To address the issues of insufficient modeling accuracy of the adjustable capacity of Internet data centers with energy storage (IDCE) and the lack of coordination between energy storage systems and temperature-controlled server room loads in existing studies, which limit the full utilization of IDCE regulation potential under both normal and extreme grid operating conditions, this paper proposes a frequency regulation trading bidding dispatch method for IDCE considering physical-virtual energy storage coupling, aiming to explore a normalized profitability strategy for IDCE while ensuring basic operational requirements. First, based on workload dispatch characteristics and the virtual energy storage behavior of server rooms, the coupled operation mechanism between physical and virtual energy storage within IDCE is analyzed, and a corresponding coupled adjustable capacity model is developed. Then, to maximize daily operating revenue, a frequency regulation trading and bidding dispatch model is established considering workload uncertainty and frequency regulation service compensation prices. The nonlinear constraints from the server power consumption model based on dynamic voltage-frequency regulation are addressed using a relaxation approximation approach. Finally, the effectiveness of the method proposed in this paper is validated through a numerical case study. The proposed method can realize regular profitability of IDCE on the premise of meeting basic operation requirements.

To address the issues of the insufficient regulation resources and flexibility in current active distribution networks, this paper proposes a two-stage regulation method for electric vehicle (EV) participation in the optimization of active distribution networks. Considering the impact of EV regulation on charging demand of users under the uncertainty of their travel behavior, the proposed method intends to fully exploit the regulation potential of EVs while reducing the impact of regulation on the travel of vehicle owners. First, a power deviation index is established to characterize the impact on EV charging behavior, and EV aggregation is performed with the power deviation taken into account. Then, the optimal regulation of active distribution networks containing EV aggregations, energy storage systems, and distributed generation units is conducted. Finally, the aggregated EV charging/ discharging commands are disaggregated to individual EVs by minimizing user-side impact. The effectiveness of the proposed method is validated through simulation on the IEEE 33-bus system. Numerical experiments show that the strategy can balance the regulation capability of EVs and charging demand of users. Compared with the traditional method, the EV power deviation is reduced by 25%, and the accommodation capacity is increased by 57%.

For oceanic island clusters with special geographical conditions, this paper proposes a rescue vessel scheduling strategy that integrates island transportation and energy supply to address resource heterogeneity among oceanic island clusters and ensure vessel safety, thereby improving the disaster resilience of rescue vessels under optimized scheduling. Based on both energy dispatch and transportation, a two-stage optimization method is adopted. In the first stage, day-ahead scheduling is performed according to the geographical relationship of the islands to determine preliminary sailing routes. In the second stage, both safe and emergency modes are considered. In the safe mode, based on the optimized routing information from the first stage, further optimal scheduling of power generation is performed to improve operational efficiency and economic performance under environmental constraints. In the emergency mode, considering generator fault, a coordinated optimization strategy between load-side management and power generation is implemented to support critical rigid loads and guarantee the normal operation of rescue vessels. Finally, simulation results verify the effectiveness of the proposed strategy, providing a theoretical reference for the sustainable development and construction of oceanic island clusters.

To fully exploit the potential regulation capacity of high-energy-consuming industrial loads and alleviate the pressure on the supply-demand balance of power systems caused by the large-scale access of renewable energy resources, this paper proposes a dual-time-scale optimal scheduling method for high-energy-consuming industrial parks considering the uncertainty of photovoltaic (PV). First, typical scenarios of PV power outputs are generated based on the conditional generation adversarial network to characterize the uncertainty of PV during the optimal scheduling process of industrial parks. Then, a dual-time-scale optimal scheduling model is established for high-energy-consuming industrial parks considering the coupling mechanism and regulation characteristics of various regulation resources in the industrial park. In this scheme, the production plan of high-energy-consuming industrial users is optimized to maximize the operation economy of the industrial park in the day-ahead stage, while power fluctuations of the industrial park are further mitigated by coordinating the tap position of electric arc furnaces and the charging/discharging power of energy storage resources in the intra-day stage. Finally, the optimal scheduling of an industrial park containing two typical high-energy-consuming industries, namely, iron and steel plants and cement plants is conducted to verify the effectiveness of the proposed method.

