Search Results (553)

Power distribution systems are witnessing a growing deployment of distributed, inverter-based renewable resources such as solar generation. This poses certain challenges such as rapid voltage fluctuations due to the intermittent nature of renewables. Volt-Var control (VVC) methods have been proposed to utilize the ability of inverters to supply or consume reactive power to mitigate fast voltage fluctuations. These methods usually require a detailed power network model including topology and impedance data. However, network models may be difficult to obtain. Thus, it is desirable to develop a model-free method that obviates the need for the network model. This paper proposes a novel model-free cooperative control method to perform voltage regulation and reduce inverter aging in power distribution systems. This method assumes the existence of time-series voltage and load data, from which the relationship between voltage and nodal power injection is derived using a feedforward artificial neural network (ANN). The node voltage sensitivity versus reactive power injection can then be calculated, based on which a cooperative control approach is proposed for mitigating voltage fluctuation. The results obtained for a modified IEEE 13-bus system using the proposed method have shown its effectiveness in mitigating fast voltage variation due to PV intermittency. Moreover, a comparative analysis between model-free and model-based methods is provided to demonstrate the feasibility of the proposed method. Full article

Ports in the Baltic Sea region play an integral role in advancing sustainable maritime practices in the area, due to their geographic interconnectedness, economic importance, and sensitivity to environmental challenges. While numerous port sustainability assessment methods exist, most of which are grounded in the Triple Bottom Line (TBL) metric, many tend to emphasise whether specific targets have been met, rather than evaluating port sustainability on a scalar basis. This study explores the sustainability strategies of seven selected ports in five Baltic Sea countries using an innovative qualitative evaluation framework developed by the Swedish Maritime Competence Centre (SMCC). The SMCC model integrates the three core pillars of sustainability-environmental, social, and economic dimensions, while incorporating energy efficiency and digitalisation as critical enablers of modern port operations. The findings reveal significant variation in sustainability performance among the selected ports, shaped by regional contexts, operational profiles, and prior engagement with sustainability initiatives. Also, the results bring into light the most common sustainable practices used in the ports, e.g., LED lightning, onshore power supply, and port information systems. Full article

This study presents transistor-level simulation results for a novel memristor emulator circuit. The design incorporates an inverter and a current-mode-controlled operational transconductance amplifier to stabilize the output voltage. Transient performance is evaluated across a 20 MHz to 100 MHz frequency range. Simulations using 0.18 μm TSMC technology confirm the circuit’s functionality, demonstrating a power consumption of 0.1 mW at a 1.2 V supply. The memristor model’s reliability is verified through corner simulations, along with Monte Carlo and temperature variation tests. Furthermore, the emulator is applied in a Memristive Integrate-and-Fire neuron circuit, a CMOS-based system that replicates biological neuron behavior for spike generation, enabling ultra-low-power computing and advanced processing in retinal prosthesis applications. Full article

The growing demand for low-emission maritime transport and efficient onboard energy management has intensified research into advanced control strategies for hybrid shipboard microgrids. These systems integrate both AC and DC power domains, incorporating renewable energy sources and battery storage to enhance fuel efficiency, reduce greenhouse gas emissions, and support operational flexibility. However, integrating renewable energy into shipboard microgrids introduces challenges, such as power fluctuations, varying line impedances, and disturbances caused by AC/DC load transitions, harmonics, and mismatches in demand and supply. These issues impact system stability and the seamless coordination of multiple distributed generators. To address these challenges, we proposed a hierarchical control strategy that supports sustainable operation by improving the voltage and frequency regulation under dynamic conditions, as demonstrated through both MATLAB/Simulink simulations and real-time hardware validation. Simulation results show that the proposed controller reduces the frequency deviation by up to 25.5% and power variation improved by 20.1% compared with conventional PI-based secondary control during load transition scenarios. Hardware implementation on the NVIDIA Jetson Nano confirms real-time feasibility, maintaining power and frequency tracking errors below 5% under dynamic loading. A comparative analysis of the classical PI and sliding mode control-based designs is conducted under various grid conditions, such as cold ironing mode of the shipboard microgrid, and load variations, considering both the AC and DC loads. The system stability and control law formulation are verified through simulations in MATLAB/SIMULINK and practical implementation. The experimental results demonstrate that the proposed secondary control architecture enhances the system robustness and ensures sustainable operation, making it a viable solution for modern shipboard microgrids transitioning towards green energy. Full article

