overlook the impact of ESS degradation on operational optimization, potentially leading to cost-ineffective systems. To address this gap, we introduce a quadratic ESS degradation model that captures intricate battery dynamics, such as State of Charge (SoC) and Depth of Discharge (DoD), using Markovian properties. Based on this model, we propose an optimal energy management framework for DC microgrids using Quadratic Programming (QP). The objective is to minimize the combined costs of degradation and electricity, considering the Time-of-Use (ToU) tariff while adhering to ESS constraints. This financially focused approach provides a pragmatic and economically aligned optimization strategy. Testing across various State of Health (SoH) scenarios demonstrates that our proposed model reduces total operational costs by 3-18%. This research advances microgrid optimization techniques and offers practical insights to enhance efficiency and economic resilience in real-world scenarios.

Electronic power converters are extensively applied in microgrids to interface distributed energy resources to the grid. Besides the primary active-power control, electronic converters support increasingly advanced and flexible control capabilities. While the former is typically bound to local needs (e.g., maximum power extraction from renewables), additional degrees of freedom can be exploited to support microgrid operation. This article proposes a control algorithm that allows the optimal provision of reactive power, negative-sequence, and zero-sequence currents by distributed converters. The resulting operation shows enhanced power quality at the point of common coupling (PCC) and limited conversion losses, which are taken into account in the optimization algorithm. Three modes of operation are discussed, that is, i) power loss minimization; ii) balanced currents at the PCC; iii) zero reactive power flow at the PCC. An algorithm based on the primal-dual method is proposed to solve the optimization problem. Results based on experimental measurements are discussed to prove the effectiveness of the proposal.

Diesel generators (gensets) are often the lowest-cost electric generation for reliable supply in remote microgrids. The development of converter-dominated diesel-backed microgrids requires accurate dynamic modeling to ensure power quality and system stability. Dynamic response derived using original genset system models often does not match those observed in field experiments. This paper presents the experimental system identification of a frequency dynamics model for a 400 kVA diesel genset. The genset is perturbed via active power load changes and a linearized dynamics model is fit based on power and frequency measurements using moving horizon estimation (MHE). The method is first simulated using a detailed genset model developed in MATLAB/Simulink. The simulation model is then validated against the frequency response obtained from a real 400 kVA genset system at the Power System Integration (PSI) Lab at the University of Alaska Fairbanks (UAF). The simulation and experimental results had model errors of 3.17% and 11.65%, respectively. The resulting genset model can then be used in microgrid frequency dynamic studies, such as for the integration of renewable energy sources.

This study proposes a novel approach to address the challenges posed by the increasing number of renewable sources in power grids, particularly focusing on networked microgrids. The proposed method integrates digital twin technology and blockchain technology to provide power support in networked microgrids. The method comprises three main components: 1) Blockchain Network: A blockchain network is established, utilizing a modified proof of stake (PoS) consensus mechanism. This network is responsible for maintaining hourly records and tracking system changes, including faults. Fault events are recorded in real-time, and a bidding mechanism is employed to determine which microgrids interconnect and provide power support to mitigate power imbalances. 2) Digital Twins: Digital twins are created for each distributed generation and energy storage unit within the microgrids. These digital twins play a crucial role in the proposed PoS consensus mechanism, providing an additional layer of validation for transaction records. 3) Long short-term memory (LSTM)-based Controller: An LSTM-based controller is implemented to ensure stable operation of the microgrids, even during fault events and interconnections with other microgrids. The controller is trained to maintain stable voltage levels within the system. The digital twins and LSTM-based controllers are optimized using Bayesian Optimization to determine the best parameters and hyperparameters. Simulation results demonstrate that the proposed method effectively records hourly system details and fault events. Additionally, power hardware-in-the-loop simulations confirm that the LSTM-based controller can maintain stable voltage levels and outperforms other controllers in terms of performance.

Microgrids driven by distributed energy resources are gaining prominence as decentralized power systems offering advantages in energy sustainability and resilience. However, optimizing microgrid operation faces challenges from the intermittent nature of renewable sources, dynamic energy demand, and varying grid electricity prices. This paper presents an AI-driven day-ahead optimal scheduling approach for a grid-connected AC microgrid with a solar panel and a battery energy storage system. Genetic Algorithm generates demand response strategies and optimizes battery dispatch, while LightGBM forecasts solar power generation and building load consumption. The approach aims to minimize operational costs and ensure microgrid sustainability, using a battery degradation cost function to extend its lifespan. Simulation results conducted in the University of Moratuwa microgrid show a significant 14.22% decrease in electricity costs under Sri Lanka’s current tariff structure, attributed to intelligent energy dispatch scheduling. Proactive demand response management has the potential to minimize costs further. This research contributes to microgrid optimization knowledge, promoting the adoption of intelligent and sustainable energy systems.

Navigating the complex terrain of microgrid energy management is challenging due to the uncertainties linked with abundant renewable resources, fluctuating demand, and a wide range of devices including batteries, distributed energy sources, electric vehicles, and compensatory devices. This paper presents an advanced two-stage robust day-ahead optimization model designed specifically for MG operations. The model primarily addresses challenges arising from the integration of power electronics-based generation units, the unpredictable nature of demand in microgrids, and the integration of small-scale renewable energy sources. The proposed model includes detailed formulations for MG energy management, covering optimal battery usage, efficient EV energy management, compensator usage, and strategic dispatching of DG resources. The multi-objective function aims to minimize various costs related to energy losses, power purchases, load curtailment, DG operation, and battery/EV expenses over a 24-hour period. To efficiently solve this optimization problem, the C&CG algorithm is utilized. Numerical simulations on a test system validate the effectiveness of the proposed model and solution algorithm, showing a significant reduction in the operating costs of the microgrid. This approach offers a robust framework to enhance the resilience and efficiency of microgrid energy management. The results conclusively demonstrate that the proposed approach surpasses comparable methods by at least 5%, highlighting its effectiveness in improving key indicators within the microgrid system.

