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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]

This study focuses on microgrid systems incorporating hybrid renewable energy sources (HRESs) with battery energy storage (BES), both essential for ensuring reliable and consistent operation in off-grid standalone systems. The proposed system includes solar energy, a wind energy source with a synchronous turbine, and BES. Hybrid particle swarm optimizer (PSO) and a genetic algorithm (GA) combined with active disturbance rejection control (ADRC) (PSO-GA-ADRC) are developed to regulate both the frequency and amplitude of the AC bus voltage via a load-side converter (LSC) under various operating conditions. This approach further enables efficient management of accessible generation and general consumption through a bidirectional battery-side converter (BSC). Additionally, the proposed method also enhances power quality across the AC link via mentoring the photovoltaic (PV) inverter to function as shunt active power filter (SAPF), providing the desired harmonic-current element to nonlinear local loads as well. Equipped with an extended state observer (ESO), the hybrid PSO-GA-ADRC provides efficient estimation of and compensation for disturbances such as modeling errors and parameter fluctuations, providing a stable control solution for interior voltage and current control loops. The positive results from hardware-in-the-loop (HIL) experimental results confirm the effectiveness and robustness of this control strategy in maintaining stable voltage and current in real-world scenarios. [ABSTRACT FROM AUTHOR]

This paper proposes a distributed robust frequency control (DRFC) scheme for industrial applications that can effectively adjust the frequency and regulate the active power-sharing ratio of islanded microgrids (MGs). The proposed method also enhances the H∞ performance and the transient response of the MGs by imposing pole placement constraints in the linear matrix inequality (LMI) framework. The proposed scheme utilizes the information on active power and frequency deviations of each distributed generator (DG) and its neighboring DGs to generate an auxiliary control signal implemented by the secondary control layer. The auxiliary control signal improves the input of the droop controller block at the primary control layer, ensuring both frequency regulation and proportional active power-sharing among the DGs. To show the robustness of this novel feature, an AC-islanded MG is simulated under various load and fault scenarios. The simulation results demonstrate the effectiveness and efficiency of the proposed scheme in stabilizing the frequency and damping ratio of the MG while maintaining an active power-sharing balance. [ABSTRACT FROM AUTHOR]

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

Abstract 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.

Abstract 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.

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 Before starting stability analysis of the multivirtual synchronous machine (n‐VISMA) power system, it is necessary to obtain the steady‐state operating points (SSOPs) of all dynamic nodes in the network. Modified traditional iterative schemes using the concept of droop bus technique in an islanded microgrid are not feasible for load flow analysis of VISMA microgrid incorporating non‐control dynamics. This paper proposes closed‐form steady‐state, fundamental‐frequency models for islanded VISMA microgrids using the concept of virtual swing buses. In this technique, the virtual internal buses of all VISMAs in the network are governed by the swing equation. The voltage at all buses is variable except the virtual buses in which the pole wheel voltages are prespecified. The algorithm was extended by a droop control localized to each VISMA. The suitability of the proposed algorithm to obtaining SSOPs of VISMA was tested on IEEE‐9 bus system with VISMA replacing electromechanical synchronous machines and also on a low‐voltage distribution system. To validate the applicability of the proposed algorithm and prove its accuracy, the case study systems were also modeled in the SIMULINK environment for detailed time domain analysis. The algorithm was found to be computationally effective for a load flow analysis of the VISMA microgrid. The results also reveal that the addition of external droop control improves the frequency stability of the system.

