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Molecular simulations in an open environment, involving ion exchange, are necessary to study various systems, from biosystems to confined electrolytes. However, grand-canonical simulations are often computationally demanding in condensed phases. A promising method [L. Belloni, J. Chem. Phys. 151, 021101 (2019)], one of the hybrid nonequilibrium molecular dynamics/Monte Carlo algorithms, was recently developed, which enables efficient computation of fluctuating number or charge density in dense fluids or ionic solutions. This method facilitates the exchange through an auxiliary dimension, orthogonal to all physical dimensions, by reducing initial steric and electrostatic clashes in three-dimensional systems. Here, we report the implementation of the method in LAMMPS with a Python interface, allowing facile access to grand-canonical molecular dynamics simulations with massively parallelized computation. We validate our implementation with two electrolytes, including a model Lennard-Jones electrolyte similar to a restricted primitive model and aqueous solutions. We find that electrostatic interactions play a crucial role in the overall efficiency due to their long-range nature, particularly for water or ion-pair exchange in aqueous solutions. With properly screened electrostatic interactions and bias-based methods, our approach enhances the efficiency of salt-pair exchange in Lennard-Jones electrolytes by approximately four orders of magnitude, compared to conventional grand-canonical Monte Carlo. Furthermore, the acceptance rate of NaCl-pair exchange in aqueous solutions at moderate concentrations reaches about 3% at the maximum efficiency. [ABSTRACT FROM AUTHOR]

A hybrid power station comprising storage pump units and conventional hydropower components holds the potential to enhance the operational flexibility of basin hydroelectric regulation. The storage pumps must possess significant power capacity and operate with high efficiency to ensure viable energy storage. This study investigates the energy dissipation within a two‐stage storage pump using entropy generation theory. The numerical solution of flow energy dissipation (FED) components was obtained for various flow rates using the steady‐state single‐phase shear stress transport k–ω turbulence model. Results indicate that the return channel contributes the most to FED generation within the entire passage, with the FED proportion decreasing from 66.7% to 41.3% as the flow rate increases from 0.5QBEP to 1.2QBEP. The FED generation percentage from the runners increases from 10.4% to 46.9% with increasing flow rate, ranking second. The FED generation percentage attributed from the spiral case ranges from 10.3% to 16.7%, ranking third. Losses from the draft tube are found to be negligible. Flow pattern analysis reveals that FED generation primarily occurs at the junction of inferior flow (flow separation and vortex flow) and the main flow, where significant velocity gradients exist. [ABSTRACT FROM AUTHOR]

The power to gas (P2G) equipment serves as a critical linkage in integrated energy systems that foster bidirectional energy flow to enhance the share of renewable energy and curb uncertainty. By bringing together P2G hydrogen production and hydrogen injection into gas grid technology, the energy conversion loss associated with P2G applications can be significantly reduced. Nonetheless, the strict criteria concerning hydrogen blending ratios pose limitations on the integration of renewable energy. This paper proposes a comprehensive energy system model that couples gas grid hydrogen injection with a hybrid P2G process, aiming to reduce energy conversion losses and overcome the constraints posed by hydrogen blending ratios. This approach promotes the efficient utilization of renewable energy in integrated energy systems (IES). For wind power uncertainty, a scenario analysis method combining Latin hypercube sampling and scenario curtailment 0–1 algorithm is employed. The model's nonlinear constraints are linearized through piecewise linearization, transforming the problem into a mixed-integer programming one for solution simulation results demonstrate that the application of the hybrid P2G process in IES systems with hydrogen injection into gas grid technology reduces certain energy conversion stages and cost inputs compared to using only power to hydrogen conversion. This approach shows significant improvements in economic and environmental performance. The hybrid P2G scheme reduces the hydrogen blending ratio of the system to the minimum operating cost from 12 % to 6 %. At the same time, its minimum value is 20.1 thousand USD lower than that of the scheme using only power to hydrogen conversion, which is about 4.7 % of the total cost. [Display omitted] • An IES based on hybrid P2G and hydrogen injection into gas grid is proposed. • The conversion rate is used to analyze the effect of renewable energy consumption. • The 0–1 algorithm is used to curtailment scenarios to capture the uncertainties. • A multi-scenario model is constructed to optimize capacities and operation. • The optimal economic performance is achieved at the hydrogen blending ratio of 6 %. [ABSTRACT FROM AUTHOR]

Now, the present power generation and distribution companies are working on renewable energy systems because their features are low-level atmospheric pollution, producing less greenhouse pollutants, more reliability, good static performance, and high robustness. In this work, the sunlight Photovoltaic (PV) system is selected because of its advantages are easily available in the atmosphere, high flexibility, zero carbon footprint, easy to maintain, and less transportation cost. However, solar networks produce nonlinear I-V characteristics. Due to the non-linear nature of the solar system, the extraction of peak voltage from the PV module is a very tough task. So, in this article, a variable modified step grey wolf method is integrated with the adaptive-neuro-fuzzy-inference-system to improve the energy production of solar systems. The features of this proposed maximum power point tracking controller are fast identification of the solar system operating point, generating the less fluctuated oriented converter load power, providing more MPP tracking accuracy, less dependence on the solar system installation, and useful for all environmental bad weather conditions. Another problem of solar systems is less voltage production which is improved by introducing a wide voltage gain-boost converter circuit. The features of this converter circuit are less development cost because it does not require more power electronics switches. Here, the proposed IGWM with an AFLC-fed sunlight system is investigated by using MATLAB/Simulink. [ABSTRACT FROM AUTHOR]