Proton exchange membrane electrolyzers (PEMEL) installed with renewable energy resources can be used for power system ancillary services such as frequency regulation and virtual inertia emulation. However, the performance of PEMEL-based frequency ancillary services is threatened by cyberattacks that compromise the communication control. Considering denial-of-service (DoS) attacks on PEMEL communication, this paper proposes an enhanced resilient model predictive control (ER-MPC) for a PEMEL controller. The proposed ER-MPC consists of two procedures. First, the combination of autoregressive (AR) model-based prediction method and hold signal method is used to reconstruct attacked signals during severe DoS attacks. Then a model predictive control (MPC) is used to compute the control signal for PEMEL stack. The objective of ER-MPC-based controlling of PEMEL is to regulate the frequency deviation during contingency and normal operation under severe DoS attack. The effectiveness of the proposed ER-MPC was compared with that of AR-based resilient MPC and resilient MPC methods. The simulation results revealed that the proposed ER-MPC successfully improved microgrid frequency regulations under severe DoS attacks. In addition, the proposed ER-MPC-based PEMEL has a performance effect over other techniques in terms of the reduction in frequency deviation and the rate of change of frequency during severe DoS attacks, disconnection, and successful connection of wind turbine generation.

This paper presents a systematic review of microgrid interoperability focusing on energy access. Drawing upon 59 studies and reports, it delves into interoperability issues and technologies across various microgrid applications. This study aims to provide a synthesized overview of the current discourse on microgrid interoperability, particularly contextualized within the realm of energy access. This objective is accomplished through a process that involves clarifying terminologies, exploring potential interoperability issues in microgrids, identifying the technologies for interoperability, and examining promising pathways to achieve interoperability. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method was used in this study. Through data extraction and content analysis, the review found that studies on interoperability in the energy field primarily focus on the smart grid topic, where information and communication technologies are regarded as key elements to facilitate interoperability. There is less emphasis on physical or electrical interoperability, with the literature primarily focusing on interoperability in the communication domain. Furthermore, interoperability in the energy access context is uncommon, as indicated by a lack of literature in remote, rural, or community settings. Adopting common industry standards is one of the strategies for ensuring interoperability, enabling microgrid systems to function effectively and reliably. This paper contributes to describing research insights, identifying gaps in knowledge, and proposing future research directions regarding microgrid interoperability.

A distributed control of a Microgrid (MG) depends on the communication network for the exchange of information among the Distributed Generators (DGs). Most of the control schemes consider ideal communication among the DGs; however, in practical systems, delays are inherited in a system that can affect the dynamic performance and even destabilize an MG. Therefore, in this research work, a distributed Secondary Controller (SC) for an islanded MG is designed considering Communication Time Delays (CTDs). Sufficient delay-independent conditions for the robust stability of a buffer-free distributed averaging proportional integral control scheme are proposed. The CTDs are analyzed using the Lyapunov Krasovskii approach. A proposed method synchronizes the frequencies and voltages of the DGs to their respective references while confirming proportionate power sharing in the presence of CTDs. Moreover, different adaptive controllers are designed to update the fixed SC gain parameters to enhance the system’s performance. Finally, the efficacy of the controller is demonstrated through simulation studies that validate its performance against load variation and plug-and-play functionality.

The increase in renewable energy resources penetration into microgrids weakens the inertia of microgrids and negatively affects their frequency stability. This reduction in inertia reduces the microgrid’s ability to handle disturbances and increases frequency instability risk. The purpose of this paper is to present a virtual inertia (VI) control strategy for an island microgrid with the use of an energy storage system (ESS) to improve inertia and frequency stability of the system. The VI controller parameters are optimized using the differential evolution (DE) algorithm. The proposed VI control strategy can effectively improve microgrid frequency stability and reduce the risk of frequency instability. The results demonstrate that the proposed strategy is effective and can provide fast and reliable virtual inertia support for the microgrid. The proposed VI control approach is verified through simulation results and performs better than the conventional inertial response for frequency stability in islanded microgrids. Virtual inertia control injects/absorbs active power into/from the microgrid during frequency deviations. Different disturbances are applied to demonstrate uncertain load and RES generation behavior. The proposed VI control method is compared with the available literature. In the proposed method, the frequency deviation is better controlled. Furthermore, the results demonstrate a more stable and efficient system operation with the developed VI controller than the existing methods.

Vehicle-sharing services are now integrating electric vehicles (EVs) into their sharing systems. Electric vehicle sharing (EVS) is a convenient and cost-effective approach to reducing the use of internal combustion engine vehicles and improving traffic flow. However, the randomness in the charging load transfer eventually affects the reliability of a distribution network that contains microgrids. This study analyzes the spatiotemporal transfer characteristics of the EVS charging load in a distribution network that contains microgrids in different geographical areas. Combined with the improved charging load model, a reliability evaluation model of the charging load transfer is developed based on the Gauss-Markov movement model, which describes the spatiotemporal transfer characteristics of a distribution network with high accuracy. Moreover, the sequential Monte Carlo method for evaluating the reliability of a distribution network that contains microgrids is improved so that considers charging load transfer. Different scenarios, i.e., randomness of charging load transfer, number of EVS, charging locations of the main feeder lines in the microgrid, and the configuration of the wind-photovoltaic-storage power generation capacity are considered in evaluating the impact of charging load transfer on a distribution network that contains microgrids. The results could serve as a basis for integrating EVS into vehicle-sharing networks considering efficient charging load transfer in distribution network that contains microgrids.