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]

Before starting stability analysis of the multivirtual synchronous machine (n‐VISMA) power system, it is necessary to obtain the steady‐state operating points (SSOPs) of all dynamic nodes in the network. Modified traditional iterative schemes using the concept of droop bus technique in an islanded microgrid are not feasible for load flow analysis of VISMA microgrid incorporating non‐control dynamics. This paper proposes closed‐form steady‐state, fundamental‐frequency models for islanded VISMA microgrids using the concept of virtual swing buses. In this technique, the virtual internal buses of all VISMAs in the network are governed by the swing equation. The voltage at all buses is variable except the virtual buses in which the pole wheel voltages are prespecified. The algorithm was extended by a droop control localized to each VISMA. The suitability of the proposed algorithm to obtaining SSOPs of VISMA was tested on IEEE‐9 bus system with VISMA replacing electromechanical synchronous machines and also on a low‐voltage distribution system. To validate the applicability of the proposed algorithm and prove its accuracy, the case study systems were also modeled in the SIMULINK environment for detailed time domain analysis. The algorithm was found to be computationally effective for a load flow analysis of the VISMA microgrid. The results also reveal that the addition of external droop control improves the frequency stability of the system. [ABSTRACT FROM AUTHOR]

For islanded microgrids (MGs), distributed control is regarded as a preferred alternative to centralized control for the frequency restoration of MGs. However, distributed control with successive communication restricts the efficiency and resilience of the control system. To address this issue, this paper proposes a distributed event-triggered control strategy for the frequency secondary control in islanded MGs. The proposed event-triggered control is Zeno behavior free and enables each DG to update and propagate its state to neighboring DGs only when a specific “event” occurs, which significantly reduces the communication burden. Compared with the existing event-triggered control, a trigger condition checking period of the proposed event-triggered control is provided to reduce the computation burden when checking the trigger condition. Furthermore, using the aperiodicity and intermittent properties of the communication, a simple detection principle is proposed to detect and isolate the compromised communication links in a timely and economic fashion, which improves the resilience of the system against FDI attacks. Finally, the control effectiveness of the proposed control scheme is validated by the simulation results of the tests on an MG with 4 DGs.

Islanded microgrids, powered by renewable energy sources, offer a sustainable electricity solution for remote areas. However, maintaining frequency stability in these systems remains a challenge due to the intermittent nature of renewables. This research proposes an approach to enhance microgrid stability by integrating a green hydrogen energy storage system (GHESS) and employing advanced AI-based control strategies. The GHESS plays a pivotal role in storing excess renewable energy as hydrogen and then converting it back to electricity when needed, reducing reliance on traditional backup generators. To optimize microgrid performance, a hybrid single-neuron PID (SNPID) controller, augmented by machine learning techniques, is developed and compared against conventional proportional, integral, and derivative PID and fuzzy self-tuning PID (FSTPID) controllers. The system’s performance was evaluated using four realistic scenarios. In all cases, the SNPID controller significantly outperformed the existing options. It achieved a 58% reduction in frequency fluctuations compared to the fuzzy self-tuning PID (FSTPID) controller and an impressive 87% reduction compared to the traditional PID controller. The Simulation results underscore the SNPID controller’s exceptional performance in frequency stability, emphasizing the transformative potential of AI for microgrid management.

The existence of imbalanced single- and two-phase loads, coupled with short-circuit faults, contributes to voltage imbalance. It is inevitable to use LCL filters to remove harmonics caused by converter switching. While effective in removing harmonic components, these filters may inadvertently amplify and propagate resonance. Both voltage imbalance and active resonance significantly degrade power quality. This paper proposes a multi-level hierarchical control system to mitigate both phenomena. At Level 1, the controller incorporates proportional-resonant regulators for current and voltage. Level 2 employs common droop control to establish microgrid voltage and frequency reference values. Level 3 introduces a washout filter with an adjustable Kd gain, capable of attenuating active resonance harmonics originating from the LCL filter. Finally, Level 4 integrates a controller based on extracting positive and negative sequence components using the dual decoupled synchronous reference frame (DDSRF) algorithm. This controller effectively compensates for negative sequence components of voltage in the load bus, balancing load voltage. Simulation results for a standalone microgrid demonstrate that the proposed 4-level hierarchical control algorithm can reduce the load bus voltage imbalance from 10% to 2.5% under the most challenging conditions. It diminishes harmonic distortion caused by active resonance from 11% to 2.5%. The presented control algorithm can perform optimally under dynamic changes of active and reactive power of nonlinear and imbalanced loads. The main merit of this approach is not to use physical devices and Flexible Alternative Current Transmission Systems (FACTS), such as Active Power Filters (APF), Distribution Static Synchronous Compensators (DsSTATCOM), SVC, etc.

