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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]

Abstract Load Frequency Control (LFC) is essential for maintaining the stability of Islanded Microgrids (IMGs) that rely extensively on Renewable Energy Sources (RES). This paper introduces a groundbreaking 1PD-PI (one + Proportional + Derivative-Proportional + Integral) controller, marking its inaugural use in improving LFC performance within IMGs. The creation of this advanced controller stems from the amalgamation of 1PD and PI control strategies. Furthermore, the paper presents the Mountaineering Team Based Optimization (MTBO) algorithm, a novel meta-heuristic technique introduced for the first time to effectively tackle LFC challenges. This algorithm, inspired by principles of intellectual and environmental evolution and coordinated human behavior, is utilized to optimize the controller gains. The effectiveness of the proposed methodology is rigorously evaluated within a simulated IMG environment using MATLAB/SIMULINK. This simulated IMG incorporates diverse power generation sources, including Diesel Engine Generators (DEGs), Microturbines (MTs), Fuel Cells (FCs), Energy Storage Systems (ESSs), and RES units like Wind Turbine Generators (WTGs) and Photovoltaics (PVs). This paper employs the Integral Time Multiplied by the Squared Error (ITSE) and Integral of Time Multiplied By Absolute Error (ITAE) indicators as the primary performance metrics, conventionally used to mitigate frequency deviations. To achieve optimal controller parameter tuning, a weighted composite objective function is formulated. This function incorporates multiple components: modified objective functions related to both ITSE and ITAE, along with a term addressing overshoot and settling time. Each component is assigned an appropriate weighting factor to prioritize specific performance aspects. By employing distinct objective functions for different aspects of control performance, the derivation of optimized controller gains is facilitated. The efficacy and contribution of the proposed methodology are rigorously demonstrated within the context of RES-based IMGs, featuring a comparative analysis with well-known optimization algorithms, including Particle Swarm Optimization (PSO) and the Whale Optimization Algorithm (WOA). These algorithms are used to optimize the 1PD-PI controller, resulting in three control schemes: 1PD-PI/MTBO, 1PD-PI/WOA, and 1PD-PI/PSO. The effectiveness of these control schemes is evaluated under various loading conditions, incorporating parametric uncertainties and nonlinear factors of physical constraints. Three case studies, presented in eight scenarios (I-VIII), are utilized to comprehensively assess the efficiency, robustness, and sensitivity of the proposed approach. This analysis extends beyond the time domain, considering the stability evaluation of the proposed control scheme. Simulation results unequivocally establish the superior performance of the MTBO algorithm-optimized 1PD-PI controller compared to its counterparts. This superiority is evident in terms of minimized settling time, reduced peak undershoot and overshoot, and enhanced error-integrating performance characteristics within the system responses. Improvements are observed in both the high range and within the 80–90% range for criteria such as overshoot, undershoot, and the numerical values of the objective functions. This paper underscores the practicality and effectiveness of the 1PD-PI/MTBO control scheme, offering valuable insights into the management of frequency disturbances in RES-based IMGs.

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

In this paper, an intelligent brain emotional learning-based intelligent controller (BELBIC) technique is presented for the secondary frequency control of microgrid. The effects of renewable energy sources and load changes, power fluctuations and dynamic disturbances along with uncertainty affecting the microgrid frequency are all considered in the studied islanded microgrid. In the intelligent method for tuning and stabilizing the microgrid frequency, the learning of BELBIC technique is based on emotional factors and is able to adjust the microgrid frequency by including nonlinear terms and overcoming the effects of model uncertainty, disturbances, environmental changes and low inertia due to renewable energy sources. The behavior of the system against various changes and disturbances is investigated and compared with the optimal PID control methods. The advantages of the proposed method include low overshoot / undershoot, short settling time and minimization of frequency deviation. According to the simulation results obtained in different scenarios, the proposed control method shows good performance for network frequency stabilization. [ABSTRACT FROM AUTHOR]

The low carbon park islanded microgrid faces operational challenges due to the high variability and uncertainty of distributed renewable energy sources. These sources cause severe random disturbances that impair the frequency control performance and increase the regulation cost of the islanded microgrid, jeopardizing its safety and stability. This paper presents a data-driven intelligent load frequency control (DDI-LFC) method to address this problem. The method replaces the conventional LFC controller with an intelligent agent based on a deep reinforcement learning algorithm. To adapt to the complex islanded microgrid environment and achieve adaptive multi-objective optimal frequency control, this paper proposes the quantum-inspired maximum entropy actor-critic (QIS-MEAC) algorithm, which incorporates the quantum-inspired principle and the maximum entropy exploration strategy into the actor-critic algorithm. The algorithm transforms the experience into a quantum state and leverages the quantum features to improve the deep reinforcement learning's experience replay mechanism, enhancing the data efficiency and robustness of the algorithm and thus the quality of DDI-LFC. The validation on the Yongxing Island isolated microgrid model of China Southern Grid (CSG) demonstrates that the proposed method utilizes the frequency regulation potential of distributed generation, and reduces the frequency deviation and generation cost. [ABSTRACT FROM AUTHOR]

Optimal power flow (OPF) analysis enables the in-depth study and examination of islanded microgrid design and operation. The development of the analysis framework, including modeling, formulating, and selecting effective OPF solvers, however, is a nontrivial task. As a result, this paper presents a tutorial on an OPF modeling framework, offering a mathematical model that can be readily implemented using established open-source software tools such as OpenDSS, Pyomo, and IPOPT. The framework is versatile, capable of representing single-phase and unbalanced three-phase islanded microgrids. Various inverter models, such as those of grid forming and following equipped with their operating characteristics, can be incorporated. The efficacy of the proposed framework is demonstrated in studying the OPF of single-phase and three-phase microgrids. [ABSTRACT FROM AUTHOR]

Abstract Modern electrical power system design will increase renewable energy sources (RES) dominance. Rotating masses, the main source of inertia in power systems, have been greatly decreased in renewable energy producing systems. Thus, load–frequency equilibrium is an important indicator of these systems' performance and safety. This work introduces a fractional‐order (FO) operator‐based cascade control structure for islanded microgrid (μG) load‐frequency control (LFC). The structure utilizes a tilt–FO derivative with filter (TFODn) in the first level to reduce noise. The second level implements the proportional integral (PI) controller's FO form (FOPI), making it a tilt–FO derivative with a filter cascaded to the FOPI controller (TOFDn‐FOPI). The optimal controller parameters for quick dynamic responses with low frequency fluctuation are determined using a unique cost function and prairie dog optimization (PDO) algorithm. The LFC control loops have frequency deviation‐based responsive loads (RL). Control signal delays, RES output changes, parameter uncertainties, and cyber vandalism are examined. Laboratory‐scale tests also assessed the controller's practicality. The proposed controller outperforms standard and FO type proportional integral derivative (PID) and PD‐PI controllers. The TFODn‐FOPI controller is suitable for complicated closed‐loop systems like islanded μGs due to its faster error clearing, reduced RL capacity, and superior time and frequency domain indicators.