Due to prevalence of distributed energy resources, especially inverter interfaced ones, conventional protection systems meet substantial challenges for fault detection. Differential protection is the most reliable protection method for current networks; however, the conventional method leads to high communication burden. We propose a new differential protection based on disturbance detection and positive phase angle differences. The disturbance detection method is proposed based on Mathematical Morphology which has high speed and accuracy. Instead of transmitting the instantaneous values or the phasor of the currents which impose high communication burden on the network, the phase angle of the current is the only data which is transmitted when a sudden change is detected. The proposed method reduces the communication burden significantly and increases the resiliency of the protection scheme. The effectiveness of the proposed method is verified in modified IEEE 9 bus 3 machine network with three distributed energy resources. PSCAD is used to simulate the microgrid and the proposed method is implemented in MATLAB. It is proved that this method works properly in both islanded and grid connected modes and despite significant changes in the grid. The robustness of method in presence of noise and its high reliability and dependability is verified through various simulation case studies.

An auction-based single-sided bidding energy transaction mechanism in a grid-connected microgrid (MG) using a multi-agent system allows for better profit sharing for all stakeholders. This can replace existing bilateral contractual trade between the stakeholders. In the bilateral contractual energy trade model, energy transactions are either long-term, medium-term, or short-term agreements or bilateral negotiations between the stakeholders based on physical limits. Whereas in an auction-based mechanism, the energy transaction is in real time based on bidding strategies and supply-demand mismatches among the stakeholders. This work proposes a single-sided auction mechanism (SSAM) to clear the market based on the asking price of the seller and the supply-demand mismatch of the microgrid. In addition, the new two bidding algorithms, namely the linear bidding algorithm (LBA) and the fuzzy logic-based bidding algorithm (FLBA), are developed for sellers to select the ‘ask’ quotes. The proposed auction-based, single-sided bidding energy transaction mechanism is tested and validated in the existing Malnad College of Engineering (MCE) grid-tied MG (bilateral contractual) trading model, Hassan-573201, Karnataka, India. The energy market simulation results yield promising findings, highlighting the advancement of proposed SSAM and bidding strategies in boosting the profit margin of sellers.

In this paper, an energy management system (EMS) using intelligent Lyapunov based adaptive fuzzy controller is designed for standalone microgrid having photovoltaic and wind turbines as primary sources, while battery storage system and diesel generator are available as secondary sources. Moreover, any remnant power available in the system is guided by the controller towards the dump load. The centralized controller based on intelligent adaptive fuzzy system helps to optimize the power flow in the system during high and low penetration of renewable energy resources under variable load conditions. Additionally, voltage regulation is carried out using nonlinear Lyapunov controller which kept the output voltages in desired range. The proposed control system helps in increasing the efficiency and reliability of standalone microgrid. Furthermore, the proposed system is validated by comparing the simulation and hardware results with the PID and sliding mode controller, which are consider as benchmark controller techniques for standalone microgrid. The outcome of the system validates the superiority of proposed techniques and achieved global asymptotic stability under variable load conditions.

A novel strategy is proposed to tackle an optimal dispatch of a microgrid in response to dynamic conditions, utilizing a water wave optimization (WWO) algorithm and considering a day-ahead load forecasting. Amongst meta-heuristic algorithms, the WWO algorithm stands out in terms of population size, parameter tuning, exploitation and exploration, convergence speed, as well as optimization mechanism. It leverages its ability to efficiently explore solution spaces and adapt to changing conditions. It is applied to the dynamic optimal dispatch of a microgrid with the uncertainty of load power considered and solved by day-ahead load forecasting. It dynamically adjusts the microgrid operation in response to these inputs, ensuring optimal decision-making in the face of varying load scenarios. With the competition of various day-ahead load forecasting techniques in the microgrid, a multi-variate linear regression (MLR) model shows its advantage features, being more transparent, more effective, and more robust than other techniques, especially transparent explainability, as well as simple and fast in model training. These are requirements to achieve the result of day-ahead load forecasting. Thus, the MLR model is proposed to forecast day-ahead load in the microgrid in this paper. The simulation results show that the percentage error (PE) between the MLR model-based forecasted and actual load powers is always less than 4.42%, the mean absolute percentage error (MAPE) of the forecasting result is 3.33%, and the execution time is 49 (s). These achievements meet the accurate and fast requirements. They are completely competitive with the results of using other techniques such as convolutional neural networks (CNN) and long short-term memory (LSTM), especially in the execution time. This has contributed to improving the efficiency of the dynamic optimal dispatch in the microgrid. Then, the diesel generation, battery energy storage, and total microgrid generation costs are 68.76 ( ${\$}$ ), 5.09 ( ${\$}$ ), and 73.85 ( ${\$}$ ) respectively by using the WWO algorithm which are better than those by using a genetic algorithm (GA), a non-dominated sorting genetic algorithm-II (NSGA-II), a particle swarm optimization (PSO) algorithm, and a transient search optimization (TSO) algorithm in the microgrid. The findings offer valuable insights for microgrid operators, energy planners, and policymakers seeking sustainable and cost-effective solutions for distributed energy resource management.