This study focuses on microgrid systems incorporating hybrid renewable energy sources (HRESs) with battery energy storage (BES), both essential for ensuring reliable and consistent operation in off-grid standalone systems. The proposed system includes solar energy, a wind energy source with a synchronous turbine, and BES. Hybrid particle swarm optimizer (PSO) and a genetic algorithm (GA) combined with active disturbance rejection control (ADRC) (PSO-GA-ADRC) are developed to regulate both the frequency and amplitude of the AC bus voltage via a load-side converter (LSC) under various operating conditions. This approach further enables efficient management of accessible generation and general consumption through a bidirectional battery-side converter (BSC). Additionally, the proposed method also enhances power quality across the AC link via mentoring the photovoltaic (PV) inverter to function as shunt active power filter (SAPF), providing the desired harmonic-current element to nonlinear local loads as well. Equipped with an extended state observer (ESO), the hybrid PSO-GA-ADRC provides efficient estimation of and compensation for disturbances such as modeling errors and parameter fluctuations, providing a stable control solution for interior voltage and current control loops. The positive results from hardware-in-the-loop (HIL) experimental results confirm the effectiveness and robustness of this control strategy in maintaining stable voltage and current in real-world scenarios.

In a renewable energy-based islanded microgrid system, frequency control is one of the major challenges. In general, frequency oscillations occur in islanded microgrids due to the stochastic nature of load and variable output power of distributed generating units (DGUs). In the presented research proposal, frequency oscillations are suppressed by implementing the proportional integral derivative (PID) controller-based control design strategy for an islanded microgrid. The modeling of the islanded microgrid is firstly presented in the form of a linearized transfer function. Further, the derived transfer function is approximated into its equivalent first-order plus dead time (FOPDT) form. The approximated FOPDT transfer function is obtained by employing the reaction curve method to calculate the parameters of the FOPDT transfer function. Furthermore, the desired frequency regulation is achieved for the manifested FOPDT transfer function by incorporating PID control design. For PID controller tuning, different rule-based methods are implemented. Additionally, comparative analysis is also performed to ensure the applicability of the comparatively better rule-based tuning method. The Wang–Chan–Juang (WCJ) method is found effective over other rule-based tuning methods. The efficacy of the WCJ method is proved in terms of transient response and frequency deviation. The tabulated data of tuning parameters, time domain specifications, and error indices along with responses are provided in support of the presented control strategy. [ABSTRACT FROM AUTHOR]

Ensuring accurate current sharing and regulating output voltage is challenging in DC microgrids. Conventional droop control has limitations in achieving voltage regulation and accurate current sharing. Unequal transmission line resistances, local loads, and constant power load (CPL) add to the complexity. To tackle this issue, this paper presents a novel consensus based distributed secondary control that determines the resistance of transmission lines by injecting an external pulse to the reference voltage of each converter, and then measuring the resulting changes in output voltage and current. The proposed secondary control signal, denoted as ψi, enables simultaneous proportional current sharing and voltage restoration in the DC microgrid. To guarantee the stability of the proposed method, a comprehensive stability analysis of the system is performed. In contrast to similar methods, the proposed method enables achieving precise proportional current sharing and voltage restoration, even when faced with varying resistive loads and CPLs. Furthermore, the proposed method exhibits remarkable features including, high convergence speed, the capability to determine transmission line resistances instead of assuming them to be known, and robustness in plug-and-play scenarios and communication system failures. The proposed method is validated through simulations in MATLAB and experimental tests, affirming its effectiveness. [ABSTRACT FROM AUTHOR]