As microgrids are the main carriers of renewable energy sources (RESs), research on them has been receiving more attention. When considering the increase in the penetration of renewable energy sources/distributed generators (DGs) in microgrids, their low inertia and high stochastic power disturbance pose more challenges for frequency control. To address these challenges, this paper proposes a model predictive control (MPC) secondary control that incorporates an unknown input observer and where RESs/DGs use a deloading virtual synchronous generator (VSG) control to improve the system's inertia. An unknown input observer is employed to estimate the system states and random power disturbance from the RESs/DGs and load to improve the effect of the predictive control. The distributed restorative power of each DG is obtained by solving the quadratic programming (QP) optimal problem with variable constraints. The RESs/DGs are given priority to participate in secondary frequency control due to the proper weighting factors being set. An islanded microgrid model consisting of multiple photovoltaic and wind power sources was built. The simulation results demonstrate that the proposed method improves the system frequency, restoration speed, and reduces frequency deviations compared with the traditional secondary control method. [ABSTRACT FROM AUTHOR]

The percentage of renewable power in conventional power generation is gradually growing due to developments in power electronic converters (PECs). Renewable energy sources (RESs) can be integrated into the main grid through PECs, which are the most prevalent method to accomplish this. Virtual oscillator control (VOC) is a well-known time-domain method to regulate grid-forming inverters. VOC objective is to model the nonlinear dynamics of deadzone oscillator in a system of voltage source inverters to give a steady AC microgrid (MG). VOC is a self-synchronizing control method that just involves the current feedback signal. In contrast, classical droop and virtual synchronous machine (VSM) controllers both require the use of low pass filters to calculate real and reactive powers. The selection of control parameters in deadzone VOC is difficult and time-consuming. The VOC parameters are designed using different optimization techniques such as Particle Swarm Optimization (PSO), Sine Cosine Algorithm (SCA), modified Sine Cosine Algorithm (mSCA), African Vulture Optimization Algorithm (AVOA), and Artificial Jellyfish Search Optimization (AJSO). MATLAB and a real-time digital simulator (Opal RT-OP5142) were used to examine the system's performance with aforesaid controllers (droop, VSM, conventional VOC, VOC-PSO, VOC-SCA, VOC-mSCA, VOC-AVOA, and VOC-AJSO). In comparison to all control methods, the proposed VOC-AJSO provides faster synchronization. The effectiveness of the suggested VOC-AJSO control approach is proved by the hardware results. • Implementation of different grid-forming inverter control methods, such as droop, VSM, and VOC. • Different optimization methods (PSO, SCA, mSCA, AVOA, and AJSO) are presented to select the VOC parameters. • The three-phase VSI is tested in hardware for standalone condition with the aforesaid control methodologies. [ABSTRACT FROM AUTHOR]

To overcome the challenges of conventional power systems, such as increasing power demand, requirements of stability and reliability, and increasing integration of renewable energy sources, the concept of microgrids was introduced and is currently one of the most important solutions for solving the mentioned problems. Generally, microgrids have two operating modes, namely grid-connected and islanded modes. Based on the literature and its unique characteristics, the islanded mode is more challenging than the other one. In this paper, a new self-adaptive comprehensive differential evolution (SACDE) algorithm is proposed for solving economic load dispatch (ELD) and combined economic emission dispatch (CEED) problems, achieving optimal power consumption in isolated microgrids. Initially, SACDE is employed for solving the ELD problem as a single-objective function, meaning that the operational cost is just considered as the objective function, and thereby, the resources are scheduled accordingly. Then, a multi-objective platform based on SACDE is also proposed to solve the CEED problem. It means two objective functions, including operational cost and emission, are simultaneously optimized. For evaluating the performance of the proposed method, three different scenarios under various cases are considered. According to the results, when SACDE is employed to solve the single objective function (cost minimization) problem, it has better performance than other methods. In terms of the bi-objective scheme (cost and emission minimization), SACDE is significantly superior to the price penalty factor technique which is frequently used in previous studies. [ABSTRACT FROM AUTHOR]

Modern electrical power system design will increase renewable energy sources (RES) dominance. Rotating masses, the main source of inertia in power systems, have been greatly decreased in renewable energy producing systems. Thus, load–frequency equilibrium is an important indicator of these systems' performance and safety. This work introduces a fractional‐order (FO) operator‐based cascade control structure for islanded microgrid (μG) load‐frequency control (LFC). The structure utilizes a tilt–FO derivative with filter (TFODn) in the first level to reduce noise. The second level implements the proportional integral (PI) controller's FO form (FOPI), making it a tilt–FO derivative with a filter cascaded to the FOPI controller (TOFDn‐FOPI). The optimal controller parameters for quick dynamic responses with low frequency fluctuation are determined using a unique cost function and prairie dog optimization (PDO) algorithm. The LFC control loops have frequency deviation‐based responsive loads (RL). Control signal delays, RES output changes, parameter uncertainties, and cyber vandalism are examined. Laboratory‐scale tests also assessed the controller's practicality. The proposed controller outperforms standard and FO type proportional integral derivative (PID) and PD‐PI controllers. The TFODn‐FOPI controller is suitable for complicated closed‐loop systems like islanded μGs due to its faster error clearing, reduced RL capacity, and superior time and frequency domain indicators. [ABSTRACT FROM AUTHOR]

Nowadays, the participation of renewable energy sources (RESs) and the integration of these sources with traditional power plants in microgrids (MGs) for providing demand-side power has rapidly grown. Although the presence of RESs in MGs reduces environmental problems, their high participation significantly affects the system's whole inertia and dynamic stability. This paper focuses on an islanded MG frequency regulation under the high participation of RESs. In this regard, a novel fractional order cascade controller (FOCC) is proposed as the secondary frequency controller. In the proposed FOCC controller structure, a fractional order proportional-integral controller is cascaded with a fractional order tilt-derivative controller. The proposed FOCC controller has a greater degree of freedom and adaptability than integer order controllers and improves the control system's efficiency. The adjustable coefficients of the proposed controller are tuned via the kidney-inspired algorithm. An energy storage system equipped with virtual inertia is also employed to improve the system inertia. The proposed FOCC controller efficiency is compared with proportional-integral-derivative (PID), tilt-integral-derivative (TID), and fractional order proportional-integral-derivative (FOPID) controllers under different disturbances and operating conditions. The results demonstrate that the presented controller provides better frequency responses compared to the other controllers. Moreover, the sensitivity analysis is performed to show the proposed controller robustness versus the parameters' changes in the system. [ABSTRACT FROM AUTHOR]