Global building energy consumption is significantly increasing. Utilizing renewable energy sources may be an effective approach to achieving low-carbon and energy-efficient buildings. A combined system incorporating solar photovoltaic–thermal (PV/T) components with an air-source heat pump (ASHP) was studied for simultaneous heating and power generation in a real residential building. The back panel of the PV/T component featured a novel polygonal Freon circulation channel design. A prototype of the combined heating and power supply system was constructed and tested in Fuzhou City, China. The results indicate that the average coefficient of performance (COP) of the system is 4.66 when the ASHP operates independently. When the PV/T component is integrated with the ASHP, the average COP increases to 5.37. On sunny days, the daily average thermal output of 32 PV/T components reaches 24 kW, while the daily average electricity generation is 64 kW·h. On cloudy days, the average daily power generation is 15.6 kW·h; however, the residual power stored in the battery from the previous day could be utilized to ensure the energy demand in the system. Compared to conventional photovoltaic (PV) systems, the overall energy utilization efficiency improves from 5.68% to 17.76%. The hot water temperature stored in the tank can reach 46.8 °C, satisfying typical household hot water requirements. In comparison to standard PV modules, the system achieves an average cooling efficiency of 45.02%. The variation rate of the system’s thermal loss coefficient is relatively low at 5.07%. The optimal water tank capacity for the system is determined to be 450 L. This system demonstrates significant potential for providing efficient combined heat and power supply for buildings, offering considerable economic and environmental benefits, thereby serving as a reference for the future development of low-carbon and energy-saving building technologies. Full article

As inevitable outcomes of the digital economy’s low-carbon development, green data centers play a crucial role in environmental impact and underlying mechanisms. This study focuses on green data center establishment as a representative practice, utilizing Chinese A-share listed companies and urban data from 2009 to 2023 to construct a multi-period difference-in-differences model. From a supply chain perspective, we investigate the impact of green data centers on corporate carbon emissions and their mechanisms. The results demonstrate that regional establishment of green data centers significantly promotes corporate carbon emission reduction, with conclusions remaining robust after a series of comprehensive robustness and endogeneity tests. This process primarily operates through two channels: green total factor energy efficiency and green attention. Green data center establishment significantly enhances green total factor energy efficiency and corporate green attention. The more developed the regional digital infrastructure and the higher the computing power development levels, the stronger the incentive effect on corporate carbon reduction. Heterogeneity analysis reveals that green data centers have more significant promoting effects on carbon emission reduction in state-owned enterprises and high-tech enterprises. This research contributes to a deeper understanding of the effects, mechanisms, and regional variations related to green data centers in facilitating corporate carbon emission reduction. Full article

Steam networks are widely used for industrial heat supply. High-temperature heat pumps (HTHPs) are an increasingly attractive low-emission solution to traditional steam generation, which could also improve the operational efficiency and energy demand flexibility of industrial processes. This work characterises 4-bar steam supply via HTHPs and aims to assess how variations in power input that result from flexible HTHP operation may affect steam flow and temperature, both with and without a downstream steam accumulator (SA). First, steady-state modelling is used for system design. Then, dynamic component models are developed and used to simulate the system response to HTHP power input variations. The performance of different SA integration layouts and sizes is evaluated. Results demonstrate that steam supply fluctuations closely follow changes in HTHP operation. A downstream SA is shown to mitigate these variations to an extent that depends on its capacity. Practical SA sizing recommendations are derived, which allow for the containment of steam supply fluctuations within acceptability. By providing a basis for evaluating the financial viability of flexible HTHP operation for steam provision, the results support clean technology’s development and uptake in industrial steam and district heating networks. Full article

The advancement of Internet of Things (IoT) technology has enabled battery-powered devices to be deployed across a wide range of applications; however, it also introduces challenges such as high energy consumption and security vulnerabilities. To address these issues, adiabatic logic circuits offer a promising solution for achieving energy efficiency and enhancing the security of IoT devices. Adiabatic logic circuits are well suited for energy harvesting systems, especially in applications such as sensor nodes, RFID tags, and other IoT implementations. In these systems, the harvested bipolar sinusoidal RF power is directly used as the power supply for the adiabatic logic circuit. However, adiabatic circuits require a peak detector to provide bulk biasing for pMOS transistors. To meet this requirement, a diode-connected MOS transistor-based voltage doubler circuit is used to convert the sinusoidal input into a usable DC signal. In this paper, we propose a novel adiabatic logic design that maintains low power consumption while optimizing energy and current fluctuations across various input transitions. By ensuring uniform and complementary current flow in each transition within the logic circuit’s functional blocks, the design reduces energy variation and enhances resistance against power analysis attacks. Evaluation under different clock frequencies and load capacitances demonstrates that the proposed adiabatic logic circuit exhibits lower fluctuation and improved security, particularly at load capacitances of 50 fF and 100 fF. The results show that the proposed circuit achieves lower power dissipation compared to conventional designs. As an application example, we implemented an ultrasonic transmitter circuit within a LoRaWAN network at the end-node sensor level, which serves as both a communication protocol and system architecture for long-range communication systems. Full article

