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The distribution system's economic operation is significantly impacted by the management of distributed generation (DG) resources, energy storage (ES), and controllable loads. The paper employs a smart distribution system that incorporates dispatchable and non‐dispatchable DG resources, as well as battery storage, in addition to the demand response (DR) scheme. New modelling was performed in hourly steps to achieve the optimal unit commitment. In smart homes, appliances are prioritized and classified into four types: adjustable, interruptible, shiftable, and uncontrollable loads. Load reduction in smart homes is also considered based on load prioritization and customer participation in the DR scheme to achieve the proposed scheme's objectives. The proposed method considers costs associated with microturbines (MTs), including manufacturing, start‐up, shutdown, and pollution. Additionally, planning is conducted to purchase or sell electricity to the upstream network. The simulation is run on an IEEE 33‐bus system to demonstrate the proposed method's effectiveness. The system is assumed to be capable of operation in both island and grid‐connected modes. The results demonstrate the proposed approach's efficacy in load reduction, operation cost, and execution time. [ABSTRACT FROM AUTHOR]

An editorial is presented on smart grid vulnerability, cybersecurity, and protection being accepted for publication. Topics include improving the stability of the entire power system with large-scale integration of renewable energy systems; and objective optimization technique for the planning of a networked microgrid based on peer-to-grid (P2G) and peer-to-peer (P2P) energy trading schemes.

The integration of distributed energy resources and smart grid technologies has increased the vulnerability and complexity of modern power systems, especially in the face of extreme events. Enhancing the resilience of power systems is crucial to ensure continuous and reliable electricity supply during and after such events. This paper proposes a preventive framework to enhance the resilience of networked microgrids (NMGs) during extreme events. The framework integrates optimal energy management strategies and preventive resilience enhancement measures, focusing on the coordination and utilization of hydrogen (H2) energy systems and controllable distributed generators (CDGs). The framework leverages capacity‐based signals to prompt H2 system owners and CDG operators to prepare for disruptions by fully charging H2 storage tanks and pre‐scheduling CDG commitments. By utilizing this proactive energy scheduling, critical loads can be supplied through stationary fuel cells and CDGs, improving overall network resilience. The proposed resilient energy management approach combines optimization techniques to improve the resilience of NMGs and reduce their dependence on the main grid. Numerical simulations demonstrate the effectiveness of the proposed linear model in enhancing the resilience of NMGs and improving their operational performance during extreme events. The contributions of this paper include advancements in understanding NMGs, the development of a preventive framework, integration of H2 energy systems and CDGs, and proposal of effective signalling models. [ABSTRACT FROM AUTHOR]

In recent years, the interest in using DC microgrids has greatly increased due to their higher efficiency, less complexity, and greater transmission power compared to AC microgrids. To address challenges in DC microgrids in the presence of electrical vehicles (EVs) and the uncertainty of charging EVs, researchers have used PV/EV combination systems with energy storage systems (ESS). Controlling DC microgrids, including PV/EV/ESS, is crucial to cope with the existing challenges. On the other hand, the research on DC microgrids' protection systems is in the early stages, and there are still many challenges in this field. In addition, differences in control systems can pose different challenges for the protection system. The simulation results in this paper demonstrate that considering the best case (use of unipolar resistance grounding system) the DC bus voltage is improved by 22.3% (based on MAPE% data comparison). In the protection part, the empirical‐mode decomposition (EMD) method and selecting a suitable intrinsic mode functions (IMF) have been used to protect the DC microgrid. The proposed protection method has been tested under pole‐to‐pole and pole‐to‐ground fault conditions. The results show that the proposed method is capable of detecting various fault types in the studied microgrid. [ABSTRACT FROM AUTHOR]

Stability issues can significantly increase the risk of hybrid microgrids (HMGs), particularly during island mode operation. The dynamic performance of the system can induce constraints and stability margins that may elevate the loss of load probability. This paper presents a new stability‐oriented risk assessment model that bridges the conventional reliability models, stability, and system risk. The proposed model ensures the risk of the system by considering the redesign or reconfiguration of HMGs to address stability issues. First, the interlinking converters (ICs) DC‐link voltage stability is analysed to determine the acceptable power flow margins in rectifying and inversion mode. Next, the new general risk assessment model is introduced. The results show that the stability margin significantly increases the risk of the HMG, particularly when considering the aging of converters. The study also examines the impact of various load characteristics and ICs with different numbers but the same total size. In some cases, the risk is acceptable for the desired loads, or it can be reduced to an admissible level by reconfiguring the ICs. Finally, the paper demonstrates the effectiveness of the proposed model in the optimal design of HMGs, aiming to guarantee the system's risk. [ABSTRACT FROM AUTHOR]

The aggregation of the remaining battery capacity of electric vehicles (EVs) can be used as distributed energy storage to participate in the microgrid optimisation through vehicle‐to‐grid (V2G) technology. However, the reliability of this service to be delivered is affected by the uncertainty of EVs' behaviour. The un‐reliable service will bring risks in the operation of microgrid when EVs' V2G electricity is regarded as an important flexibility resource. This paper, first proposes an analytical method to quantify the reliability of EV aggregation's (EVA's) V2G electricity based on the analysis of historical charging data and compound Poisson process. Based on this model, electric vehicles aggregators can evaluate the V2G bid quantity for any time slot on day t+1 according to the reliability requirements. Secondly, an optimal operation model of microgrid with consideration of V2G's reliability is considered. Finally, a test system with photovoltaics (PVs) and EVA on top of the original IEEE 33‐node system is used to verify the effectiveness of the proposed model. The results show that with the proposed reliability evaluation method of V2G electricity, the optimal operation of the microgrid can be reached with appropriate evaluation of the capability of the EVA. [ABSTRACT FROM AUTHOR]

