Search Results (16,662)

With increasing penetration of distributed renewable energy sources (RES) in distribution networks, spatiotemporal mismatches arise between static time-of-use (TOU) pricing and real-time carbon emission factors. This misalignment hinders demand-side flexibility deployment, potentially increasing high-carbon-period consumption and impeding low-carbon operations. To address this, the paper proposes an adjustable carbon emission factor (ADCEF) which decouples electricity from carbon liability using storage. The strategy leverages energy storage for carbon responsibility time-shifting to build a dynamic ADCEF model, introducing a negative-carbon incentive mechanism which quantifies the value of surplus renewables. A revenue feedback mechanism couples ADCEF with electricity prices, forming dynamic price troughs during high-RES periods to guide flexible resources toward coordinated peak shaving, valley filling, and low-carbon responses. Validated on a modified IEEE 33-bus system across multiple scenarios, the strategy shifts resources to carbon-negative periods, achieving 100% on-site excess RES utilization in high-penetration scenarios and, compared to traditional TOU approaches, a 27.9% emission reduction and 8.3% revenue increase. Full article

Efficiently predicting the lifespan of lithium iron phosphate (LFP) batteries early in their operational life is critical to accelerating the development of energy storage systems while reducing testing time, cost, and resource consumption. Traditional degradation models rely on full-cycle testing to estimate long-term performance, which is both time- and resource-intensive. This study proposes a novel semi-empirical degradation model that leverages a small fraction of early-cycle data with just 5% to accurately forecast full-lifetime performance with high accuracy, with less than 1.5% mean absolute percentage error. The model integrates fundamental degradation physics with data-driven calibration, using an improved musical chairs algorithm modified with chaotic map dynamics to optimize model parameters efficiently. Trained and validated on a diverse dataset of 27 LFP cells cycled under varying depths of discharge, current rates, and temperatures, the proposed method demonstrates superior convergence speed, robustness across LFP operating conditions, and predictive accuracy compared to traditional approaches. These results provide a scalable framework for rapid battery evaluation and deployment, supporting advances in electric mobility and grid-scale storage. Full article

Vapor injection (VPI) can significantly enhance the heating performance of electric vehicle (EV) heat pump systems under low ambient temperatures, making the integrated design and control of the VPI loop essential. This study uses R290 as the working fluid and investigates the gas–liquid flow characteristics of the vapor-injection electronic expansion valve (VPI-EXV) in the VPI loop. The evaporation coefficient in the Lee model is calibrated using four typical operating conditions, keeping the relative errors of both total mass flow rate and injection ratio predictions within 10%. Results show that valve opening is the dominant factor: as the opening increases from 10% to 100%, the injection ratio rises from 0.24 to 0.83, while increasing outlet pressure from 0.58 MPa to 0.78 MPa and inlet subcooling from 0 °C to 10 °C reduces it by about 18% and 9%, respectively. The 90° turning structure inside the VPI-EXV induces recirculation and high turbulent kinetic energy downstream of the throttling region, modifying the outlet gas-liquid distribution, based on which an injection ratio control strategy with valve opening as the primary variable is proposed. Full article

Compared to the traditional non-load-bearing multi-legged robots, the heavy-duty multi-legged robots typically not only have larger body weight, larger volume, and larger load ratio but also require greater energy dissipation. Traditional path planning often focuses on the problem of finding the shortest path. However, the substantial load capacity and multi-jointed structure of heavy-duty multi-legged robots impose stringent requirements on path smoothness. Consequently, the smoothness requirement makes the traditional A* algorithm unsuitable for applications where low-energy operation is critical. An improved low-energy path planning method based on the A* algorithm is presented for an electrically driven heavy-duty six-legged robot. Then, the environment is discretized by using the grid method to facilitate path searching. To address the path zigzagging problem caused by the traditional A* algorithm, the Bézier curve smoothing technique is adopted. The continuous curvature transitions are employed to significantly improve the smoothness of path. The heuristic function in the A* algorithm is enhanced through a dynamic weight adjustment mechanism. The nonlinear suppression strategy is introduced to prevent data changes and improve the robustness of the algorithm. The effectiveness of the proposed method is verified through the MATLAB simulation platform system. The simulation experiments show that, in various environments with different obstacle densities (0.17–0.37%), compared with the traditional A* algorithm, the method proposed in this paper reduces the average path length by 7.2%, the number of turning points by 25.9%, and the energy consumption by 5.75%. The proposed improved A* algorithm can significantly overcome the problem of insufficient smoothness in traditional A* algorithms and reduce the number of nodes generated by the control data stack, which improves the optimization efficiency during path planning. As a result, the heavy-duty six-legged robots can walk farther and operate for longer periods of time while carrying the limited energy sources. Full article

