State perception of power equipment is one of the key issues in the construction of smart grid. Digital twin (DT) technology can map and quickly predict the physical state of equipment in real-time, but existing modeling methods are difficult to meet the real-time computing requirements. This paper proposes a DT modeling method for distribution cables temperature field based on reduced-order model. The method first establishes a multi-physics full-order model of cables, builds a reduced-order model of the steady-state temperature field based on singular value decomposition and response surface interpolation methods. Combining the steady-state reduced-order model and temperature measurement data, the multi-circuit cables heat transfer inverse problem is solved to reconstruct the transient temperature field inside the cables in real-time, and the correctness is verified based on the temperature rise test results. Furthermore, the proposed method is employed to reconstruct the internal transient temperature field of 10 kV cables in actual operation. Taking the reconstructed result as the known initial state, the cable conductor temperature during emergency operation is quickly predicted based on the reduced-order model and the improved superposition method. As compared with the simulation results of the full-order model, the maximum relative error of the conductor temperature DT reconstruction value is 1.76%, the conductor temperature prediction error during emergency operation is 1.01%. The calculation time of single reconstruction and prediction is 8.1 s and 3.6 s, and the calculation efficiency is about 35 555 times and 6 000 times that of the full-order model, respectively. The new method takes calculation speed, calculation accuracy and modeling cost into consideration, and has reference significance for the temperature field DT modeling of other types of power equipment.

The optimization of the number of central air-conditioning cooling source units and their operating parameters is a collaborative optimization problem involving both discrete and continuous variables, which poses challenges for classical reinforcement learning algorithms. To address this problem, this paper proposed an energy-saving optimization control strategy for central air-conditioning cooling source systems based on a combination of the options-critic and actor-critic frameworks. Firstly, a hierarchical actor-critic (H-AC) algorithm was utilized to hierarchically optimize the number of units and operating parameters, with both the high-level and low-level models sharing a Q-network to evaluate state values, thereby addressing optimization challenges across multiple time scales. Secondly, the H-AC algorithm was improved in terms of agent architecture, policy, and network update mechanisms to accelerate the convergence of the agent. Finally, the proposed method was validated on the cooling source system of a research building located in a hot summer and warm winter region, using a TRNSYS simulation platform for experiments. The results demonstrate that, under conditions where the average indoor comfort time proportion is increased by 14.08, 11.23, 29.70 and 9.07 percentage points, respectively, the system energy consumption based on the improved H-AC algorithm is reduced by 32.28%, 28.55%, 28.64%, and 11.53% compared to four classical DRL algorithms. Although the system energy consumption of the improved H-AC algorithm is 0.27% higher than that of the options-critic framework, it achieves a more stable learning process and increases the average indoor comfort time proportion by 4.8%. This approach offers effective technical solutions for energy-saving optimization of central air-conditioning cold source systems in various building types, contributing to the achievement of buildings’ dual-carbon goals.

To achieve the goal of building a country with strong transportation network, it is necessary to highly develop infrastructure, establish an efficient operational system, promote technological innovation, and achieve sustainable development. Under the dual carbon goals, the sector of transportation must achieve deep carbon reduction through energy transition. This aligns with the sustainable development element in the process of building a country with strong transportation network. However, it is also essential to avoid negative impacts on resource allocation for transportation infrastructure and operational systems due to errors or overly hasty decisions in energy transition. Such impacts could hinder the smooth advancement of the strategy to build a country with strong transportation network. Because energy transition involves many key decision factors such as vehicle power technology, infrastructure building, user conduct, and policy-making, its complexity and importance can’t be ignored. This paper aims to delve into the significance, methods, tools, and experiences of decision-making analysis for transportation energy transition at the intersection of building a country with strong transportation network and achieving the dual carbon goals. By combining literature reviews and case studies, it seeks to provide theoretical and practical reference for national policies and corporate decision-making, while also fostering greater attention and research on related decision-making analyses. Firstly, this paper describes the meaning and motivation of transportation energy decision-making analysis and abstractly summarizes it relying on an optimization model framework. Secondly, based on the vehicle power technology, infrastructure, user conduct, and policy-making, a detailed demonstration is carried out by using the optimization model framework mentioned above and citing analytical cases in existing cases which include mileage optimization of an electric vehicle, charging network planning, user conduct of making choice, emission reduction and fairness of policy, and so on. Thirdly, the impact that improper assumptions in the decision-making and analysis of transportation energy transition on industry and society is pointed out according to the published cases or policy implementations. Finally, this paper summarizes the significance and feasibility of decision-making analysis in the transformation of transportation energy and proposes several research topics to encourage further exploration in this field. By integrating the strategy of building a country with strong transportation network with the dual carbon goals, this paper aims to provide comprehensive theoretical support and practical gui-dance for decision-making in transportation energy transformation, thereby promoting sustainable development in China’s transportation sector.