The multi‐microgrids operation can greatly improve the proportion of renewable energy integration and power reliability of rural microgrids through energy trading between adjacent microgrids. Therefore, this paper proposes an optimization method for the low‐carbon economic operation of rural microgrids which contain wind power, photovoltaic, biogas, and other common rural renewable energy sources. Using the asymmetric Nash bargaining model which can form the non‐linear energy mapping function to quantify the bargaining power of each microgrid to establish the multi‐microgrids energy trading model, and the non‐convex trading optimization problem is equivalently transformed into two convex optimization subproblems, subproblem 1 is microgrids alliance costs containing the emission costs, and subproblem 2 is cooperative benefits distribution. Then, the accelerated Alternating Direction Method of Multipliers (ADMM) method is used to solve subproblem 1 and subproblem 2, respectively, and the privacy of transaction information is also protected. The results show that: compared with the single‐microgrid operation, the combined operation of rural multi‐microgrids with biogas can greatly improve the renewable energy consumption rate, reduce carbon emission, and improve the profit income of the alliance and each microgrid. Meanwhile, the accelerated ADMM algorithm used can greatly improve the solution speed. [ABSTRACT FROM AUTHOR]

Fast and accurate fault location in DC power systems is of particular importance to ensure their reliable operation. One of the approaches for implementing a fast‐tripping protection scheme is to use Traveling waves (TW) initiated by a fault scenario. This paper proposes a physics‐informed machine learning approach that utilizes TWs for fault location in DC microgrids. TWs are extracted by the so‐called multiresolution analysis which identifies the TW's wavelet coefficients for multiple frequency ranges. This paper deploys Parseval's theorem to find the energy of wavelet coefficients as a quantitative metric for describing TWs. The hypothesis of this paper is that once the Parseval energy curves for a specific cable are extracted, they can be utilized to locate faults along with that cable regardless of the DC system in which the cable is deployed. The fault location algorithm uses Parseval energy curves to train a Gaussian Process (GP) estimator. With the Parseval energy values of measured current at the protection device location, the GP estimator is able to estimate fault locations with high accuracy. The effectiveness of the proposed algorithm is verified by simulating a DC microgrid system in PSCAD/EMTDC. [ABSTRACT FROM AUTHOR]

This paper emphasizes the integration of wind and photovoltaic (PV) generation with battery energy storage systems (BESS) in distribution networks (DNs) to enhance grid sustainability, reliability, and flexibility. A novel multi‐objective optimization framework is introduced in this study to minimize energy supply costs, emissions, and energy losses while improving voltage deviation (VD) and voltage stability index (VSI). The proposed framework comprising normal boundary intersection (NBI) and decomposition‐based evolutionary algorithms (DBEA) determines the optimal siting and sizing of renewable‐based distributed resources, considering load demand variations and the intermittency of wind and solar outputs. The comparative analysis establishes that the proposed strategy performs better than many contemporary algorithms, specifically when all the objective functions are optimized simultaneously. The validation of the proposed framework was carried out on the standard IEEE‐33 bus test network, which demonstrates significant percentage savings in energy supply costs (49.6%), emission rate (62.2%), and energy loss (92.3%), along with enormous improvements in VSI (91.9%) and VD (99.8953%). The obtained results categorically underline the efficiency, reliability, and robustness of the proposed approach when employed on any complex distribution network comprising multiple renewable energy sources and battery storage systems. [ABSTRACT FROM AUTHOR]

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

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

This paper summarizes our recent work on Grid‐Forming Inverter (GFM) application to power systems. We have developed a novel design of GFM, a single‐phase synchronous inverter (SSI) for the conventional 100/200V distribution network based on the concept of "non‐interference core (NIC) dynamic model." This paper first explains the design concept of NIC‐SSI and a simulation model of SSI for power system analysis. Then the stabilization effect is investigated, where the installation of SSI on the single‐phase consumer side is assumed as a new concept. The improvement of frequency and transient stability are evaluated. The SSI model is verified by comparing the developed root mean square (RMS) simulation, hardware‐in‐the‐loop (HIL) simulation, and the experiment using SSI hardware. The simulation results show that the SSI has the considerable ability of grid stabilization. Singlephase micro‐grid (SMG) operations using SSIs are also presented. The effectiveness of SMG operations is shown based on laboratory experiments. [ABSTRACT FROM AUTHOR]

Renewable energy sources (RES) are increasingly being developed and used to address the energy crisis and protect the environment. However, the large‐scale integration of wind and solar energy into the power grid is still challenging and limits the adoption of these new energy sources. Microgrids (MGs) are small‐scale power generation and distribution systems that can effectively integrate renewable energy, electric loads, and energy storage systems (ESS). By using MGs, it is possible to consume renewable energy locally and reduce energy losses from long‐distance transmission. This paper proposes a deep reinforcement learning (DRL)‐based energy management system (EMS) called DRL‐MG to process and schedule energy purchase requests from customers in real‐time. Specifically, the aim of this paper is to enhance the quality of service (QoS) for customers and reduce their electricity costs by proposing an approach that utilizes a Deep Q‐learning Network (DQN) model. The experimental results indicate that the proposed method outperforms commonly used real‐time scheduling methods significantly. [ABSTRACT FROM AUTHOR]

Residential buildings may use energy storage, flexible loads, and renewable energy sources to reduce energy consumption and increase demand side flexibility. The flexibility of a single building can be coordinated with other facilities in a transactive energy (TE) market to reduce energy costs. In addition, cloud energy storage (CES) has been proposed to provide storage services for residential buildings with more economic benefits than individual energy storage units in recent years. Although the TE market and CES implementation have received much attention in previous works, a suitable structure for CES participation in TE market has not been addressed. Furthermore, previous studies ignored all or some sources of uncertainties in the TE decision making process. This paper presents a stochastic optimization model in a transactive energy framework based on a distributed optimization algorithm for peer‐to‐peer energy trading using the alternating direction method of multipliers in the presence of CES. This paper considers the uncertainties of the inflexible load demand, renewable energy generations, and market prices using an artificial neural network‐based scenario generation and reduction methodology. Numerical results show improvements toward addressing the challenges of the uncertainties while maximizing the CES's owner revenue and minimizing the customers' costs in the proposed model. [ABSTRACT FROM AUTHOR]

