Search

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]