Pre-synchronization control is needed when the microgrid changes from an off-grid state to a grid-connected state. Aiming to resolve the problems of frequency overstep and voltage fluctuation in traditional pre-synchronous grid-connection schemes, a micro-grid pre-synchronous grid-connection scheme is proposed that can solve both frequency overstep and voltage fluctuation at the same time. Firstly, the feedforward disturbance compensation V/f control strategy of the load voltage and current is introduced to the energy storage system to suppress voltage fluctuations caused by power changes. Then, the V/f control strategy of the energy storage system is improved to suppress voltage fluctuations caused by frequency fluctuations when the starting phase of pre-synchronization is enacted. Finally, PI parameters are optimized based on the golden jackal optimization algorithm to meet the requirements of frequency fluctuation and grid-connection speed in the pre-synchronization process. A large number of simulation experiments demonstrate the effectiveness of the proposed method. [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]

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.

In order to solve the problems such as significant frequency fluctuation and slow dynamic response under load disturbance in islanded microgrid which has a diesel generator (DG) as the primary source, an auxiliary frequency regulation method is proposed to improve the dynamic response process of the system. Firstly, the model of diesel generator system is established to analyze the impact on transient state due to main parameters, including inertia and response delay. Secondly, the energy storage system (ESS) with auxiliary frequency regulation based on droop control is proposed, and the engineering parameter and ESS′s power margin design method are given. In addition, a small signal model of the DG-ESS system is established to prove the stability of the system. Finally, the experimental platform of DG-ESS microgrid is built, and the results verify that the proposed control strategy effectively reduces the transient frequency fluctuation under the change of static or motor load.

The interest in the integration of distributed energy resources in microgrids increased significantly in the last decade. The stochastic nature of some energy sources and the dynamic power demand has brought difficulties regarding the optimal control of microgrids. This current study addresses the energy management challenge in an islanded hybrid energy microgrid that includes three types of renewable energy resources (photovoltaic, geothermal and biomass) and a battery storage system, using intelligent management methods that optimize energy production costs. The proposed intelligent management methods are based on cost function minimization. These ensure optimal energy management operation under uncertain weather conditions, keeping to the minimum the price of the energy produced by the microgrid. Due to the erratic nature of solar irradiance and considering that solar energy is cheaper than its counterparts (geothermal and biomass in this case), forecasting the photovoltaic power production is an essential task for guaranteeing optimal microgrid operation. Day-ahead photovoltaic power forecasts are issued based on solar irradiance forecasts provided by the Global Forecasting System and further integrated into the day-ahead scheduling of the microgrid. This algorithm, together with the cost-optimization algorithms, gives intelligence to the management protocol generating the input parameters (power and operating time for each generating unit) for the microgrid Energy Management Controller. Different cost optimization methods are studied and compared, including mathematical models, such as Mixed Integer Linear Programming, or nature-inspired optimization algorithms, such as Genetic Algorithm, Harmony Search Algorithm, and Particle Swarm Optimization. It is shown that these algorithms provided suitable solutions for the energy balance and scheduling ensuring minimal operation costs for the microgrid. The study also highlights the differences and limitations of these algorithms and shows a method on how these energy management methods can be integrated into a real-world microgrid providing a step forward in the development of intelligent microgrids.