Recent advances in electric grid technology have led to sustainable, modern, decentralized, bidirectional microgrids (MGs). The MGs can support energy storage, renewable energy sources (RESs), power electronics converters, and energy management systems. The MG system is less costly and creates less CO2 than traditional power systems, which have significant operational and fuel expenses. In this paper, the proposed hybrid MG adopts renewable energies, including solar photovoltaic (PV), wind turbines (WT), biomass gasifiers (biogasifier), batteries' storage energies, and a backup diesel generator. The energy management system of the adopted MG resources is intended to satisfy the load demand of Basra, a city in southern Iraq, considering the city's real climate and demand data. For optimal sizing of the proposed MG components, a meta-heuristic optimization algorithm (Hybrid Grey Wolf with Cuckoo Search Optimization (GWCSO)) is applied. The simulation results are compared with those achieved using Particle Swarm Optimization (PSO), Genetic Algorithms (GA), Grey Wolf Optimization (GWO), Cuckoo Search Optimization (CSO), and Antlion Optimization (ALO) to evaluate the optimal sizing results with minimum costs. Since the adopted GWCSO has the lowest deviation, it is more robust than the other algorithms, and their optimal number of component units, annual cost, and Levelized Cost Of Energy (LCOE) are superior to the other ones. According to the optimal annual analysis, LCOE is 0.1192 and the overall system will cost about USD 2.6918 billion. [ABSTRACT FROM AUTHOR]

Islanded, renewable energy microgrids for use at remote operating facilities reduce logistical burdens associated with fossil fuel based electrical power sources and provide greater operational flexibility; however, energy generation can be intrinsically intermittent on renewable microgrids. This intermittent electrical generation can be mitigated with electrical energy storage. Liquid air energy storage (LAES) is one promising technology proposed to meet this energy storage issue due to its high energy density and lack of geographical constraints. Small-scale microgrids may not have enough excess capacity to store pressurized liquid air (LA), and instead may rely on unpressurized LA storage and their associated unpressurized power recovery systems. Using commercial off-the-shelf components, this article conducts a performance-based tradespace study for several dual opening, unpressurized Dewar designs for use with Stirling- or Peltier-based power recovery cycles. The dual opening Dewar design is found to be ineffective for the short-term LA storage times necessary for small-scale microgrid use, primarily due to excessive conductive thermal losses; however, the design may be useful as a LA receiver and immediate use energy storage medium for a connected Stirling generator. A proposed alternative solution using a self-pressurized Dewar for LA storage and transport for microgrid applications is presented for future work. [ABSTRACT FROM AUTHOR]

Generally, an islanded microgrid depends on its Distributed Generators (DG) which are generally intermittent renewable energy sources such as wind turbines, photovoltaic, etc. From the various sources, effective power-sharing is required to compensate the entire microgrid demands. When there is a power outage, the existing droop methods are insufficient to operate an islanded microgrid smoothly. As a result, a hybrid optimization algorithm named Moth Flame Optimization (MFO) and Improved Firefly Algorithm (IFFA) is introduced to study the control issues of an islanded microgrid. A single battery unit is included to serve as a storage unit for intermittent sources and produce the required power when the available sources fail. Due to harmonic distortion, voltage unbalance occurs in the presence of unbalanced/non-linear loads, which can lead to voltage collapse. A comprehensive algorithm known as MFO-IFFA control is proposed to offer DC offset/steady-state error removal and fast dynamic response in an islanded microgrid. The simulation results are validated on MATLAB, which shows that the proposed MFO-IFFA achieves Total Harmonic Distortion (THD) of 1.60 % and Voltage Unbalance Factor (VUF) of 1.748 % which is much better than the existing 3IMPL and DDSRF methods. [ABSTRACT FROM AUTHOR]

To meet the rising energy needs of the world, the participation of renewable energy sources (RESs) in the power system has been boosted exponentially, primarily due to environmental issues. Islanded operation of power grids as microgrids (MGs) is more common now. Although this has several advantages, the diminished inertia constant has emerged as one of the serious consequences of high penetration level of RESs and islanded operations, which worsens the frequency stability with a higher value of rate of change of frequency (RoCoF). To address this issue, this article proposes a modified form of internal model control (IMC)-based virtual inertia (VI) control, which provides the necessary VI power in low inertia MG, regulates the system’s frequency deviation and prevents system instability. The proposed controller has been optimally designed through the grey wolf optimizer (GWO) algorithm with integral time absolute error (ITAE) as the cost function. To demonstrate the effectiveness of the proposed controller, a comparative analysis with existing methods, such as a conventional VI controller, VI emulator based on model-predictive controller, MATLAB-based IMC tuning algorithm for proportional–integral (PI) and proportional–integral–derivative (PID) controller is given under different disturbance and operating conditions. Compared with the conventional VI control method, the frequency deviation has been reduced by almost 80.91% when using the proposed method, for normal operating conditions of system inertia and load damping. Similarly, 79.13% and 73.93% have been reduced in frequency deviation for medium and low operating conditions of system inertia and load damping, respectively. The outcomes from MATLAB simulation show that the given controller offers better frequency responses with improved transient response in comparison with other controllers. [ABSTRACT FROM AUTHOR]

• A general component connection method (CCM) for modelling an islanded microgrid with multiple power sources is developed. • State-space models of nine scenarios with hybrid synchronous machines, grid-following converters, grid-forming (GFM) converters, and motor-generator pair (MGP) are investigated. • Small-signal dynamics of different scenarios based on GFM power sources are compared. • The damping ratios of an islanded grid can be improved by GFM converters and MGPs. • Critical ranges for control parameters, GFM converter penetration and electrical distance in typical scenarios are investigated. Grid-forming (GFM) technologies based on converters or synchronous machines (SMs) were proposed as emerging approaches for improving inertia, damping, and strength of power girds with increasing renewable energy penetration. Although the stability of GFM converters has been widely studied, the dynamics of a grid-connected generator can be dictated by the bulk power systems. Therefore, the stability of islanded microgrids with hybrid GFM sources should be investigated. To fulfill this gap, this paper adopts a general component connection method for modelling islanded microgrids integrated with multiple generators, and models for SMs, grid-following (GFL) and GFM converters are developed. Additionally, a new GFM technology known as the motor-generator pair (MGP) is investigated, and the model for small signal analysis is derived. Then, full-order state-space models for nine scenarios with hybrid GFM sources as well as simulation models which are used to verify their effectiveness are built. Finally, small-signal dynamics of different scenarios are fully examined. In the scenarios with SMs integrated, the range of oscillation frequency is wider than all converter-based scenarios, and mechanical dynamics can dominate stability; and in scenarios with only GFM converters, though the damping can be improved compared to GFL converters, low frequency oscillation may occur, and the dominant factor changes from the phase locked loop and power controller to voltage controller; then improvement in damping can be observed in MGP-based scenarios, but roots that dominate stability are sensitive to current controller and excitation. Further, critical ranges for control parameters, GFM converter penetration and electrical distance in different scenarios are investigated. [ABSTRACT FROM AUTHOR]