A microgrid (MG) may lose its grounding provided by the main distribution grid in islanded mode, which could cause equipment insulation damage, hazards to personnel, and protection malfunction. Existing MG grounding schemes include the grounding transformer-based scheme and distributed energy resource (DER) transformer-based scheme. However, the grounding transformer-based scheme will increase MG’s cost, and the DER transformer approach will affect the main grid in the grid-connected mode. Moreover, future MGs may have multiple source locations. In each source location, the source and critical load can potentially operate as a sub-MG, requiring a grounding when it stands alone. In this scenario, the drawbacks of existing grounding schemes will be further magnified. In this paper, a novel controllable DER transformer-based grounding scheme is proposed, where a controllable switch is added to the neutral wire of the transformer. The proposed scheme can disable grounding capability in the grid-connected mode and enable it in the islanded mode by changing the transformer connection. The proposed approach can avoid impacts on the main distribution grid and eliminate the need for additional transformers. The design methodology of the proposed grounding scheme is provided. Simulation verification is conducted on a realistic MG model and experimental verification is conducted.

The concept of utilizing microgrids (MGs) to convert buildings into prosumers is gaining massive popularity because of its economic and environmental benefits. These pro-sumer buildings consist of renewable energy sources and usually install battery energy storage systems (BESSs) to deal with the uncertain nature of renewable energy sources. However, because of the high capital investment of BESS and the limitation of available energy, there is a need for an effective energy management strategy for prosumer buildings that maximizes the profit of building owner and increases the operating life span of BESS. In this regard, this paper proposes an improved energy management strategy (IEMS) for the prosumer building to minimize the operating cost of MG and degradation factor of BESS. Moreover, to estimate the practical operating life span of BESS, this paper utilizes a non-linear battery degradation model. In addition, a flexible load shifting (FLS) scheme is also developed and integrated into the proposed strategy to further improve its performance. The proposed strategy is tested for the real-time annual data of a grid-tied solar photovoltaic (PV) and BESS-powered AC-DC hybrid MG installed at a commercial building. Moreover, the scenario reduction technique is used to handle the uncertainty associated with generation and load demand. To validate the performance of the proposed strategy, the results of IEMS are compared with the well-established energy management strategies. The simulation results verify that the proposed strategy substantially increases the profit of the building owner and operating life span of BESS. Moreover, FLS enhances the performance of IEMS by further improving the financial profit of MG owner and the life span of BESS, thus making the operation of prosumer building more economical and efficient.

In rural territories, the communities use energy sources based on fossil fuels to supply themselves with electricity, which may address two main problems: greenhouse gas emissions and high fuel prices. Hence, there is an opportunity to include renewable resources in the energy mix. This paper develops an optimization model to determine the optimal sizing, the total annual investment cost in renewable generation, and other operating costs of the components of a hybrid microgrid. By running a k-means clustering algorithm on a meteorological dataset of the community under study, the hourly representative values become input parameters in the proposed optimization model. The method for the optimal design of hybrid microgrid is analyzed in six operating scenarios considering: (1) 24-hour continuous power supply; (2) load shedding percentage; (3) disel power generator (genset) curtailment; (4) the worst meteorological conditions; (5) the use of renewable energy sources including battery energy storage systems (BESSs); and (6) the use of genset. A mathematical programming language (AMPL) tool is used to find solutions of the proposed optimization model. Results show that the total costs of microgrid in the scenarios that cover 100% of the load demand (without considering the scenario with 100% renewables) increase by over 16% compared with the scenario with genset operation limitation. For the designs with power supply restrictions, the total cost of microgrid in the scenario with load shedding is reduced by over 27% compared with that without load shedding.

Benefits accrued by virtue of the presence of microgrids have led to their increased deployment beyond their original objective of supplying power to the remote communities. However, in order to achieve a zero emission energy sector, the challenge is to design a carbon-neutral microgrid. This paper presents a novel, optimal design for a decarbonized microgrid taking into consideration the concept of sector-coupling, by integrating the electric, heat/thermal, hydrogen and transport sectors. The microgrid also includes wind facilities, solar PV panels, green hydrogen system (fuel cells, electrolyzers, storage tanks), Fuel Cell Electric Vehicles (FCEVs) and Battery Energy Storage Systems (BESSs). The real isolated microgrid of Kasabonika Lake First Nation (KLFN) in northern Ontario, Canada, is considered for the design studies and to evaluate the techno-economic feasibility. The effect of (US) Inflation Reduction Act of 2022 (IRA2022) is examined. Results demonstrate the practicability and techno-economic merits of the proposed Decarbonized Sector-coupled Microgrid (DCSCMG). The proposed DCSCMG is compared to the existing diesel-based KLFN microgrid on economic metrics, levelized Cost of Energy (COE) and emissions. Further, the advantages offered by inclusion of BESSs and/or sector-coupling are investigated in the context of net-zero.

This paper presents a dual-layer approach for managing and controlling an AC Microgrid (MG). The MG integrates a Photovoltaic System (PVS), Wind Turbine System (WTS), a Battery Storage System (BSS), all interconnected with the utility grid. The dual-layer is structured into a Control Layer (CL) and an Energy Management System Layer (EMS-L). The CL proposes an efficient model coupled with a system control, ensuring high power quality for the AC microgrid. This system employs a Particle Swarm Optimization (PSO) algorithm to optimize the set point tracking performance. Simultaneously, the EMS-L utilizes a Sparrow Search Algorithm (SSA) with the objective of minimizing the total operating costs of the AC microgrid. This is achieved by employing a 15-minutes step time over a 24-hours period, enhancing both precision and rapidity of the system’s operation. This enhanced temporal resolution effectively models and responds to fluctuations in energy demand, supply variability and environmental factors. This synergistic integration allows for nuanced and efficient energy flow management in order to optimise the MG performance. A significant aspect of the research is the comparative analysis of the proposed SSA-EMS with PSO and Genetic Algorithm (GA), alongside an unoptimized system. This analysis highlights the hybrid methodology’s superior efficiency and effectiveness. The robust framework, combining SSA at the EMS layer with PSO at the control layer, has been empirically tested using a real world data to confirm its effectiveness and resilience in practical scenarios. These tests provide solid evidence of the methodology’s potential in boosting the sustainable and reliable operation of microgrids. Significantly, the SSA-EMS showcases notable cost efficiency, achieving reductions of 33.34% and 41.18% compared to PSO and GA. Impressively, compared to an unoptimized system, the SSA-EMS demonstrates an even more remarkable cost reduction of 49.43% and 59.10% while considering the impact on battery health constraints.