In recent years, the use of high-power semiconductor devices has seen growing demand across various applications, including data centers, electric vehicles, and traction systems. However, increasing power densities may increase challenges in ensuring the reliability of devices, particularly under high surge currents. These surge events may result in excessive power dissipation and rapid temperature increases, leading to device performance degradation and potential failure. Therefore, accurate temperature estimation is critical. However, existing approaches in the literature are mostly oversimplified and constrained by static I–V characteristics, limiting their accuracy. To encounter these limitations, this article proposes a forward-voltage-based temperature evaluation methodology for high-power diodes subjected to 10 ms surge events. The proposed model integrates rated electrical parameters with thermal simulation data to enable the accurate estimation of dynamic slope resistance and forward voltage during transient surge operation. The proposed framework shows strong agreement with the experimental results and provides a reliable tool for surge capability assessment. This approach enhances device modeling accuracy under very-high-current stress and offers valuable insights for electro-thermal design and thermal management in next-generation power semiconductor devices. Full article

With the rapid development of renewable energy sources in power generation, utility boilers need to perform load regulation over a wide range to maintain the stability of the power supply system. Preheating combustion technology is a potential approach to achieve wide load range operation, improve combustion stability, and lower NO_x emissions from utility boilers. Preheating combustion devices (PCDs) were designed and installed in the reduction zone of a boiler. These devices preheated the coal at an excess air ratio ranging from 0.35 to 0.7 to generate high-temperature gas and char, which effectively reduced NO_x formation in the furnace. Numerical studies were conducted to evaluate the combustion performance and nitrogen oxides emissions of a 330 MW utility boiler retrofitted with PCDs at different loads. The simulations were conducted over a load range of 20% to 100% of the rated load, corresponding to an electrical power of 66 MW to 330 MW. The preheated combustion device’s previous experimental data served as the boundary conditions of the preheated product nozzles. The simulation results demonstrated that the retrofitted boiler could operate stably from 20% to 100% of the rated load, maintaining acceptable combustion efficiency and lower NO_x emissions. The combustion efficiency gradually dropped with decreasing boiler load, reaching a minimum value of 95.6%. As the load declined, the size of the imaginary tangent circle of the boiler shrank, while the ignition distance increased. Additionally, the variation in NO_x concentration with load was complex. The NO_x concentration at the furnace outlet was between 102.7 and 220.3 mg/m³, and the preheated products effectively reduced the nitrogen oxides produced by combustion in the furnace at all loads. Full article

Rydberg atoms are neutral atoms excited to high principal quantum number states, which endows them with exaggerated properties such as large electric dipole moments, long lifetimes, and extreme sensitivity to external electromagnetic fields. These characteristics form the foundation of Rydberg atom-based sensors, an emerging class of quantum devices capable of optically detecting electric fields across frequencies from DC to the terahertz regime. Rydberg-based electrometry operates through both Autler–Townes (AT) splitting of resonant Rydberg transitions and Stark-shift measurements for high-frequency or far-detuned fields, enabling broadband field sensing from DC to the THz regime. Using ladder-type electromagnetically induced transparency (EIT) and AT splitting, these sensors enable non-invasive, SI-traceable measurements of field amplitude, frequency, phase, and polarization. Recent developments have demonstrated broadband electric field probes, voltage calibration standards, and compact RF receivers based on thermal vapor cells and integrated photonic architectures. Furthermore, innovations in multi-photon EIT, superheterodyne readout, and multi wave mixing have expanded the dynamic range and bandwidth of Rydberg-based electrometry. Despite challenges related to environmental perturbations, linewidth broadening, and laser stabilization, ongoing advances in atomic control, hybrid photonic integration, and EIT-based readout promise scalable, chip-compatible sensors. This review summarizes the physical principles, experimental progress, and emerging applications of Rydberg atom-based sensing, emphasizing their potential for next generation quantum metrology, wireless communication, and precision field mapping. Full article