At present, the research on distributed energy cluster scheduling is mostly limited to a single scenario and lacks efficient and accurate algorithms. Aiming at these problems, this paper proposed a multi-scenario scheduling method for distributed energy clusters based on evolutionary algorithm experience-guided deep reinforcement learning (EA-RL). The individual models of power supply, energy storage and load in distributed energy cluster were established, respectively. Based on the individual scheduling model, a multi-scenario distributed energy cluster optimal scheduling model including auxiliary peak regulation and frequency modulation was established. Based on the framework of evolutionary reinforcement learning algorithm, an EA-RL algorithm was proposed. The algorithm combines genetic algorithm (GA) and deep deterministic policy gradient (DDPG) algorithm. The empirical sequence was used as the individual of genetic algorithm for crossover, mutation and selection. The high-quality experience was selected to join the DDPG algorithm experience pool to guide the training of the agent to improve the search efficiency and convergence of the algorithm. According to the multi-scenario scheduling model, the state space and action space of the multi-scenario scheduling problem of distributed energy cluster were constructed. Then, the reward function was constructed by minimizing the scheduling cost, the deviation of the auxiliary service scheduling instruction, the over-limit power of the tie line and the power difference between the source and the load, and the reinforcement learning model was established. To validate the effectiveness of the proposed algorithm and model, offline training of scheduling agents was conducted based on multi-scenario simulation cases, resulting in agents capable of adapting to various grid scenarios. Verification was carried out through online decision-making, and their scheduling decision-making capabilities were assessed based on decision outcomes. The validity of the algorithm was further verified through comparison with the DDPG algorithm. Finally, the trained agents undergo 60 consecutive days of online decision-making tests incorporating varying degrees of disturbances to validate their posterior effectiveness and robustness.

To quantitatively investigate the impact of interactions among control loops in a double-fed wind farm integrated with the grid via an MMC-HVDC system on sub-/super-synchronous oscillations, an analytical method combining modal analysis and relative gain array (RGA) is proposed. Firstly, a small-signal model of the double-fed wind farm integrated with the grid through MMC-HVDC is established, with its accuracy being verified by comparing its step response with that of an electromagnetic transient simulation model. Secondly, modal analysis is employed to identify the dominant sub-/super-synchronous oscillation modes affecting system stability, and the primary participating variables of these oscillation modes are determined through participation factor calculations, laying a foundation for subsequent analysis of the influence of interactions among different control loops. Thirdly, the RGA is introduced to confirm the existence of interactions, quantify and compare the strength of interactions among control loops associated with the primary variables of the dominant oscillation modes. This focuses subsequent research on the rotor-side converter (RSC) control loop of the wind farm and the fixed V/f control loop in the MMC-HVDC system. Finally, based on the variation of RGA values with influencing factors, the effects of the electrical distance of grid connection and the controller parameters on the degree of interaction among control loops are quantitatively evaluated and verified using time-domain simulation. The study reveals that, when the electrical distance increases or the proportional coefficient of the fixed V/f control on the MMC-HVDC side rises, the interaction between the RSC control loop on the double-fed wind turbine side and the fixed V/f control loop intensifies, leading to a decrease in system stability.

Liquid-solid two-phase flow technology is widely applied to the heat transfer enhancement in heat exchanger design, the key lies in guiding low-volume-fraction particles to the wall region to disrupt the thermal boundary layer and thereby improve the heat transfer efficiency. The movement behavior of particles is a key factor for deep analysis of heat transfer enhancement mechanism. Non-spherical particles have better disturbance effects and more complex movement behaviors due to the anisotropy of their shapes. This paper takes regular tetrahedral particle groups as the research subject to analyze their motion and distribution law in liquid-solid two-phase flow in vertical uppipe. In the investigation, the effects of particle inlet volume fraction (1%, 2%, 3%, 4% and 5%) and liquid inlet velocity (1.0, 1.2, 1.5, 1.8 and 2.0 m/s) on the average velocity and relative volume fraction distribution of particle groups in tubes are simulated based on CFD-DEM (Computational Fluid Dynamics-Discrete Element Model) coupling method, and the accuracy of the numerical simulation is verified by PIV (Particle Image Veloci-metry) experiments. The results show that, within the studied parameter range, the average velocity of particle groups exhibits axial fluctuations, with a fluctuation amplitude intensifying as the liquid inlet velocity increases, and decreases radially from the pipe center to the wall. Furthermore, the velocity distribution becomes increasingly centralized as the fluid flow develops axially. Along the radial direction, the relative volume fraction of particles follows the double peak law, that is, being higher in the central area and near the wall of the tube, while being lower in the transition area. When the particle inlet volume fraction is 1% and the liquid inlet velocity is 2.0 m/s, the particle volume fraction near the pipe wall is the highest.