This work tackles the scheduling challenge of microgrids for smart homes, aiming to optimize energy management with both renewable and non-renewable sources. A power control center orchestrates the microgrid, coordinating distributed energy resources (DERs) for peak demand fulfillment and excess energy utilization. We propose a proportional-integral control system for efficient demand response, achieving reduced post-scheduling costs and a peak-to-average ratio. Comparative analysis reveals Ant Colony Optimization outperforms Binary Particle Swarm Optimization in cost and peak-to-average ratio reduction. Simulations explore two scenarios: Case 1 integrates with the main grid for reliability, while Case 2 utilizes solely renewable energy sources. Although Case 2 exhibits superior performance, Case 1’s dependence on the main grid offers greater real-world feasibility. Therefore, Case 1 with optimized DER scheduling emerges as the recommended solution for enhancing microgrid efficiency and ensuring reliable power supply in smart homes.

Designing a properly coordinated protection scheme for a multi-microgrid is very challenging because of their distinct characteristics. A microgrid can be operated in a grid-connected mode through direct connection to utility or through another grid-connected microgrid. It can also operate in an islanded mode or get connected with another independent microgrid. The various operation modes and topologies under which the system may operate bring major challenges such as the bi-directional flow of power and substantial variation in fault current. Such characteristics make protection schemes of conventional radial distribution systems unreliable options. In this paper, a centralized communication-assisted protection scheme based on artificial neural networks is proposed. The scheme operates in a cascaded process. In the first stage, a central protection controller is responsible of identifying and isolating the microgrid or tie line which has the fault. In the second stage, the local protection controller will be activated due to detection of islanding condition. It will then identify and isolate the faulty line accordingly. Identification of fault location is accomplished through neural networks trained with massive amount of three phase voltage and current measurements of all buses and lines during different fault scenarios using MATLAB/Simulink environment. The proposed scheme utilizes IEC 61850 based standardized communication to monitor the multi-microgrid and send the trip commands. Finally, the performance evaluation of the proposed scheme in terms of end-to-end delays including neural networks computational delay and communication network delay through extensive simulations is also presented which proves the effectiveness of the proposed protection scheme.

As photovoltaic (PV) systems have been successfully adopted worldwide, accurate power generation forecasting becomes increasingly essential to stable power grid operation and smart grid applications to cope with the variability of PV systems. Several data-driven models have recently been proposed for the more accurate prediction of PV power generation and have shown good performance. In particular, hybrid models that combine the characteristics of single-structure deep learning-based models have achieved better accuracies. To this end, a novel ultra-short term PV power generation forecasting model with a hybrid structure is proposed for instantaneous response to PV fluctuations. For higher forecasting accuracy, the proposed model decomposes the input feature data into trend and residual components and employs customized sub-models such as the linear, Transformer, and long short-term memory (LSTM). Furthermore, the proposed model is trained with data from the self-built PV site to implement a model suitable to real-world applications. Finally, the experimental results demonstrate that the proposed model has the best forecasting performance compared to conventional and state-of-the-art deep learning-based forecasting models with reasonable computational complexity.

The emergence of microgrids and the increasing adoption of Distributed Generation Systems (DGS) have created an opportunity to replace traditional fossil fuels with renewable resources. Such a shift poses security and power quality challenges that must be addressed by academics and industrial research paradigms. Unintentional islanding is an important security concern, as it can result in power quality degradation, electrical hazards, and equipment damage. To address this problem and find efficient solutions, many anti-islanding techniques to detect and eliminate the phenomenon can be found in the specialized literature. These solutions can be classified as passive, active, remote, hybrid, or based on machine learning and signal processing techniques. In this context, this paper provides a comprehensive review of existing anti-islanding methods, highlighting their importance in preventing dangerous situations. The review includes a detailed analysis of the advantages and limitations found for each method, as well as its suitability for practical applications. The goal is to provide a valuable resource for researchers and practitioners in the field of distributed power systems, enabling them to choose the most appropriate anti-islanding method for their specific needs. Overall, this paper aims to address the challenges posed by unintentional islanding and promote the adoption of renewable energy resources for a more sustainable future.

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

The smart distribution network (SDN) is integrating increasing distributed generation (DG) and energy storage (ES). Hosting capacity evaluation is important for SDN planning with DG. DG and ES are usually invested by users or a third party, and they may form friendly microgrids (MGs) and operate independently. Traditional centralized dispatching method no longer suits for hosting capacity evaluation of SDN. A quick hosting capacity evaluation method based on distributed optimal dispatching is proposed. Firstly, a multi-objective DG hosting capacity evaluation model is established, and the hosting capacity for DG is determined by the optimal DG planning schemes. The steady-state security region method is applied to speed up the solving process of the DG hosting capacity evaluation model. Then, the optimal dispatching models are established for MG and SDN respectively to realize the operating simulation. Under the distributed dispatching strategy, the dualside optimal operation of SDN-MGs can be realized by several iterations of power exchange requirement. Finally, an SDN with four MGs is conducted considering multiple flexible resources. It shows that the DG hosting capacity of SDN oversteps the sum of the maximum active power demand and the rated branch capacity. Besides, the annual DG electricity oversteps the maximum active power demand value.