This paper proposes a novel configuration for a multiport boost converter (MPBC) with a single inductor (SI), accounting for equivalent series resistances (ESRs) and minimizing input switching stress. The MPBC performance is evaluated and compared with other established topologies. The proposed MPBC interfaces two unidirectional input DC power ports and a rechargeable port for an energy storage element (ESE) with two output ports. The design integrates two renewable sources with the ESE as a third source. One output is for higher voltage, linked to a single‐phase inverter for AC loads. The other output is for lower DC voltage, used for DC loads. The configuration can be adjusted based on requirements. This converter has numerous applications in renewable energy systems, electric vehicles, and agriculture. The steady‐state and small signal modeling of MPBC has been done to derive the mathematical expressions for analyzing stability, stresses (both voltage and current), and performance considering ESRs. A 240 W, MPBC is fabricated along with improved switching strategies using DSP TMS320F28379D. Experimental and simulation results are compared to show the effectiveness of proposed scheme on stability, stresses, and efficient power transfer. Output power is regulated effectively by sharing the input power thereby reducing voltage stress on switches. [ABSTRACT FROM AUTHOR]

The integration of fuel cell device and supercapacitor device into a singular hybrid power source at the electrode level can make the best use of the advantages of each individual device and more importantly, eliminate the energy loss by virtue of the conversion of chemical energy to electric energy on electrocatalyst material and the storage of electric energy on energy storage material simultaneously. To avoid the use of noble metal electrocatalyst and exploit the dual electrocatalytic and capacitive function of polyaniline, a noble-metal-free fuel cell-supercapacitor hybrid power source employing polyaniline electrode as both anode and cathode is rationally constructed. Based on electrochemical in-situ Raman spectroscopy, this work proposes that the open circuit potential (OCP) of polyaniline electrode is a simple and quick indicator of the redox state of polyaniline. After carefully selecting the fuel and carbon-based support, it is found that the ascorbic acid fuel and the carbon fiber cloth supported polyaniline contributes to the lowest OCP threshold in the anode, and thus the fastest self-charge and largest stabilized open circuit voltage of hybrid power source. The hybrid power source can be self-charged to 0.32 V and yield a peak power density of 0.21 mW cm−2. [Display omitted] • Noble-metal-free fuel cell-supercapacitor hybrid power source on electrode level. • Dual electrocatalytic and capacitive function of polyaniline. • Open circuit potential of electrode can represent the redox state of polyaniline. • Widening redox state threshold enhances the performance of hybrid power source. • Self-charge and discharge performance of hybrid power source. [ABSTRACT FROM AUTHOR]

Critical systems, in which failure and malfunction may result in severe human, environmental, and financial damages, are essential components in various sectors and particularly in energy domains. Although undesirable, integration error problems in the supervision of critical systems do occur, incurring significant expenses due to an operator's subjective analysis and hardware topology failures. In this work, a malleable model design approach is proposed to formulate and solve the integration error problem in critical systems' supervision in terms of reliability. A real hybrid power plant (HPP) case is considered for a case study with simulated data. A method framework with an informal approach (C4 diagram) and formal approach (hierarchical colored Petri nets) in a radial spectrum is applied to the HPP supervision design. In using formal methods, a formulation and solution to this problem through structured, scalable, and compact mathematical representations are possible. This malleable model is intended to guarantee the functional correctness and also reliability of the plant supervision system based on system software architecture. The outcomes suggest that the malleable model is appropriate for the energy domain and can be used for other types of critical systems, bringing all the benefits of this methodology to the context in which it will be applied. [ABSTRACT FROM AUTHOR]

The hybrid active power filter (HAPF) can effectively suppress the 75–125 Hz low-frequency harmonic current generated by the EAST poloidal field power supply. However, the low-frequency harmonic current suppression performance of HAPF is greatly affected by its control parameters when the circuit parameters are determined. In this paper, the state–space equation of the HAPF system is established, and the relationship between the eigenvalues of the state–space matrix and the HAPF control parameters is analyzed using the Lyapunov method. Based on the variation law of the eigenvalues, by optimizing the control parameters of the HAPF system, the low-frequency harmonic current suppression performance of HAPF is improved by 41.9%. [ABSTRACT FROM AUTHOR]

For lithium-ion batteries and supercapacitors in hybrid power storage facilities, both steady degradation and random shock contribute to their failure. To this end, in this paper, we propose to introduce the degradation-threshold-shock (DTS) model for their remaining useful life (RUL) prediction. Non-homogeneous compound Poisson process (NHCP) is proposed to simulate the shock effect in the DTS model. Considering the long-range dependence and heavy-tailed characteristics of the degradation process, fractional Weibull process (fWp) is employed in the diffusion term of the stochastic degradation model. Furthermore, the drift and diffusion coefficients are constantly updated to describe the environmental interference. Prior to the model training, steady degradation and shock data must be separated, based on the three-sigma principle. Degradation data for the lithium-ion batteries (LIBs) and ultracapacitors are employed for model verification under different operation protocols in the power system. Recent deep learning models and stochastic process-based methods are utilized for model comparison, and the proposed model shows higher prediction accuracy. [ABSTRACT FROM AUTHOR]

