Search Results (135)

In complex energy storage operating scenarios, batteries seldom undergo complete charge–discharge cycles required for periodic capacity calibration. Methods based on accelerated aging experiments can indicate possible aging paths; however, due to uncertainties like changing operating conditions, environmental variations, and manufacturing inconsistencies, the degradation information obtained from such experiments may not be applicable to the entire lifecycle. To address this, we developed a stage-wise state-of-health (SOH) prediction approach that combined offline training with online updating. During the offline training phase, multiple single-cell experiments were conducted under various combinations of depth of discharge (DOD) and C-rate. Multi-dimensional health features (HFs) were extracted, and an accelerated aging probability $p_{AA}$ was defined. Based on the correlation statistics between HFs, $k_{HF}$, the SOH, and $p_{AA}$, all cells in the dataset were divided into general early, middle, and late aging stages. For each stage, cells were further classified by their longevity (long, medium, and short), and multiple models were trained offline for each category. The results show that models trained on cells following similar aging paths achieve significantly better performance than a model trained on all data combined. Meanwhile, HF optimization was performed via a three-step process: an initial screening based on expert knowledge, a second screening using Spearman correlation coefficients, and an automatic feature importance ranking using a random forest regression (RFR) model. The proposed method is innovative in the following ways: (1) The stage-wise multi-model strategy significantly improves the SOH prediction accuracy across the entire lifecycle, maintaining the mean absolute percentage error (MAPE) within 1%. (2) The improved model provides uncertainty quantification, issuing a warning signal at least 50 cycles before the onset of accelerated aging. (3) The analysis of feature importance from the model outputs allows the indirect identification of the primary aging mechanisms at different stages. (4) The model is robust against missing or low-quality HFs. If certain features cannot be obtained or are of poor quality, the prediction process does not fail. Full article

With the integrated development of new energy and oil and gas production, introducing wind–solar–storage microgrids in coalbed methane well screw pump discharge systems enhances the renewable energy proportion while promoting green development. However, the cyclical, volatile, and random characteristics of wind and photovoltaic generation create scheduling challenges, with insufficient green power consumption reducing renewable energy utilization efficiency and increasing grid dependence. This study establishes an operation scheduling optimization model for coalbed methane well screw pump discharge systems under wind–solar–storage microgrids, minimizing daily operation costs with screw pump rotational speed as decision variables. The model incorporates power constraints of generation units and production constraints of screw pumps, solved using particle swarm optimization. Results demonstrate that energy storage batteries effectively smooth wind and photovoltaic fluctuations, enhance regulation capabilities, and improve green power utilization while reducing grid purchases and system operation costs. At different coalbed methane extraction stages, the model optimally adjusts screw pump rotational speed according to renewable generation, ensuring high pump efficiency while minimizing operation costs, enhancing green power consumption capacity, and meeting daily drainage requirements. Full article

The increasing share of renewable energy sources (RES) in modern power systems necessitates the development of efficient, large-scale energy storage technologies capable of mitigating generation variability. Liquid Air Energy Storage (LAES), particularly in its adiabatic form, has emerged as a promising candidate by leveraging thermal energy storage and high-pressure air liquefaction and regasification processes. Although LAES has been widely studied, the impact of ambient temperature on its performance remains insufficiently explored. This study addresses that gap by examining the thermodynamic response of an adiabatic LAES system under varying ambient air temperatures, ranging from 0 °C to 35 °C. A detailed mathematical model was developed and implemented in Aspen Hysys to simulate the system, incorporating dual refrigeration loops (methanol and propane), thermal oil intercooling, and multi-stage compression/expansion. Simulations were conducted for a reference charging power of 42.4 MW at 15 °C. The influence of external temperature was evaluated on key parameters including mass flow rate, unit energy consumption during liquefaction, energy recovery during expansion, and round-trip efficiency. Results indicate that ambient temperature has a marginal effect on overall LAES performance. Round-trip efficiency varied by only ±0.1% across the temperature spectrum, remaining around 58.3%. Mass flow rates and power output varied slightly, with changes in discharging power attributed to temperature-driven improvements in expansion process efficiency. These findings suggest that LAES installations can operate reliably across diverse climate zones with negligible performance loss, reinforcing their suitability for global deployment in grid-scale energy storage applications. Full article

