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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]

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]

he microgrid (MG) is a complicated cyber-physical system that operates based on interactions between physical processes and computational components, which make it vulnerable to varied cyber-attacks. In this article, the impact of data integrity attack (DIA) has been considered, as one of the most dangerous cyber threats to MGs, on the steady-state operation of hybrid microgrids (HMGs). Additionally, a novel method based on the sequential hypothesis testing approach is proposed to detect DIA on the renewable energy sources’ metering infrastructure and improve the data security within the HMGs. The proposed method generates a binary sample, which is used to compute a test statistic that is further used against two thresholds to decide among three alternatives. The performance of the suggested method is examined using an IEEE standard test system. The results illustrated the acceptable performance of the proposed methodology in detection of DIAs. In addition, to evaluate the effect of DIA on the operation of the HMGs, DIAs with different severities are launched on the measured power generation of renewable energy resources like wind turbines. The results showed that a successful DIA on renewable units can severely affect the operation of electric grids and cause serious damage. [ABSTRACT FROM AUTHOR]

The fault detection method (FDM) plays a crucial role in controlling and operating microgrids (MGs), because it allows for systems to rapidly isolate and restore faults. Due to the fact that MGs use inverter-interfaced distributed production, conventional FDMs are no longer appropriate because they are dependent on substantial fault currents. This study presents a smart FDM for MGs based on the Hilbert–Huang transform (HHT) and deep neural networks (DNNs). The suggested layout aims to prepare the fast detection of fault kind, phase, and place data to protect MGs and restore services. The HHT pre-processes the branch current measurements obtained from the protective relays to extract the characteristics, and singular value decomposition (SVD) is used to extract some features from intrinsic mode functions (IMFs) that are obtained from HHT to use as input of DNNs. As part of the fault data development, all the information eventually enters the DNNs. Compared with prior studies, this suggested method provides considerably superior fault-type identification accuracy. It is also possible to determine new fault locations. A detailed assessment analysis of this suggested FDM was conducted on IEEE 34-bus and MG systems to demonstrate its effectiveness. The simulations indicated that the proposed method is effective for detecting precision, computing time, and robustness to measurement uncertainties. [ABSTRACT FROM AUTHOR]

Today, many countries are facing water shortage due to population increase as well as groundwater pollution. To overcome this problem, a variety of techniques have been developed and implemented, one of the most recent being the condensation of water from humid air. Aqueous vapour in the Earth's atmosphere includes a significant amount of water that could be used to supply potable water. One of the methods for condensing water vapour in humid air is to use thermoelectric coolers. In this study, production of potable water by thermoelectric coolers, which is one of the optimal methods for trapping moisture in order to produce fresh water by condensation, has been studied. Fundamental concepts in the design of thermoelectric coolers for the production of potable water have been discussed by the thermoelectric element. According to the specification of temperature, pressure, relative humidity and desired geographical location variables, the maximum amount of water production can be achieved. Modelling results demonstrate higher water production with the thermoelectric method for Ramsar (318 L) in comparison to Bandar‐Abbas. Similarly, for this geographical point, the proposed hybrid solar panels efficiency results in 62.67% by mid‐December with 49.33% on thermal efficiency and 13.33% on electrical efficiency. [ABSTRACT FROM AUTHOR]

In this article, a new procedure is proposed on the basis of Hilbert-Huang Transform and deep learning for cyber-attacks detection in direct current (DC) micro-grids (MGs) as well as detection of the attacks in distributed generation (DG) units and its sensors. An advanced elective group deep learning method with Krill Herd Optimization (KHO) algorithm is proposed. At first, Hilbert-Huang Transform is used with the aim of extracting the signals feature and next these features are applied as the multiple deep input basis models are made with the aim of capturing automatically sentient traits from raw fluctuation signals. At third, to make sure the variety of the basis patterns, linear decoder, denoising autoencoder and sparse autoencoder are applied to make various deep autoencoders, respectively. Further, Bootstrap is applied with the aim of designing separate educational data subsets for any base model. Fourth, for implementing selective ensemble learning, a combination strategy of enhanced weighted voting (EWV) with class-particular thresholds is studied. Eventually, KHO algorithm is applied with the aim of adaptive selecting the optimal class-specific thresholds. In the offered tactic, firstly, a DC micro-grid is functioned and controlled with the lack of any false data injection attacks (FDIAs) to collect adequate information within the usual operation needed for the educating of deep learning networks. It is noteworthy that, in the procedure of datum production, load variable is also determined with the aim of having distinctive datasets for cyber-attack scenarios and load variables. Also, to provide more realistic method, the smart plug-in electric vehicle is also considered in the model. Outcomes of Simulation in various scenarios are applied with the aim of verifying the benefit of the offered procedure. The outcomes propose that the offered procedure is able to more accurate and robust know various type of false data injection attack over than 93.76% accuracy detection of true rate. [ABSTRACT FROM AUTHOR]

