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The purpose of this paper is to examine cyber security issues related to smart grids (SGs), such as prior cyber‐attacks, possible vulnerability issues, and enhanced cyber‐security procedures for SGs on a technical and management basis. When designing public policies for SGs, it is important to consider both the motivations and obstacles to cyber protection and socio‐economic conditions. Furthermore, the importance of suggested policies is viewed from various perspectives. The present paper provides an assessment of a group of policies specified by various stakeholders as having the potential for developing SG cyber security. The methodology for the formation of such a group of policies includes a perfect examination of global experience and meetings with experts from various fields. Subsequently, such policies are evaluated using a Delphi questionnaire aimed at assessing their advantages in pursuing investment goals for cyber security. The first result is that every policy is considered a positive effect of all goals, different in the priority assigned to each of them. The policies that received the most attention were: "regulatory changes policy to foster innovation in cyber security of SGs," "regulation of new business models to improve cyber security and detect malicious attacks" and "establish a cyber‐security governance strategy." [ABSTRACT FROM AUTHOR]

This paper investigates the false data injection attacks (FDIA) in an AC smart island and the detection solution of the attack on distributed energy resources in a smart island. In this study, a new scheme of FDIA detection is proposed based on wavelet singular values as input index of deep learning algorithm. In the proposed method, switching surface based on sliding mode control breaks down for adjusting accurate factors of wavelet transform and then features of wavelet coefficients are extracted by singular value decomposition. Indexes are determined according to the wavelet singular values in switching surface of voltage and current which defines the input indexes of deep machine learning and detecting FDIA. This cyber‐protection plan has been put forward for cyber diagnostic and examined in different types of attacks happening in voltage and current signals derivation of measuring sensors as well as sending and receiving data from communication and control systems. The main priority of the suggested detection plan is the high capability to detect FDIA with a high accuracy. To show the effectiveness of the proposed method, simulation studies are performed on AC smart island in MATLAB/Simulink environment. [ABSTRACT FROM AUTHOR]

Within microgrids (MGs), the integration of renewable energy resources (RERs), plug-in hybrid electric vehicles (PHEVs), combined heat and power (CHP) systems, demand response (DR) initiatives, and energy storage solutions poses intricate scheduling challenges. Coordinating these diverse components is pivotal for optimizing MG performance. This study presents an innovative stochastic framework to streamline energy management in MGs, covering proton exchange membrane fuel cell–CHP (PEMFC-CHP) units, RERs, PHEVs, and various storage methods. To tackle uncertainties in PHEV and RER models, we employ the robust Monte Carlo Simulation (MCS) technique. Challenges related to hydrogen storage strategies in PEMFC-CHP units are addressed through a customized mixed-integer nonlinear programming (MINLP) approach. The integration of intelligent charging protocols governing PHEV charging dynamics is emphasized. Our primary goal centers on maximizing market profits, serving as the foundation for our optimization endeavors. At the heart of our approach is the Meerkat Optimization Algorithm (MOA), unraveling optimal MG operation amidst the intermittent nature of uncertain parameters. To amplify its exploratory capabilities and expedite global optima discovery, we enhance the MOA algorithm. The revised summary commences by outlining the overall goal and core algorithm, followed by a detailed explanation of optimization points for each MG component. Rigorous validation is executed using a conventional test system across diverse planning horizons. A comprehensive comparative analysis spanning varied scenarios establishes our proposed method as a benchmark against existing alternatives. [ABSTRACT FROM AUTHOR]

In islanding microgrids (MGs), distributed generation units are in charge of controlling voltage, frequency, and current of the grid on their own without any assistance from the main grid. Therefore, it is of utmost importance to select and design a controller that is robust against disturbances and load variations. In this study, a new sliding mode controller with a rotating reference voltage algorithm is proposed that improves the load sharing between distributed generators (DGs) in an islanded mode MG. In the proposed algorithm, in order to improve the performance and convergence rate of the controller, the amplitude of the reference voltage signal of the controller is adaptively modified. As a case study, the proposed strategy is studied based on the assumption that there are three DGs in the grid. One of the DGs is in charge of regulating voltage and frequency based on a reference signal, and the two other DGs are responsible for load sharing and loads the current control mode. The MG under study consists of three low-voltage distributed generation units operating in parallel mode. In order to have a realistic case study, it is assumed that the MG consists of different types of loads such as balanced/unbalanced resistive, inductive, and nonlinear loads. The extensive simulations are applied to indicate that the proposed framework is able to provide more desirable total harmonic distortion (THD), lower steady-state error, and faster response compared with classic sliding mode controllers. [ABSTRACT FROM AUTHOR]