Renewable energies equipped with LIRs as wind turbines and photovoltaic arrays provide negative impacts on power system dynamic securities. For this issue, developing adaptive controlling strategies play important role on controlling the system dynamic oscillations. In this paper, based on HVDC link, an ANFDC damping controller is proposed for controlling the system dynamic behavior through different LIRs. To do this, a fuzzy linguistic role proposed for tuning ANFDC parameters which the input dynamic signals are transferred into linguistic variables through offline working mode. Then, considering microgrid consists of different LIRs as offshore and onshore WTs, PVs and SSSGs integrated with together, ANFDC is trained which then in online working mode, the system damping performances through different operational and technological structures are evaluated. The proposed scheme is an online and non-model-based controller which uses the advantages of both neural and fuzzy logics together for providing a fast and secure structure of damping controller through online evaluations. The merit and effectiveness of the proposed approach investigated on a grid-connected microgrid consisting of PV, WT and SSSG connected in an incorporated HVDC link which considering different short circuit faults, damping performances are evaluated. Results indicate effectiveness of the proposed scheme for damping dynamic oscillations of LIRs with high damping ratios subject against severe fault events.

Energy management plays a pivotal role in enhancing the economic efficiency of power systems. However, it is noteworthy that a substantial number of microgrids (MGs) exhibit inherent unbalances that impose a range of critical issues, including voltage instability, elevated losses, power quality violations, safety concerns, and inefficiencies in energy management. Fast-acting power electronic converters present a relevant and efficacious solution for balancing such complex networks. This paper investigates the application of such converters within the realm of 3-phase unbalanced networks, wherein the proposed algorithm not only ameliorates network imbalances but also yields substantial reductions in operational costs, power losses, voltage deviations, and emissions. Demand response (DR) program has been applied to the model to enhance the system efficiency. The uncertainty about electric demand and renewable energy sources is considered in the simulation model for precise results. By implementing DR programs and penetrating distributed generators (DGs), the proposed model has been shown to reduce network losses and operation costs by 23% and 80%, respectively. Also, the total up-to-down voltage deviation of the voltage profile has been significantly reduced by 400%.

In the advent of recent developments in a higher efficiency Direct Current (DC) system, the DC microgrid proves to be a permissible future to match the load demand with distributed power generation. The distribution of the DC power system benefits more than the Alternate Current (AC) power system, and due to recent developments, many researchers focus on DC microgrid. DC microgrid system has the advantage of direct interfacing with Renewable energy sources (RES) and energy storage systems (ESS), making the system highly efficient and distinct. Since the protective devices used in AC power systems do not support DC applications, there are challenges in designing and implementing a proper protection device for DC microgrid, which includes the breaking of fault current and the rapid response in handling fault current, capability to overcome the fault and cost of implementing. Unlike traditional circuit breakers that work with a fixed voltage source, Z-Source Breakers are capable of interrupting current in systems with variable voltage sources, making it appropriate for applications involving renewable energy sources, microgrids and energy storage systems. These breakers use a unique Z-Source network to provide enhanced flexibility and reliability in managing electrical faults in modern power grids. This study intends to analyses the recent growth in the impedance-based DC circuit breaker and DC microgrid’s fault characteristics.

Escalating energy demands and climate change challenges necessitate the adaptation of renewable-based microgrid systems in the energy sector. The proposed work employs a robust Multi Agent System (MAS) technique to achieve efficient and automated control of the hybrid microgrid operation. The hybrid microgrid system incorporates Renewable Energy Sources (RES), a diesel generator, and a battery storage system. The operation of the hybrid microgrid consists of three distinct modes: islanded, transition to grid, and grid-oriented mode. The system’s performance is optimized by considering factors like climatic patterns, energy costs, connected source characteristics, and load demand. Different climatic scenarios are assessed for each mode of operation, where the best, extreme sunny, extreme cloudy, and worst climate conditions are considered for islanded mode; sunny and cloudy scenarios are considered for transition to grid mode as well as grid-feed and grid-tied modes are considered for grid-oriented operation of the microgrid. The simulation studies are performed using the MATLAB/Simulink R2021a environment. Furthermore, Particle Swarm Optimization (PSO) is implemented to optimize power allocation within the microgrid and enhance its cost-effectiveness. The optimization results demonstrate efficient utilization of available energy sources along with effective energy management facilitated by the MAS control system. The results emphasize the importance of adopting a MAS approach for achieving smart energy management through comprehensive analysis and integrating decentralized energy management techniques for optimal accommodation of distributed energy resources in hybrid microgrids.

This paper proposes a novel primary level controller and coupling LCL filter design methodology for microgrid prosumer units The so-called decentralized peer-to-peer-based power flow control algorithm introduces a power exchange communication link between two contractees, namely a prosumer unit and any other unit, on the time scales of primary power flow controller. This can be regarded as feed-forwarding of power flow information between the units in close to real-time in order to attain almost instant power balancing within the system. When coupled with the conventional voltage and frequency droop primary control, as presented in the paper, and tested on a simulation model of one radial microgrid in the off-grid mode of operation, it showed enhanced static and dynamic performance. The study also investigated adverse influence of the communication link time delay. The paper addressed prosumer units microgrid voltage-forming role by proposing design and control methodology for the prosumer units’ LCL coupling filter. To that aim, thorough voltage and current harmonic emission analysis has been conducted, taking into account realistic span of a microgrid parameters’ values and presence of other units. The study and the simulation model tests revealed that the limited total voltage harmonic distortion becomes critical requirement in the off-grid model of operation.