The increasing interest in renewable energy-based power systems globally is driven by their abundance and environmentally friendly attributes. Islanded hybrid microgrid systems (IHMS) are a relatively new development in this field and involve the integration of two or more sustainable sources, such as wind turbines, solar photovoltaic (PV) systems, and other forms of renewable energy such as the ocean, wave, and geothermal energy. In order to ensure an uninterrupted power supply for the growing community and industrial sector of Perhentian Island, Malaysia, alternative power sources must be properly synchronized and managed through an energy management system. To this end, the main contribution of this study is the comprehensive analysis of various optimization methods in terms of net present cost (NPC) and convergence rate. The results of the analysis indicate that HOMER proved to be relatively faster in terms of convergence rate, with the NPC recorded as 387,185$ and the Levelized Cost of Energy (LCOE) recorded as 0.64$/kWh, which are the least among the other techniques evaluated. The hybrid energy system was designed to acquire the optimal quantity and size of power-generating modules, including PV systems, wind turbines, batteries, and diesel generators, while also meeting the load requirements. The optimization problem incorporated the LCOE and NPC into the cost function. Various optimization techniques were developed and tested. In addition, an advanced control method, which includes the use of Proportional–integral–derivative (PID) control and Fuzzy Logic Controller (FLC) with automated tuning, was applied to manage voltage and frequency. The control strategy was implemented in MATLAB Simulink, along with a full model of the islanded hybrid microgrid system. The simulation results demonstrate the effectiveness of the proposed FLC in maintaining the voltage and frequency within the acceptable range during various operating conditions. In conclusion, this manuscript provides a comprehensive study on the optimization and control of a solar-wind islanded hybrid microgrid. The proposed approach can be used as a valuable tool for the design and operation of solar-wind islanded hybrid microgrids in remote and islanded communities.

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.

Augmenting the range of the active droop gain in inverter-based microgrids (IBMGs) is required for faster power sharing and improved control at the secondary level. However, IBMG stability is degraded when active droop gain is increased, which reveals an obvious tradeoff. Therefore, fractional-order control (FOC) is used in this paper to increase the inverter-based (IB) MGs’ stability margin by increasing the stable range of the active droop gain. So, a thorough small-signal model of IBMGs is developed to assess the MG stability and tune the control parameters for the case of using FOCs. Also, a fractional-order low-pass filter (FO-LPF) is proposed for the outer control loop which increases the damping factor and reduces the power oscillations at the same active droop gain. Moreover, a further boost in stability margin is achieved by combining the proposed FO-LPF in the power control loop with FO-PI controllers in the inner control loops. The stability results of the developed FO small-signal model are validated on a MATLAB/SIMULINK MG stability benchmark system. Also, a comparative case study has shown the superiority of the proposed approach over recent ones in the case of a reconfigured and meshed network. Moreover, the discretization scheme of FOCs is outlined and tested under a load disturbance event.

Islanded microgrids with low-inertia distributed energy resources (DERs) are prone to frequency fluctuations. With the increasing integration of DERs in microgrids, the complexity of control and stability has also increased. Moreover, the integration of DERs into microgrids may result in a power imbalance between energy supply and demand during sudden changes in load or energy generation. This can cause frequency variations in the microgrid, which could have disastrous consequences such as equipment damage or even blackouts. This paper proposes a control strategy to ensure the efficient operation of an islanded hybrid microgrid consisting of a PV generator, battery energy storage system (BESS), and emergency diesel generator. According to Energy Exchange Model proposed in this paper, the hybrid system presented operates independently without being connected to the electrical grid, where the PV system and BESS act as the main energy sources, while the emergency diesel generator provides active power backup with voltage and frequency regulation. The novel in this paper is also that DER aids in frequency regulation during active power transients by delivering and absorbing active power in accordance with the inverter's suggested P droop control strategy. In this way inverter droop control decreases system frequency nadir emulating so called "synthetic inertia". To design both the islanded hybrid system and the proposed control strategy, the MATLAB/Simulink environment is utilized. Based on the results, it can be concluded that the analyzed microgrid system is capable of maintaining stability and operating efficiently in a range of operating conditions. [ABSTRACT FROM AUTHOR]

Controlled power converters have found many applications in the power industry during the last decade. Virtual synchronous generator (VSG) is a truly good example of these converters that can be used to induce desired dynamics to the network. Despite the excellent performance of VSGs in balanced conditions, they do not carry the flexibility to suppress unbalanced components. To deal with this conflicting issue, a decoupled double synchronous reference frame-based VSG is proposed in this paper. Applying this method to the VSG structure under unbalanced voltage conditions by decoupling the positive and negative components, the oscillating terms are mostly eliminated, and DC values are extracted with the aim that the controllers can perform better. The benefits of the proposed scheme include higher accuracy, better controllability, effective component separation, and robust performance under heavy load conditions. The simulation results prove the validity and effectiveness of the proposed strategy against traditional VSG and previous techniques presented to enhance VSGs under unbalanced conditions in an islanded microgrid. [ABSTRACT FROM AUTHOR]