Resilience is one of the main features of smart distribution networks, and a microgrid (MG) access to the distribution network provides an effective way to improve resilience. MG and distribution network belong to different interests, so it is necessary that MGs and flexible resources are actively guided through price leverage. In this way, MGs take part in the post‐disaster restoration and enhance its resilience. Firstly, this paper proposes a dynamic restoration electricity price response mechanism after extreme disasters and constructs a power response model for loads and electric vehicles within the MGs. Secondly, the optimal scheduling model of the distribution network with multiple‐microgrids (MMG) is proposed to improve the restoration rate of critical loads (RRCL). Single microgrid achieves the largest microgrid revenue and restoration contribution, and MMG uses the power headroom index to optimize the dynamic restoration electricity price to achieve the smallest power purchase cost of distribution network. Finally, the optimal scheduling method for resilience enhancement of distribution networks with MMG considering dynamic restoration electricity price response mechanism is validated by dual microgrid access to an IEEE 33‐node distribution system. The simulation results show that the proposed optimization method effectively improves the RRCL of distribution network. [ABSTRACT FROM AUTHOR]

Autonomous microgrid is known to lack appropriate inertia and damping for grid stabilization. Due to this, virtual synchronous machine (VISMA) has been introduced to provide necessary ancillary services through control of power converters. In a multi‐VISMA (n‐VISMA) microgrid, relative rotor angle stability of the power system is dependent on the active power balance after small perturbation. Thus, the use of relevant analytical models are essential issues for microgrid stability analysis. This paper presents a comprehensive small‐signal stability analysis to study inherent electromechanical oscillations in the virtual rotors. The subsystems of the microgrid consisting of VISMA, network, load and the outer power control were all modelled in Synchronous Reference Frame. The small‐signal model (SSM) was tested on IEEE‐9 bus system with VISMA replacing electromechanical synchronous machines on the network. To validate the developed numerical analytics, dynamic responses of the SSM are compared with those of the non‐linear (NL) system dynamics and the results reveal that the developed linearized SSM is sufficient to accurately characterize behaviour of the VISMA microgrid when operated in autonomous mode. Eigenvalues analysis and parameter sensitivities of the critical modes were investigated. Oscillatory participations of the VISMAs and steady state stability limit of the microgrid have also been investigated. [ABSTRACT FROM AUTHOR]

A machine learning‐based optimized droop method is suggested here to simultaneously reduce the production cost (PC) and power line losses (PLL) for a class of direct current (DC) microgrids (MGs). Traditionally, a communication‐less technique known as the hybrid droop method has been employed to decrease PC and PLL in DC MGs. However, achieving the desired reduction in either PC or PLL requires arbitrary adjustments of weighting coefficients for each distributed generator in the conventional hybrid droop method. To address this challenge, this paper introduces a systematic approach that capitalizes on the benefits of artificial intelligence to accurately predict both the PC and PLL in a DC MG. Furthermore, an optimization technique relying on the gradient descendent method is employed to independently optimize both PC and PLL for each scenario. The effectiveness of the proposed method is confirmed through a comparative study with classical and hybrid droop coordination schemes under various scenarios such as rapid load changes. [ABSTRACT FROM AUTHOR]

Proper current sharing, DC bus voltage deviation reduction, and SOCs balancing, along with ensuring stability are the vital challenges of DC microgrids control algorithms. Addressing these challenges without communication links and a central controller is one of the priorities of control methods. Motivated by the above mentions, this paper presents a novel communication‐free control method. In this regard, a new parameter called "virtual current" is defined according to the unit current and its SOC. Then using a piecewise droop curve and the droop curve shift technique, the virtual current for each unit is determined. The units control coefficients and the relationship of the virtual current are allocated based on the location and power of the loads and RESs such that in the worst case; 1) SOCs are converged; 2) the DC bus voltage deviation is reduced; and 3) the current is appropriately distributed. The simulation and experimental results confirm that the proposed method can balance SOCs like SOC‐based methods and share power properly like piecewise droop methods while reducing DC bus voltage deviation. [ABSTRACT FROM AUTHOR]

The paper presents a ground fault location algorithm (GFLA) in double‐circuit transmission lines (DCTL) with a T‐off connection to an industrial microgrid (IMG) containing electric arc furnaces (EAF), renewable distributed generations (RDG), and static VAR compensator (SVC). The design presented utilizes the information on the current and voltage phasors of one side of the transmission lines (TL) during the fault period, and there is no need for the availability of information on the type of faulty phase(s) and even the faulty section. To find the exact location of the fault in this design, the voltage phasor equations governing the fault point in positive, negative, and zero‐sequence (PNZS) domains, the zero‐sequence current (ZSC) equation of the fault point, and the boundary condition equation of the fault point have been used. The simulation system is implemented in Simulink/MATLAB using accurate modelling of all network components. The algorithm has been tested and evaluated in different fault conditions, including various locations, phase types, phase‐to‐ground connection resistance, occurrence time, and microgrid (MG) operation modes. The successful results of various tests of the proposed design as well as the analysis of various sensitivities confirm the satisfying performance of the suggested algorithm. [ABSTRACT FROM AUTHOR]