This research article details the design and implementation of a nonlinear adaptive filtering (NAF) technique using an exponential functional link network (EFLN) for a shunt hybrid active power filter (SHAPF) control to solve the current-associated power quality issues on the utility side at the distribution level of electrical power systems. Separation of the fundamental component from the harmonics, achieving unity power factor operation, reducing the reactive power drawn from the source, balancing the currents during transients, and reduction of total harmonic distortion (THD) of the source current are the issues considered to resolve. The proposed technique solves these issues by generating the sinusoidal reference current and separating the fundamental current from the harmonics. When compared to conventional and existing adaptive filtering techniques such as least mean square (LMS), least mean fourth (LMF), and variable step size LMS (VSS-LMS), the proposed EFLN-NAF method excels in terms of speedy convergence, adaptability in noise-specific environments and reduced steady-state coefficient error. MATLAB/Simulink software is utilized to perform the simulations to examine the suggested strategy for the chosen SHAPF topology both in static and dynamic scenarios. For a 15 kW and 3kVAr requirement of the nonlinear load, simulation results proved that the designed PPF for 2kVAr is able to share the reactive power with the APF, thereby reducing its rating and cost. The proposed method of filtering has been proven to be fast converging with 0.049 s, and the THD in steady state is brought to 1.32 % in steady-state and to 3.77 % during transient conditions, which are under standard limits. A hardware prototype of the experimental setup is constructed at the laboratory scale with OPAL-RT (OP4510) as the controller. With an active and reactive power demand of 1.1 kW and 210VAr, the designed PPF supplies 110 VAr, whereas the rest is supplied by the APF. The practical THD in source current is observed to be 2.081 %, which meets the standards. The results from both simulations and experiments are validated, and the efficacy of the proposed technique in mitigating the aforementioned power quality issues is proved. [Display omitted] • A passive power filter design to share the reactive power requirement in addition to mitigating the predominant harmonics of the 5th and 7th order. • A robust nonlinear adaptive filter implementation based on exponential functional link network (EFLN-NAF) algorithm to the shunt hybrid active power filter (SHAPF) control for reference current generation and fundamental component separation from harmonics. • Solution for current-associated power quality issues such as reducing the reactive power burden, achieving unity power factor operation, and mitigating the harmonics, thereby reducing the source current THD value. • Simulation of the proposed technique for the control of shunt hybrid active power filter and evaluation of the outcomes for resolving the power quality issues in both steady-state and dynamic conditions. • Development of a hardware prototype at a laboratory scale using OPAL-RT (OP4510) real-time simulator as a controller for the proposed technique for the control of SHAPF and evaluation of the outcomes. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper proposes a hybrid microwave power receiving (MPR) metasurface array with efficient dual matching of surface impedance and phase gradient. The hybrid array comprises three components: a reflective phase gradient metasurface (R-PGM) array, a surface wave focusing array, and an energy harvesting port. The R-PGMs efficiently convert incident electromagnetic waves into surface waves. The surface wave focusing array then concentrates the energy onto the integrated harvesting port through dual matching of surface impedance and phase gradient. Connecting a rectifier circuit enables efficient microwave energy reception and RF-DC conversion, avoiding the need for multiple rectifiers or complex feeding networks in traditional MPR designs. Numerical analysis and experimental tests verify the superior performance of this hybrid array design in efficient microwave energy harvesting and RF-DC conversion, achieving 90.84% plane wave-to-surface wave conversion efficiency, 76.67% surface wave energy harvesting efficiency, and 49.28% overall RF-DC conversion efficiency. Across the wideband range of 5.6–6.0 GHz, the energy harvesting efficiency remains consistently high, demonstrating the superior characteristics and promising potential of this design in MPR device development. [ABSTRACT FROM AUTHOR]

Today, hydrogen fuel cells (HFCs) have become very popular in various applications because of their ability to be a clean energy source. One of the difficulties associated with HFCs is their sluggish response to variations in the load. This paper presents a nested control strategy based on a type-2 neuro-fuzzy controller (T2NFC) to improve the HFC's dynamic response in a hybrid stand-alone power network using HFC and battery. A system model is constructed using Matlab-Simulink. An interleaved converter is used to draw the maximal power from the HFC and reduce the ripple in the HFC's current. Two T2NFCs control DC voltage and battery charge/discharge current. The robustness of the T2NFC is evaluated for input disturbance, output disturbance, and both disturbances. The results show that the proposed control strategy is robust against input and output disturbances. Also, it provides improved dynamic response of the HFC, lower ripple in HFC current, and less overshoot or undershoot in DC voltage both in transient and steady-state. For step reference input, the proposed controller improves settling time of 24.06 % and overshoot of 59.55 % compared to the conventional PI controller. The results verify the effectiveness of the proposed controller under different operating conditions of hybrid stand-alone power network with fuel cell and battery. • The response of HFC is enhanced, leading to faster and more accurate power generation. • The hydrogen fuel cell's current is experienced a significant reduction in ripples. • The hydrogen fuel cell is now being operated at its maximum power point. • A Type-2 Neuro-Fuzzy Controller is developed to provide robust control of the hydrogen fuel cell's output voltage. [ABSTRACT FROM AUTHOR]