This paper analyzes the transient voltage stability of the dual-source DC power system. The system’s equivalent model is first established. Subsequently, the effect mechanisms of line parameters and voltage-source rectifiers’ current control inner loops on the system’s transient voltage instability are investigated. It indicates that these factors reduce the power supply capacity of the source, increasing the risk of transient instability in the system. Then, considering the influence of fault depths, the influence of different large disturbances on the transient voltage stability is investigated. Furthermore, the critical cutting voltage and critical cutting time for DC power systems are determined and then validated on the MATLAB R2023b/Simulink platform. Finally, based on the mixed potential function theory, the impact of system parameter variations on stability boundaries is analyzed quantitatively. Simulation verification is conducted on the MATLAB R2023b/Simulink platform, and experimental verification is conducted on the RT-LAB Hardware-in-the-Loop platform. The results of the quantitative analysis and experiments corroborate the conclusions drawn from the mechanistic analysis, underscoring the critical role of line parameters and converter control parameters in the system’s transient voltage stability. Full article

A comprehensive diagnosis of eddy–mean flow interaction in the Kuroshio Current (KC) region and the associated energy conversion pathway is conducted employing a state-of-the-art high-resolution global ocean–sea ice coupled model. The spatial distributions of the energy reservoirs and their conversions exhibit significant complexity. The cross-stream variation is found in the energy conversion pattern in the along-coast region, whereas a mixed positive–negative conversion pattern is observed in the off-coast region. Considering the area-integrated conversion rates between energy reservoirs, barotropic and baroclinic instabilities dominate the energy transferring from the mean flow to eddy field in the KC region. When the KC separates from the coast, it becomes highly unstable and the energy conversion rates intensify visibly; moreover, the local variations of the energy conversion are significantly influenced by the topography in the KC extension region. The mean available potential energy is the total energetic source to drive the barotropic and baroclinic energy pathway in the whole KC region, while the mean kinetic energy supplies the total energy in the extension region. For the whole KC region, the mean current transfers 84.9 GW of kinetic energy and 37.3 GW of available potential energy to the eddy field. The eddy kinetic energy is generated by mixed barotropic and baroclinic processes, amounting to 84.9 GW and 15.03 GW, respectively, indicating that topography dominates the generation of mesoscale eddy. Mean kinetic energy amounts to 11.08 GW of power from the mean available potential energy and subsequently supplies the barotropic pathway. Full article

This study investigates the external load and positional constraints’ impact on the accuracy and performance of an autonomous adaptive electrohydraulic actuator with pump control intended for mobile equipment. An actuator simulation model was developed in the MATLAB/Simulink (version R2021A) environment, and a full-scale experimental setup was constructed to validate this model. Various motion trajectories under different load conditions were analyzed to evaluate discrepancies between simulated and experimental results and to identify key performance characteristics across operational modes. The results demonstrate that the simulation model adequately replicates the actuator’s dynamic behavior, although deviations emerge under high-load conditions. Notably, in the absence of external load, the static positioning error does not exceed 0.025 mm (0.05% of the 50 mm target value), while under the maximum load of 8000 N, the error increases to 0.075 mm (0.15% of the 50 mm target value). These limitations are primarily due to current constraints imposed by the actuator’s power supply capacity (up to 300 W at 24 V), which restrict pressure buildup rates under heavy loads. Nevertheless, the proposed control system exhibits robustness to load variations and ensures positioning accuracy within acceptable limits, demonstrating its practical suitability for mobile machinery applications. The developed simulation model also serves as a valuable tool for control system tuning and testing in the absence of a physical prototype. Full article

Dams are critical infrastructures that provide essential services such as water supply, hydroelectric power generation, and flood control. As many dams age, the risk of structural failure increases, making safety assurance more urgent than ever. Traditional monitoring systems typically employ predictive models—based on techniques such as the finite element method (FEM) or machine learning (ML)—to compare real-time data against expected performance. However, these models often rely on static warning thresholds, which fail to reflect the dynamic conditions affecting dam behavior, including fluctuating water levels, temperature variations, and extreme weather events. This study introduces an adaptive warning threshold methodology for dam safety based on kernel density estimation (KDE). The approach incorporates a boosted regression tree (BRT) model for predictive analysis, identifying influential variables such as reservoir levels and ambient temperatures. KDE is then used to estimate the density of historical data, allowing for dynamic calibration of warning thresholds. In regions of low data density—where prediction uncertainty is higher—the thresholds are widened to reduce false alarms, while in high-density regions, stricter thresholds are maintained to preserve sensitivity. The methodology was validated using data from an arch dam, demonstrating improved anomaly detection capabilities. It successfully reduced false positives in data-sparse conditions while maintaining high sensitivity to true anomalies in denser data regions. These results confirm that the proposed methodology successfully meets the goals of enhancing reliability and adaptability in dam safety monitoring. This adaptive framework offers a robust enhancement to dam safety monitoring systems, enabling more reliable detection of structural issues under variable operating conditions. Full article