The integration of the renewable energy sources (RESs) into the power grid, drives a significant transformation in the conventional power generation landscape. This transition from traditional synchronous generators to inverter based RESs introduces unique challenges in maintaining the grid frequency stability due to the reduced system inertia. The inherent stochastic nature of the RES power generation, load demand, and grid inertia includes further complexity in the assessment of frequency stability. Existing studies have limitations, including neglecting the stochastic nature of RES generation and load demand fluctuations, relying on limited metrics, and lacking a comprehensive day‐to‐day assessment. To address these shortcomings of the existing approaches, this paper introduces a novel methodology for assessing frequency stability in power grids with high RES penetration. It proposes three indices for evaluating grid frequency sensitivity, resiliency, and permissibility amidst varying RES integration. Utilizing a stochastic approach, the study incorporates uncertainties in RES generation and load demand, offering a comprehensive framework for day‐to‐day frequency stability analysis. Additionally, it presents a systematic method to ascertain the necessary inertial support for maintaining desired frequency reliability in RES‐dominated grids. The effectiveness of these methodologies is validated through a case study on a modified IEEE 39‐bus test system, demonstrating their applicability in ensuring reliable grid operation under high RES scenarios. [ABSTRACT FROM AUTHOR]

This paper proposes a fully distributed scheme to solve the day‐ahead optimal power scheduling of networked microgrids in the presence of different renewable energy resources, such as photovoltaics and wind turbines, considering energy storage systems. The proposed method enables the optimization of the power scheduling problem through local computation of agents in the system and private communication between existing agents, without any centralized scheduling unit. In this paper, a cloud‐fog‐based framework is also introduced as a fast and economical infrastructure for the proposed distributed method. The suggested optimized energy framework proposes an area to regulate and update policies, detect misbehaving elements, and execute punishments centrally, while the general power scheduling problem is optimized in a distributed manner using the proposed method. The suggested cloud‐fog‐based method eliminates the need to invest in local databases and computing systems. The proposed scheme is examined on a small‐scale microgrid and also a larger test networked microgrid, including 4 microgrids and 15 areas in a 24‐h time period, to illustrate the scalability, convergence, and accuracy of the framework. The simulation results substantiate the fast and precise performance of the proposed framework for networked microgrids compared with other existing centralized and distributed methods. [ABSTRACT FROM AUTHOR]

In the process of accessing distributed power sources in microgrid, there will be serious coupling between active power and reactive power, which will lead to significant changes in the inverter outlet voltage and impedance and increase the circulating current phenomenon. The generation of circulating current will accelerate the aging of devices, reduce the inverter efficiency, and threaten the stable operation of the system. In this paper, the mechanism of circulating current generation is analysed, the basic characteristics of circulating current are studied, and an inverter parallel circulating current suppression strategy with adaptive virtual composite impedance and droop control dynamic adjustment algorithm is proposed. The virtual impedance setting can weaken the influence of line difference on system stability, make the system equivalent output impedance resistive, weaken the inductive device characteristics and reduce the circulating current amplitude; the adaptive droop algorithm implantation dynamically correlates the control power with the droop coefficient, enhance the power decoupling accuracy and weaken the circulating current influenced by power changes. The simulation platform and experimental setup established by the large‐scale simulation software PSCAD/EMTDC are used to study the basic scenarios of the inverter under two conditions of equal and unequal capacity, and the rationality of the proposed control strategy is tested. [ABSTRACT FROM AUTHOR]

Considering the importance of the renewable energy sector in the distribution systems, energy operation, and management which are connected to the distribution network (DN) in the form of multiple microgrids (MMGs) is crucial in reducing cost and pollution. Hence, this paper aims to propose optimal energy management for MMGs in the DN. Different objective functions have been taken into account in this optimization, including network cost, pollution reduction, and distribution network power losses. To design the multi‐objective optimization problem, a fuzzy method has been adopted for simultaneous multi‐objective calculations. Furthermore, the effect of the placement of distributed generations (DGs) and microgrids (MGs) is considered to reduce the distribution network power losses. Information gap decision theory (IGDT) has formulated uncertainties about renewable sources and consumers. To solve this optimization problem, a new method of the modified Harris Hawk optimization (MHHO) algorithm has been implemented, compared with the original HHO and genetic algorithm (GA). Finally, the proposed method has been analysed under the IEEE 33‐bus distribution network for a 24‐hour time horizon, including three MGs considering different renewable energy sources (RESs). The simulation results have demonstrated the high performance of the allocated network with the MHHO algorithm compared to the other scenarios. [ABSTRACT FROM AUTHOR]

In voltage‐controlled voltage source inverters (VSIs)‐based microgrids (MGs), the inner control is of prime interest task for guaranteeing safe and stable operation. In this paper, an in‐depth investigation of the modelling, control design, and analysis of the voltage and current inner control loops intended for single‐phase voltage‐controlled VSIs is established. The main objective of this work is to provide a comprehensive study of the mathematical modelling, control design, and performance evaluation of the inner control's loops considering different proportional‐integral (PI) controller types with and without compensation, and to determine the optimal scheme that can offer better performance in terms of implementation simplicity, robustness, and transient and steady‐state responses. Thus, the mathematical closed‐loop models of designed outer voltage and inner current control schemes based on PI, P, and feedforward controllers with and without compensation are, first, derived. Following this, a systematic and effective control design for tuning the different PI controllers' parameters is proposed. Furthermore, an analysis revealing the performance of the designed voltage and current control schemes is provided. This analysis enables us to choose a P controller and PI feedforward controller for the current control loop and the voltage control loop, respectively. The chosen P and PI controllers should be simple; meanwhile, they should offer a wide bandwidth. A simulation study is carried out in MATLAB/Simulink software to assess the performance of the adopted inner control scheme for both linear and non‐linear loads. In addition, an experimental setup, based on a TMS320F2837xD μC, of a single‐phase VSI supplying linear and non‐linear loads is built to verify the effectiveness and the robustness of the adopted inner controller. The results demonstrated: (1) the necessity of introducing the compensation term, which is responsible for offering control improvement against voltage perturbation, (2) the high tracking performance of the chosen controller in terms of dynamic and steady‐state responses as well as its simplicity of implementation. [ABSTRACT FROM AUTHOR]

