Energy Science & Engineering最新文献

This paper presents the Lyapunov stability scheme based nonlinear power transfer matrix (NLPTM) model for controlling and modeling the permanent magnet synchronous generator (PMSG) based wind turbine. The proposed model considers active and reactive power as system state variables; thus, the rotor flux has no impact on the changes in stator active and reactive powers. This approach is based on a nonlinear technique employed on the machine-side ac/dc converter (MSC) and the grid-side dc/ac converter (GSC) of the PMSG wind turbine to reduce the oscillating current during the energy conversion process. The effectiveness of the proposed Lyapunov-integrated NLPTM approach is evaluated on a nonlinear controller-integrated PMSG wind system through simulation and is validated against an existing control scheme under transient operating conditions. The results proved the proposed approach's superiority in enhancing the power flow and suppressing the transient currents to improve the stability of the PMSG wind turbine.

During ultra-deep well drilling, the narrow safety density window of the drilling fluid makes it prone to complex conditions such as gas invasion. The high-temperature and high-pressure environment causes changes in the properties of the drilling fluid, making the gas invasion process highly covert and increasing the difficulty of well control. To address the challenge of accurately predicting gas migration velocity under gas invasion conditions in ultra-deep wells, this study considers the impact of temperature and pressure on the properties of the drilling fluid. A fluid state equation for ultra-deep wells was established. Based on the coupling effects of various parameters, a gas migration velocity model applicable to multi-well types and multi-factor coupling influences was developed. This model can calculate conditions for high-viscosity drilling fluids and analyze the impact of different parameters on gas migration velocity in vertical and horizontal wells. Additionally, suppression methods for gas migration velocity in ultra-deep wells were proposed. The study shows that an increase in drilling fluid viscosity, drilling fluid density, annular backpressure, and gas-liquid density ratio reduces the gas content in the wellbore and suppresses the increase in gas migration velocity. An increase in formation permeability results in a higher gas content in the wellbore, promoting an increase in gas migration velocity. The impact of drilling fluid displacement on wellbore gas content is complex. To reduce gas migration velocity, high-density and high-viscosity drilling fluids can be used, along with appropriate increases in wellhead backpressure and drilling fluid flow rate, to prevent gas invasion. This study helps to better understand the multiphase flow characteristics in the wellbore under gas invasion conditions in ultra-deep wells, ensuring the smooth operation of ultra-deep well drilling.

Solar photovoltaic (PV) systems can be significantly enhanced through the use of accurate solar cell models. Unfortunately, the absence of precise parameters from manufacturers limits the accuracy of these models. Given the impossibility of reliable modeling without such parameters, this paper introduces a multi-objective optimization algorithm to estimate the necessary parameters effectively. The problem of suboptimal optimization results often arises due to local minima and premature convergence of the optimization algorithm, even though there are a number of optimization algorithms that address this issue. This paper is intended to examine the reliability of the proposed algorithm to determine if it is reliable. For the purpose of showing the proficiency of the proposed optimization algorithms, their performance is compared with that of some other well-known algorithms to show their superiority. The performance of the algorithm is validated by comparing experimental results, including analyses based on statistical data, with estimated parameters based on statistical analysis. Furthermore, the results obtained with the proposed algorithms indicate that they are better suited for estimating solar PV models than the other algorithms i.e., rmse of the proposed algorithm for three diode model is 4.21E−13 as well as 3.20E−13 for four diode model. A simple structure and high accuracy are the main characteristics of the proposed algorithm, which indicates its potential for a variety of applications in the solar energy field in the future. Moreover, the proposed algorithm is computationally efficient as well as easy to use and can be applied to a number of applications.