The growing demand for clean, reliable and efficient power supply has driven the adoption of renewable energy sources in the package of distributed generation (DG) at the distribution segment of the power system. Despite advancements in DG allocation methodologies, a significant research gap exists regarding the simultaneous evaluation of DG sizing, location and power factor optimization, and their economic implications. This study presents the Mountain Gazelle Optimizer (MGO), a recent optimization approach to address the challenges of sizing, locating, and optimizing the power factor of multi-type DG units in a radial distribution network (RDN). In this work, the MGO is employed to reduce voltage variations, reactive power losses, real power losses, and costs while improving the bus voltage in the RDNs. The methodology involves extensive simulations across multiple scenarios covering one to three DG allocations with varying power factors (unity, fixed, and optimal). Key performance metrics evaluated included real and reactive loss reductions, voltage profile index (VPI), voltage stability index (VSI), and cost reductions due to energy losses compared to base cases. The proposed approach was implemented on the standard 33- and 69-bus networks, and the findings demonstrate that the MGO much outperforms other optimization approaches in the existing literature, realizing considerable decreases in real power losses (up to 98.10%) and reactive power losses (up to 93.38%), alongside notable cost savings. This research showcases the critical importance of optimizing DG power factors, a largely neglected aspect in most prior studies. In conclusion, this work fills a vital gap by integrating power factor optimization into the DG allocation framework, offering a comprehensive approach to enhancing the electricity distribution networks’ dependability, efficacy, and sustainability. Full article

When connecting a renewable energy source to a medium-voltage grid, it has to fulfil grid codes and be able to work in a medium-voltage range (>10 kV). Multilevel converters (MLCs) are recognized for their low total harmonic distortion (THD) and ability to work at high voltage compared to other converter types, making them ideal for applications connected to medium-voltage grids whilst being compliant with grid codes and voltage ratings. Cascaded H-bridge multilevel converters (CHBs-MLC) are a type of MLC topology, and they does not need any capacitors or diodes for clamping like other MLC topologies. One of the problems in these types of converters involves the double-frequency harmonics in the DC linking voltage and power, which can increase the size of the capacitors and converters. The use of line frequency transformers for isolation is another factor that increases the system’s size. This paper proposes an isolated CHBs-MLC topology that effectively overcomes double-line frequency harmonics and offers isolation. In the proposed topology, each DC source (renewable energy source) supplies a three-phase load rather than a single-phase load that is seen in conventional MLCs. This is achieved by employing a multi-winding high-frequency transformer (HFT). The primary winding consists of a winding connected to the DC sources. The secondary windings consist of three windings, each supplying one phase of the load. This configuration reduces the DC voltage link ripples, thus improving the power quality. Photovoltaic (PV) renewable energy sources are considered as the DC sources. A case study of a 1.0 MW and 13.8 kV photovoltaic (PV) system is presented, considering two scenarios: variations in solar irradiation and 25% partial panel shedding. The simulations and design results show the benefits of the proposed topology, including a seven-fold reduction in capacitor volume, a 2.7-fold reduction in transformer core volume, a 50% decrease in the current THD, and a 30% reduction in the voltage THD compared to conventional MLCs. The main challenge of the proposed topology is the use of more switches compared to conventional MLCs. However, with advancing technology, the cost is expected to decrease over time. Full article

Accurate forecasting of photovoltaic (PV) power generation is crucial for optimizing grid operation and ensuring a reliable power supply. However, the inherent volatility and intermittency of solar energy pose significant challenges to grid stability and energy management. This paper proposes a learning model named CECSVB-LSTM, which integrates several advanced techniques: a bidirectional long short-term memory (BILSTM) network, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), variational mode decomposition (VMD), and the Sparrow Search Algorithm (CSSSA) incorporating circle chaos mapping and the Sine Cosine Algorithm. The model first uses CEEMDAN to decompose PV power data into Intrinsic Mode Functions (IMFs), capturing complex nonlinear features. Then, the CSSSA is employed to optimize VMD parameters, particularly the number of modes and the penalty factor, ensuring optimal signal decomposition. Subsequently, BILSTM is used to model time dependencies and predict future PV power output. Empirical tests on a PV dataset from an Australian solar power plant show that the proposed CECSVB-LSTM model significantly outperforms traditional single models and combination models with different decomposition methods, improving R² by more than 7.98% and reducing the root mean square error (RMSE) and mean absolute error (MAE) by at least 60% and 55%, respectively. Full article