The increasing penetration of renewable energy sources and the retirement of conventional generation units have decreased system inertia, making power systems more vulnerable to resilience and stability issues. To address this problem, this paper proposes a novel approach using grid‐supportive loads (GSLs) to provide a fast and concise primary frequency response and a deep deterministic policy gradient agent‐based secondary controller to restore the system frequency to the nominal value. The proposed method is evaluated on the single‐area and multi‐area test systems. The simulation results demonstrate that using GSLs enhances the power system's stability and resilience. Compared to conventional controllers, the frequency nadir is improved with GSLs. Additionally, the proposed method effectively enhances resilience even with high penetration. These findings indicate that the proposed approach can improve the resilience and stability of power systems and provide a promising solution for future power systems. The results of this study underscore the importance of utilizing innovative approaches to enhance the stability and resilience of power systems in the context of high penetration of renewable energy sources and the retirement of conventional generation. [ABSTRACT FROM AUTHOR]

Distributed energy resources (DERs) provide flexible load restoration strategies, which can effectively enhance the resilience of active distribution systems (ADSs). Whereas, the widespread DERs in ADSs complicates the supply‐demand relationship and make the system resilience difficult to access. Therefore, this paper proposes a simulation‐based resilience assessment algorithm of ADSs considering the microgrid formation based on grid‐edge DERs. Microgrid formation is used to depict the resilience gain of grid‐edge DERs on ADSs. Specifically, a resilience assessment framework for ADSs is firstly proposed, where the uncertainty of component state and supply‐demand is modelled based on probability statistics. Then the mixed integer linear programming is used to search for optimal load restoration strategies with microgrid formation. On this basis, a set of resilience indices are defined to quantitatively analyse the resilience of ADSs, and a resilience assessment algorithm with uncertainty scenario generation is proposed to obtain these indices. Furthermore, extensive numerical results based on a modified IEEE 123‐bus feeder validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The high penetration of distributed generation systems poses challenges in effectively managing both DC bus voltage and power‐sharing in DC microgrids (MGs). To ensure precise voltage regulation across various distributed generation systems and maintain overall system stability, this paper studies the modelling and control of an islanded DC MG described by a set of nonlinear‐nonaffine differential equations. In the proposed DC MG, a battery storage, a photovoltaic (PV) power plant, and a back‐end power converter with DC loads have been connected to a common DC bus. First, a dynamic model of a DC MG was developed. Then, to find the maximum power point of solar PV, a straightforward method based on an artificial neural net is employed. Afterward, a nonlinear local state feedback controller through output regulation theory is applied to achieve voltage stability, overcoming the drawbacks of droop‐based techniques. Moreover, partial state feedback capability simplifies controller design. To verify the effectiveness of the proposed approach, various scenarios, including sudden load variation, illumination change, and changes in the MG configuration, have been examined. Finally, numerical studies were carried out in a simulation environment to demonstrate the superiority of the proposed nonlinear control scheme over constant feedback control. [ABSTRACT FROM AUTHOR]

As an important form of renewable energy utilization, microgrid (MG) is considered to be the main bearing form of distributed generation in the future. One of the most concerning issues in the operation of MG is how to realize its economic dispatch (ED). Nowadays, distributed algorithms have been increasingly used to solve the economic dispatch problem of MG. However, the MG based on the distributed optimization architecture must bear higher cyber‐attack risks. To address this issue, this paper investigates the distributed robust ED problem of MG. Firstly, a multi‐objective dispatch model of MG using a linear weighted sum (LWS) algorithm is developed, which considers the environmental and economic costs. On this basis, an event‐triggered fully distributed algorithm is proposed, which can effectively reduce communication times. Furthermore, an attack resilient strategy against false data injection (FDI) attacks is implemented in the proposed fully distributed algorithm, which has strong robustness against various colluding attacks and non‐colluding attacks, and can eliminate incorrect measurement of incremental cost and power generation data. Finally, the effectiveness of the proposed distributed control strategy is demonstrated through case studies in this paper. [ABSTRACT FROM AUTHOR]

The droop mechanism is widely utilized in a stand‐alone microgrid (MG) to regulate power‐sharing among distributed generators (DG). However, over the years, the droop phenomena are continually modified to lessen the deviation in voltage and frequency parameters caused due to classical droop. This study suggests computing the droop coefficient for voltage–current (V–I) droop to take into account for proportional power distribution among DGs. In grid utility networks, the conservation voltage reduction (CVR) strategy is widely used to curtail the use of energy. Hence, this paper investigates the CVR's performance for stand‐alone MG by performing the adaptive vector control scheme in the two‐phase d–q reference frame. In addition to it, the paper is intended to develop a coordinated control strategy for stand‐alone MG involving classical P–f droop and self‐sustained V–I droop employed to perform the function of CVR during peak demand and overloading conditions. Further, a load‐shedding control approach is also considered to cut out some segments of load during overloading conditions to operate the MG in the stable zone. The validation of the proposed strategy is conducted on MATLAB/Simulink software. [ABSTRACT FROM AUTHOR]

The article discusses incentivised by environmental concerns and energy security risks, converter-interfaced generation and a fleet of renewable energy sources have extensively penetrated for the world. Topics include maintaining rate-of-change of-frequency and voltage angle within satisfactory boundaries has critical to prevent unnecessary operation of protective relays; and the proposed control scheme adjusts the droop factors dynamically for the transient events.