DC voltage is one of the important parameters of active power filters. Since the series hybrid active power filter does not withstand the fundamental voltage, it cannot absorb energy from the power grid, making it too difficult to control its DC voltage. In order to solve the DC voltage control problem, an improved control method for the series hybrid active power filter with magnetic flux compensation is proposed in this paper. In this improved method, the fundamental magnetic flux compensation coefficient is equal to 1, meeting the condition of fundamental magnetic flux compensation. This improved method does not rely on the precise detection and phase-locking of the fundamental voltage at the port of the series transformer. A phase-fixed active current component is generated directly by the inverter, enabling the active power filter (APF) to absorb active power from the power grid. Consequently, the active power absorbed by the APF from the power grid has a linear relationship with the active current component. Both simulation and experimentation verified the correctness and effectiveness of this proposed method. [ABSTRACT FROM AUTHOR]

A hydro–solar hybrid system is an important solution for expanding renewable generation capacity under the percepts of the energy transition. This type of association allows for the coordinated dispatch of solar and hydropower plants, resulting in operational benefits in terms of energy generation and reservoir management, that is, the better use of available water and energy resources. As in this case, the operation of the hydropower plant is associated with the cascade in which it operates, when it is hybridized (for example, by associating with a solar power plant), in addition to local changes, there are impacts on the operating conditions of the other hydropower plants in the same cascade. From such a perspective, this study presents an energy system management model for hybrid power plants composed of hydro and solar sources, aiming to optimize the joint operation and measure the operational consequences at the local level and in the cascade. The results from a case study of a hydro–solar power plant hybridization in the Tietê River (Brazil) revealed increased energy production and improvement in the operating conditions of the cascade's reservoirs, while the grid capacity was found to be an important constraint that limits the capture of synergies resulting from the generation sources complementarity and thus on the benefits to the cascade. [ABSTRACT FROM AUTHOR]

Hybrid power plants have recently emerged as reliable and flexible electricity generation stations by combining multiple renewable energy sources, energy storage systems (ESS), and fossil-based output. However, the effective operation of the hybrid power plants to ensure continuous energy dispatch under challenging conditions is a complex task. This paper proposes a dispatch engine (DE) based on mixed-integer linear programming (MILP) for the planning and management of hybrid power plants. To maintain the committed electricity output, the dispatch engine will provide schedules for operation over extended time periods as well as monitor and reschedule the operation in real time. Through precise prediction of the load and the photovoltaic (PV) and wind power outputs, the proposed approach guarantees optimum scheduling. The precise predictions of the load, PV, and wind power levels are achieved by employing a predictor of the Feed-Forward Neural Network (FFNN) type. With such a dispatch engine, the operational costs of the hybrid power plants and the use of diesel generators (DGs) are both minimized. A case study is carried out to assess the feasibility of the proposed dispatch engine. Real-time measurement data pertaining to load and the wind and PV power outputs are obtained from different locations in the Sultanate of Oman. The real-time data are utilized to predict the future levels of power output from PV and from the wind farm over the course of 24 h. The predicted power levels are then used in combination with a PV–Wind–DG–ESS–Grid hybrid plant to evaluate the performance of the proposed dispatch engine. The proposed approach is implemented and simulated using MATLAB. The results of the simulation reveal the proposed FFNN's powerful forecasting abilities. In addition, the results demonstrate that adopting the proposed DE can minimize the use of DG units and reduce a plant's running expenses. [ABSTRACT FROM AUTHOR]

An improved model predictive control (MPC) with Luenberger state observer is proposed in this article for LC‐coupling hybrid active power filter (LC‐HAPF). Traditionally, the implementation of MPC for LC‐HAPF requires additional sensors to measure coupling capacitor voltage, thereby increases the system cost and reduces reliability. The existence of one‐step sampling delay in practice will also affect the final compensation performance. To relax these problems, both continuous and discrete models are deduced first. Then, the Luenberger observer is utilized based on the discrete model to mitigate the steady‐state error without additional sensors. In addition, the sampling delay compensation is provided by improving the prediction horizon. Finally, the effectiveness of the improved MPC for LC‐HAPF is verified by experiment results, indicating that the proposed method has fast dynamic response and low steady‐state error. [ABSTRACT FROM AUTHOR]

