Search Results (170)

The large-scale integration of renewable energy into power systems poses significant challenges to reactive power and voltage stability. To enhance system stability, this work proposes a cluster partitioning and distributed control strategy for distribution networks with high-penetration distributed PV integration. Firstly, a comprehensive clustering index system, including electrical distance, voltage sensitivity, and regulation ability, is established. Considering the voltage and reactive power support capability of regional clusters, the distribution network is divided into clusters. Subsequently, based on the results of cluster division, a hierarchical partition optimization model is constructed with voltage and reactive power as the optimization objectives. Finally, a distributed optimization algorithm based on ADMM is proposed to solve the optimization model and maximize the utilization of distribution network control resources. The simulation results based on the IEEE 33-node distribution system verify the effectiveness of the proposed distributed optimization strategy. Full article

Responding to the challenges of global climate change and domestic air pollution, Taiwan revised its energy policy in recent years, introducing an energy transition strategy focused on low-carbon and clean energy. However, if photovoltaic installations are not properly sited, they may have negative impacts on the local environment. Previous research on renewable energy has primarily focused on policy evaluation, with limited attention given to case studies that examine the suitability of site selection for PV system installations. Thus, this study incorporates the Fuzzy Delphi Method (FDM) and the Analytic Hierarchy Process (AHP) to explore the criteria for evaluating site suitability for ground-mounted PV systems. This study considers existing sites with completed ground-mounted PV systems in Tainan City as case study subjects. The results indicate that the most important factor, as prioritized by experts, is the distance from Class I environmentally sensitive areas, followed by the duration of insolation, proximity to the electrical grid, and distance from residential areas. The evaluation model developed in this study provides a valuable reference for future site selection of ground-mounted PV systems. Establishing dedicated PV energy parks also may offer a viable solution to mitigate disputes related to the deployment of ground-mounted PV systems. Full article

Aiming at the issues that the field mill type electric field sensor lacks an accurate and complete mathematical model, and its signal is weak and contains a large amount of harmonic noise, on the basis of establishing the mathematical model of the sensor’s induction electrode, the finite element method was adopted to analyze the influence laws of parameters such as the thickness of the shielding electrode and the distance between the induction electrode and the shielding electrode on the sensor sensitivity. On this basis, the above parameters were optimized. A signal processing circuit incorporating a pre-integral transformation circuit, a differential amplification circuit, and a bias circuit was investigated, and a completed mathematical model of the input and output of the field mill type electric field sensor was established. An improved harmonic detection method combining fast Fourier transform and back propagation neural network (FFT-BP) was proposed, the learning rate, momentum factor, and excitation function jointly participated in the adjustment of the network, and the iterative search range of the algorithm was limited by the threshold interval, further improving the accuracy and rapidity of the sensor measurement. Experimental results indicate that within the simulated electric field intensity range of 0–20 kV/m in the laboratory, the measurement resolution of this system can reach 18.7 V/m, and the measurement linearity is more than 99%. The designed system is capable of measuring the atmospheric electric field intensity in real time, providing necessary data support for lightning monitoring and early warning. Full article

Green data centers (GDCs) are increasingly deployed worldwide to power digital infrastructure sustainably. These centers integrate renewable energy sources, such as solar and wind, to reduce dependence on grid electricity and lower operational costs. When distributed geographically, GDCs face considerable challenges due to spatial variations in renewable energy availability, electricity pricing, and bandwidth costs. This paper addresses the joint optimization of operational cost and service quality for delay-sensitive applications scheduled across distributed green data centers (GDDCs). We formulate a multi-objective optimization problem that minimizes total operational costs while reducing the Average Task Loss Probability (ATLP), a key Quality of Service (QoS) metric. To solve this, we propose an Adaptive Firefly-Based Bi-Objective Optimization (AFBO) algorithm that introduces multiple adaptive mechanisms to improve convergence and diversity. The minimum Manhattan distance method is adopted to select a representative knee solution from each algorithm’s Pareto front, determining optimal task service rates and ISP task splits into each time slot. AFBO is evaluated using real-world trace-driven simulations and compared against benchmark multi-objective algorithms, including multi-objective particle swarm optimization (MOPSO), simulated annealing-based bi-objective differential evolution (SBDE), and the baseline Multi-Objective Firefly Algorithm (MOFA). The results show that AFBO achieves up to 64-fold reductions in operational cost and produces an extremely low ATLP value ($1.875\times {10}^{-7}$) that is nearly two orders of magnitude lower than SBDE and MOFA and several orders better than MOPSO. These findings confirm AFBO’s superior capability to balance energy cost savings and Quality of Service (QoS), outperforming existing methods in both solution quality and convergence speed. Full article