In recent years, the manufacturing industry and power sector have collectively accounted for nearly 60% of global carbon emissions, presenting a formidable obstacle to achieving net-zero targets by 2050. To address the urgent need for industrial decarbonization, this paper proposes a profit-driven framework for low-carbon manufacturing that synergistically integrates green certificates, demand response, distributed generation, and carbon capture, utilization, and storage (CCUS) technologies. A comprehensive optimization model is formulated to enable manufacturers to maximize profits through strategic participation in electricity, carbon, green certificate, and industrial manufacturing product markets simultaneously. By solving this optimization problem, manufacturers can derive optimal production decisions. The framework’s effectiveness is demonstrated through a case study on lithium-ion battery manufacturing, which reveals promising outcomes: meaningful profit growth, substantial carbon emission reductions, and only minimal impacts on production output. Furthermore, the proposed demand response strategy achieves significant reductions in electricity consumption during peak hours, while the integration of distributed generation systems markedly decreases reliance on the main grid. The incorporation of CCUS extends the clean operation periods of thermal power units, generating additional revenue from carbon trading and CO₂ utilization. In summary, the proposed model represents the first unified profit-maximizing optimization framework for low-carbon manufacturing industries, shifting from traditional cost minimization to profitability optimization, addressing gaps in fragmented low-carbon strategies, and providing a replicable blueprint for carbon-neutral operations while enhancing profitability. Full article

Detecting anomalies in electrical equipment and improving maintenance efficiency are critical for ensuring operational safety, reliability, and sustainability. To address the structural limitations of conventional manual and visual inspection methods, this study developed an object-recognition-based automated damage diagnosis system for lightning rods and insulators (ADS-LI), which enabled non-contact and fully automated diagnosis of lightning rods and insulators. ADS-LI employs a dual-module architecture. The first module precisely detects lightning rods and insulators using the PointRend algorithm applied to drone-acquired aerial imagery. The second module is a formula-based diagnostic model that quantitatively determines structural anomalies using the geometric attributes of the detected objects. Specifically, anomalies in lightning rods are identified by analyzing variations in inclination derived from center-coordinate shifts ($\Delta x$), while insulator anomalies are evaluated based on the mask area conservation ratio ($r$). The performance of ADS-LI was validated using 90 independent test datasets, achieving a 0.89 F1-score and 99% overall accuracy. These results demonstrate that ADS-LI effectively automates labor-intensive diagnostic tasks that previously relied on skilled experts. Furthermore, by quantifying anomaly detection criteria, it ensures consistency and reproducibility for diagnostic outcomes. This study is also expected to contribute, in the long term, to the transition of elevated electrical installations toward a sustainable maintenance regime. Full article

Accurate multi-energy load forecasting is essential for the low-carbon, efficient, and resilient operation of park-level Integrated Energy Systems (PIESs), where cooling, heating, and electricity networks interact closely and increasingly incorporate renewable energy resources. However, forecasting in such systems remains challenging due to complex cross-energy coupling, high-dimensional feature interactions, and pronounced nonlinearities under diverse meteorological and operational conditions. To address these challenges, this study develops a novel three-stage hybrid forecasting framework that integrates Recursive Feature Elimination with Cross-Validation (RFECV), a Multi-Task Long Short-Term Memory network (MTL-LSTM), and Random Forest (RF). In the first stage, RFECV performs adaptive and interpretable feature selection, ensuring robust model inputs and capturing meteorological drivers relevant to renewable energy dynamics. The second stage employs MTL-LSTM to jointly learn shared temporal dependencies and intrinsic coupling relationships among multiple energy loads. The final RF-based residual correction enhances local accuracy by capturing nonlinear residual patterns overlooked by deep learning. A real-world case study from an East China PIES verifies the superior predictive performance of the proposed framework, achieving mean absolute percentage errors of 4.65%, 2.79%, and 3.01% for cooling, heating, and electricity loads, respectively—substantially outperforming benchmark models. These results demonstrate that the proposed method offers a reliable, interpretable, and data-driven solution to support refined scheduling, renewable energy integration, and sustainable operational planning in modern multi-energy systems. Full article

Enhancing the efficiency of mechanized oil production remains a critical objective in the industry. This paper presents a comparative analysis of existing methods aimed at improving the energy efficiency of oil extraction systems, outlining their respective advantages and limitations. A novel approach is proposed, based on the use of a submersible compensator of reactive power to optimize the performance of electric submersible pumps (ESPs). A mathematical model of the ESP’s electrical system is developed to support the proposed method. Theoretical findings are validated by the experimental studies conducted on operational oil wells. Test results demonstrate a reduction in current consumption by 14.5–20% and an improvement in the power factor from 0.62 to 0.96. These outcomes confirm the effectiveness of the proposed solution in enhancing energy efficiency and reducing electrical losses in oil production processes. Full article