Nowadays, the participation of renewable energy sources (RESs) and the integration of these sources with traditional power plants in microgrids (MGs) for providing demand-side power has rapidly grown. Although the presence of RESs in MGs reduces environmental problems, their high participation significantly affects the system’s whole inertia and dynamic stability. This paper focuses on an islanded MG frequency regulation under the high participation of RESs. In this regard, a novel fractional order cascade controller (FOCC) is proposed as the secondary frequency controller. In the proposed FOCC controller structure, a fractional order proportional-integral controller is cascaded with a fractional order tilt-derivative controller. The proposed FOCC controller has a greater degree of freedom and adaptability than integer order controllers and improves the control system’s efficiency. The adjustable coefficients of the proposed controller are tuned via the kidney-inspired algorithm. An energy storage system equipped with virtual inertia is also employed to improve the system inertia. The proposed FOCC controller efficiency is compared with proportional-integral-derivative (PID), tilt-integral-derivative (TID), and fractional order proportional-integral-derivative (FOPID) controllers under different disturbances and operating conditions. The results demonstrate that the presented controller provides better frequency responses compared to the other controllers. Moreover, the sensitivity analysis is performed to show the proposed controller robustness versus the parameters’ changes in the system.

Recent advances in electric grid technology have led to sustainable, modern, decentralized, bidirectional microgrids (MGs). The MGs can support energy storage, renewable energy sources (RESs), power electronics converters, and energy management systems. The MG system is less costly and creates less CO2 than traditional power systems, which have significant operational and fuel expenses. In this paper, the proposed hybrid MG adopts renewable energies, including solar photovoltaic (PV), wind turbines (WT), biomass gasifiers (biogasifier), batteries’ storage energies, and a backup diesel generator. The energy management system of the adopted MG resources is intended to satisfy the load demand of Basra, a city in southern Iraq, considering the city’s real climate and demand data. For optimal sizing of the proposed MG components, a meta-heuristic optimization algorithm (Hybrid Grey Wolf with Cuckoo Search Optimization (GWCSO)) is applied. The simulation results are compared with those achieved using Particle Swarm Optimization (PSO), Genetic Algorithms (GA), Grey Wolf Optimization (GWO), Cuckoo Search Optimization (CSO), and Antlion Optimization (ALO) to evaluate the optimal sizing results with minimum costs. Since the adopted GWCSO has the lowest deviation, it is more robust than the other algorithms, and their optimal number of component units, annual cost, and Levelized Cost Of Energy (LCOE) are superior to the other ones. According to the optimal annual analysis, LCOE is 0.1192 and the overall system will cost about USD 2.6918 billion.

Load and supply parameters may be uncertain in microgrids (MGs) due for instance to the intermittent nature of renewable energy sources among others. Guaranteeing reliable and stable MGs despite parameter uncertainties is crucial for their correct operation. Their stability and dynamical features are directly related to the controllers' parameters and power-sharing coefficients. Hence, to maintain power good quality within the desirable range of system parameters and to have a satisfactory response to sudden load changes, careful selection of the controllers and power-sharing coefficients are necessary. In this paper, a simple design approach for the optimal design of controllers' parameters is presented in an islanded MG. To that aim, an optimization problem is formulated based on a small-signal state-space model and solved by three different optimization techniques including particle swarm optimization (PSO), genetic algorithm (GA), and a proposed approach based on the combination of both PSO and GA. The optimized coefficients are selected to guarantee desirable static and dynamic responses in a wide range of operations regardless of the number of inverters, system configuration, output impedance differences, and load types. Through the proposed design and tuning method, the performance of the MG is improved as compared to those obtained using state-of-art techniques. This fact is demonstrated by using numerical simulations performed on a detailed model implemented in PSIM© software. [ABSTRACT FROM AUTHOR]

In remote arctic communities, where access to a bulk electrical grid interconnection is not available, the implementation of islanded microgrids has been the most viable way to produce and distribute electricity services to their inhabitants. Historically, these islanded grids have relied primarily on diesel generators or hydropower resources to supply the baseload. However, this practice can result in increased expense due to the high costs associated with fuel transportation and the significant amounts of on-site storage necessary when fuel transportation is unavailable during winter months. In order to mitigate this problem, arctic microgrids have started to transition to a hybrid-source operational mode by incorporating renewable energy sources that are inherently variable in nature, such as wind or solar. Due to their highly stochastic behavior, these hybrid-source islanded microgrids can pose potential issues related to power quality due to introduction of rapid net load fluctuations and inability of diesel generators to respond rapidly. In addition, non-firm stochastic sources can require significant idling diesel generator resources to serve as spinning reserves, which is inefficient and wasteful. This work studies the problems that may arise in the transient dynamics of a real-world hybrid diesel microgrid when subjected to a loss of wind generation. Moreover, this work proposes a transition from a diesel spinning reserve to a battery energy-storage system (BESS) operating reserve scheme. The study of the proposed transition is important in establishing the fundamental implication of transient dynamics and the potential benefits of integrating a BESS as a spinning reserve in terms of stability, frequency nadir, and transient voltage deviation. The methods to investigate and validate the transient dynamics relied on both electromagnetic simulation models of GFMIs and a commercially available GFMI in an experimental power hardware-in-the-loop setup. The simulation results showed that the proposed operating reserve scheme improves the power quality of the system in terms of voltage deviation and frequency nadir when the microgrid is subjected to a loss of wind generation scenario. Depending on the simulation cases, adding a GFMI reduced the frequency nadir between 65.3% and 86.7%. Moreover, the reductions in the voltage deviations improved between 3.6% and 23.0%. From these results it can be concluded that the integration of a GFMI can reduce the frequency nadir in a hybrid diesel microgrid, and in turn, reduce diesel consumption, which improves system reliability while reducing fuel expenses. Furthermore, the novelty of this work relies on the fact that the offline simulation results were validated using a power hardware-in-the-loop platform that incorporated a 100 kVA commercially available GFMI as the device under test. [ABSTRACT FROM AUTHOR]