The large-scale integration of highly uncertain renewable energy poses significant challenges to the planning and operation of power systems. To address the joint generation and transmission planning problem in renewable-dominated power systems, this paper proposes a distributionally robust planning method considering multi-energy complementarity and direct current (DC) transmission. First, with the objective of minimizing total annual planning costs, a joint generation-transmission planning model incorporating wind-storage complementarity and adjustable DC transmission is established. Then, considering the high uncertainty of wind power output, an improved generative adversarial network is adopted to construct typical planning scenarios. On this basis, a two-stage distributionally robust generation-transmission planning model based on probabilistic scenario fuzzy sets is constructed and solved using a parallelizable column-and-constraint generation algorithm that requires no dual formulation. Finally, case studies on a modified Graver-6 node system verify that the proposed method yields a planning scheme that balances improved economic performance with controllable conservativeness.

Frequency deviations emerge at different nodes after a power grid disturbance, which is defined as the spatial distribution of system frequency. Previous studies have proposed a frequency divider formula to estimate this frequency distribution. However, the assumptions and simplifications adopted in its derivation introduce considerable errors. To address this issue, this paper proposes an accurate formula for estimating frequency distribution based on the concept of complex frequency, considering the variations in the magnitude and phase angle of voltage vectors. The proposed formula allows the spatial distribution mechanism of grid frequency to be analytically described, revealing the inherent law of frequency distribution and proving that the frequency distribution level is correlated with the impedance distribution and voltage level of the network. Compared with the existing frequency divider formula, the proposed formula more reasonably accounts for factors such as network resistance, loads, and the magnitudes and angles of node voltages and generator internal potentials. Its rigorous derivation leads to higher accuracy. Time-domain simulation results in the IEEE 39-bus system and 145-bus system verify the accuracy of the proposed formula.

Dynamic equivalent modeling of doubly-fed wind farms relies on specific disturbances, making it challenging to develop equivalent models with strong universality. To address this issue, this paper proposes a data-driven approach for dynamic equivalent modeling of doubly-fed wind farms. First, the mathematical model of doubly-fed wind turbine units is simplified into a set of equations. Then, neural network components with similarity to these equations are selected and reasonably combined to build a residual combination neural network consisting of feature memory layers, information flow acceleration layers, and data relationship mapping layers, aiming to simplify the detailed wind farm model equivalently. Furthermore, genetic algorithms are employed to optimize the main parameters of the three components in this combined network. Finally, a typical wind farm in Henan Province is used as a test case. The results show that the proposed method based on residual combination neural networks accurately captures the output response characteristics of wind farms and achieves higher modeling accuracy.

Hydrogen production from photovoltaic (PV) power generation is an important approach to improving the consumption of renewable energy and reducing the impact on the power grid. To suppress power fluctuation from PV generation and loads in an electro-hydrogen system and reduce variations in the hydrogen storage state charge, and avoid frequent start-stop operations of hydrogen storage equipment caused by state-of-hydrogen limit violations, an electro-hydrogen coupled direct current (DC) microgrid cooperative control strategy is proposed. First, a fuzzy control algorithm is used to optimize and regulate the power allocation between hydrogen storage systems and lithium battery storage. Then, the DC microgrid operating conditions are divided into normal and extreme conditions according to the hydrogen storage state, and the hydrogen storage state of the hydrogen storage tank is dynamically adjusted by the proposed hydrogen storage variable-parameter sag control strategy to inhibit the speed of state of hydrogen of the hydrogen storage moving towards the overcharge and overdischarge intervals by different control strategies, and to improve the regulation capability of the DC microgrid. Finally, simulation analysis by MATLAB/Simulink verifies that the proposed control strategy can enhance the ability of the electro-hydrogen coupled DC microgrid to suppress source load fluctuations, maintain the stability of the DC bus voltage, and alleviate overcharge and overdischarge in the hydrogen storage energy systems, thus extending the service life of the equipment.

High electricity costs remain one of the primary obstacles limiting the large-scale deployment of wind-photovoltaic (PV) hydrogen production systems. In such systems, the output characteristics of renewable power, the wind-PV-storage-load configuration ratio, and the grid-connected/off-grid operation mode affect the utilization rate of renewable power generation and the electricity consumption structure for hydrogen production, thereby influencing the variable and fixed costs per unit of hydrogen production. This paper develops a bi-level levelized cost of hydrogen (BLCOH) analysis model for grid-connected wind-PV-storage hydrogen production systems, explicitly considering dynamic variations in renewable energy supply and hydrogen production electricity demand. The model is applied to compare the costs of three system configurations: off-grid wind-PV hydrogen production, off-grid wind-PV-storage hydrogen production, and grid-connected wind-PV-storage hydrogen production. Furthermore, the impacts of renewable energy utilization hours, hydrogen production utilization hours, and peak-shaving electricity pricing on hydrogen production costs are quantitatively analyzed. The results indicate that integrating energy storage improves renewable energy utilization and enables additional revenue through grid peak-shaving services, while grid connection enhances the operational reliability of hydrogen production systems. Compared with the other two approaches, the grid-connected wind-PV-storage hydrogen production system achieves lower hydrogen production cost, making it promising in the future. Additionally, this paper derives analytical expressions for estimating critical thresholds of storage cost, grid electricity price, and the combined cost of storage and grid connection for wind-PV hydrogen production systems.