The interference of metal working surfaces on the electric field can lead to performance variations between the flush mounting and non-flush mounting of capacitive proximity sensors in industrial applications. Traditional active shielding circuit designs are complex, while grounding shields not only reduce the sensor sensitivity but are also unsuitable for grounded sensors. To address this issue, this paper proposes an innovative near-ground (NG) shielding structure. This structure effectively concentrates the electric field between the sensing electrode and ground by adding a common ground electrode around the sensing electrode, thereby reducing the electrical coupling between the metal working surface and the sensing electrode and achieving the desired shielding effect. Through finite element analysis and experimental verification, this study performed an in-depth investigation of the capacitance difference $C_d$ and the rate of change of capacitance with the target distance of sensors under the two mounting methods. The proposed structure achieved a performance comparable with active shielding (17 fF $C_d$) while operating passively, which addressed a critical cost–adaptability trade-off in industrial CPS designs. The results show that although the performance of the NG shielding was slightly inferior to active shielding, it was significantly better than traditional grounding shielding, and its structure was simple and low cost, showing great potential in practical applications. Full article

The surface-borehole transient electromagnetic method exhibits significant advantages in identifying deep targets, as its closer distance to subsurface targets results in more pronounced effective anomalies when compared to surface-based techniques. The grounded-wire source TEM demonstrates enhanced capabilities for deep exploration, featuring increased penetration depth, enhanced signal response, superior resolution, and minimized volume effects, which render it especially effective for examining intricate deep reservoirs. This study utilizes a time-domain finite-element method with unstructured tetrahedral grids to conduct three-dimensional numerical simulations of grounded-wire source SBTEM in complex terrains, capitalizing on the flexibility and precision of this method for modeling detailed geological structures. A comparative analysis of electromagnetic field responses between conductive and high-resistivity targets indicates that the detection capability of magnetic field components decreases more markedly than that of the vertical electric field Ez as the burial depth of the target increases. The grounded-wire source SBTEM exhibits enhanced sensitivity and better identification capabilities for conductive targets when compared to high-resistivity alternatives. The present research represents a detailed analysis of the impact of complex terrain on the detection capabilities of grounded-wire source SBTEM, utilizing electromagnetic response simulations of typical three-dimensional complex geological models. The results provide robust theoretical backing and empirical evidence for an enhanced understanding of subsurface resource exploration. Full article

To address the enhanced coupling characteristics in reactive power partitioning of power grids with high-penetration renewable energy integration, this paper proposes an optimized reactive power partitioning method that integrates dynamic source–load correlation characteristics and regional coupling degree evaluation. Conventional static electrical distance-based partitioning methods struggle to adapt to dynamic coupling effects caused by renewable energy output fluctuations, leading to degraded partition decoupling performance. This study innovatively constructs a Copula function-based joint probability distribution model for source–load correlation. By employing non-parametric estimation and undetermined coefficient methods to solve marginal distribution parameters, and utilizing the K-means clustering algorithm to generate typical scenario sets, a comprehensive source–load coupling evaluation framework is established, incorporating the renewable energy output proportion and time-varying correlation index. For electrical distance calculation, a generalized construction method for extended sensitivity matrices is proposed, featuring dynamic weight adjustment through regional coupling degree correction factors. Simulation results demonstrate that in practical case studies, compared with traditional partitioning schemes, the proposed method reduces the regional coupling degree metric by 4.216% and enhances the regional reactive power imbalance index suppression by 11.082%, validating its effectiveness in achieving reactive power local balance and reactive power partitioning. This research breaks through the theoretical limitations of static partitioning and provides theoretical support for dynamic zonal control in modern power systems with high renewable penetration. Full article

Near-field microwave microscopy (NSMM) is a promising technique for the non-destructive, high-resolution imaging of electrical and dielectric properties at the microscale. However, its performance is highly sensitive to the probe-to-sample distance, often requiring extremely close proximity, which limits its practical application in device manufacturing, especially in scenarios involving coatings and packaging. In this study, we propose a distance inversion method based on a dual-port symmetrical microwave probe to improve imaging performance at larger, safer scanning distances. This method utilizes the correlation between probe height and resonant frequency to compensate for distance-induced signal distortions. The experimental results demonstrate that even at a probe–sample distance of 80 µm, clear and distinguishable NSMM images of printed circuit boards (PCBs) can be obtained. The imaging resolution reached 13 µm. The defect structure with dimensions of 130 × 130 µm² on the PCB was successfully identified. The signal-to-noise ratio was significantly enhanced after applying the correction method. This approach not only improves the robustness and flexibility of NSMM in industrial scenarios but also extends its applicability to packaged or coated electronic devices, offering a valuable tool for advanced non-destructive testing. Full article