Frequency stability is one of the major challenges in renewable energy source-based microgrids. This issue can be resolved by employing virtual inertia control that helps in improving damping properties and stability. In applying this control method, a phase-locked loop (PLL) is used to acquire the assessment of the frequency data of the microgrid. Nonetheless, the application of PLL in microgrids produces larger frequency oscillation due to its system dynamics. To solve such issues, we propose a robust Interval Type-2 Fuzzy Logic PID Controller (IT2FPID) for the virtual inertia control to limit the unwanted frequency measurement impacts while simultaneously improving the microgrid system stability. Further, a novel improved parasitism predation algorithm (IPPA) algorithm is proposed to tune the controller parameters. The performance of our proposed solutions is studied by comparing the simulation results with a few standard techniques considering the change in system parameters and different paces of renewable energy source penetration. [ABSTRACT FROM AUTHOR]

An islanded microgrid (IMG) having renewable energy sources (RES), dispatchable sources and battery energy storage (BES) is an effective approach to supply the electricity in remote areas. The reliable operation of IMG is quite complex and challenging task, due to uncertain behaviour of RES, loads and energy bid. In this paper, an IMG is simulated to maximise the revenue considering a dynamic optimisation objective subjected to various constraints of system components for two different cases, i.e. considering the stationary and the dynamic energy bid. Artificial bee colony (ABC) and its variants based on global best (GABC), and global and personal best with asynchronous scaling factors (GPSABC) are implemented and the obtained results are also compared with the existing optimisation techniques. Results of GPSABC improve the revenues and offers better accuracy and stability parameters. Further, the effect on revenue results with different ramp rates of diesel generators is described through GPSABC.

With the high integration of power electronic technologies in microgrids, the reliability assessment considering power electronic devices has become a hot topic. However, so far no research has considered the impact of the operation failure probability of power electronic equipment on the overall reliability of the microgrid. This paper aims to construct a holistic operation failure rate model of power electronic systems based on the overall reliability assessment of islanded microgrid with high penetration of renewable energy sources (RESs). In addition, to improve the reliability of islanded microgrid, the conventional battery energy storage system (BESS) is replaced by the hybrid energy storage system (HESS). Based on the proposed model, the operation failure models for the power electronic modules in microgrid are built and tested, and then the sensitivity analysis is performed for exploring the influence of various factors on the reliability of the microgrid.

Load flow analysis is a significant tool for proper planning, operation, and dynamic analysis of a power system that provides the steady-state values of voltage magnitudes and angles at the fundamental frequency. However, due to the absence of a slack bus in an islanded microgrid, modified load flow algorithms should be adopted considering the system frequency as one of the solution variables. This paper proposes the application of nature-inspired hybrid optimization algorithms for solving the load flow problem of islanded microgrids. Several nature-inspired algorithms such as genetic algorithm (GA), differential evolution (DE), flower pollination algorithm (FPA), and grasshopper optimization algorithm (GOA) are separately merged with imperialistic competitive algorithm (ICA) to form four hybrid algorithms named as ICGA, ICDE, ICFPA, and ICGOA. Performances of these algorithms are tested on the 6-bus test system and the modified IEEE 37-bus test system. A comparison among the proposed algorithms is carried out in terms of statistical analysis conducted using SPSS statistics software. From the statistical analysis, it is identified that on an average, ICDE takes less number of iterations and consequently needs less execution time compared with other algorithms in solving the load flow problem of islanded microgrids.

Today’s clean technologies related to microgrids are approaching towards the smart nanogrid system. It fulfils the demand of the electricity throughout the world by proper using of renewable energy sources and energy storage systems. Still, the microgrid (MG) power plant control has enriched to a level that it will require complicated and smooth control in the grid interaction including distributed islanding operation. The load dynamics and uncertainties are the common issues which hampers the frequency, voltage and power profile of the MG that is responsible to damage the load and power system. The design of robust fuzzy logic controller (FLC) has been proposed in this research article to regulate the performances of three-phase islanded MG. The performance of the proposed FLC has been examined under different loading condition whose robustness has been evaluated under faulty condition. The investigated performances of the MG ensure high tracking and robust performance of the proposed FLC. [ABSTRACT FROM AUTHOR]

As the penetration of renewable energy, generally, uncertainties, increase in microgrids, a more dynamic and complex system is emerging that makes the load frequency control (LFC) of islanded microgrids more challenging due to their stochastic dynamic nature. This stochastic nature can create oscillatory frequency response, which eventually leads to deteriorating control function and instability even under primary and traditional secondary controllers. Due to the uncertainties, the measurements and microgrid states are continually perturbed; hence, they cannot be directly utilized for feedback control. Therefore, it is required to utilize an observer-based stochastic feedback control that correctly recognizes these uncertainties and sieves incorrect states, frequency deviation here, and can properly maintain the balance between the energy sources and the demand. This article develops an optimal observer-based secondary control for LFC, framed in two layers that are the stochastic unknown input observer (SUIO) and linear quadratic regulator. The proposed SUIO not only can address the uncertainties, e.g., renewable energy, load, and measurement noise, with efficient control effort but also performs robust against parameter changes of the microgrid. The simulation results confirm the efficacy of this proposed control as compared to the traditional secondary control method. [ABSTRACT FROM AUTHOR]

Microgrids have become more and more popular their usefulness as a renewable energy resource has been recognized. The core ability and promise of microgrids is addressing the environmental concerns due to climate change that have been growing during recent years. The innovation of microgrids is that they are designed to operate either in island mode or interconnected with the main grid system. However, when the microgrid operates in islanded mode, faults may occur which can cause system collapse or even blackout. Load curtailment schemes can be utilized to decrease the quantity of associated load to a level that can be securely supported by accessible generation in isolated mode. The main goal of this research is to evaluate the optimal amount of shedding power considering sustainable power sources, with the help of primary and secondary adjustments of the generator to restore the frequency to the allowed range. Particle Swarm Optimization algorithm is applied in this paper to determine the distributed shedding power on each demand load bus which can improve the voltage quality of the isolated microgrid system. The effectiveness of the proposed method is demonstrated through the simulation of IEEE 16- bus microgrid. [ABSTRACT FROM AUTHOR]