With the emergence of renewable energy sources (RESs), the power grid all over the world is going through a paradigm shift. The inverter based RESs are replacing the conventional rotating synchronous generators and this trend is expected to continue in coming years. Consequently, the grid strength is decreasing, which can pose significant challenges on the grid stability, especially during integration of IRESs. This paper presents a thorough analysis on the integration challenges of IRESs in weak grids. The paper also presents a comprehensive investigation on the impacts of factors, such as available fault level at the point of interconnection (POI), feeder length and grid nature on the POI voltage, and maximum allowable power injection by IRES. This is followed by maximum allowable IRES power injection sensitivity analysis to quantify the impacts of the aforementioned factors. The paper concludes with a thorough case study on the IEEE 39‐bus test system to numerically show the impact of diminishing grid strength on the integration of IRESs. [ABSTRACT FROM AUTHOR]

The development of advanced and intelligent measurement instruments in recent years has increased the intelligence of modern energy systems, especially power systems. Besides, with the advancement of energy conversion technologies, these systems benefit from multi‐carrier energy resources. Accordingly, this paper presents a model of smart city which considers various components, including smart transportation system (STS), microgrid (MG), and smart energy hub (SEH) with the ability of energy transformation. The proposed model addresses the islanded operation of a smart city that makes it a smart island. This island deploys the energy carriers of electricity, heat, gas and water as well. In addition, STS includes electric vehicle (EV) parking lots as well as metro system (MS) that can interactively exchange energy. More precisely, the different components of the smart island are modelled on the assumption of energy interdependency. In the proposed model, the water supply unit in SEH is provided which can be effective in reducing the cost of components by supplying water to them. In order to exchange energy within STS, metro stations have been optimally allocated using intelligent water drops (IWD) optimization method. In addition to smart island modelling, this paper quantifies the uncertainties within STS and MG using cloud theory. Eventually, the proposed model is simulated to ensure its effectiveness and accuracy. [ABSTRACT FROM AUTHOR]

This paper presents a multi‐objective restoration scheme for improving the resilience of integrated electricity and natural gas distribution systems against extreme weather events. The coupling constraints of electricity and gas networks are tackled properly using a linearized optimal power flow (OPF). Distributed generators, power‐to‐gas facility, rescheduling of generation/storage units, and microgrid formation are employed as operational resources/measures for restoring the integrated energy system after the event landfall. An adaptable directed multi‐commodity flow‐based microgrid formation is utilized, that is, the network configuration is dynamically changed in accordance with time‐variant load priority weights. The proposed method was successfully examined on an integrated electricity and natural gas distribution system comprised of the modified IEEE 33‐bus distribution network and a 14‐node natural gas distribution network. Numerical results showed that using microgrid formation increased the supplied critical load of integrated electricity and natural gas distribution system by about 16%. Moreover, due to making benefit of the power‐to‐gas unit, the supplied critical load increased by about 12.3%. respectively. While utilizing energy storage systems along with the power‐to‐gas unit facilitated the exchange of energy between the power distribution network and natural gas distribution network regarding time‐variant load priority weights, the supplied critical load increased by about 13%. [ABSTRACT FROM AUTHOR]

Microgrids are a new concept resulting from decentralized power sources, which have changed energy utility grid topography. As this promising field is penetrated, control must be streamlined immediately. This paper analyses key control mechanisms in energy utility networks, including centralized, decentralized, and distributed setups. The focus is on the auxiliary category as the various benefits and drawbacks of each approach is investigated. Information interchange architectures that enable multiple control techniques in the auxiliary control domain are examined using distributed energy networks. The research emphasizes the necessity of auxiliary control in ensuring reliable and effective utility infrastructure operation but notes the lack of a comprehensive scholarly reference. Additionally, the study explores key concerns in utility grids' auxiliary control mechanisms, emphasizing information‐sharing system limits. The goal is to demonstrate the importance of auxiliary control and evaluate its challenges, such as latency, which require new solutions for a seamless transition to a sustainable energy future through rigorous academic assessment. At the auxiliary level, complex, decentralized regulatory mechanisms are recommended to handle transmission channel bandwidth and operational overhead in energy utility networks. These innovative measures have the potential to transform the fundamental structure of our energy landscape, heralding in unprecedented efficiency and sustainability. [ABSTRACT FROM AUTHOR]

Integrated hydrogen energy systems (IHESs) have become attractive alternatives to cope with the depletion of fossil fuels and increasingly severe climate change problems. This paper proposes an adaptively optimal energy scheduling method based on deep deterministic policy gradient (DDPG) to improve the operational efficiency of IHES. The optimal scheduling problem is formulated as a Markov decision process problem with action space, environmental states, and action‐value function. The DDPG‐based optimal energy management algorithm with actor‐critic structure is proposed based on policy gradients and neural networks. Through actor‐critic network training and policy iteration, the energy management scheme can be adaptively optimized according to the dynamic responses of IHES. The benefits of the proposed algorithm are analysed through time‐domain simulations, and the scheduling robustness under different uncertain conditions is verified. [ABSTRACT FROM AUTHOR]

Nowadays, due to network expansions, system operators are dealing with natural events including system failures. Evaluating the system readiness to maintain the network interconnections during severe faults are known as an important challenge of today's distribution networks. To do this, this paper presents an online non‐model‐based scheme of evaluating the system resiliency and supplying critical loads within severe natural conditions. In this case, by developing system resiliency as multi‐metric decision making problem and proposing the Choquet integral and graph theoretic methodology, the system resiliency is evaluated. The proposed resiliency scheme is an online non‐model‐based approach can be used for all distribution system configurations with the potential of natural event failures. In this case, by proposing online resiliency criteria based on the system critical loads and branches and developing the proposed criteria on Choquet integral scheme in the form of optimization procedure, the system resiliency is evaluated. The validity of the proposed approach is carried out on two different systems including the IEEE 123‐bus test system and the Consortium for Electric Reliability Technology Solutions (CERTS) microgrid. Simulation results present effectiveness of the proposed scheme to provide proper protective switching strategy and design the system resiliency criteria for future distribution systems. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various reports within the issue on topics including planning and operating approach for active distribution grids, voltage control for photovoltaic inverters, and optimal scheduling model of microgrid considering battery lifetime.