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Proportional reactive power sharing among the distributed generators (DGs) is still a challenge in an islanded microgrid. The disparity in output voltage of each DG is the main cause of the error in reactive power sharing in DGs. The output voltage difference of DGs is caused by unequal line impedances and connected local loads. In order to reduce the error in reactive power sharing among the DGs, a Robust Volt-VAr control scheme (RVVCS) with an adaptive nominal voltage is proposed in this study. Since the suggested strategy is entirely decentralized, it does not need knowledge of feeder impedance or network configurations. Also, it does not require any communication link or optimization technique. The technique proposed is tested on several network configurations, including various loads, radial networks, reconfigured networks, and mesh networks, and is found satisfactory in each case. In one of the many cases presented in this study, with n q = 3 e - 4 V/VAr, β = 0.2, β add = 0.002 and local as well as common load is connected, the suggested RVVCS minimises the error in reactive power output of D G 1 , D G 2 and D G 3 to (42.68%, - 8.92%, - 33.82%), respectively, compared to the error obtained in RDCS (72.21 %, - 15.61%, - 56.6%), SADCS (72.6%, - 16%, - 56.6%) and CDCS (243.74%, - 69.87%, - 173.87%). A similar reduction pattern in reactive power output is also observed in all the case studies performed in this research. The stability of the proposed strategy is investigated in terms of eigenvalue analysis. The practicability of the proposed technique is validated in time-domain simulation in MATLAB/Simulink. [ABSTRACT FROM AUTHOR]

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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]

According to the characteristics of islanded microgrid with renewable energy and load uncertainty, a robust economic dispatch algorithm for islanded microgrid was proposed based on echelon deviation uncertainty set. The microgrid model was set up for its structural characteristics, and the uncertainty set was converted into the uncertainty set with echelon deviation. In the day-ahead scheduling stage, the outputs of each device of the microgrid were optimized to obtain the day-ahead scheduling scheme with the optimal solution for all scenarios feasible and performance of the expected set of scenarios optimal. In the intra-day scheduling stage, it maintains the power output of the energy storage system obtained in the day-ahead scheduling stage, and adjusts the day-ahead optimal solution of micro-gas turbine, curtailment of photovoltaic power and wind power, and shiftable load to improve the economic performance through a secondary optimization utilizing the real-time data of new energy and load. The results show that the proposed method achieves better control performance and improves the economy of microgrid dispatch. Finally, the effectiveness of the method is verified by a simulation case. [ABSTRACT FROM AUTHOR]

In a high percentage of new energy-islanded microgrids, the overall inertia of the system gradually decreases, and the transient stability requirements of the microgrid frequency and voltage become more and more demanding under low-inertia conditions. To improve the transient stability of low-inertia islanded microgrid frequencies and voltages, this paper proposes a transient stability enhancement strategy for islanded microgrids based on energy storage system (ESS)–virtual synchronous generator (VSG) control. Model predictive control (MPC) is added within the active control loop of the VSG to achieve dynamic correction of the active power reference value of the VSG; PI control link is added within the reactive control loop to achieve a fast dynamic response of the reactive power command value. The ESS achieves fast and accurate regulation of frequency and voltage according to the power reference value of the VSG active control loop and the power command value of the reactive control loop simultaneously. Considering the need to ensure the ability of VSG to operate stably during transients, a comprehensive current-limiting technique combining virtual impedance and phase limiting is used to limit the fault current of VSG and maintain its synchronization and stability. Finally, the simulation results verify the strategy's effectiveness and the superiority of the transient stability enhancement effect. [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]