This article addresses a voltage control and energy management strategy of active distribution systems with a grid-connected dc microgrid as well as for an islanded dc microgrid with hybrid energy resources. In the islanded mode, a control and management strategy using a backup diesel generator (DG), a renewable energy source (RES), and an energy storage system plays a vital role in maintaining the microgrid bus voltages within the limits. However, operating backup diesel generator (DG) has its own challenges including startup delay, frequent switching, and uneven loading when operated along with a RES. Additionally, fuel efficiency and emission characteristics vary with loading since most of DGs are driven by constant-speed diesel engines. Hence, an exhaustive power management scheme (PMS) is proposed by utilizing the hybrid energy storage system. Real-time simulation and experimental validation of the proposed scheme are provided using a real-time digital simulator (RTDS) and a laboratory-scale prototype, respectively. Extreme scenarios including DG failure/scheduled maintenance, low power generation, and battery charge are analyzed in the islanded mode. Furthermore, a dc microgrid is connected to an IEEE active distribution system feeder to analyze control and management challenges for the grid connected mode with contribution from a microgrid and with no contribution from a microgrid. These scenarios resemble more realistic unbalanced utility grid conditions. A centralized optimization problem is formulated at an advanced distribution management system level to maintain all the node voltages within limits in the IEEE test system. RTDS is used to simulate dc microgrid connected with the IEEE test system and an optimization algorithm is implemented in MATLAB. Superior performance of the developed algorithms are demonstrated and validated for coordination between centralized optimization at ADMS and the microgrid energy management system. [ABSTRACT FROM AUTHOR]

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An islanded microgrid (IMG) having renewable energy sources (RES), dispatchable sources and battery energy storage (BES) is an effective approach to supply the electricity in remote areas. The reliable operation of IMG is quite complex and challenging task, due to uncertain behaviour of RES, loads and energy bid. In this paper, an IMG is simulated to maximise the revenue considering a dynamic optimisation objective subjected to various constraints of system components for two different cases, i.e. considering the stationary and the dynamic energy bid. Artificial bee colony (ABC) and its variants based on global best (GABC), and global and personal best with asynchronous scaling factors (GPSABC) are implemented and the obtained results are also compared with the existing optimisation techniques. Results of GPSABC improve the revenues and offers better accuracy and stability parameters. Further, the effect on revenue results with different ramp rates of diesel generators is described through GPSABC. [ABSTRACT FROM AUTHOR]

Isochronous controlled battery energy storage system (ESS) can maintain constant frequency in an islanded ac microgrid, but its power and state of charge (SoC) may go beyond limit due to intermittent renewable energy resources and impact load change. This paper introduces a control strategy integrated with bang-bang and backlash mechanism to coordinate distributed energy resources (DERs). When overloading will take place, ESS will change from isochronous control to droop control, with droop gain varying based on bang-bang control, by which the overloaded power will be spontaneously transferred from ESS to other DERs. Moreover, frequency will be automatically restored when impact load disappears, which eliminates the effort of secondary frequency control. When SoC of ESS comes to limit, the diesel generator will change from droop control to isochronous control, whereas ESS will adopt virtual inertia and grid-supporting droop control to help stabilize frequency. A backlash mechanism is also employed to latch the control mode near SoC limit. Comprehensive tests on real-time simulator demonstrated the effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

This paper proposes a new control strategy of microgrids for the improved voltage quality. In the existing control techniques, the droop control is commonly adopted as a decentralized power sharing method at the cost of voltage deviations. Besides, the conventional cascaded control featuring relatively slow dynamic response shows difficulties in handling the fluctuation of renewable energy outputs, leading to further voltage quality deterioration. In this paper, an advanced model predictive power control strategy by considering the battery constraints is proposed for the bidirectional dc–dc converters to smooth the solar photovoltaic (PV) outputs and stabilize the dc-bus voltages. A model predictive voltage control scheme taking into account the voltage changing trend is then developed to control the distributed inverters to improve the output ac voltages. Furthermore, a washout-filter-based power sharing approach with the plug-and-play capability is adopted to achieve a proper load sharing among parallel inverters and mitigate the voltage deviation. The proposed control strategy is numerically simulated in MATLAB/Simulink and experimentally verified by hardware-in-the-loop tests under the condition of fluctuating PV outputs and variable power demands. (This paper is accompanied by a video showing the hardware-in-the-loop test.) [ABSTRACT FROM AUTHOR]

• Microgrid is a small grid that uses renewable energy sources to generate power. • Performance of microgrid may deteriorate due to the uncertainty in load dynamics. • A controller is proposed to control the voltage, current and power of a microgrid. • The controller is designed for both single and three phase islanded microgrid. • Performance of the controller is measured and compared against various loads. Future grids will consist of many small and large microgrids that will require complex control of grid interaction as well as islanding operation. This paper addresses the control of islanded microgrids under dynamic system conditions. In this paper, a robust model reference modified adaptive proportional integral derivative (PID) controller is designed for voltage and current control augmentation of islanded microgrid against various load dynamics. The variation of load dynamics may cause the unsafe operation of the microgrid. The objective of the designed controller is to confirm the safe operation against voltage and current control under different load dynamics. The controller is designed based on selecting the desired reference model and controlling parameters. The effectiveness of the proposed controller is evaluated against the presence of load dynamics, harmonic sources, asynchronous machines, nonlinear and unknown loads. The results show that the proposed controller is capable of providing high tracking performance and safe operation for both the single and three phase islanded microgrid system. [ABSTRACT FROM AUTHOR]

This work aims to investigate the impact of electric vehicles (EVs) charging/discharging strategies on the optimal operation of islanded microgrid (MG). For this purpose, firstly, load model of EVs under four different charging/discharging strategies, namely, uncoordinated charging mode, maximum renewable mode, valley filling mode and charging discharging mode is established. Thereafter, impact of these strategies on the optimal operation of MG is investigated. A 2m point estimate method is used to study the impact of forecast errors associated with load demand and renewable energy sources on the MG total cost under different EV charging/discharging strategies. Finally, a sensitivity analysis of variations of battery parameters on the MG total cost is also presented.

One of the major issues in the operation of an islanded microgrid is the frequency deviations caused by the variation of power production coming from non‐dispatchable renewable energy sources. Using an appropriate control system may allow the system to deal with the frequency deviation and allow the system frequency to be restored to its rated value. A wide‐area system frequency controller sends a control signal to each of the dispatchable generating sources to enforce the frequency control by using communication channels. However, the communication channels are susceptible to the time delays, which affect the performance of the frequency control system and potentially the system security. Also, the system parameters are uncertain due to the error in the modelling, variation in network topology, generations, loads etc. This study proposes a robust proportional and integral (PI) system frequency controller based on Kharitonov theorem (KT) considering the time delay of the communication system and the system parametric uncertainties. Simulation results demonstrate that the KT‐based controller model has enhanced robustness compared to the conventional approaches of controller design. [ABSTRACT FROM AUTHOR]