Islanded AC–DC hybrid microgrids composed of new energy sources, constant power loads and energy storage system are typical non‐linear systems, and guaranteeing large signal stability is a key issue. In this paper, the non‐linear model of islanded AC–DC hybrid microgrids is established, and large signal stability criteria are obtained. Considering reference current limitation of the energy DC–AC converter, the derived large signal stability criterion gives important constraints on the constant power loads power, the power of energy storage unit, DC bus voltage, the inductor and equivalent resistance of the AC filter, DC capacitor, the proportional parameter of the inner current controller and the maximum instantaneous power of the DC–AC converter. The paper is summarized as follows. Firstly, the equivalent model of the AC–DC hybrid microgrids in DQ rotating frame is established. Then based on mixed potential theory, the mixed potential model is constructed, and large signal stability criterion is derived and optimized considering the reference current limitation of the DC–AC converter. Finally, the presented criterion is verified by simulation and experimental results. [ABSTRACT FROM AUTHOR]

An introduction is presented which discusses various reports published within the issue including microgrid and virtual power plant for power supply, voltage reactive power control of a power system, and virtual power plant real-time energy management along with a brief profile of guest editors.

DC microgrids (DCMGs) have significant advantages, but their protection can be a challenge for DCMGs expansion expressly in distributed generation sources' (DGS) tendance. This paper proposes a quick, and precise strategy to perceive and trip any fault in DCMGs in the free tendance of DGS. A bi‐level multi‐agent system (MAS) is proposed to protect the DCMG and DGs. The MAS first level perceives the fault in DCMG via a discrete wavelet transform (DWT). The multiply of two harvest levels' summation of DWT is exerted to detect and distinguish DC lines faults. The MAS second level detects the fault in DGS and their switchers to the point of common connection (PCC) via a differential pole (DP) controller. A four bus DCMG containing solar PV and wind sources is implemented, and the results demonstrate that the proposed plan can recognize and trip the fault by 100% of reliability. [ABSTRACT FROM AUTHOR]

In an AC microgrid, the active/reactive power is usually shared among its distributed generators (DGs) based on the frequency‐active power (F−P$F-P$) droop and the voltage‐reactive power (V−Q$V-Q$) droop. By increasing the resistant/inductance ratio (R/X$R/X$) of feeder lines; however, adverse effects of interactions between these two control loops are intensified. In this paper, an adaptive multi‐input multi‐output (MIMO) current control structure is proposed to tackle this problem in AC microgrids with arbitrary numbers of DGs in the primary control level. A deep analysis based on the relative gain array (RGA) matrix and the diagonal dominance concept is provided to systematically design MIMO controllers. The proposed technique is based on the Lyapunov's stability theory, and the asymptotic stability of the whole microgrid is guaranteed. For each DG, the suggested design procedure is started by defining a model reference in which the desired control objectives, including the settling time and the steady‐state error, are considered. Then, a feedback‐feedforward controller is established where its gains are adaptively tuned by some rules derived from a Lyapunov function. Moreover, a predictor is used to estimate the adverse effects of other DGs which are taken into account as external disturbances during the design process of the adaptive controller. By considering some realistic scenarios through time‐domain simulations in MATLAB/SIMULINK, it is shown that the proposed strategy can be successfully used to solve the power sharing problem in AC microgrids. [ABSTRACT FROM AUTHOR]

With the advancement of carbon peaking and carbon neutrality goals, microgrid clusters have attracted widespread attention due to their high stability and energy efficiency. This article proposes a scheduling architecture for microgrid clusters that integrates blockchain smart contracts, consensus mechanisms, and encryption technologies to address the issues of data trust, privacy protection, and security reliability in microgrid clusters. An economic adjustment model for microgrid clusters is constructed that combines economic and environmental objectives. Finally, based on the RPBFT (Rotating Proof of Believability) algorithm and group architecture of FISCO BCOS blockchain technology, an economic scheduling method for microgrid clusters is designed. The authors built a microgrid cluster model on the FISCO BCOS blockchain platform and conducted experiments on the effectiveness, economy, fault tolerance, and scalability of economic scheduling methods. The experimental results showed that the economic scheduling method proposed in this paper can effectively reduce the risk of nodes tampering with data in a microgrid cluster system, ensure the privacy of submicrogrids, and support the deployment of large‐scale microgrid clusters, the realization of multiparty collaborative autonomy and the stable, low‐carbon, economic operation of microgrid clusters. [ABSTRACT FROM AUTHOR]

This paper proposes an approach for segmentation of power systems to prevent uncontrolled islanding. With the development of microgrids (MGs) and increased penetration of renewable energy sources (RESs) in the power system, it is necessary to consider the effect of MGs and the stochastic behaviour of RESs in the controlled islanding process. First, the enormous search space for finding the boundary points is reduced by the clustering of the coherent generators and the buses which are placed in sections that cannot be separated from the coherent groups. Then, linear AC power flow is defined as a sub‐problem and solved by linear programming to satisfy the power system and MG constraints. In this step, an optimization model is defined, and a mixed‐integer linear programming (MILP)‐based model is suggested to find cut‐sets and islands arrangement with reduced load shedding. In view of the fact that the fluctuation of RESs may have the significant effects on the power imbalance, to cover the uncertainties a scenario based stochastic simulation is considered. Finally, the effect of uncertainties on cut‐sets and load curtailment is evaluated. The effectiveness of the proposed methodology is demonstrated by the simulation on the test system. [ABSTRACT FROM AUTHOR]