This paper focuses on the stability of synchronous operation (SSO) of three-stage hydropower stations cascaded by regulating reservoirs (THSRRs). Firstly, the model of synchronous operation of THSRRs is established. According to the model, the Hopf bifurcation theory is utilized to analyze the SSO of THSRRs. Then, the critical stable sectional areas(CSSAs) of the first and second-stage regulating reservoirs (FSRR and SSRR) are identified. The role of the regulating reservoir areas on the stable domain is explored. Finally, the coupling effects of the governor-regulating reservoirs on the system are analyzed. The results show that the largest stable domain is found in the third-stage hydropower station(TSHS). The smallest one is found in the second-stage hydropower station(SSHS). The increase in area of regulating reservoir is beneficial to the upstream hydropower station but detrimental to the downstream hydropower station. Under the parameters of the hydropower stations, there are two positive solutions for the CSSAs of the adjacent regulating reservoirs and only one positive solution for the nonadjacent reservoir. The increase in integral gain is detrimental to the stable domain. As the proportional gain increases, the stable domain firstly increases and then decreases. This study provides technical support for the secure running of THSRRs.

Climate change continues to challenge both environmental stability and economic systems worldwide. Among available mitigation strategies, nature-based carbon management (NBCM) methods provide the dual advantage of reducing atmospheric carbon dioxide (CO₂) and restoring ecosystems. This review examines four main NBCM approaches, forest and grassland restoration, wetland and blue-carbon ecosystems, urban green spaces, and regenerative agriculture, to test the hypothesis that biochar-based regenerative agriculture is the most sustainable and practical pathway for long-term carbon sequestration. Confirming this hypothesis is essential because policy and investment decisions increasingly depend on identifying NBCM options that combine scientific effectiveness with economic feasibility. The study reviews papers, patents, and reports published between 2016 and 2024, integrating environmental, technological, and policy findings. The review reveals several key insights. Nature-based methods collectively offer significant global potential for carbon reduction, yet their success depends on ecological and socioeconomic conditions. Biochar-based regenerative systems stand out by showing persistent improvements in soil-organic-carbon storage, crop productivity, and greenhouse-gas mitigation, supported by numerous international case studies. The analysis also identifies the main constraints, production cost, infrastructure requirements, and limited awareness, that determine the pace of large-scale adoption. The contribution of this review lies in linking biochar's environmental durability with its socioeconomic applicability, providing a bridge between climate science, agriculture, and sustainable development policy. Future efforts should focus on field validation across climates, cost optimization for biomass-to-biochar chains, and supportive policy frameworks to encourage wider adoption. These findings present a clear pathway for scaling NBCM solutions, positioning biochar as a leading nature-based strategy for long-term climate mitigation.

Hybrid microgrids, integrating renewable, and conventional energy sources are critical for sustainable and resilient power systems. Their dynamic performance is affected by uncertainties in load demand, generation variability, and control strategies. This paper investigates the performance of a grid-connected inverter in a hybrid microgrid and compares different controllers, including Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and a Reinforcement Learning (RL) agent. The proposed system integrates solar panels and wind turbines with traditional sources such as batteries and fuel cell stacks, with maximum power extraction achieved using a hill-climb MPPT technique. Four converters regulate the microgrid DC link voltage, and the RL agent's performance is evaluated under both static and dynamic conditions. Simulation results, validated in MATLAB/Simulink, demonstrate that the RL agent outperforms ANN and ANFIS controllers in terms of stability, power quality, and dynamic response.

Partial shading is crucial in reducing the performance of the photovoltaic (PV) array. The static reconfiguration is a viable option for alleviating the partial shading. The scattering of shade relies on its puzzle pattern. This research proposes a static Kropki puzzle-based reconfiguration to scatter the shade effectively, thereby alleviating the consequence of partial shading. The Kropki puzzle pattern offers the benefits of simple rules and faster execution. The position of the panels is modified in accordance with the Kropki puzzle pattern and tested experimentally on a 4 × 4 total-cross-tied array. The Kropki reconfiguration is validated by relating to total-cross-tied, Sudoku, Skyscraper, and Odd–Even configurations by performance parameters. Overall, the experimental results show that the Kropki puzzle has superior shade capability and improved PE by 54% compared to conventional TCT.