To reduce the operating costs of container terminals and improve energy utilization efficiency, a day-ahead coordinated scheduling model for container terminal microgrids with potential energy feedback is developed. First, a logistics-energy coupling system of container terminals is established, based on which, a quay crane operation model with potential energy feedback and an automatic guided vehicle energy management model are developed. Next, the operation characteristics of various logistics equipment in the terminal are analyzed, and a logistics-energy coordinated optimization model with the objective of minimizing daily operating costs is established. The model is then solved using an adaptive simulated annealing genetic algorithm to obtain the optimal collaborative scheduling scheme for quay cranes and automated guided vehicles. Finally, a container terminal in Shanghai is adopted for simulation verification. Compared with the traditional scheduling method, the proposed scheduling scheme reduces the operating cost by 4.96%, increases the utilization rate of potential energy feedback by 18.69%, and effectively shortens the investment payback period of the quay crane potential energy feedback device transformation.

Aiming at the multi-stage tensioning control of cables for long-span cable-stayed bridges under the influence of sag nonlinearity, based on the fundamental principles of multi-stage equivalent tensioning of cables and sag nonlinearity effects, the multi-stage equivalent tensioning process is theoretically derived, providing correction methods for various influencing factors in theoretical calculations during the construction process, and a new theory for multi-stage equivalent tensioning calculation is proposed. Through comparison between numerical analysis and measured results, the maximum error of tension control using the proposed calculation theory is -1.25%, and the maximum beam alignment error is 3.3 cm. Compared with conventional linear methods, the proposed method can significantly improve the uniformity and accuracy of cable force calculation without multiple iterative calculations, meeting practical engineering application requirements and effectively guiding cable tensioning control in the construction of long-span cable-stayed bridges.

Floating production storage and offloading (FPSO) units are critical equipment in offshore oil development. When mooring at a dock in harsh environmental conditions such as typhoons, their large main dimensions and simultaneous exposure to combined wind, wave, and current forces impose high demands on the load-bearing capacity of the mooring system. Additionally, the mooring lines are arranged in a complex manner, with nearly 60 lines, making it almost infeasible to use a comparative method to find the optimal parameter combination for all mooring lines due to the extensive computational workload. Therefore, this paper comprehensively considers multi-directional wind, wave, and current loads and develops an iterative optimization method for FPSO typhoon-resistant mooring line system parameters based on an improved genetic algorithm. The method takes line lengths as variable parameters, with different combinations of line lengths regarded as individuals in the genetic iteration. By checking the line load of each combination under typical conditions, the fitness of each combination is evaluated for iterative optimization, ultimately yielding an optimized mooring line system. The comparison of mooring schemes before and after optimization reveals that the optimized mooring line length parameters show a trend to tighten longer lines and relax shorter lines, thereby improving the utilization of lines that previously had lower loads. The optimized mooring scheme reduces the maximum mooring line load under all conditions by 12.2% without significantly affecting FPSO motion amplitudes. By merely adjusting the tension of the mooring lines, the optimized scheme meets safety standard checks without altering other parameters. This optimization method can enhance the safety of mooring systems within the constraints of limited dock resources and provide a reference for docking mooring operations.

In the field of ship engineering, the three-dimensional (3D) small sample model is a simplified 3D digital representation of the basic geometric shape and general structure of an actual object, commonly used to validate and optimize the structure and performance of ships. Traditional 3D small sample models are represented by a series of computer-aided design (CAD) operations, which are complex and dependent on specialized modeling expertise. Large language models (LLMs) have shown excellent performance in various fields, but their application in 3D modeling suffers from the phenomenon of “hallucinations”, leading to poor accuracy and robustness. Therefore, an LLM-driven enhanced 3D small sample modeling method for ships is proposed, using a parametric modeling instruction set. First, based on retrieval-augmented generation, existing 3D small sample model code instructions are searched and called, with LLM tools being used to drive the parameters defined in the model, thereby reducing hallucinations in the generation of 3D small sample models. Then, an external ship knowledge database is introduced to supplement information to the generated 3D model with additional information such as model features, process requirements, etc., enhancing the model utility in downstream applications. The results show that the LLM-driven enhanced 3D small sample modeling method for ships can effectively handle different modeling needs, quickly generating ship 3D small sample models with up to 15 features, and significantly improving modeling efficiency.