A second-order compensation link is adopted to control voltage-controlled inverters (VCIs) in microgrid systems to enhance the performance of the power synchronization process of the inverter. The second-order compensation link is classified as both a real pole compensator (RPC) and a complex pole compensator (CPC) according to the pole position. Given a model for the VCI power output, the design process for the second-order compensation link, which is equipped with an RPC and a CPC, is detailed. Moreover, the frequency-domain compensation effects of the RPC and CPC are analyzed using the root locus and Bode diagrams of the system before and after compensation. Finally, the compensation effects of the two types of second-order compensators are compared with the commonly used high-pass filter using MATLAB/Simulink, which verifies the RPC and CPC strategies. Simulation results show that the two types of compensators designed in this study can effectively increase the system cutting frequency and improve the phase margin in the frequency domain while accelerating the power synchronization process, simultaneously making it smoother and reducing overshoot in the time domain. The RPC has better gain robustness, whereas the CPC has better time constant robustness. By implementing an RPC or a CPC, the dynamic time of the power synchronization compensation strategy is reduced within 0.5 s, and the overshoot is reduced within 10% in the experiments with two inverters.

With the integration of distributed generation (DG) into a microgrid, fault detection has become a major task to accomplish. A scheme for microgrid feeder protection based on a newly proposed feature, $\Delta \pmb{RP}$, defined as the ratio of the sum of positive-sequence real power (PSRP) at the two ends of the feeder to the larger of the PSRPs among the two ends, is proposed. If $\Delta \pmb{RP}$ is larger than a threshold, an internal fault is detected; otherwise, the fault is external or there is no fault. The proposed scheme is tested in various scenarios, including fault type, fault resistance, fault location, fault inception angle, varying DG penetration levels, and simultaneous, evolving, and composite faults. In addition to this, the proposed scheme offers a robust performance when subjected to noise, synchronization error, changes in sampling frequency, and changes in the topology of a microgrid. The dominancy of the proposed scheme is proven by a comprehensive comparative study with various available recent schemes. Test results on the IEEE 13-bus network indicate the viability of the proposed protection scheme for a microgrid. Finally, the proposed scheme has been validated on a real-time simulator.

The ever-increasing dependence on electrical power has posed more challenges to power system engineers to deliver secure, stable, and sustained energy to electricity consumers. Due to the increasing occurrence of short- and long-term power interruptions in the power system, the need for a systematic approach to mitigate the negative impacts of such events is further manifested. Self-healing and its control strategies are generally accepted as a solution for this concern. Due to the importance of self-healing subject in power distribution systems, this paper conducts a comprehensive literature review on self-healing from existing published papers. The concept of self-healing is briefly described, and the published papers in this area are categorized based on key factors such as self-healing optimization goals, available control actions, and solution methods. Some proficient techniques adopted for self-healing improvements are also classified to have a better comparison and selection of methods for new investigators. Moreover, future research directions that need to be explored to improve self-healing operations in modern power distribution systems are investigated and described at the end of this paper.

Nowadays, the combined offshore wind farm and hydrogen production plant (COWHP) has been installed all around the world and will play an important role in reducing the carbon dioxide emissions. However, the cooperation of the COWHP, the hydrogen pipeline transportation, the power network transmission, and the hydrogen fuel cell electric vehicles (HFCEVs) scheduling is still an essential problem. This problem falls into the category of mixed-integer nonlinear optimization, which involves a significant number of decision variables and constraints. Existing methods have often struggled to effectively solve this problem due to its inherent complexity and non-linear nature. In this paper, a price-guided method is proposed to cooperate with this complex system. First, the COWHP model, the hydrogen pipeline network scheduling model, the power network flow model, the HFCEVs refuelling microgrid model, and the HFCEV traffic flow model in real-world transportation networks are presented. Second, the price-based cooperation model is proposed. Third, price strategies are deployed to cooperate with the complex system. The decision making trial and evaluation laboratory-the technique for order preference by similarity to ideal solution (Dematel-TOPSIS) method is deployed to evaluate the real-time performance of different strategies. The simulation results reveal that in the small vehicle flow transportation network cases, deep deterministic policy gradient (DDPG) has the best real-time evaluation performance; whereas for the large vehicle flow transportation network cases, long short term memory (LSTM) has the best real-time performance. From the posterior evaluation view, LSTM has the best performance for all cases to reduce total operation cost, and also reduce the total waiting time.

The motivation to write this paper is a need to contribute to research of methods of defence against intentional attacks on components of hybrid power systems (microgrids) based on renewable energy sources and storage systems. Spatially distributed electrical energy sources and energy storage devices are gradually being developed and installed, but the security protection of microgrids or individual components of a microgrid is not being improved accordingly. A good example is a fleet of EVs in an urban environment connected to a microgrid with low level of security measures in place (or sometimes none). The term security refers to protection against intentional attacks, not protection against accidents and malfunctions (safety). Lithium batteries, as potentially the most dangerous devices in a microgrid system, i.e. the most likely targets of intentional attacks, can be protected by adequate protection of their power converters and BMSs. This paper presents some of the possible intentional attacks on a battery energy storage system in a microgrid, as well as proposed improvements to the protection of communication channels, power converters’ process data logging, and physical protection of battery energy storage systems.