This study presents a techno-economic analysis of hydrogen transportation via liquid organic hydrogen carriers by road, comparing this option with compressed hydrogen (350 bar) and liquefied hydrogen. The analysis includes the simulation of hydrogenation and dehydrogenation reactors for the dibenzyltoluene/perhydro-dibenzyltoluene system using ASPEN Plus, along with a cost assessment of compression, liquefaction, and trucking. A sensitivity analysis is also carried out, evaluating hydrogen transport at varying daily demand levels (1, 2, and 4 t/d) and transport distances (50, 150, and 300 km), with varying electricity prices and capital expenditures for hydrogenation and dehydrogenation units. Results indicate that compressed hydrogen is the most cost-effective solution for short distances up to 150 km, with a levelized cost of transported hydrogen ranging from 1.10 to 1.61 EUR/kg. However, LOHC technology becomes more competitive at longer distances, with LCOTH values between 1.49 and 1.90 EUR/kg at 300 km across all demand levels. Liquefied hydrogen remains the least competitive option, reaching costs up to 5.35 EUR/kg, although it requires fewer annual trips due to higher trailer capacity. Notably, at 150 km, LOHC transport becomes more cost-effective than compressed hydrogen when electricity prices exceed 0.22 EUR/kWh or when the capital costs for hydrogenation and dehydrogenation units are minimized. From an environmental perspective, switching from compressed to liquid hydrogen carriers significantly reduces CO₂ emissions—by 56% for LOHCs and 78% for liquid hydrogen—highlighting the potential of these technologies to support the decarbonization of hydrogen logistics. Full article

As the adoption of electric vehicles (EVs) rapidly increases, the expansion of charging infrastructure has become a critical issue. Unlike internal combustion engine vehicles, EV charging is sensitive to factors such as the time and location for charging, depending on the charging speed and capacity of the battery. Therefore, recommending an appropriate charging station that comprehensively considers not only the user’s preference but also the charging time, waiting time, charging fee rates, and power supply status is crucial for the user’s convenience. Currently, charging station recommendation services suggest suitable charging stations near a designated location and provide information on charging capacity, fee rates, and availability of chargers. Furthermore, research is being conducted on EV charging station recommendations that take into account various charging environments, such as power grid and renewable energy conditions. To solve these optimization problems, a large amount of information about the user’s history and conditions is required. In this paper, we propose a real-time charging station recommendation method based on minimal and simple current information while driving to the destination. We first propose a preference objective function that considers the factors of distance, time, and fees, and then analyze the recommendation results based on both synthetic and real-world charging environments. We also observe the recommendation results for different combinations of the weights for these factors. If we set all the weights equally, we can obtain appropriate recommendations for charging stations that reflect driving distance, trip time, and charging fees in a balanced way. On the other hand, as the number of charging stations in a given area increases, it has been found that gradually increasing the weighting of charging fees is necessary to alleviate the phenomenon of rising fee rates and provide balanced recommendations. Full article

Biomass pellets are increasingly recognized as a cost-effective and sustainable renewable energy source worldwide. However, comprehensive sustainability assessments of their production processes are scarce. To address this gap, three distinct scenarios in Northeast China were evaluated using emergy, economic, and environmental analysis methods: corn single production, corn–pellet co-production, and pellet production. A modified method for calculating the environmental loading rate (ELR) was proposed, which accounts for the environmental benefits associated with replacing coal with biomass pellets for heating. The results showed that corn–pellet co-production demonstrates superior energy efficiency compared to corn-only production, but presents a contrasting economic profile. The ELR for corn single production and corn–pellet co-production are 1.57 and 1.63, respectively, with corresponding emergy sustainability indices (ESI) of 0.89 and 0.84. After applying the modified method, the ELR and ESI for corn–pellet co-production were adjusted to 0.84 and 1.63, respectively, and the ESI of pellet production increased from 8.24 to 21.15. Furthermore, processing corn straw into biomass pellets for heating can reduce heating costs by approximately USD 254.26/hm² and reduce emissions of SO₂, NO_x, CO, PM_2.5, and CO₂ by 9.12, 19.82, 580.31, 65.86, and 13,060.66 kg/hm², respectively. Sensitivity analysis revealed that transportation distance and renewable electricity have a greater impact on pellet production than corn–pellet co-production. The ESI for pellet production decreases from 21.15 to 14.02 as transport distance increases from 20 km to 100 km, while it rises to 57.81 as the proportion of renewable energy in the power supply increases from 0% to 100%. Full article