This paper proposes a new method of fault detection and classification in asymmetrical distribution systems with dispersed generation to detect islanding and perform protective action based on applying a combination of wavelet singular entropy and fuzzy logic. In this method, positive components of currents at common coupling points are decomposed to adjust detailed coefficients of wavelet transforms and singular value matrices, and expected entropy values are calculated via stochastic process. Indexes are defined based on the wavelet singular entropy in positive components and three phase currents to detect and classify the fault. This protection scheme is put forward for fault detection and is investigated in different types of faults such as single-phase to ground, double-phase to ground, three-phase to ground and line to line in distribution lines in the presence of distributed generations, and different locations of faults are verified when the distributed generation is connected to the utility. The major priority of the proposed protection scheme is its reduction in time (10 ms from the event inception) in distinguishing islanding and protection transmission lines in the presence of distributed generations. [ABSTRACT FROM AUTHOR]

To control a smart-island (which comprises three distributed generation (DG) units with their autonomous converters), based on a nonlinear backstepping control (BSC) scheme, an adaptive reference signal, and a state observer are designed to improve the steady-state performance of the system. An adaptive reference value has been designed and used instead of a constant reference signal to improve the system's steady-state performance, e.g., total harmonic distortion (THD), peak value, and effective value in the voltage control mode. The presented adaptive reference is changed based on the error between the reference signal and the output voltage signal to tend the error to zero. Compared to the classical backstepping controllers, the reference signal is pursued with a fast response and a low steady-state of loads alternations. To demonstrate the efficiency, authenticity, and compatibility of the proposed control strategy, offline digital time-domain simulation studies are carried out on a master-slave organized inverter-based microgrids in various load variations like linear and nonlinear loads in MATLAB/Simulink software environment. The obtained results are compared with previously reported techniques. Moreover, the obtained simulation results are verified by performing laboratory-based experimental tests based on digital signal processing (DSP), which validate the proposed control strategy's accuracy, authenticity, and effectiveness under different conditions. [Display omitted] • A Novel adaptive reference signal has been presented. • The system steady-state performance has been enhanced by applying a state observer in a classic backstepping controller. • The reference signal is pursued with a fast response and a low steady-state ahead of loads alternations. • a master-slave organized inverter-based microgrids has been proposed. [ABSTRACT FROM AUTHOR]

Smart Island (SI) refers to the condition where distributed generation (DG) units e.g. wind turbines (WT), fuel cell (FC), and photovoltaic cells (PV) have to control current, voltage, and frequency of the grid by themselves without any support from the main grid. Therefore, design and implementation of robust controller to overcome disturbances and load variations is very crucial. This paper presents a new approach using General Type-II Fuzzy controller to control smart island in combination with a novel modified optimization algorithm to increase the load sharing throughout the DGs operating in an islanding mode. This case study assumes that there are two DGs in the smart island where each DG has its own responsibilities. In particular, one of the DGs regulates the frequency and voltage based on a reference feedback, while the other DG operates in load current control mode to share load between each DG properly. The simulation results indicate that the suggested controller has a quick response to load alternations with a low steady state error and low THD. Moreover, the proposed controller is independent from system states and other units. The experimental analysis indicate that the suggested control system is effectively capable to coordinate the operations of the DG units in smart island to validate the stable operation of the overall smart island. Image 1 • The main contributions of this paper are as follows: • Introducing a novel and intelligent control model for controlling the smart island, • Establishing a fuzzy based control over load sharing between DG units, voltage, frequency, and current, • Applying fixed frequency PWM modulation which leads to the switching frequency to be constant, • Developing a real-time measuring and amount of desired (reference) signal as an exact proportion of load current. [ABSTRACT FROM AUTHOR]