The rapid advancement of high-resolution remote sensing technology has significantly expanded observational capabilities in the oil and gas sector, enabling more precise identification of petroleum infrastructure. Remote sensing now plays a critical role in providing real-time, continuous monitoring. Manual interpretation remains the predominant approach, yet is plagued by multiple limitations. To overcome the limitations of manual interpretation in large-scale monitoring of upstream petroleum assets, this study develops an end-to-end, deep learning-driven framework for intelligent extraction of key oilfield targets from high-resolution remote sensing imagery. Specific aims are as follows: (1) To leverage temporal diversity in imagery to construct a representative training dataset. (2) To automate multi-class detection of well sites, production discharge pools, and storage facilities with high precision. This study proposes an intelligent monitoring framework based on deep learning for the automatic extraction of petroleum-related features from high-resolution remote sensing imagery. Leveraging the temporal richness of multi-temporal satellite data, a geolocation-based sampling strategy was adopted to construct a dedicated petroleum remote sensing dataset. The dataset comprises over 8000 images and more than 30,000 annotated targets across three key classes: well pads, production ponds, and storage facilities. Four state-of-the-art object detection models were evaluated—two-stage frameworks (Faster R-CNN, Mask R-CNN) and single-stage algorithms (YOLOv3, YOLOv4)—with the integration of transfer learning to improve accuracy, generalization, and robustness. Experimental results demonstrate that two-stage detectors significantly outperform their single-stage counterparts in terms of mean Average Precision (mAP). Specifically, the Mask R-CNN model, enhanced through transfer learning, achieved an mAP of 89.2% across all classes, exceeding the best-performing single-stage model (YOLOv4) by 11 percentage points. This performance gap highlights the trade-off between speed and accuracy inherent in single-shot detection models, which prioritize real-time inference at the expense of precision. Additionally, comparative analysis among similar architectures confirmed that newer versions (e.g., YOLOv4 over YOLOv3) and the incorporation of transfer learning consistently yield accuracy improvements of 2–4%, underscoring its effectiveness in remote sensing applications. Three oilfield areas were selected for practical application. The results indicate that the constructed model can automatically extract multiple target categories simultaneously, with average detection accuracies of 84% for well sites and 77% for production ponds. For multi-class targets over 100 square kilometers, manual detection previously required one day but now takes only one hour. Full article

This paper presents a side-by-side study of CFD predictions and experimental measurements for a novel counter-flow heat exchanger installed in the sidewall of a dishwasher (HEBS). The work aims to improve appliance efficiency by transferring heat from discharged hot wastewater to the incoming cold supply. Motivated by sustainability goals and tightening EU energy rules, the research targets the high losses typical of conventional machines. This approach combines detailed ANSYS Fluent 2022R2 simulations with controlled laboratory tests on a bespoke test rig. The measured data show a repeatable rise in the cold-water temperature of roughly 8 K, corresponding to an approximate 15% gain in thermal performance for the heat-recovery stage. While the simulations and experiments efficiently agree based on trends and qualitative behavior, there are noticeable quantitative differences in the total energy transfer, indicating the models need further refinement. The validation carried out here forms a solid basis for design optimization and for reducing energy consumption in household dishwashers. This work overcomes the limitations of previous studies which typically rely on external storage tanks or static heat recovery analysis. The primary novelty of this paper lies in the empirical validation of a high-efficiency heat exchanger integrated into the extremely constrained sidewall volume of the appliance, tested under transient, on-the-fly flow conditions, providing a verified methodology for constrained industrial applications. Full article

Against the backdrop of rapidly growing distributed photovoltaics (DPVs) and mounting pressure on conventional frequency-regulation (FR) resources, this study proposes a day-ahead–intraday two-stage optimal scheduling strategy for aggregators of DPV + advanced energy storage participating in a joint energy–FR market. In the day-ahead stage (hourly resolution), a multi-aggregator-independent offering model is formulated that explicitly accounts for PV curtailment costs and storage operating/lifecycle costs. Subject to constraints on buy–sell transactions, PV output, storage charging/discharging power and state of charge (SOC), FR capacity, and power balance, the model co-optimizes energy and FR-capacity offers to maximize profit. In the intraday stage (15 min resolution), bidding deviation penalties are introduced, and a rolling optimization is employed to jointly adjust energy and FR dispatch/offers, reconfigure storage SOC in real time, reduce deviations from day-ahead schedules, and enhance economic performance. A three-aggregator case study indicates that, with deviation penalties considered, regulation-command tracking remains at a high level and PV utilization remains very high, while clearing costs decline and system frequency-response capability improves. The results demonstrate the proposed strategy’s implementability, economic efficiency, and scalability, enabling high-quality participation in ancillary services and promoting high-quality renewable integration under high-penetration distributed scenarios. Full article