This paper proposes a homotopy-based approach to solve the power flow problem (PFP) in islanded microgrid networks with droop-controlled distributed generation (DG) units. The technique is based on modifying an "easy" problem solution that evolves with the computation of intermediate results to the PFP solution of interest. These intermediate results require the solution of nonlinear equations through Newton–Raphson (NR) method. In favor of convergence, the intermediate solutions are close to each other, strengthening the convergence qualities of the technique for the solution of interest. The DG units are modeled with operational power limits and three types of droop-control strategies, while the loads are both magnitude voltage- and frequency-dependent. To evaluate the method performance, simulations are performed considering the proposed and classical NR methods, both departing from a flat start estimation. Tests are carried out in three test systems. Different load and DG unit scenarios are implemented for a 6-, 38-, and 69-bus test system. A base case is studied for all systems, while for the two larger models, a loading factor is used to simulate the load augmenting up to the maximum value. The results demonstrated that for the largest-size model system, only the homotopy-based approach could solve the PFP for stringent requirements such as the diversification of the load profile and hard loading operation point. [ABSTRACT FROM AUTHOR]

When multiple paralleled distributed generation (DG) units operate in an islanded microgrid, accurate power sharing of each DG unit cannot be achieved with a conventional droop control strategy due to mismatched feeder impedance. In this paper, a small AC signal (SACS)-injection-based modified droop control method is presented for accurate active and reactive power sharing among DG units. The proposed control method adjusts the voltage amplitude of each DG unit by injecting small AC signals to form a reactive power control loop. This strategy does not need communication links or to specifically obtain the physical parameter of the feeder impedance and only requires the local information. Moreover, the parameter design procedure and stability analysis are given full consideration. Finally, simulation and experimental results verify the effectiveness of the proposed control scheme, and accurate active and reactive power sharing can be achieved. [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]

Abstract The high proportion of nonlinear and unbalanced loads results in power quality issues in islanded microgrids. This paper presents a novel control strategy for harmonic and unbalanced power allocation among distributed generators (DGs) in microgrids. Different from the existing sharing strategies that allocate the harmonic and unbalanced power according to the rated capacities of DGs, the proposed control strategy intends to shape the lowest output impedances of DGs to optimize the power quality of the microgrid. To achieve this goal, the feasible range of virtual impedance is analyzed in detail by eigenvalue analysis, and the findings suggest a simultaneous adjustment of real and imaginary parts of virtual impedance. Because virtual impedance is an open-loop control that imposes DG to the risk of overload, a new closed-loop structure is designed that uses residual capacity and absorbed power as feedback. Accordingly, virtual impedance can be safely adjusted in the feasible range until the power limit is reached. In addition, a fuzzy integral controller is adopted to improve the dynamics and convergence of the power distribution, and its performance is found to be superior to linear integral controllers. Finally, simulations and control hardware-in-the-loop experiments are conducted to verify the effectiveness and usefulness of the proposed control strategy.

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.

This article provides insights into the dynamic performance of indirect and direct control strategies for grid-forming (GFM) inverters. The indirect method for grid-forming inverters utilizes three cascaded stages to produce the reference signal sent to the PWM generator. However, the direct method has only one stage. The indirect method consists of the outer loop, which controls the power, the middle loop, which regulates voltage, and the inner loop, which controls the output current. The direct method does not require the middle and inner control loops. The article compares the performance and sensitivity of both indirect and direct control methods under various conditions, including static, inductive and dynamic loads, by adjusting controller gain parameters. A virtual reactance inrush current mitigation technique is implemented with the direct method to provide momentary control over inrush caused by the dynamic loads. The direct method incorporates a feedforward technique known as virtual reactance inrush current mitigation to outperform the performance of the direct control method in the presence of dynamic and inductive loads. The performance of both control methods is experimentally compared, and the direct method outperforms the indirect method. Experimental analyses are performed in a laboratory-scaled microgrid supplied by a 208V, one 5kVA SiC inverter, and one 10kVA Si-based inverter to compare the performance of the indirect and direct methods under different test scenarios, and the outcomes are presented in this article.