• Different methods and possible solutions in cyber security in SGs are summarized. • The interactive features of cyber security, modeling and mechanisms are discussed in detail. • The characteristics and applicability of various cyber-attack models are technically presented and analyzed. • The cutting-edge cyber security techniques in SGs and energy systems in various research are discussed. • Possible potential research directions regarding CPSs are highlighted. • Decisive problem-solving approaches are analyzed and concluded comprehensively. Due to the advancement in communication networks, metering and smart control systems, as well as the prevalent use of Internet-based structures, new forms of power systems have seen moderate changes with respect to several aspects of contradictory Cyber–Physical Power Systems (CPPSs). These structures usually have connections between power sections and cyber parts. CPPSs confront newly emerging issues including stability, resiliency, reliability, vulnerability and also security. Studying, analyzing and providing solutions to mitigate or solve these problems highly depend on accurate modeling methods and examining the interaction mechanisms associated with the cyber-security of Smart Grids (SGs). This paper aims to systematically summarize different methods and techniques and to review corresponding solution approaches in cyber-security in energy systems. In the first step, we discuss the interactive features of cyber-security; then, their modeling and mechanisms are reviewed and summarized in detail. Furthermore, the characteristics and applicability of different cyber-attack models are technically discussed and analyzed. The cutting-edge cyber security approaches such as blockchain and quantum computing in SGs and power systems are stated, and recent research directions are highlighted. The decisive problem-solving approaches and defense mechanisms are presented. Finally, some points regarding the role of cyber-security in the future of SGs are presented. [ABSTRACT FROM AUTHOR]

In current study, in order to find the control of general uncertain nonlinear systems, a new optimal hybrid control approach called Optimal General Type II Fuzzy Sliding Mode (OGT2FSM) is presented. In order to estimate unknown nonlinear activities in monitoring dynamic uncertainties, the benefits of general type‐2 fuzzy logic systems (GT2FLS) is utilized by the suggested controller. The suggested controller not only warranties the constancy and hardiness against uncertainties of the lumped resulted by external disturbances and un‐modeled dynamics, but also considerably decreases the control chattering inherent in traditional sliding mode control. Moreover, the Modified Backtracking Search Algorithm (MBSA), which is a new heuristic algorithm, is used in the offered controller. A comparison is performed between the results of current study and the results of some of the most recent studies on the same topic, which are an Adaptive Interval Type‐2 Fuzzy Logic Controller (AGT2FLC) and Conventional Sliding Mode Controller (CSMC), to assess the efficiency of the suggested controller. The suggested control scheme is finally used to the Electric Vehicles type as a case study. Results of simulation confirm the good performance the suggested controller. [ABSTRACT FROM AUTHOR]

• Optimal allocation of various automation equipment in a real distribution network. • Switches optimal configuration plan is guided to the actual project in various cases. • Branch lines influence is modeled on the automatic equipment configuration. Sectionalizing switches (SSs) have been installed in Distribution networks with the aim of providing maneuver and ring points, so raising service reliability. It is usually assumed that these switches are totally reliable although, in practice, they have not ideal efficiency at all times. In fact, the switches might occasionally face failure, which decreases their capability with the aim of enhancing system reliability. A model with the aim of considering the failure probability of the switches in their optimal placement issue is proposed in this paper. The model aids with the aim of minimizing the whole costs of SSs and the interruption costs incurred through interrupted users. This study uses the discrete Markov chain model with the aim of obtaining the malfunction possibility under various states. Afterwards, the placement problem is solved with the aim of obtaining the global optimal solution with genetic algorithm. The advantage of the suggested model has been investigated according to sensitivity analysis and various scenarios. The research illustrates how the outcomes of the placement issue have been influenced through the uncertainty arising from the possible failure of the switches and how ignoring the uncertainty is able to cause deviation of the anticipated profit from the real one. The efficiency of the suggested procedure has been evaluated and shown through investigating on the Ahwaz city distribution network. The outcomes of simulation prove the ability and precision of the suggested procedure. [ABSTRACT FROM AUTHOR]