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This paper provides a comprehensive analysis of a grid-connected hybrid microgrid system that seamlessly integrates renewable energy sources, encompassing wind generators, solar arrays, and Fuel Cells (FCs). Emphasis is placed on the pivotal role of power electronic converters in optimizing control and energy management strategies for these diverse sources. The wind and solar subsystems employ Perturb and Observe (P&O) controllers to achieve Maximum Power Point Tracking (MPPT). Additionally, the study delves into the analysis and control design of the grid-connected hybrid system inverter, employing a Proportional-Integral (PI) control technique in the synchronous d-q frame to maximize the output voltage response and active power. In managing renewable grid energy based on artificial neural networks (ANNs), the main goal is to address grid availability concerns by prioritizing renewable sources. The hybrid system acts as a backup during grid unavailability and simultaneously produces hydrogen via electrolysis. The excess energy is seamlessly supplied to the grid upon filling the hydrogen tank. The proposed solution shows great promise for use in renewable energy management systems that combine hybrid technologies. [ABSTRACT FROM AUTHOR]

Embracing the potential of cloud technology in the field of electric vehicle advancements, this paper explores the application of support vector regression (SVR) for accurate state of charge (SOC) estimation of lithium-ion batteries in various computational landscapes. This study aims to scrutinize and compare the performance of SOC estimation, with a specific focus on precision, computational efficiency, and execution speed. The investigation is conducted across diverse environments, including a traditional non-cloud setup and two cloud-based platforms-a standard cloud environment employing Amazon web services (AWS) EC2 servers and an enhanced configuration utilizing the MATLAB production server. The investigation not only emphasizes the effectiveness of cloud integration but also provides valuable insights into the strengths and weaknesses of the proposed methodology. The experimental results contribute to a nuanced understanding of the methodology's performance, shedding light on its potential implications for advancing electric vehicle technologies. This study thus extends its significance beyond technical considerations, providing a broader perspective on its relevance to global electrification initiatives. [ABSTRACT FROM AUTHOR]

As the demand for renewable energy continues to increase, wind power has emerged as a prominent source of clean energy. However, incorporating wind energy into the power generation system at a high level can significantly impact the dynamic performance of the power system, resulting in increased uncertainties during operation. This study investigated the effectiveness of the Multi-Band Power System Stabilizer (MBPSS), a new power system stabilizer, in suppressing dynamic oscillations in a multi-machine power system connected to a wind farm. This research focused on analyzing the transient stability of a nine-bus network, commonly known as the Western System Coordinating Council (WSCC), integrated with a Doubly Fed Induction Generator (DFIG) using MATLAB/Simulink. The study evaluated the dynamic performance of the proposed system under fault conditions, including Line-to-Line-to-Line-to-Ground (LLLG) faults. Simulation results showed that MBPSS effectively dampened oscillations and improved the stability of the power system, even in the presence of severe faults and high-level integration of wind farms. [ABSTRACT FROM AUTHOR]

The prevalent tendency in power transmission systems is to operate closer and closer to the energy limit, rendering system voltage instability a commonly widespread phenomenon. It is, therefore, necessary that certain remedial corrective controls need be undertaken whenever these systems tend towards failure. In this respect, load shedding stands as a major correction mechanism and such a failure can be prevented and nominal system voltage can be resumed. It is worth noting however that load shedding must be implemented very carefully to ensure the satisfaction of both the customer and the electricity-production company. In this context, our focus of interest is laid on load and machine shedding against voltage collapse as an effective corrective method. It is important to note that such a problem turns out to be commonly defined as an optimization problem under constraints. Using genetic algorithms as resolution methods, the application of the proposed methods was implemented on the 14-node IEEE test network, while considering a number of different case studies. [ABSTRACT FROM AUTHOR]