A hybrid technique is proposed to enhance the power quality (PQ) on the distribution sides of the utility grid (UG) by controlling a unified power quality conditioner (UPQC) connected to hybrid sources (photovoltaic [PV] and wind turbine [WT]). The proposed hybrid method integrates the implementation of the pelican optimization algorithm and the Aquila optimizer; hence, it is called the improved aquila optimizer (IAO) technique. The objective of the proposed method is to lessen the total harmonic distortion (THD), voltage instability, and PQ issues during load fluctuation situations. The improved Aquila Optimizer technique optimizes the control parameters of the UPQC to achieve optimal PQ development. The series controller is attached to the grid‐side to enhance grid PQ, while the shunt hybrid active power filter (SAPF) and shunt active power filter (SHAPF) generate the best control pulses based on load and source conditions. The proposed solution addresses power loss, THD, and voltage instability problems during load fluctuation conditions. The series controller reduces voltage sag by 14% and voltage swell by 15%. The THD for the proposed technique is 0.8%. The PQ of the proposed technique is improved, and various characteristics are reduced. The efficiency of the proposed technique is examined by using MATLAB and is compared to existing approaches. The PQ characteristics are significantly improved, and the proposed technique is better than the existing techniques. [ABSTRACT FROM AUTHOR]

Featured Application: The LM-IEKF algorithm proposed in this paper can effectively estimate the state of charge of a lithium-ion battery, and it is suitable for the estimation of an electric vehicle. The error covariance matrix in the IKEF process is modified by the LM algorithm, and it can still maintain a good convergence speed and estimation accuracy in the face of severe current changes. With the application of lithium batteries more and more widely, in order to accurately estimate the state of charge (SoC) of the battery, this paper uses the iterated extended Kalman filter (IEKF) algorithm to estimate the SoC. The Levenberg–Marquardt (LM) method is used to optimize the error covariance matrix of IKEF. Based on the hybrid pulse power characteristics experiment, a second-order Thevenin model with variable parameters is established on the MATLAB platform. The experimental results show that the proposed model is effective under the constant current discharge condition, the Federal Urban Driving Schedule (FUDS) condition, and the Beijing dynamic stress test (BJDST) condition. The results show that the simulation error of the improved LM-IEKF algorithm is less than 2% under different working conditions, which is lower than that of the IKEF algorithm. The improved algorithm has a fast convergence speed to the true value, and it has a good estimation accuracy in the case of large changes in external input current. Additionally, the fluctuation of error is relatively stable, which proves the reliability of the algorithm. [ABSTRACT FROM AUTHOR]

With the development of lithium-ion batteries and fuel cells, the application of hybrid power systems is becoming more and more widespread. To better optimize the energy management problem of fuel cell hybrid systems, the accuracy of system modeling and simulation is very important. The hybrid system is formed by connecting the battery to the fuel cell through an active topology. Digital twin technology is applicable to the mapping of physical entities to each other with high interactivity and fast optimization iterations. In this paper, a relevant model based on mathematical logic is established by collecting actual operational data; subsequently, the accuracy of the model is verified by combining relevant operating conditions and simulating the model. Subsequently, a three-dimensional visualization model of a hybrid power system-based sightseeing vehicle and its operating environment was established using digital twin technology to improve the model simulation of the fuel cell hybrid power system. At low speeds, the simulation results of the hybrid power system-based sightseeing vehicle have a small error compared with the actual running state, and the accuracy of the data related to each internal subcomponent is high. In the simple interaction between the model display vehicle and the environment, the communication state can meet the basic requirements of the digital twin model because the amount of data to be transferred is small. This study makes a preliminary attempt at digital parallelism by combining mathematical logic with visualization models and can be used as a basis for the subsequent development of more mature digital twin models. [ABSTRACT FROM AUTHOR]

This manuscript proposes a hybrid energy harvest and management system to manage harvested ambient thermal and radio frequency (RF) energy and provide constant voltage for an electronic water meter. It mainly includes an antenna, a rectifier, thermoelectric generators (TEGs), and an energy management circuit. The antenna harvests the ambient RF power, and the rectifier converts it to DC power. The harvested RF and thermal powers are stored in a capacitor and managed by an FEH710 energy management circuit to power an electronic water meter. Eight thermoelectric generators convert thermal energy into DC power. The proposed hybrid energy harvesting and management system has been evaluated by simulation and measurement. The antenna's reflection coefficient and peak gain at 2.45 GHz are -30 dB and 3.6 dBi, respectively. The rectifier's measured RF-DC power conversion efficiency (PCE) is 66.7% at 0 dBm. As a demonstration, a commercial electronic water meter works stably by the harvested ambient RF and thermal energy. The proposed hybrid energy harvesting system is expected to find potential practical applications for the Internet of Things (IoT) in environments with RF radiation coverage and temperature gradients. [ABSTRACT FROM AUTHOR]

Water desalination systems integrated with stand-alone hybrid energy sources offer a remarkable solution to the water–energy challenge. Given the complexity of these systems, selecting an appropriate energy management system is crucial. In this regard, employing artificial intelligence techniques to develop and validate an energy management system can be an effective approach for handling such intricate systems. Therefore, this paper presents an ANN-based energy management system (ANNEMS) for a pumping and desalination system connected to an isolated hybrid renewable energy source. Thus, a parametric sensitivity algorithm was developed to identify the optimal neural network architecture. The water–energy management-based supervised multi-layer perceptron neural network demonstrated effective power sharing within a short time frame, achieving accuracy criteria of RMSE, R, and R² between the actual and estimated electrical power of the three motor pumps. The ANNEMS is defined to facilitate real-time power sharing distribution among the various system motor pumps on the test bench, considering the generated power profile and water tank levels. The proposed strategy employs power field oriented control to maintain DC bus voltage stability. Experimental results from the implementation of the proposed ANNEMS are provided. Therein, the power levels of the three motor pumps demonstrated consistent adherence to their reference values. In summary, this study highlights the significance of selecting appropriate energy management for real-time experimental validation. [ABSTRACT FROM AUTHOR]