This paper presents a novel supervisory control method for an islanded hybrid ac/dc microgrid (HMG) with a high renewable energy source (RES) penetration. Droop control can respond to inevitable uncertainties in the load and RES. However, this leads to frequency and voltage deviations, and the generator output power may deviate from the optimal point. To restore the frequency and voltage of both ac and dc grids while reducing generation cost at short time intervals, a model predictive control based optimization problem is formulated to update the set-points of the available resources including the interlinking converter and distributed generators. With the sensitivities, which help to linearly express the frequency and voltage for resource output power, the problem can be approximated to a quadratic programming model. In addition, to consider the effect of the reactive power on the frequency and improve the accuracy, the second-order frequency sensitivity is calculated and applied selectively. The proposed method was tested on a modified IEEE 33-bus system connected to a sample dc grid. The simulation results validate its performance for reliable and economic operation in an islanded HMG. [ABSTRACT FROM AUTHOR]

• An APD–MS control strategy is proposed to reduce the dependence of the BSWBIM on the master DG and maintain the SOC of the main DG, improving the reliability of the system. • An MT control method in BSWBIM is proposed to improve the utilization of biogas by considering the relationship between the MT load rate and generated efficiency. • An APD strategy based on the SOC and power limitation of BESSs is proposed to distribute the charging power among BESSs, improving the stability of the system by ensuring the regulation capacity for the master DG. As many countries increase their investment in the field of renewable energy, technological research and development has increased on establishing biogas–solar–wind battery islanded microgrids (BSWBIMs) based on biogas generation technology. In BSWBIMs, multiple distributed generators (DGs) are controlled to share either the lack or surplus of power throughout the microgrid. To improve the reliability and economic efficiency of the system, a reasonable control strategy is indispensable. In this paper, a novel adaptive power distribution master–slave (APD–MS) control strategy is proposed to maintain the system voltage and frequency within the Chinese national grid requirements. Moreover, compared with the conventional master-slave control strategy, the proposed control strategy can solve the problem of high dependence on the master DG. Notably, the proposed control strategy can improve the biogas utilization efficiency, system reliability and system stability. The theoretical analysis and software simulation based on BSWBIMs at low voltage levels demonstrate the effectiveness and superiority of the proposed APD–MS control strategy. [ABSTRACT FROM AUTHOR]

Summary: With the rapid growth of single‐phase renewable energy source units within three‐phase microgrids, the accuracy of the power sharing per phase among distributed generation (DG) units is necessary. This paper proposes an adaptive power control strategy based on new sliding mode control for islanding operation of a microgrid with multiple three‐phase and single‐phase DGs. The studied microgrid consists of two three‐phase photovoltaic (PV) arrays and three single‐phase DG units, which include PV, fuel cell, and battery systems. In order to control the three‐phase DGs properly, an observer is used to estimate the uncertain system parameters. Then, according to the estimated values, a controller is adapted to the new system conditions. In this control strategy, one of the three‐phase PV arrays is responsible for adjusting the voltage and frequency, and the other is responsible for controlling the load current to manage the power of three‐phase inverters. To control the single‐phase DGs, their power and voltage references are assigned based on the corresponding phase of the three‐phase DGs responsible for power and voltage control. Subsequently, the power and voltage control operation is performed in a per‐unit system. The proposed control strategy is strongly stable against variation of conditions, disturbances, and microgrid parameter uncertainties, and it regulates the active and reactive powers injected by DGs. The feasibility of the proposed control strategy is validated using simulation studies on a multibus islanded microgrid with balanced and unbalanced loading conditions via MATLAB/Simulink software. The results indicate that the proposed approach is able to provide faster response and lower steady‐state error compared with the conventional proportional integral controller. [ABSTRACT FROM AUTHOR]

This study presents a digital coordination strategy of load frequency control (LFC) and Over/Under Frequency Relay (OUFR) protection for an isolated microgrid (MG) considering high penetration of renewable energy sources (RESs). In such MGs, the reduction in system inertia due to the replacement of traditional generating units with a large amount of RESs causes undesirable influence to MG frequency stability, leading to weakening of the MG. Furthermore, sudden load change and short circuits caused large frequency fluctuations which threaten the system security. In order to handle these challenges, this study proposes a specific design of the digital OUFR, which will operate for both conditions of over and under frequency in coordination with digital PID controller based on mapping technique in discretization process to protect the MG against high‐frequency variations. To prove the effectiveness of the proposed digital coordination strategy, a small MG was investigated for the simulation considering load change, varying the penetration level of RESs and the system inertia. The results reveal the robustness of the proposed coordination to maintain the MG frequency stability and security. In addition, the superiority of the digital OUFR has been approved in terms of accuracy and speed response during high disturbances. [ABSTRACT FROM AUTHOR]

Microgrid (MG) clustering is regarded as an important driver in improving the robustness of MGs. However, little research has been conducted on providing appropriate MG clustering. This article addresses this shortfall. It proposes a novel multi-objective optimization approach for finding optimal clustering of autonomous MGs by focusing on variables such as distributed generation (DG) droop parameters, the location and capacity of DG units, renewable energy sources, capacitors and powerline transmission. Power losses are minimized and voltage stability is improved while virtual cut-set lines with minimum power transmission for clustering MGs are obtained. A novel chaotic grey wolf optimizer (CGWO) algorithm is applied to solve the proposed multi-objective problem. The performance of the approach is evaluated by utilizing a 69-bus MG in several scenarios. [ABSTRACT FROM AUTHOR]

This paper investigates the frequency performance problem of microgrids (MGs) integrated with renewable employing an energy storage system (ESS) equipped with virtual inertial control (VIC) support. To tackle the uncertainties related to the system operation, a two-layer multiple model predictive control (TLMMPC) method, consisting of nominal and ancillary MMPCs, is proposed to submit effective control signals to the ESS for improving system frequency performance. The ancillary MMPC generates the control commands for the VIC-based ESS utilizing the signals provided by the nominal MMPC and the frequency deviation signal of the actual system considering uncertainties and operating constraints. The control commands are generated to attain the minimum value of frequency response error with the least control endeavor while considering various operational and physical limitations. The TLMMPC method has the capability to work with different state of charge (SoC) levels to obtain the desired SoC and highest efficiency from the ESS and preserve the ESS's longevity. The dynamic performance of the proposed TLMMPC technique is investigated on an islanded MG and compared to model predictive control (MPC), fractional-order MPC, and tilt-integral-derivative controllers under different scenarios. The results validate that the proposed TLMMPC technique significantly improves the system frequency response from viewpoints of settling time, peak overshoot, and undershoot and obtains the most efficient ESS compared to the other methods. • This paper presents a TLMMPC method to control a VIC-based ESS in the LFC problem of an islanded MG. • The TLMMPC works with different SoC levels to obtain the highest efficiency from the ESS. • The performance of the TLMMPC method is compared to other methods under different scenarios. • The sensitivity analysis is done to study the TLMMPC's robustness against parameters' changes. [ABSTRACT FROM AUTHOR]

