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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 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]

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]

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]