Hybrid precoding is a significant procedure for decreasing the hardware complexity and power usage in massive multiple-input multiple-output (MIMO) systems. However, the effectiveness of hybrid precoding is highly dependent on precise channel state info and designing of the beamforming matrix. In recent years, deep learning-based approaches have emerged as a promising solution to address these challenges. This research focuses on improving the performance of massive MIMO systems. However, several methods have been introduced to develop the hybrid precoding model, but these models suffer from several issues such as complexity, interference and quantization error. Currently, deep learning-based methods have gained huge attention in this domain where these methods learn from the data and try to overcome the challenges. Here, a deep learning-based model is presented where our main aim is to develop a hybrid precoder along with the deep learning-based optimal power allocation model. Therefore, the proposed model overcomes the issue of hybrid precoding and power distribution resulting in improving the overall performance of massive MIMO systems on the parameters such as spectral efficiency (SE) and the sum rate. [ABSTRACT FROM AUTHOR]

Objective Optimal design of hybrid power train configuration involves system cost, energy consumption and operation requirements. Therefore, it is necessary to study the hybrid power simulation software with global optimization function. Method The function and architecture of a hybrid power traction simulation software with global optimization function is introduced, the calculation model and calculation principle of the software key modules are expounded. With a hybrid power tram project as an example, the system optimization simulation of the hybrid power on-board energy storage and ground charging scheme is carried out to verify the application effect of the software. Result & Conclusion The global optimization hybrid power traction calculation and simulation software is used to comprehensively optimize the layout scheme of the train energy storage devices and line charging piles. After the optimization, not only is the cost of the optimized scheme reduced compared to the original scheme, but the optimized scheme can fully meet the normal operation requirements of the train.

Ensuring the accuracy of wind power prediction is paramount for the reliable and stable operation of power systems. This study introduces a novel approach aimed at enhancing the precision of wind power prediction through the development of a multiscale hybrid model. This model integrates advanced methodologies including Improved Intrinsic Mode Function with Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), permutation entropy (PE), Least Squares Support Vector Regression (LSSVR), Regularized Extreme Learning Machine (RELM), multi-head attention (MHA), and Bidirectional Gated Recurrent Unit (BiGRU). Firstly, the ICEEMDAN technique is employed to decompose the non-stationary raw wind power data into multiple relatively stable sub-modes, while concurrently utilizing PE to assess the complexity of each sub-mode. Secondly, the dataset is reconstituted into three distinct components as follows: high-frequency, mid-frequency, and low-frequency, to alleviate data complexity. Following this, the LSSVR, RELM, and MHA-BiGRU models are individually applied to predict the high-, mid-, and low-frequency components, respectively. Thirdly, the parameters of the low-frequency prediction model are optimized utilizing the Dung Beetle Optimizer (DBO) algorithm. Ultimately, the predicted results of each component are aggregated to derive the final prediction. The empirical findings illustrate the exceptional predictive performance of the multiscale hybrid model incorporating LSSVR, RELM, and MHA-BiGRU. In comparison with other benchmark models, the proposed model exhibits a reduction in Root Mean Squared Error (RMSE) values of over 10%, conclusively affirming its superior predictive accuracy. [ABSTRACT FROM AUTHOR]

Hybrid power systems can be affected by various uncertain parameters such as technical, economic, and environmental factors. These parameters may have both positive and negative impacts on the overall performance of the system. Therefore, in this study, an effective optimization method for modeling and optimization of a hybrid solar-battery-diesel power structure for remote consumers is proposed. The purpose of this method is to find the optimal configuration of the hybrid structure from the technical-economical-environmental point of view to cover the load demand requirements of remote consumers. To verify the performance of the hybrid microgrid, the results of the hybrid system based on the hourly meteorological data and load profile are compared with the results of the conventional diesel generator (DG). The optimization problem is solved using a harmony search optimization algorithm. The results show that the solar/battery/DG system is more suitable than the DG-only system in different uncertainty indexes based on economic and environmental aspects. Also, by the increase of the uncertainty index from 0.5 to 10%, the optimal total cost of the system decreases by 36.5%, while at the same time the unavailability value increases from 0.2259% to 9.9568%. Additionally, when the interest rate value increases from 1 to 7%, the total cost value increases around 17. 4%. [ABSTRACT FROM AUTHOR]