The active power‐virtual frequency (P‐ω′) and reactive power‐virtual voltage (Q‐V′) droop control scheme has been suggested for coordinated control of distributed energy resources (DERs) in islanded microgrids. While this method mitigates the issue of active and reactive power coupling in the droop controller, its main limitation is power‐sharing error. Due to the local property of the virtual frequency and voltage, significant steady‐state errors occur for both active and reactive power sharing. In this paper, a decentralized method is proposed to eliminate the mentioned errors without utilization of communication links. In the proposed scheme, P‐ω′ and Q‐V′ droops are realized at the microgrid point of common coupling (PCC). By employing the PCC virtual frequency/voltage for the active/reactive power sharing of all DERs, the sharing errors caused by the local property are eliminated. Small signal stability of the proposed scheme is studied, and Hardware‐in‐the‐Loop (HIL) experimental results are presented to validate the proposed method and highlight the improvements compared with the conventional P‐ω′/Q‐V′ scheme. The results verify the efficacy of the proposed method for providing accurate active and reactive power sharing while regulating the frequency and voltage close to the rated values. [ABSTRACT FROM AUTHOR]

This paper presents a novel fully decentralized and intelligent energy management system (EMS) for a smart microgrid based on reinforcement learning (RL) strategy. The purpose of the proposed EMS is to maximize the benefit of all microgrid entities comprising customers and distributed energy resources (DERs). Due to unpredictable features of renewable energy sources and variability of consumers' demands, designing the microgrid EMS is a complicated task. To overcome this issue, the multi‐agent hour‐ahead energy management problem is modelled as a finite Markov decision process. The microgrid entities are considered as intelligent agents. The optimal policy of agents is obtained through a newly developed framework of the model‐free Q‐learning algorithm to maximize the benefit of all renewable and non‐renewable energy resources and battery energy storage system. The degradation model of the battery is considered to reduce the number of battery replacements. To ensure customers' comfort, customers' expenses are decreased without demand curtailment via introducing two types of load shifting techniques. The microgrid operation is analysed under four scenarios comprising no‐learning, generator‐learning, customer‐learning, and whole‐learning. the performance of the proposed algorithm is compared to the Monte Carlo method and simulation results on the real power‐grid dataset show the superiority of the algorithm. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive control framework for the flexible and effective management and control of clustered DC nano‐grids (NGs) in an islanded DC microgrid system. It is assumed that each NG contains a photovoltaic (PV) system, a battery energy storage system (BESS), local loads, and a gateway (GW) module. Each NG has a hierarchical control system consisting of a decision‐making module and low‐level controllers. The decision‐making module ensures various desirable features including plug‐and‐play operation of NGs, maximum utilization of PV power generations, and avoiding state of charge (SoC) violation of batteries. Moreover, an adaptive model predictive control (AMPC) strategy is proposed to regulate the voltage of the NG local DC buses in the presence of non‐linear loads. This approach improves the performance of the NG voltage control system and reduces the current ripples of BESSs, thereby enhancing the lifetime of the batteries. In addition, a smart switching consensus‐based control strategy is designed that provides flexible power sharing among the NGs to balance the SoC of BESSs in which the BESSs altogether imitate the behaviour of a single cloud energy storage system (ESS). Finally, the performance of the proposed control system is verified by simulating the DC microgrid in MATLAB/Simulink. [ABSTRACT FROM AUTHOR]

Presenting cloud energy storage system (CESS) in the landscape of storage devices exposes microgrids (MGs) to a substantial change. Employing a specific type of inverter namely synchronverter as the connecting interface for CESS gains high inertial response in MGs with high penetration level of inverter‐based distributed generations (IBDGs). However, high fault contribution of synchronverters can lead to challenging issues. Nevertheless, presence of fault resistance which results in malfunctioning of the recloser‐fuse pair intensifies the cruciality of the dilemma. In this paper, at the outset, the effects of contribution of synchronverter‐based CESS and also fault resistance on recloser‐fuse protection are explored and then, a new algorithm is proposed for tackling these bottlenecks. To overcome these hurdles, the conventional recloser‐fuse scheme is applied at first. Then, by using the obtained schemes for fast and slow operations of the recloser, a specific part of them is set in a manner that the fuse‐saving task is performed properly even when the synchronverter‐based CESS is deployed. On the other hand, the other part of the recloser scheme is altered in a way that the maloperation of the recloser‐fuse pair owing to the fault resistance is alleviated. Adequate numerical analyses are conducted to evaluate the proposed scheme. [ABSTRACT FROM AUTHOR]

Energy storage devices become an indispensable part of modern power systems with high renewable energy penetration level. To reduce the operating costs, it is a promising way to allow the sharing and leasing of energy storage devices. In this paper, a bi‐lever optimized dispatch scheme is proposed to improve the usage efficiency of cloud energy storage in multi microgrids (MMG) system. Minimizing the operating costs of shared cloud energy storage is the main task of the upper lever while maximizing the profits of MMG is the goal of the lower lever. Moreover, the transaction cost and benefit between the two levers play an important role in system level optimization. This leads to a hybrid optimization problem with both discrete decision variables and continuous decision variables. To solve the problem, a relaxation‐based bi‐lever reformulation and decomposition algorithm is developed. The effectiveness of the proposed bi‐lever dispatch optimization model is verified by carrying out numerical experiments in three scenarios. It is shown that the proposed cloud energy storage service can effectively reduce the operating cost of MMG. [ABSTRACT FROM AUTHOR]

A novel agent‐based online prediction method is presented in this paper, to predict the status of both transient and short‐term voltage (STV) stability, against fault occurrence. In the proposed method, the trajectories of the relative frequency deviation (ΔF) and the power imbalance (ΔP) are estimated by employing the third‐degree polynomial curve fitting method. By tracking the estimated trajectories on ΔF–ΔP plane and checking some simple defined rules, an early prediction of both transient and STV instability is achieved in an organized multi‐agent system (MAS). In order to evaluate the performance of the proposed algorithm, the method has been tested on IEEE 39‐bus system, IEEE 118‐bus system and IEEE Nordic test system. Based on the obtained results, the proposed algorithm has an overall accuracy of 99.5% under symmetrical and asymmetrical faults, PMU measurement error, different operating points and topological changes. [ABSTRACT FROM AUTHOR]