Bike-sharing services contribute to reducing emissions and conserving natural resources within urban transportation systems. They also promote public health by encouraging physical activity and generate economic benefits through shorter travel times, lower transportation costs, and decreased demand for parking infrastructure. This paper examines the use of shared micro-mobility services in the Italian cities of Florence and Bologna, based on an analysis of GPS origin–destination data and associated temporal coordinates provided by the RideMovi company. Given the still-limited number of studies on free-floating and electric-scooter-sharing systems, the objective of this work is to quantify the performance of electric bikes and e-scooters in bike-sharing schemes and compare it to traditional, muscular bikes. Trips were reconstructed starting from GPS data of origin and destination of the trip with a shortest path criteria that considers the availability of bike lanes. Results show that e-bikes are from 22 to 26% faster on average with respect to muscular bikes, extending trip range in Bologna but not in Florence. Electric modes attract more users than traditional bikes, e-bikes have from 40 to 128% higher daily turnover in Bologna and Florence and e-scooters from 33 to 62% higher in Florence with respect to traditional bikes. Overall, turnover is fairly low, with less than two trips per vehicle per day. The performance is measured in terms of trip duration, speed, and distance. Further characteristics such as daily turnover by transport mode are investigated and compared. Finally, spatial analysis was conducted to observe demand asymmetries in the two case studies. The results aim to support planners and operators in designing and managing more efficient and user-oriented services. Full article

This paper develops an optimal dispatch model for an integrated wind–solar hydrogen-to-ammonia system to address the mismatch between renewable-energy fluctuations and chemical production loads. The model incorporates renewable variability, electrolyzer dynamics, hydrogen-storage regulation, and ammonia-synthesis load constraints, and is solved using a multi-time-scale MILP framework. An efficiency-priority power allocation strategy is further introduced to account for performance differences among electrolyzers. Using real wind–solar output data, a 72-h case study compares three operational schemes: the Balanced Scheme, the Steady-State Scheme, and the Following Scheme. The proposed Balanced Scheme reduces renewable curtailment to 2.4%, lowers ammonia load fluctuations relative to the Following Scheme, and decreases electricity consumption per ton of ammonia by 19.4% compared with the Steady-State Scheme. These results demonstrate that the integrated dispatch model and electrolyzer-cluster control strategy enhance system flexibility, energy efficiency, and overall economic performance in renewable-powered ammonia production. Full article

Accurate real-time dosimetry is key in megavoltage radiotherapy; however, many detectors require external biasing or complex instrumentation. This study evaluated thin-film CdTe solar cells operating in photovoltaic (zero-bias) mode as medical dosimeters. Superstrate ITO/CdS/CdTe/Cu/Au devices were fabricated and irradiated with 6-MV photons from a clinical linear accelerator to 20 kGy cumulative dose. Electrical and dosimetric properties were assessed based on AM 1.5 current–voltage measurements, external quantum efficiency (EQE), dose linearity, dose-rate dependence, field-size dependence, percentage depth dose (PDD), and one-month reproducibility. With increasing dose (5–20 kGy), the open-circuit voltage and fill factor decreased by ~2–3%, the short-circuit current density by ~10%, retaining ~87% initial efficiency. Series and shunt resistances were stable, while EQE decreased uniformly (~5%), indicating degradation mainly from increased nonradiative recombination. Dose–signal linearity remained intact, and post-irradiation sensitivity loss was corrected with a single calibration factor. Dose-rate dependence was minor; low reverse bias (~3–7 V) enhanced response without nonlinearity. Field-size and PDD responses agreed with ionization chamber data within ~1%, and weekly stability was within ~1%. Parallel stacking of two cells increased signal nearly linearly. CdTe solar-cell detectors thus enable zero-bias, real-time, stable, and scalable dosimetry and strongly agree with reference standards. Full article

Technical constraints to be faced in microgrids have become more frequent with high renewable integration. In this context, Thermal Energy Storage (TES) has emerged as a promising solution to enable consumers’ flexibility to contribute to the solution of such operational issues. This paper examines the integration of the novel system ECHO-TES (a Thermal Energy Storage System developed within the European Project ECHO) in microgrids to address technical constraints, utilizing OpenDSS and Python simulations. Building on that, the Efficient Compact Modular Transaction Simulation System (ECHO-TSS) adds a layer of virtual automated transactions, coordinating multiple ECHO-TES assets to simulate not only energy flows and electricity consumption, but also the associated economic interactions. The study explores the critical role of TES in enhancing microgrid efficiency, flexibility, and sustainability, particularly when coupled with renewable energy sources. By analyzing diverse demand scenarios, the research aims to assess its impact on grid stability and management. The paper highlights the importance of advanced modeling tools like OpenDSS in simulating complex microgrid operations, including the dynamic behavior of TES systems. It also investigates demand-side management strategies and the potential of TES to mitigate challenges associated with renewable energy variability. The findings contribute to the development of robust, adaptive microgrid systems and support the global transition towards sustainable energy infrastructure. Full article