Concentrated solar power (CSP)—photovoltaic (PV) hybrid power plants allow for the generation of cheap electrical energy with a high capacity factor (CF). A deep integration of both technologies offers synergies, using parts of the PV generated electricity for heating the thermal storage tank of the CSP unit. Such configurations have been previously studied for systems coupled by an electric resistance heater (ERH). In this work, the coupling of a CSP and a PV plant using a heat pump (HP) was analyzed due to the higher efficiency of heat pumps. The heat pump is used as a booster to lift the salt temperature in the storage system from 383 to 565 °C in order to reach higher turbine efficiency. A techno-economic analysis of the system was performed using the levelized cost of electricity (LCOE), the capacity factor and nighttime electricity fraction as variables for the representation. The CSP–PV hybrid with a booster heat pump was compared with other technologies such as a CSP–PV hybrid plant coupled by an electric heater, a standalone parabolic trough plant (PT), a photovoltaic system with battery storage (PV–BESS), and a PV thermal power plant (PVTP) consisting of a PV plant with an electric heater, thermal energy storage (TES) and a power block (PB). [ABSTRACT FROM AUTHOR]

This paper introduces a novel hybrid filter topology that combines passive and active components to enhance harmonic filtering and resonance damping in electrical power systems. The design integrates a three-phase two-level voltage-source converter with a double-tuned passive filter in parallel, significantly reducing the power rating and operational costs while maintaining good harmonic filtering performance and reactive current compensation. Double-tuned passive filters, compared to single-tuned ones, offer improved harmonic attenuation at multiple frequencies, enhancing overall system efficiency. Moreover, when used with the proposed hybrid filter topology, the double-tuned version allows for even lower dimensions of the active part, thereby further reducing system cost. A state-feedback controller is designed to enhance the performance of the hybrid filter, proving particularly effective in environments with complex impedance conditions. This paper also examines the impact of variations in passive component parameters, demonstrating the design's robustness against potential deviations expected over the operational lifespan. The results indicate that the hybrid filter effectively mitigates harmonics and maintains operational stability under various transient conditions, as confirmed by analytical and simulation studies on a real industrial network model. These findings underline the hybrid filter's potential to significantly improve power quality in modern electrical networks. [ABSTRACT FROM AUTHOR]

The H-bridge MLI has cascaded to addicted cascaded the diode with switches of lower rated power, consumption of power reactive and active. A new system is being developed to increase effectiveness of MLI (multilevel inverter). within the electricity system, to inverter is critical for DC-AC voltage conversion. MLI focuses more on Industry Needs. MLI is typically used to provide high power at high voltage applications. In comparison to inverters flying capacitors and the diode-clamped, it has the fewest components and the voltage level it same number to achieve the voltage balancing capacitors or diodes any voltage clamping. This design incorporates a multilayer inverter with a cascaded H Bridge based on a Boost circuit (BCCHB). The job of the voltage to increase the boost circuit of the multilevel inverter. Boost circuits are essential in an application of wide range, including photovoltaic and the use of fuel cells. ML Inverter with Cascaded H Bridge is a Voltage Controlled Source Inverter with the advantages of being small in size and having an output waveform that is independent of load type. A cascaded H Bridge prototype model based on a boost circuit is validated, and the results simulation has been compared with the result. [ABSTRACT FROM AUTHOR]

Reducing power consumption with tolerable performance degradation is a fundamental challenge in today's containerized High Performance Computing (HPC). Most Dynamic Power Management (DPM) approaches proposed for the HPC environment are based on profile-guided power-performance prediction techniques. However, the complexity of DPM approaches in a multi-tenant containerized HPC environment (cHPCe) increases significantly due to the varying demands of users and the contention of shared resources. Moreover, there is limited research into software-level monitoring of power consumption in the popular Docker container environment since it is not designed keeping HPC in mind. The proposed research in this paper aims to present a real-time hybrid power-performance prediction approach using Long Short-Term Memory (LSTM) machine learning model. Unlike state-of-the-art techniques, LSTM with the rolling update mechanism accurately predicts the relationships between the tail latencies in time-series power performance data. It updates the training sample sequences according to the currently predicted power consumption and duration to predict sporadic power surges. It also proposes a power-cap determination framework with resource contention awareness to fine-tune power consumption in real time at the thread level. The proposed containerized environment is designed from scratch keeping HPC requirements in mind. Hence, we name the proposed approach as power-aware cHPCe (pcHPCe) and is evaluated and compared with native BareMetal execution using the NAS Parallel Benchmark (NPB) and HPC Challenge benchmark (HPCC) applications. Our experimental results show that the power-performance prediction model achieves an accuracy of 91.39% on average in real-time with an overhead of 1.6% of the total computing power per node. Our resource contention-aware power-cap selection framework attains significant power saving up to 13.1%. [ABSTRACT FROM AUTHOR]

Hybrid lateral closed-wedge high tibial osteotomy (HBHTO) carries certain advantages over medial open-wedge high tibial osteotomy (OWHTO). We investigated the potential difference in the required correction angle between HBHTO and OWHTO to achieve an equal amount of whole lower-extremity alignment correction, retrospectively analyzing the preoperative plain radiographic images of 100 patients. The medial proximal tibial angle (MPTA), joint line convergence angle (JLCA), mechanical lateral distal femoral angle (mLDFA), hip–knee–ankle axis (HKA), length of the tibia, width of the tibial plateau, length of the lower limb (leg length), and location of the center of deformity (CD) were measured. Differences in the required correction angle at the hinge point between the two techniques (CAD) were compared, and correlation analysis was performed to reveal the influential factors. The mean difference in CAD between HBHTO and OWHTO was 0.78 ± 0.22 (0.4~1.5)°, and mean WBL position change per correction angle was 3.9 ± 0.3 (3.0~4.6)% in HBHTO and 4.1 ± 0.3 (3.1~4.7)% in OWHTO. Correlation analysis revealed a strong positive correlation between CAD and HKA. mLDFA, JLCA, MPTA, leg length, OWCD, HBCD, and HCD were also significantly correlated with CAD. HBHTO required a 5.6% larger correction angle at the hinge point to achieve the same amount of alignment correction as OWHTO. [ABSTRACT FROM AUTHOR]

