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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]

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]

• Study various applications of Blockchain technology in AC smart Microgrid. • Securing the datum transfer in various control layer on the basis of Blockchain technology. • Cyber-attack mitigation in smart microgrids. • Practical and applicable method for the power and energy society. Industrial Internet of Things (IIoT) has been defined as an architecture that uses the Internet of Things (IoT) and cloud computing to facilitate distributed control of modern industrial systems like AC smart microgrids (MGs). This paper proposes a novel secure energy policy and load sharing approach for renewable MGs for independent utilization of off-grid MGs with power electronic jointing (PEJ) on the basis of master-slave (M-S) which is formed in the IIoT environment. Assume that computations for system dispatch are performed by an upper layer however a lower layer calculates proper control proceedings for the PEJ. A decentralized multi-agent system (MAS) realizes the upper layer of intelligent control on the basis of communication. The layer has 2 control mechanisms: economic dispatch and MAS power balance control. Numerous operating, controlling, and planning to be in the energy industry pay special attention to Blockchain technology. In addition to allowing a common and distributed database, Blockchain technology (B.CT) enables safe, automated, transparent, and economic operations in power distribution systems. If a hacker manipulates and alters the data exchanged between agents, it will result in disrupting system performance in terms of economy and stability MG voltage profile, load distribution, optimized parameters including cost, environmental pollution, and unit output. Therefore, it is necessary to maintain the cyber security of AC smart MG and increase the security of data measured in the sensors and the transaction data between agents. In this paper, B.CT is presented to secure the exchanged data against malicious cyber-attacks in an AC smart MG whose control layers are M-S organized. The simulated system consists of the MG with several distributed generation units that examine cyber-attack points and then compare the results in normal mode and cyber-attack mode and B.CT is presented to increase the cyber security of AC smart MG. [ABSTRACT FROM AUTHOR]

• Proposes a novel secured management method for renewable microgrids. • Considering the policies required for diagnosing cyber-attacks happening in the communication networks. • A new framework for cyber-attack detection in DC smart MG. • Ability of detecting cyber-attacks with great precision and distinguishing cyber-attack from load changes. This study proposes a novel secured management method for renewable microgrids considering the policies required for diagnosing cyber-attacks happening in the communication networks, usually applied in the secondary control layer of microgrids (MGs). Due to the so long stochastic and bad information entering the systems in order to make malicious attacks, their location and time data links have the ability of straying of those acting in normal conditions that attempt to have an effect on the precise voltage regulation and current dividing via influencing sensors of current and voltage. The ability to extract high-level features due to the usage of fast fourier transform (FFT) and deep learning (DL) for attack detection in cyberspace has made them to be considered as a strong technique in the face of small mutations or new attacks. These self-educated and compaction abilities of DL architectures have been considered as basic techniques for hidden scheme detection from the training datum for this reason attacks have been distinguished from benign traffic. A novel method, deep learning and FFT, for cyber-security has been used in the following paper with the aim of enabling the attacks detection in DC smart MG. The deep model and traditional machine learning way are evaluated in terms of performance, and distributed attack detection has been compared to the centralized diagnosing procedure. The tests proved that the distributed attack detection system studied can be more advanced in comparison with centralized detection systems applying FFT in the role of the input index of the DL model. This suggested distributed method enables for scalable monitoring of a MG and has the ability of detecting the existence of cyber-attacks in the communications between distributed generation agents (DGAs) controlled via a control on the basis of consensus and isolating the communication link over that the attack has been injected. Any local attack detection needs restricted information about its neighbor's dynamics. The most important factor of the proposed detection plan can be that has the ability of detecting cyber-attacks with great precision and distinguishing cyber-attack from load changes.in addition, this has been shown that the suggested model can be further useful in the detection of the attack. [ABSTRACT FROM AUTHOR]