To achieve the “dual carbon” goals, modern power systems must integrate large-scale renewable energy, whose inherent intermittency poses challenges to grid stability. Multi-energy microgrids combining wind, photovoltaic (PV), and energy storage systems provide an effective solution but still face issues in coordinated control, fault ride-through, and seamless grid transitions. This paper develops a hybrid microgrid model comprising a Doubly Fed Induction Generator (DFIG), a PV array, and a battery energy storage system, and proposes a coordinated control framework. The DFIG employs its grid-side converter (GSC) to regulate DC bus voltage and its rotor-side converter (RSC) for maximum power point tracking (MPPT). The PV inverter similarly employs MPPT control, while the battery operates in constant power mode under grid-connected conditions and in droop control under islanded conditions. To enhance the Low Voltage Ride-Through (LVRT) capability, a flexible crowbar strategy based on rotor current, voltage sag depth, and DC bus voltage is proposed. Additionally, a pre-synchronization module based on voltage magnitude and phase angle is integrated with the battery control to ensure smooth transitions between islanded and grid-connected modes. Simulation studies conducted in MATLAB/Simulink show that the proposed flexible crowbar achieves the largest reduction among the three stator currents (from 1.25 pu to 1.01 pu, 19.2%) and among the three rotor currents (from 2.0 pu to 1.46 pu, 27%), effectively suppressing fault currents and improving equipment safety. The pre-synchronization scheme further enables seamless reconnection of the microgrid to the main grid, while sensitivity analyses of the voltage- and angle-loop control gains confirm the robustness and adaptability of the proposed strategy during mode transitions.

Obtaining fast, reliable, and low-cost predictions in the petroleum industry is an important task in reservoir engineering that can help develop future development plans efficiently and increase recovery rates. In this paper, the Kolmogorov–Arnold Network enhanced time-series net (KAN) is designed for predicting oil production in Carbon Capture, Utilization, and Storage-Enhanced Oil Recovery scenarios. By comparing algorithms of the same type, the study revealed significant advantages of KANs, such as improved prediction accuracy and improved parameter efficiency. Targets demonstrate that KANs consistently surpassed other techniques by exhibiting lower error metrics, indicating more accurate predictions. The study concludes that KANs' effectiveness and efficiency position them as a viable alternative to conventional networks.

Fracture interference is commonly observed during hydraulic fracturing operations in tight reservoirs. While such interference can contribute to the development of complex fracture networks, it can also impede fracture initiation and propagation. Consequently, it is crucial to clarify the influence of pre-existing structural weaknesses on the resulting fracture network characteristics in tight reservoirs. Although several numerical models have been developed to investigate the impact of natural fractures on hydraulic fracture propagation, these models often require a detailed characterization of natural fracture properties. To address the above limitations, this study presents a three-dimensional (3D) numerical model for simulating multi-fracture propagation. The accuracy of the model's fracture extension calculations have been validated through rigorous verification. The results indicate that: (1) During simultaneous fracturing of multiple segments within a single stage, intersegment interference inhibits the opening of intermediate fractures, resulting in nonplanar fracture geometries with a noticeable curvature. (2) In sequentially fractured multi-fracture stages, fractures in later segments exhibit larger apertures compared to those in earlier segments, concurrent with continued fluid leak-off into the previously fractured segments. (3) In parallel fracturing of multiple horizontal wells, fractures in the central regions of adjacent wells tend to be drawn towards each other, potentially leading to premature fracture intersection. Simultaneously, shear stresses between fractures within the same wellbore are reduced, causing the fractures to rotate outwards. (4) During nonsimultaneous, cross-fracturing of multiple horizontal wells, fractures propagating into regions of tensile stress exhibit enhanced growth, while fractures subjected to compressive stresses from adjacent fractures tend to propagate outwards. In conclusion, the strategic application of diverse hydraulic fracturing techniques offers a viable approach to enhancing fracture network complexity. The findings of this study provide valuable guidance for optimizing the effective development and exploitation of tight reservoir resources.