Cyber-physical threats as false data injection attacks (FDIAs) in islanded smart microgrids (ISMGs) are typical accretion attacks, which need urgent consideration. In this regard, this paper proposes a novel cyber-attack detection model to detect FDIAs based on singular value decomposition (SVD) and fast Fourier transform (FFT). Since new research are mostly focusing on FDIAs detection in DC systems, paying attention to AC systems attack detection is also necessary; hence, AC state estimation (SE) have been used in SI analysis and in considering renewable energy sources effect. Whenever malicious data are added into the system state vectors, vectors' temporal and spatial datum relations might drift from usual operating conditions. In this approach, switching surface based on sliding mode controllers is dialyzed to regulate detailed FFT's coefficients to calculate singular values. Indexes are determined according to the composition of FFT and SVD in voltage/current switching surface to distinguish the potential cyber-attack. This protection layout is presented for cyber-attack detection and is studied in various types of FDIA forms like amplitude and vector derivation of signals, which exchanged between agents such as smart sensor, control units, smart loads, etc. The prominent advantage of the proposed detection layout is to reduce the time (less than 10 milliseconds from the attack outset) in several kinds of case studies. The proposed method can detect more than 96% accuracy from 2967 sample tests. The performances of the method are carried out on AC-ISMG in MATLAB/Simulink environment. [ABSTRACT FROM AUTHOR]

Today, in various leading power utilities in developing countries, achieving optimal operational energy management and planning, taking into account the costs reduction of generation, transmission and distribution of electricity, and also reducing the emission of an environmental pollutant becomes more and more important. Optimal use of renewable energy sources (RESs) is an effective way to achieve these goals. In this regard, in this research article, an improved multi-objective differential evolutionary (IMODE) optimization algorithm is suggested and performed to dispatch electricity generations in a smart microgrid (MG) system, taking into account economy and emission as competitive issues. In this paper, a nonlinear equation of multi-objective optimization issue with various equality and inequality limitations is formulated in order to lower the total operational costs of the MG considering environmental pollution effects simultaneously. In order to address the issue of optimal operation of the MG in single-objective and multi-objective forms, an intelligent method according to the improved differential evolutionary (IDE) optimization is utilized and performed and the proposed algorithm is implemented on different problems. First, it is assumed that there is no limit to the exchange of power overhead, and secondly, the limitation of power exchange with the upstream grid is considered. In multi-objective mode, these two modes are also considered. In order to show the impact of renewable energy on the cost, in the third part of the simulations, the operation is solved with maximum participation of renewable energy sources. In the final section, the sensitivity analysis on the number of populations in this problem is performed. The obtained results of the simulation are compared to differential evolutionary (DE) and particle swarm optimization (PSO) techniques. The effectiveness of the suggested multi-operational energy management method is confirmed by applying a study case system. [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]

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]

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]

Nowadays, the role of cyber-physical systems (CPSs) is of paramount importance in power system security since they are more vulnerable to different cyber-attacks. Detection of cyber-attacks on a direct current microgrid (DC-MG) has become a pivotal issue due to the increasing use of them in various electrical engineering applications, from renewable power generations to the distribution of electricity and power system of public transportation and subway electric network. In this study, a novel strategy was provided to diagnose possible false data injection attacks (FDIA) in DC-MGs to enhance the cyber-security of electrical systems. Accordingly, to diagnose cyber-attacks in DC-MG and to identify the FDIA to distributed energy resource (DER) unit, a new procedure of wavelet transform (WT) and singular value decomposition (SVD) based on deep machine learning was proposed. Additionally, this paper presents a developed selective ensemble deep learning (DL) approach using the gray wolf optimization (GWO) algorithm to identify the FDIA in DC-MG. In the first stage, in the paper, to gather sufficient data within the ordinary performance required for the training of the DL network, a DC-MG was operated and controlled with no FDIAs. In the information generation procedure, load changing was considered to have diagnosing datasets for cyber-attack and load variation schemes. The obtained simulation results were compared with the new Shallow model and Hilbert Huang Transform methods, and the results confirmed that the presented approach could more precisely and robustly identify multiple forms of FDIAs with more than 95% precision. [ABSTRACT FROM AUTHOR]

Using blockchain technology as one of the new methods to enhance the cyber and physical security of power systems has grown in importance over the past few years. Blockchain can also be used to improve social welfare and provide sustainable energy for consumers. In this article, the effect of distributed generation (DG) resources on the transmission power lines and consequently fixing its conjunction and reaching the optimal goals and policies of this issue to exploit these resources is investigated. In order to evaluate the system security level, a false data injection attack (FDIA) is launched on the information exchanged between independent system operation (ISO) and under-operating agents. The results are analyzed based on the cyber-attack, wherein the loss of network stability as well as economic losses to the operator would be the outcomes. It is demonstrated that cyber-attacks can cause the operation of distributed production resources to not be carried out correctly and the network conjunction will fall to a large extent; with the elimination of social welfare, the main goals and policies of an independent system operator as an upstream entity are not fulfilled. Besides, the contracts between independent system operators with distributed production resources are not properly closed. In order to stop malicious attacks, a secured policy architecture based on blockchain is developed to keep the security of the data exchanged between ISO and under-operating agents. The obtained results of the simulation confirm the effectiveness of using blockchain to enhance the social welfare for power system users. Besides, it is demonstrated that ISO can modify its polices and use the potential and benefits of distributed generation units to increase social welfare and reduce line density by concluding contracts in accordance with the production values given. [ABSTRACT FROM AUTHOR]