Abstract Flow battery technology offers advantages such as environmental friendliness, safety and reliability, decoupled power and capacity units, and flexible configuration. It can be applied across various energy storage scenarios, including generation, grid, and load, and holds significant development potential. This article focuses on a domestic (China) perspective, primarily examining all-vanadium flow batteries through a literature review. It provides an overview of recent research achievements in flow battery technology from four aspects: electrolyte materials, electrode materials, membrane materials, and structural design of the battery/stack. The article categorizes and deeply analyzes the technical characteristics, research progress, and challenges of existing flow batteries. Additionally, it compiles representative flow battery engineering projects and proposes relevant recommendations for the future development of flow battery technology.
Abstract The inherent stochasticity of wind power presents significant challenges to power grid stability and the economic viability of wind energy assets. This research develops and validates a novel short-term forecasting framework designed to transcend conventional accuracy metrics by producing forecasts that are both statistically robust and operationally valuable. Utilizing high-frequency multisite power data from a regional wind farm cluster, the methodology centers on comprehensive feature engineering to capture critical spatiotemporal dependencies. A diverse suite of machine-learning algorithms, including Elastic Net, gradient-boosted trees, ensembles, and a temporal convolutional network (TCN) baseline, was systematically evaluated. The performance of key models was further enhanced through automated hyperparameter optimization (HPO) using the Optuna framework. The results demonstrate exceptional predictive performance, with a regularized Elastic Net and an optimized Categorical Boosting (CatBoost) model achieving the highest accuracy on unseen test data, yielding coefficient of determination ( R 2 ) values of 0.996 and 0.986, respectively. A critical finding is that these precise forecasts significantly mitigate grid operating expenses, reducing the required spinning reserve capacity to 12.2 MW and limiting real-time balancing costs to 42.4 thousand United States Dollars (kUSD), which generates approximately 157,000 kUSD in annualized savings compared to a naive persistence baseline. Feature importance analysis consistently reveals that the immediate historical state of the system, such as recent power ramps and rolling averages, was the most dominant predictor. The developed framework provides a validated, high-fidelity tool capable of delivering actionable intelligence to grid operators, thereby supporting more secure grid management and improving the financial performance of wind energy integration.
Abstract This study addresses key challenges in shale oil reservoir development using multistage fractured horizontal wells, where complex oil-water two-phase flow mechanisms limit the accuracy of conventional single-phase flow models in interpreting reservoir parameters and predicting production. A mathematical model for oil-water two-phase flow was established, incorporating the time-varying effects of water saturation and relative permeability. Pseudo-pressure and pseudo-time functions were introduced to linearize the governing partial differential equations. A semianalytical solution was derived to effectively characterize the complex two-phase flow behavior within the fracture-stimulated region. Based on type-curve analysis and history matching, an inversion workflow was proposed to determine key parameters, including fracture half-length and matrix permeability, and to predict dynamic production. Applied to Well Zhenye 1 in the Subei Basin, the productivity type curves clearly exhibit three flow regimes: linear flow, transition flow, and boundary-dominated flow. The inverted parameters align well with actual reservoir conditions, enabling high-accuracy production forecasts with an average parameter inversion error of less than 10%. This approach significantly reduces reliance on high-cost monitoring technologies and provides a practical, economical solution to support the efficient development of shale oil reservoirs.
Abstract Shale reservoirs in the Dagang Oilfield are characterized by developed bedding and microfractures, leading to prominent wellbore instability issues during drilling. Existing models remain insufficient in characterizing the competitive “matrix-multiple weak planes” failure mechanism in such formations. Consequently, this study employs the ubiquitous joint model and Mohr–Coulomb criterion to establish a coupled numerical model that comprehensively considers bedding orientation, in situ stress difference, and the time-dependent hydration effects of drilling fluid. The results indicate that wellbore instability exhibits a dual failure mechanism: matrix shear failure is concentrated in symmetrical areas on both sides of the borehole, whereas weak-plane slippage occurs at four specific azimuths. The morphology of the collapse zone evolves dynamically with the bedding dip angle, transitioning from a symmetrical ellipse to an asymmetrical quadrilateral and undergoing rotation; this effect diminishes when the dip angle approaches 0° or 90°. The in situ stress difference controls the dominant direction of collapse, whereas hydration simultaneously weakens both matrix and weak-plane strength, causing the volume of the failure zone to expand significantly during the initial period (0–5 days). This research reveals the dynamic evolution patterns of wellbore collapse under multifactor coupling, providing a quantitative basis for optimizing well trajectories and safe drilling fluid density windows for deep shale oil in this region.
Proton exchange membrane fuel cells (PEMFCs) are widely applied in transportation and portable power systems; however, the frequent occurrence of voltage overshoot and undershoot during dynamic operation significantly compromises system performance and durability. To address this issue, this study investigates the transient voltage regulation of PEMFCs by integrating the classical proportional-integral-derivative (PID) control with advanced model predictive control (MPC). We established a dynamic PEMFC model and designed an MPC controller based on system linearization, with the oxygen excess ratio selected as the manipulated variable. We conducted comparative simulations of PID and MPC in the Simulink environment. The results reveal that MPC achieves superior performance in suppressing voltage overshoot and undershoot, leading to enhanced system stability and faster dynamic response compared with PID. These findings provide theoretical support and practical guidance for the optimization of control strategies in PEMFC systems.