Compressed carbon dioxide energy storage (CCES) has emerged as a promising solution for long-duration energy storage owing to its high energy density, adaptability to diverse environments, and compatibility with carbon capture technologies. This study develops a dynamic MATLAB 2024a/Simscape model for a 10 MW × 8 h gas–liquid CCES (GL-CCES) system featuring two-stage compression and two-stage expansion. Constant-pressure operation is maintained by check and throttle valves at the boundaries of the high-pressure tank. After startup, all system variables except those associated with the storage tank stabilize rapidly. The analysis reveals several critical dynamic phenomena: (1) a persistent mass-flow imbalance between charging and discharging processes under constant-pressure operation; (2) distinct phase transitions within the high-pressure tank that produce inflection points in thermodynamic evolution; and (3) strong ambient-temperature sensitivity that dictates system stability and efficiency boundaries. The system achieves a round-trip efficiency of 70.52% at 25 °C, which decreases to 67.01% at 21 °C. More importantly, the dynamic energy density (5.15 kWh m⁻³) is only 12.7% of the steady-state reference value. These results demonstrate the feasibility of GL-CCES for large-scale, long-duration energy storage, while also highlighting its pronounced sensitivity to ambient conditions, underscoring the need for optimized design and adaptive operational strategies. Full article

Modern electrical vehicles (EVs) are equipped with sizable batteries that possess significant potential as energy prosumers. EVs are poised to be transformative assets and pivotal contributors to the virtual power plant (VPP), enhancing the performance and profitability of VPPs. The number of household EVs is increasing yearly, and this poses new challenges to the optimization of VPP operations. The computational cost increases exponentially as the number of decision variables rises with the increasing participation of EVs. This paper explores the role of a large number of EVs as prosumers, interacting with a VPP consisting of a photovoltaic system and battery energy storage system. To accommodate the large quantity of EVs in the modeling, this research adopts the decentralized control structure. It optimizes EV operations by regulating their charging and discharging behavior in response to pricing signals from the VPP. A two-stage optimization framework is proposed for VPP-EV operation using a reinforcement algorithm and gradient-based programming. Action masking for reinforcement learning is explored to eliminate invalid actions, reducing ineffective exploration, thereby accelerating the convergence of the algorithm. The proposed approach is capable of handling a substantial number of EVs and addressing the stochastic characteristics of EV charging and discharging behaviors. Simulation results demonstrate that the VPP-EV operation optimization increases the revenue of the VPP and significantly reduces the electricity costs for EV owners. Through the optimization of EV operations, the charging cost of 1000 EVs participating in the V2G services is reduced by 26.38% compared to those that opt out of the scheme, and VPP revenue increases by 27.83% accordingly. Full article

This study presents a pulse generator employing a saturable pulse transformer (SPT) in conjunction with a fast recovery diode, integrated within an all-solid-state pulse-forming network (PFN). The saturation inductance of the SPT serves as a component of the initial LC section of the PFN, thereby contributing to the preservation of output waveform integrity. The secondary energy storage capacitor is charged through the primary circuit and the SPT, subsequently discharging into the load under the regulation of the SPT. An increase in the SPT’s transformation ratio corresponds to a rise in its saturated inductance, which in turn prolongs the pulse rise time. To mitigate this effect, a fast recovery diode is incorporated to sharpen the pulse front. Specifically, upon saturation of the SPT, current reverses through the fast recovery diode, effectively short-circuiting the load. When the inductor current attains a predetermined threshold, the diode reverts to reverse cut-off and rapidly switches off, enabling the PFN to discharge swiftly into the load and generate a high-voltage pulse characterized by a rapid rising edge. Furthermore, augmenting the number of secondary windings on the SPT—each connected to a PFN module—and arranging multiple PFNs in series facilitates an increase in output voltage. Experimental evaluations demonstrated that a three-stage PFN pulse generator attained a peak voltage of −16.9 kV on an 80 Ω matched load, with pulse currents exceeding 200 A while maintaining a 19 ns front edge. These results indicate that the proposed approach is effective for producing high-voltage, narrow pulses with rapid rise times. Additionally, the pulse power generator is capable of delivering repetitive pulses of −16.9 kV at a frequency of 20 kHz in burst mode. Full article