Microgrids (MGs) may represent a solution in the near future to many problems in the energy and electric world scenarios; such as pollution, high reliability, efficiency and so on. In particular, MGs' capability to work in an islanded configuration represents one of their most interesting features in terms of the improvement of the reliability of the system, the integration of renewable energy sources and the exploitation of the quick response and flexibility of power electronic devices in a stand-alone system. In order to study and validate innovative solutions and control strategies for islanded operation, there is a need to develop models for MG structures that can be reliable and sufficiently simple to be used for the purpose of the design and validation of innovative control systems. This paper proposes a simplified, first harmonic model for a generic structure of MG characterized by its use of only electronic power converter interfaced generation. The main advantages of the proposed method lie in the model's simplicity and its reduced solving time, thanks to the limited number of necessary parameters to describe the system. Moreover, the developed formulation allows the avoidance of specific (and often licensed) software to simulate the system. The performances of the proposed model have been validated by means of a comparative analysis of the results obtained against a more accurate representation of the system performed in the power system CAD--electromagnetic transient and DC (PSCAD--EMTDC) environment, which allows for the representation of each component with a very high level of detail. Such comparison has been performed using the University of Genoa Savona Campus Smart Polygeneration Microgrid testbed facility, due to the availability of all the necessary numerical values. [ABSTRACT FROM AUTHOR]

Integration of highly fluctuating renewable energy source (RES)-based distributed generation units (DGUs) and a low inertia of an islanded microgrid (MG), resulting from absence of direct coupling between the various DGUs and the MG, can stimulate severe frequency deviation problems. In such a scenario implementation of the energy storage units (ESUs) becomes imperative. Consequently, this paper examines the impact of diverse mix of two widely employed ESUs namely electric vehicle (EV) battery and superconducting magnetic energy storage (SMES) on the dynamic responses of an islanded MG. A novel discrete-time Laguerre functions-based intelligent model predictive control (LiMPC) approach is administered as a secondary controller. Realization of the Laguerre functions-based MPC approach, as compared to the classical MPC approach, reduces the number of parameters to be optimized and simultaneously downsizes the computational time of the control vector trajectory. Simulation results clearly validate the potency of the energy storage-based LiMPC approach, when both the ESUs are engaged, in improving the dynamic responses of the MG and simultaneously satisfying the load frequency control (LFC) requirements. A maximum percentage improvement of 41.91% in peak overshoot and 51.98% in peak undershoot of the dynamic responses, and 94.93% in the performance index value is observed. Furthermore, the superiority of the suggested control approach over a few widely used control approaches is shown. Finally, the robustness to parametric modifications of the proposed LiMPC approach is verified. • Energy storage based load frequency control (LFC) of an islanded MG is presented. • Battery of an EV and superconducting magnetic energy storage (SMES) are considered. • A novel Laguerre functions-based intelligent MPC (LiMPC) approach is proposed. • Exceptional LFC performance considering both energy storage units are ratified. • Competence of the LiMPC approach is proved over other reputed control approaches. [ABSTRACT FROM AUTHOR]

• The LPV representation of a nonlinear isolated hybrid microgrid model is investigated. • The intermittent nature of the wind speed, solar irradiation, and loads, and several parameter uncertainties are considered in the frequency control design procedure. • A dynamic robust LPV controller synthesis based on the three-step algorithm is proposed for the nonlinear model. • The proposed secondary frequency control approach suggests a practical dynamic output feedback controller with lower order than the IHMG model. • The algorithm superiority is verified in various disturbance-based and uncertain parameter-based scenarios. The applicability of a robust linear parameter varying (LPV) control for frequency fluctuation damping in an islanded hybrid microgrid (IHMG) system (DEG/WTG /PV/FC/ESSs) is investigated in this paper. Due to the erratic behavior of the majority of the renewable energy sources (RESs) and the varying time nature of loads, frequency deviation from the nominal value is inevitable, especially in the islanded mode. Load variation, solar irradiation, and wind power disturbance, as well as system parametric uncertainties, can significantly disturb the system frequency operation. In this paper, the wind speed and rotated speed of the nonlinear wind turbine are highlighted as scheduling parameters, and the nonlinearity of the wind turbine is hidden by the LPV approach. In the proposed method, a dynamic output feedback controller is obtained based on the power balance equation, by solving three LMIs to minimize the upper bound of the L 2 -Norm of the uncertain IHMG in an algorithmic approach. For better evaluation, the ultimate LPV control approach is compared with two other methods on the understudy IHMG model. The result represents a noticeable advantage of the proposed robust method compared to other methods in terms of frequency stability based on time-domain and norms characteristics in presence of the simultaneous disturbances and parametric uncertainties. [ABSTRACT FROM AUTHOR]

A variation in load on a microgrid (MG) system has a significant impact on the MG's frequency. In addition, wind and photovoltaic power sources are significantly affected by weather fluctuations; thus, the system experiences frequent oscillations. This paper proposes an integral sliding mode control system that incorporates a disturbance observer (ISMCDO) for establishing a power balance and frequency regulation in a MG system. MATLAB/ Simulink is used to compare the results of this method and those of integral sliding mode controller (ISMC) and disturbance observer-based controller (DOBC). In addition, their performance indices are compared as well, where the results can verify the superiority of ISMCDO method in comparison with other techniques. Consequently, Utilizing the ISMCDO controller, the secondary controller performances in the MG system may be improved, which will ensure stability, flexibility, quick response, and maintenance of the load balance when sudden changes in loads and weather conditions occur. [ABSTRACT FROM AUTHOR]

The application of the four-leg inverter as an effective interface for renewable and sustainable distributed energy resources (DERs) is gaining more attention with the advances in power electronics technology. One of the key technologies in inverter-based distributed generation (DG) systems is the four-leg voltage source inverter (VSI) that is utilized to operate in autonomous four-wire microgrids. Four-leg VSIs are becoming increasingly popular in four-wire microgrid, because they can not only achieve a proper control scheme in autonomous mode but also cope with the prescribed power quality requirements. The aim of this paper is to provide an overview of the main characteristics of recently used control strategies for four-leg VSIs operating in autonomous microgrids. First, two commonly-used four-wire inverter configurations are discussed, and their advantages and disadvantages are compared. Afterwards, the most up to date control techniques for three-phase four-leg VSIs operating in islanded microgrid from the reference frame point of view are described. Lastly, a comparative analysis is carried out where the benefits and drawbacks of each strategy are assessed, and then some suggestions are put forward for the future research. [ABSTRACT FROM AUTHOR]