The integration of renewable energy sources, such as wind and solar, into co-located hybrid power plants (HPPs) has gained significant attention as an innovative solution to address the intermittency and variability inherent in renewable systems among plant developers because of advancements in technology, economies of scale, and government policies. However, it is essential to examine different challenges and aspects during the development of a major work on large-scale hybrid plants. This includes the need for optimization, sizing, energy management, and a control strategy. Hence, this research offers a thorough examination of the present state of co-located utility-scale wind–solar-based HPPs, with a specific emphasis on the problems related to their sizing, optimization, and energy management and control strategies. The authors developed a review approach that includes compiling a database of articles, formulating inclusion and exclusion criteria, and conducting comprehensive analyses. This review highlights the limited number of peer-reviewed studies on utility-scale HPPs, indicating the need for further research, particularly in comparative studies. The integration of machine learning, artificial intelligence, and advanced optimization algorithms for real-time decision-making is highlighted as a potential avenue for addressing complex energy management challenges. The insights provided in this manuscript will be valuable for researchers aiming to further explore HPPs, contributing to the development of a cleaner, economically viable, efficient, and reliable power system. [ABSTRACT FROM AUTHOR]

Distributed energy generation refers to producing energy at the macro-scale by employing renewable and nonrenewable distributed energy generation (DEG) resources. The hybridization of energy sources is required to ensure cost effective and regulated supply to loads. However, to have voltage regulation in the power system, the hybrid renewable sources are integrated with the hybrid storage system. The power flow in a hybrid storage system concerning battery and superconducting magnetic energy storage (SMES) is controlled by Aquila Optimizer (AqO). Moreover, the optimal power flow to the load is maintained using a Pyramid squeeze attention-based ResNet (PSA-ResNet) controller. The fire hawk optimization (FHO) algorithm tunes the hyperparameters of the proposed PSA-ResNet controller. The simulation is performed through MATLAB/Simulink software to determine the efficiency of a proposed controller. The characteristics response for the parameters such as voltage, current and power for ON and OFF grid modes are analysed. The result clearly shows that the proposed method provides high stability with a reduced THD of 1.2%. [ABSTRACT FROM AUTHOR]

Solar power plants are desirable because of their low environmental impact and operating costs. However, the efficiency of solar photovoltaic (PV) systems remains challenging due to their nonlinear characteristics. This research proposes novel techniques for tracking the maximal hybrid power point with the goal of improving PV system efficiency. Two well-known shortcomings of traditional maximum power point tracking (MPPT) algorithms are their volatility around the maximum power point (MPP) and their failure accuracy under frequently changing isolation. In order to get over these problems, we put out a theory that uses the Proportional Integration (PI) controller to combine beluga whale optimization (BWO) with perturb and observe (P&O). The effectiveness, intricacy, and simplicity of use of this innovative technique, created by the BWO algorithm and utilizing a Proportional Integration (PI) controller, are compared with traditional P&O and incremental conductance (IC) algorithms in this study. The suggested hybrid PI-BWO approach provides a voltage gain of 2.341 and an average tracking efficiency of about 96.8%, according to MATLAB simulation testing. The P&O theory yielded an efficiency of 93.2% and a voltage gain of 2.25, but the IC technique provided an average efficiency of 95.7% and a voltage gain of 2.28. This method is characterized by its durability, stability, and convenience of use. [ABSTRACT FROM AUTHOR]

As new energy technologies mature and become more widely available, hybrid vehicles are increasingly being adopted by consumers. However, the existence of at least two power modes and energy management methods has had a significant impact on their related operating costs. This paper proposes an energy management algorithm for this type of vehicles based on an improved deep Q-Learning neural network. This way the energy consumption for hybrid vehicles could be reduced, the efficiency of their energy utilisation could be improved, and their related cost advantages could be enhanced in comparison with traditional fuel-powered vehicles. By using a mixture of multiple representative operating conditions for testing purposes, the conducted experiment confirmed that the fuel consumption per 100 kilometers for the algorithm based on double deep Q-networks under random operating conditions 1 and 2 was 4.05L/100km and 3.64L/100km, respectively. Moreover, the average fuel consumption per 100 seconds for this algorithm was 43ml/100s, which was significantly lower than that of the other three employed automotive powertrain energy management algorithms. The obtained experimental results proved that the energy management algorithm for the hybrid electric vehicle powertrain presented in this paper featured excellent energy control and management capabilities. To sum up, this study could have certain reference significance for improving the cost-effectiveness of hybrid electric vehicles in China. [ABSTRACT FROM AUTHOR]

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Copyright of Urban Mass Transit is the property of Urban Mass Transit Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)