With the increasing penetration of renewable energy such as solar and wind power into the grid as well as the addition of modern types of versatile loads such as electric vehicles, the grid system is more prone to system failure and instability. One of the possible solutions to mitigate these conditions and increase the system efficiency is the integration of virtual power plants into the system. Virtual power plants can aggregate distributed energy resources such as renewable energy systems, electric vehicles, flexible loads, and energy storage, thus allowing for better coordination and optimization of these resources. This paper proposes a genetic algorithm-based optimization to coordinate the different elements of the energy management system of a virtual power plant, such as the energy storage system and charging/discharging of electric vehicles. It also deals with the random behavior of the genetic algorithm and its failure to meet certain constraints in the final solution. A novel method is proposed to mitigate these problems that combines a genetic algorithm in the first stage, followed by a gradient-based method in the second stage, consequently reducing the overall electricity bill by 50.2% and the simulation time by almost 95%. The performance is evaluated considering the reference set-points of operation from the obtained solution of the energy storage and electric vehicles by performing tests using a detailed model where power electronics converters and their local controllers are also taken into account. Full article

In recent years, frequent typhoon-induced disasters have significantly increased the risk of power grid outages, posing severe challenges to the secure and stable operation of distribution grids with high penetration of distributed photovoltaic (PV) systems. Furthermore, during post-disaster recovery, the dual uncertainties of distributed PV output and the charging/discharging behavior of flexible resources such as electric vehicles (EVs) complicate the configuration and scheduling of mobile energy storage systems (MESS). To address these challenges, this paper proposes a two-stage robust optimization framework for dynamic recovery of distribution grids: Firstly, a multi-stage decision framework is developed, incorporating MESS site selection, network reconfiguration, and resource scheduling. Secondly, a spatiotemporal coupling model is designed to integrate the dynamic dispatch behavior of MESS with the temporal and spatial evolution of disaster scenarios, enabling dynamic path planning. Finally, a nested column-and-constraint generation (NC&CG) algorithm is employed to address the uncertainties in PV output intervals and EV demand fluctuations. Simulations on the IEEE 33-node system demonstrate that the proposed method improves grid resilience and economic efficiency while reducing operational risks. Full article

Addressing the problems of wind power’s anti-peak regulation characteristics, increasing system peak regulation difficulty, and wind power uncertainty causing frequency deviation leading to power imbalance, this paper considers the peak shaving and valley filling function and frequency regulation characteristics of energy storage, establishing a day-ahead and intraday coordinated two-stage optimization scheduling model for research. Stage 1 establishes a deterministic wind power prediction model based on time series Autoregressive Integrated Moving Average (ARIMA), adopts dynamic peak-valley identification method to divide energy storage operation periods, designs energy storage peak regulation working interval and reserves frequency regulation capacity, and establishes a day-ahead 24 h optimization model with minimum cost as the objective to determine the basic output of each power source and the charging and discharging plan of energy storage participating in peak regulation. Stage 2 still takes the minimum cost as the objective, based on the output of each power source determined in Stage 1, adopts Monte Carlo scenario generation and improved scenario reduction technology to model wind power uncertainty. On one hand, it considers how energy storage improves wind power system inertia support to ensure the initial rate of change of frequency meets requirements. On the other hand, considering energy storage reserve capacity responding to frequency deviation, it introduces dynamic power flow theory, where wind, thermal, load, and storage resources share unbalanced power proportionally based on their frequency characteristic coefficients, establishing an intraday real-time scheduling scheme that satisfies the initial rate of change of frequency and steady-state frequency deviation constraints. The study employs improved chaotic mapping and an adaptive weight Particle Swarm Optimization (PSO) algorithm to solve the two-stage optimization model and finally takes the improved IEEE 14-node system as an example to verify the proposed scheme through simulation. Results demonstrate that the proposed method improves the system net load peak-valley difference by 35.9%, controls frequency deviation within ±0.2 Hz range, and reduces generation cost by 7.2%. The proposed optimization scheduling model has high engineering application value. Full article