The graph convolutional network (GCN) algorithm is applied to geological profile prediction, and an improved GCN algorithm is proposed through targeted modifications. This improved GCN algorithm can flexibly establish connection graphs based on unequal spacing and sparse boreholes within the site, enabling the prediction of two-dimensional geological profiles. A boundary accuracy metric is introduced to better evaluate the accuracy of the predicted geological profile. Applied to actual geological profile cases and compared with the existing Markov random field and IC-XGBoost methods, the proposed method improves both global accuracy and boundary accuracy, which indicates its accuracy in predicting two-dimensional geological profiles. Finally, the influence of the borehole spacing and unequal spacing on preliction results is also discussed. The results indicate that the reduction of the borehole spacing generally improves the accuracy of the predicted geological profile boundary. However, the relationship between the reduction of borehole spacing and the improvement in prediction accuracy is not simply linear. The improvement in prediction accuracy depends on the soil layer spatial information provided by the additional boreholes. When the number of boreholes is fixed, the influence of unequal spacing depends on the soil layer spatial information provided by the locations of the boreholes. The more soil layer spatial information provided by the boreholes, the more accurate the prediction will be. The improved GCN method applied in Hangzhou to a real case to predict the geological profile, with all the measurement accuracy of validated boreholes above 0.8.

To address the data imbalance problem in the fault diagnosis of oilfield plunger injection pumps, a multi-level Inception-long short-term memory (LSTM) network model integrated with wavelet packet decomposition (WPD) and efficient channel attention (ECA) mechanism is proposed. The model utilizes WPD technology to decompose the low-frequency and high-frequency components of vibration signals. The Inception module extracts multiple-scale data features, while the LSTM module captures temporal correlations in the data. Furthermore, the ECA mechanism further enhances the model’s capability to exploit cross-channel data correlations, thereby improving the accuracy of feature representation. Experiments are conducted using plunger pump vibration data collected from an actual oilfield operation site. The results show that the proposed model achieves optimal performance with a diagnostic accuracy of 99.38%, which demonstrates its effectiveness and superiority.

Turbine blades are key hot components of gas turbines, which operates in high temperature and harsh environment. Accurate and efficient prediction of turbine blade temperature field is of great significance for the safety and stability of gas turbine service. In this paper, first, a turbine blade fluid-thermal-solid coupling model is developed for different start-up conditions of gas turbines. Then, numerical simulations are conducted to form a dataset of turbine blade temperature field. Finally, an efficient prediction method for turbine blade temperature field is proposed by combining neural network with model order reduction technique, which can predict the temperature field of the turbine blade using on-site sensor data. The results show that compared with the high-fidelity fluid-thermal-solid numerical simulations, the proposed method can efficiently predict the temperature field of the turbine blade in milliseconds with a relative error less than 12%, which provides a feasible approach for real-time monitoring of three-dimensional temperature field of turbine blades.

SiCp/Al is one of the key materials for lightweight manufacturing of large aerospace structural components. However, its multi-phase heterogeneous characteristics lead to severe machining damage and challenges in surface quality control. Tensile tests across a wide temperature range from -196 ℃ to 20 ℃ are conducted in this study, revealing the evolution of mechanical properties dominated by cryogenic strengthening effects in SiCp/Al materials. Comparative experimental studies on cryogenic and room-temperature milling are performed to analyze the macro- and micro-scale mechanisms by which cryogenic characteristics and process parameters influence cutting forces and surface topography. The results demonstrate that the material exhibits higher yield strength and tensile strength in cryogenic environments, while liquid nitrogen cooling medium reduces cutting temperature and weakens local thermal softening effects, resulting in higher milling forces compared to room-temperature machining. However, the enhanced interfacial strength under cryogenic cooling reduces the extent of surface pits and crack damage, producing better surface roughness than conventional processing. This paper provides a novel theoretical foundation and engineering basis for precision manufacturing of difficult-to-machine aerospace composites.