Current Issue : July-September Volume : 2024 Issue Number : 3 Articles : 5 Articles
This research delineates a pivotal advancement in the domain of sustainable energy systems, with a focused emphasis on the integration of renewable energy sources—predominantly wind and solar power—into the hydrogen production paradigm. At the core of this scientific endeavor is the formulation and implementation of a deep-learning-based framework for short-term localized weather forecasting, specifically designed to enhance the efficiency of hydrogen production derived from renewable energy sources. The study presents a comprehensive evaluation of the efficacy of fully connected neural networks (FCNs) and convolutional neural networks (CNNs) within the realm of deep learning, aimed at refining the accuracy of renewable energy forecasts. These methodologies have demonstrated remarkable proficiency in navigating the inherent complexities and variabilities associated with renewable energy systems, thereby significantly improving the reliability and precision of predictions pertaining to energy output. The cornerstone of this investigation is the deployment of an artificial intelligence (AI)-driven weather forecasting system, which meticulously analyzes data procured from 25 distinct weather monitoring stations across Latvia. This system is specifically tailored to deliver short-term (1 h ahead) forecasts, employing a comprehensive sensor fusion approach to accurately predicting wind and solar power outputs. A major finding of this research is the achievement of a mean squared error (MSE) of 1.36 in the forecasting model, underscoring the potential of this approach in optimizing renewable energy utilization for hydrogen production. Furthermore, the paper elucidates the construction of the forecasting model, revealing that the integration of sensor fusion significantly enhances the model’s predictive capabilities by leveraging data from multiple sources to generate a more accurate and robust forecast. The entire codebase developed during this research endeavor has been made available on an open access GIT server....
Rectifier plays a pivotal role in wireless power transfer systems. While numerous studies have concentrated on enhancing efficiency and bandwidth at specific high-power levels, practical scenarios often involve unpredictable power inputs. Consequently, a distinct need arises for a rectifier that demonstrates superior efficiency across a broad range of input power levels. This paper introduces a high-power RF-to-DC rectifier designed for WPT applications, featuring an ultrawide dynamic range of input power. The rectification process leverages a GaN (gallium nitride) high electron mobility transistor (HEMT) to efficiently handle high power levels up to 12.6 W. The matching circuit was designed to ensure that the rectifier will operate in class-F mode. A Schottky diode is incorporated into the design for relatively lower-power rectification. Seamless switching between the rectification modes of the two circuits is accomplished through the integration of a circulator. The proposed rectifier exhibits a 27.5 dB dynamic range, achieving an efficiency exceeding 55% at 2.4 GHz. Substantial improvement in power handling and dynamic range over traditional rectifiers is demonstrated....
The appearance of ultra-low-frequency oscillations in the grid at the sending end, after asynchronous grid interconnection, poses a significant threat to the stable operation of the system. For post-asynchronous interconnection in a multi-DC transmission system, an investigation is conducted to analyze the causes of ultra-low-frequency oscillations and the utilization of a Frequency Limit Controller (FLC) which aims to suppress these oscillations. Furthermore, a method is developed to rank DC sensitivity, considering the hydroelectric distribution in the sending-end grid, by combining the DC FLC impact factor and DC control sensitivity. Subsequently, a novel approach for ultralow- frequency oscillation suppression is proposed. This approach employs the stochastic subspace method for parameter estimation and the NSGA-II optimization algorithm to convert the multi-DC optimization challenge into multiple sequential cyclic optimization problems, each focusing on a single DC, ensuring a more effective suppression of ultra-low-frequency oscillations. The proposed scheme’s effectiveness is validated through simulations using a specific locations’ interconnected power grid....
When systems experience a severe fault, splitting, as the final line of defense to ensure the stability of the power system, holds immense significance. The precise selection of splitting sections has become the current focal point of research. Addressing the challenges of a large search space and unclear splitting sections, this paper introduces a grid structure optimization algorithm based on electrical coupling degree. Firstly, employing the theory of slow coherency, a generalized characteristic analysis of the system is conducted, leading to an initial division of coherency groups. Subsequently, an electrical coupling degree index, taking into account the inertia of generators, is proposed. This index can reflect the clarity of grid splitting. Furthermore, a two-layer optimization model for grid structure is constructed, utilizing the Proximal Policy Optimization (PPO) algorithm to optimize the grid structure. This process reduces the size of the splitting space and mitigates the difficulty of acquiring splitting sections. Finally, simulation validation is performed using the IEEE-118-bus system to demonstrate the effectiveness of the proposed optimization algorithm....
Zinc-ion hybrid capacitors (ZICs) can achieve high energy and power density, ultralong cycle life, and a wide operating voltage window, and they are widely used in wearable devices, portable electronics devices, and other energy storage fields. The design of advanced ZICs with high specific capacity and energy density remains a challenge. In this work, a novel kind of V, N dual-doped Ti3C2 film with a three-dimensional (3D) porous structure (3D V-, N-Ti3C2) based on Zn-ion pre-intercalation can be fabricated via a simple synthetic process. The stable 3D structure and heteroatom doping provide abundant ion transport channels and numerous surface active sites. The prepared 3D V-, N-Ti3C2 film can deliver unexpectedly high specific capacitance of 855 F g−1 (309 mAh g−1) and demonstrates 95.26% capacitance retention after 5000 charge/discharge cycles. In addition, the energy storage mechanism of 3D V-, N-Ti3C2 electrodes is the chemical adsorption of H+/Zn2+, which is confirmed by ex situ XRD and ex situ XPS. ZIC full cells with a competitive energy density (103 Wh kg−1) consist of a 3D V-, N-Ti3C2 cathode and a zinc foil anode. The impressive results provide a feasible strategy for developing high-performance MXene-based energy storage devices in various energy-related fields....
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