The large-scale utilization of hydrogen energy is currently hindered by challenges in low-cost production, storage, and transportation. This study focused on investigating the impact of the diaphragm cavity profile on the movement behavior and stress distribution of a dual-stage diaphragm compressor. Firstly, an experimental platform was established to test the gas mass flowrate and fluid pressures under various preset conditions. Secondly, a simulation path integrating the finite element method simulation, theoretical stress model, and movement model was developed and experimentally validated to analyze the diaphragm stress distribution and deformation characteristics. Finally, comparative optimization analyses were conducted on different types of diaphragm cavity profiles. The results indicated that the driving pressure differences at the top dead center position reached 85.58 kPa for the first-stage diaphragm and 75.49 kPa for the second-stage diaphragm. Under experimental conditions of 1.6 MPa suction pressure, 8 MPa second-stage discharge pressure, and 200 rpm rotational speed, the first-stage and second-stage diaphragms reached the maximum center deflections of 4.14 mm and 2.53 mm, respectively, at the bottom dead center position. Moreover, the cavity profile optimization analysis indicated that the double-arc profile (DAP) achieved better cavity volume and diaphragm stress characteristics. The first-stage diaphragm within the optimized DAP-type cavity exhibited 173.95 MPa maximum principal stress with a swept volume of 0.001129 m³, whereas the second-stage optimized configuration reached 172.57 MPa stress with a swept volume of 0.0003835 m³. This research offers valuable insights for enhancing the reliability and performance of diaphragm compressors. Full article

With the increasing integration of renewable energy sources (RESs) into power systems, batteries are playing a critical role in ensuring grid reliability and flexibility. Among them, vanadium redox flow batteries (VRFBs) have emerged as a promising solution for large-scale storage due to their long cycle life, scalability, and deep discharge capability. However, achieving optimal control and system-level integration of VRFBs requires accurate, real-time modeling and parameter estimation, challenging tasks given the multi-physics nature and time-varying dynamics of such systems. This paper presents a lightweight physics-informed neural network (PINN) framework tailored for VRFBs, which directly embeds the discrete-time state-space dynamics into the network architecture. The model simultaneously predicts terminal voltage and estimates five discrete-time physical parameters associated with RC dynamics and internal resistance, while avoiding hidden layers to enhance interpretability and computational efficiency. The resulting PINN model is integrated into a modulated model predictive control (MMPC) scheme for a dual-stage DC-AC converter interfacing the VRFB with low-voltage AC grids. Simulation and hardware-in-the-loop results demonstrate that adaptive tuning of the PINN-estimated parameters enables precise tracking of battery parameter variations, thereby improving the robustness and performance of the MMPC controller under varying operating conditions. Full article

The biological aging of titanium implants, marked by increased surface hydrophobicity and organic contamination, reduces bioactivity and delays osseointegration. A major challenge in implant dentistry is determining how to preserve surface hydrophilicity during storage, as conventional atmospheric conditions accelerate surface degradation. This pilot in vivo study aimed to evaluate ozone nanobubble water (NBW3) as a storage medium to prevent biological aging and enhance the early-stage osseointegration of glow discharge-treated titanium implants. Screw-type implants were stored in either NBW3 or atmospheric conditions and then implanted into femoral bone defects in Sprague Dawley rats. Removal torque testing, scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), and histological analysis of bone-to-implant contact (BIC) were performed 14 and 28 days post-implantation. At 14 days, the NBW3-stored implants demonstrated significantly higher removal torque (2.08 ± 0.12 vs. 1.37 ± 0.20 N·cm), BIC (65.74 ± 12.65% vs. 44.04 ± 14.25%), and Ca/P atomic ratio (1.20 ± 0.32 vs. 1.00 ± 0.22) than the controls. These differences were not observed at 28 days, indicating NBW3’s primary role in accelerating early osseointegration. The findings suggest that using NBW3 is a simple, effective approach to maintain implant surface bioactivity during storage, potentially improving clinical outcomes under early or immediate loading protocols. Full article