With the advancement of power electronics, control systems, and related technologies, devices such as unmanned aerial vehicles (UAVs), airships, and electric vehicles (EVs) have become integral to modern life and industry. However, limited battery capacity, short battery life, attenuated battery performance, environmental sensitivity, and long charging time result in range anxiety in electrically driven devices, which has become an important factor restricting their development. This paper reviews the current status of power supply technologies for moving targets, categorizing them into contact charging, autonomous power supply, and wireless power transfer (WPT) methods. The principles, advantages, disadvantages, and applications of each approach are thoroughly analyzed. Comparative analysis highlights that WPT technology, which eliminates the need for electrical connections between the transmitter and receiver, offers notable advantages, including high flexibility, extended charging distances, and simultaneous power delivery to multiple targets. These features make it particularly well suited for the energy requirements of moving devices. Accordingly, this paper emphasizes the key technologies and future development directions of microwave WPT (MWPT) and laser WPT (LWPT) to facilitate the broader adoption of dynamic wireless power supply systems for moving targets. Full article

The increasing uncertainties and model computational complexity of large-scale power system state estimation have led to the emergence of a class of multi-source metrology devices to provide vector data for the grid to improve the observability. Considering the difficult problem of optimizing the configuration of multi-source measurement devices due to the large number of nodes, a distributed optimal configuration framework for multi-source measurement data is proposed. First, based on the concepts of sensitivity and electrical distance, the sensitivity electrical distance is derived and the power system is partitioned using the improved community partitioning principle; considering the problem of partitioning information exchange, synchronized phase measurement units are configured at the boundary nodes. Secondly, within the aforementioned partition, the optimal configuration of feeder terminal units and smart meters is carried out by combining the requirements of zero-injection nodes and viewability. Finally, the proposed method is verified in the IEEE33 node example, and the results show that the proposed method significantly reduces the configuration cost of the equipment on both sides of the system while guaranteeing the system viewability, which is highly feasible and economical. Full article

In complex electromagnetic environments, cardiac pacemakers may be interfered with easily. Microwave ovens, as common household appliances, may display electromagnetic leakage, which may pose risks to pacemaker wearers. This work evaluates the electromagnetic exposure of pacemaker wearers under various conditions. One involves different distances from the microwave oven to the human body, and the other involves a distinct oven door gap. This work uses COMSOL Multiphysics to establish a human thoracic cavity model with a heart and unipolar pacemaker, as well as a model of a microwave oven with contact-type doors. The results show that the specific absorption rate (SAR_10g) and temperature increase in the thoracic cavity and heart tissue are inversely proportional to the distance from the microwave source. They are directly proportional to the oven door gap size. The induced electric field intensity, the temperature increase, and the induced voltage in the pacemaker show the same trend. When the human body is closest to the microwave oven with the largest door gap (D = 100 mm, d = 0.3 mm), the SAR_10g and temperature increase of the thoracic cavity and heart tissue reach their maximum values, which are significantly below the safety standards recommended by ICNIRP. Similarly, the maximum value of the temperature increase and the induced electric field intensity in the pacemaker are below the safety standard recommended by ISO 14708-3 (+2 °C) and IEC 60601-1-2 (28 V/m). The maximum induced voltage at the pacemaker electrode is 5.322 mV, which exceeds the sensing sensitivity setting recommended by ISO 14117 (2 mV) for unipolar pacemakers. These findings demonstrate that microwave ovens with contact-type doors electromagnetic radiation do not threaten human health under normal usage conditions. However, the maximum value of the induced voltage exceeds the sensing sensitivity of some unipolar pacemakers, which may affect the operation of the unipolar pacemaker. This phenomenon requires attention from clinicians and patients. We still recommend that pacemaker wearers keep a distance from microwave ovens when using them. Full article

With the gradual penetration of new energy generation and storage to the building side, the short-term prediction of building power demand plays an increasingly important role in peak demand response and energy supply/demand balance. The low occurring frequency of peak electrical loads in buildings leads to insufficient data sampling for model training, which is currently an important factor affecting the performance of short-term electrical load prediction. To address this issue, by using peak data clustering and knowledge transfer from similar buildings, a short-term electrical load forecasting method is proposed. First, a building’s electrical peak loads are clustered through peak/valley data analysis and K-nearest neighbors categorization method, thereby addressing the challenge of data clustering in data-sparse scenarios. Second, for peak/valley data clusters, an instance-based transfer learning (IBTL) strategy is used to transfer similar data from multi-source domains to enhance the target prediction’s accuracy. During the process, a two-stage similar data selection strategy is applied based on Wasserstein distance and locality sensitive hashing. An IBTL strategy, iTrAdaboost-Elman, is designed to construct the predictive model. The performance of proposed method is validated on a public dataset. Results show that the data clustering and transfer learning method reduces the error by 49.22% (MAE) compared to the Elman model. Compared to the same transfer learning model without data clustering, the proposed approach also achieves higher prediction accuracy (1.96% vs. 2.63%, MAPE). The proposed method is also applied to forecast hourly/daily power demands of two real campus buildings in the USA and China, respectively. The effects of data clustering and knowledge transfer are both analyzed and compared in detail. Full article