The microgrid under VSG control is conducive to the security and stability of the microgrid system. However, it still belongs to the primary regulation of frequency, and the microgrid in islanding mode has the problem that the frequency cannot be stabilised at the rated value after the load change. To address this problem, in order to eliminate the steady state error caused by the primary frequency regulation structure of the traditional VSG control technique and keep the load-induced frequency offset near the rated value, this paper proposes a fractional-order self-resilient-based secondary frequency regulation control strategy. Firstly, a three-phase circuit topology based on VSG is established, and the mechanism of primary frequency regulation is analysed with it as the research object. Then, the proposed fractional-order self-resilient control algorithm is applied to the rotor equations of the active frequency regulation characteristics of the VSG, so as to achieve the quadratic non-differential regulation of the frequency, and to eliminate the frequency error caused by frequency fluctuation of the virtual synchronous generator in the islanding mode. Finally, for the case of sudden load change, the VSG-based islanded microgrid model is constructed in Matlab/Simulink and compared with other control strategies, which verifies the effectiveness of the control strategy proposed in this paper. The control strategy in this paper can effectively eliminate the steady state error caused by the primary frequency modulation and improve the response speed.

Integration of renewable energy sources into the power grid has become a critical research topic in recent years. Microgrid technology has emerged as a promising option to integrate distributed generation and facilitate the widespread use of grid-connected renewable energy. However, ensuring appropriate power quality (PQ) in microgrids is challenging. High PQ is crucial for achieving energy efficiency and proper operation of equipment. This comprehensive review paper offers an overview of PQ issues in microgrids, covering various types of PQ disturbances, their key features, and the most relevant PQ standards. Additionally, it provides an extensive case study review of published research on PQ analysis of microgrid and renewable energy based systems. The key findings of each case study and their investigation method are emphasized. The paper also discusses in detail the different approaches and models employed in these studies to assess PQ issues. A wide range of microgrid systems is presented, featuring various sizes, configurations, topologies, and components, such as photovoltaic and wind systems, energy storage systems, and electric vehicles. Overall, the paper emphasizes the importance of maintaining high PQ in microgrids and provides a valuable resource for researchers and practitioners interested in this topic.

In recent years, integrating renewable energy sources (RESs) has achieved significant attention due to the growing demand for sustainable energy solutions. Inverter-interfaced Islanded Microgrids (IGs) have appeared as an advantageous approach to integrating RESs into the power grid. Grid-forming inverters (GFIs) are a critical component of IGs, and their synchronization is essential for stable and reliable operation. The literature has widely proposed soft transition, pre-synchronization, and re-synchronization to synchronize IGs to the main grid. However, methods for synchronizing GFIs in the islanded microgrid are restricted. Parallel operation of GFIs is required to guarantee the high-power demand of IG and improve voltage-frequency stability. For parallel operation, GFIs must be synchronized with each other. In the conventional synchronization control systems that are highly nonlinear, the linear proportional-integral (PI) controllers are commonly used in synchronization loops without considering the nonlinearity resulting from the initial condition dependency and cross-coupling. Thus, conventional synchronization methods can be exposed to concerns of stable operation, narrow operation area, and performance degradation. This study proposes a new linearized synchronization control system for GFIs in IGs. In this way, it is possible to analytically design robust linear controllers and ensure a stable operation, high performance, and wide (full) operation area. In addition, a new soft-commissioning method is proposed to deactivate synchronization loops and soft-start the synced GFI. The proposed system has been tested in real-time and CHIL hardware setups for two 550 kW GFIs operating in parallel, and the results in the perfect agreement are presented in this study.

Accurate net load forecasting is a cost-effective technique, crucial for the planning, stability, reliability, and integration of variable solar photovoltaic (PV) systems in modern power systems. This work presents a direct short-term net load forecasting (STNLF) methodology for solar-integrated microgrids by leveraging machine learning (ML) principles. The proposed data-driven method comprises of an initial input feature engineering and filtering step, construction of forecasting model using Bayesian neural networks, and an optimization stage. The performance of the proposed model was validated on historical net load data obtained from a university campus solar-powered microgrid. The results demonstrated the effectiveness of the model for providing accurate and robust STNLF. Specifically, the optimally constructed model yielded a normalized root mean square error of 3.98% when benchmarked using a 1-year historical microgrid data. The $k$ -fold cross-validation method was then used and proved the stability of the forecasting model. Finally, the obtained ML-based forecasts demonstrated improvements of 17.77% when compared against forecasts of a baseline naïve persistence model. To this end, this work provides insights on how to construct high-performance STNLF models for solar-integrated microgrids. Such insights on the development of accurate STNLF architectures can have positive implications in actual microgrid decision-making by utilities/operators.

The main aim of this paper is to design a local controller for DC/DC converter in a battery energy storage system (BESS) and a controller based on a virtual synchronous generator (VSG) for photovoltaic (PV) converter connected to AC grid. In the DC/DC converter design, the state feedback method is used so that the voltage and current control loops are combined, leading to higher flexibility and improved damping. This flexibility, which maintains the state of charge (SOC) of BESS, is supported by the design of virtual resistance and virtual capacitor statically and dynamically. In this design method, the BESS can be independently connected to each DC bus node and detect disturbances through local measurements. In addition, the improved Pre-Parallelism (PREP) method has been used to improve the transient performance caused by the disconnection and connection of additional loads, the existence of faults, and the inherent inertia difference in parallel operation between VSG and synchronous generator (SG). In this method, the problem of phase jump caused by transient disturbances is solved by considering a cosine function in VSG design. Also, to solve the problem of inertia difference between the units, a small signal model has been presented, in which, by considering the capacity ratio of the units on the AC side, the necessary inertia for VSG can be included in the design. The proposed method is simulated by considering different scenarios in MATLAB software, so the results demonstrate the superiority of the proposed controller compared to other existing methods.