Accurately predicting the emission concentration of NO _x at the outlet of the selective catalytic reduction (SCR) denitrification system in the waste incineration process is of great significance for enhancing data quality and optimizing ammonia injection. However, the waste incineration process exhibits significant nonlinearity, multivariate coupling, and time-series characteristics. These factors pose substantial challenges to achieving accurate prediction of NO _x emissions. To solve this problem, this paper presents a prediction model for the emission concentration of NO _x at the outlet of SCR denitrification system by integrating maximum information coefficient (MIC), principal component analysis (PCA) and long short-term memory (LSTM) neural networks. First, MIC is employed to assess the maximum normalized mutual information values among variables, and the input variables that exhibit the strong-est correlation with NO _x emission concentration are selected while the redundant variables are eliminated based on the principle of maximum redundancy. Then, PCA is utilized to obtain the cumulative contribution rate of the va-riance of each principal component, extract the principal component features, and obtain the optimal input feature variable set. Finally, an emission prediction model of NO _x at the outlet of SCR denitrification system is established based on the LSTM neural network. The results indicate that, as compared with the back propagation neural network model and the support vector machine model, the proposed model exhibits higher accuracy and generalization ability, achieving a mean absolute percentage error of 6.33%, a root mean squared error of 4.71 mg/m³ and a determination coefficient of 0.90. This research lays a theoretical foundation for achieving the intelligent control of SCR denitrification system in the waste incineration process.

As laser point cloud models are crucial for distribution line inspection and management, most distribution channels have constructed laser point cloud models at present. With the increase of the number of models, extracting key component locations (e.g., conductors, insulators) becomes vital. In order to enhance the accuracy and efficiency of segmenting key components such as lines, towers and insulators, this paper presents a segmentation algorithm for laser point cloud of distribution lines based on a fusion Transformer model. Given the need for detailed features in the point clouds of distribution lines, a dual-channel parallel feature extraction module is designed to capture high-frequency and low-frequency features. The low-frequency features are processed via average pooling and a fusion Transformer-based extractor, while the high-frequency features are handled through max pooling and a multi-layer perceptron (MLP) module with convolutional layers. The feature vectors from both channels are then fused to improve the ability of detail feature extraction. Additionally, the fused features are fed back into the MLP module for further refinement, achieving precise point cloud target segmentation. Extensive experiments demonstrate the accuracy and effectiveness of the proposed algorithm. It has potential advantages in many aspects, such as improving the inspection accuracy of unmanned aerial vehicles, enhancing the level of automation, improving the robustness, integrating multi-source data and reducing inspection costs.

Studying the effect of Ozone (O₃) on polycyclic aromatic hydrocarbon (PAH) during the combustion process of biodiesel can provide new insights for reducing soot emissions. A skeletal reaction mechanism of biodiesel surrogates coupled with an O₃ reaction mechanism and a PAH reaction mechanism was constructed for modeling the effect and mechanism of O₃ on PAH formation in a counterflow flame of biodiesel surrogates. The final mechanism consists of 138 species and 608 reactions. Analysis show that the addition of O₃ creates a localized rapid temperature rise zone on the fuel side. As the initial O₃ mole fraction increases, the temperature rise rate in this zone intensifies and its position shifts closer to the fuel outlet, resulting from the preliminary oxidation of the fuel releasing heat. Furthermore, the maximum mole fraction of PAH initially increases and subsequently decreases with increasing initial O₃ mole fraction. When initial O₃ mole fraction increases to 0.04, the maximum mole fraction of major PAH such as benzene (A₁), naphthalene (A₂), anthracene (A₃), and pyrene (A₄) are 4.57, 6.76, 16.16, 12.38 times that at initial O₃ mole fraction of 0.00, respectively. The addition of O₃ has a significant impact on the concentration of PAH, and has the greatest impact on A₃. At the same time, the pathway of benzene (A₁) generation shifts from C₂H₂-dominated to C₂H₃-dominated mechanisms. And when initial O₃ mole fraction increases to 0.12, the maximum mole fractions of A₁, A₂, A₃, and A₄ are 0.880, 0.357, 0.375, and 0.143 times that at initial O₃ mole fraction of 0.00. It is because that the C₂H₃ radicals are oxidized, thereby inhibiting the production of A₁.

Proton exchange membrane fuel cells (PEMFCs) have attracted significant attention in the fields of transportation, marine engineering, and aerospace due to their advantages of pollution-free operation, high efficiency, and low noise. However, reliability issues hinder their large-scale commercialization. To further enhance fuel cell reliability, this paper proposed a fault prediction method based on deep learning. First, for operational monitoring data including voltage, current, humidity, and temperature, feature parameters for fault diagnosis were selected based on fuel cell failure mechanisms. This approach reduces data dimensionality, suppresses redundant information, and improves the computational efficiency of the prediction model. Additionally, pre-processing techniques such as normalization and sliding time windows were employed to eliminate the effects of differing dimensions among monitoring parameters. Then, a fuel cell state prediction model based on the long short-term memory (LSTM) network was constructed. Its inputs were preprocessed multidimensional feature sequences, and its output predicts the fuel cell state for the next T time steps. Finally, the predicted state data was fed into a convolutional neural network (CNN)-based fault identification model to achieve fuel cell fault state prediction. The proposed method was validated using experimental fault data from fuel cell tests, and the results show that the model can predict failures in advance. By virtue of effective data preprocessing, future state prediction via LSTM, and fault recognition through CNN, this deep learning-based approach enables early prediction of operational anomalies in proton exchange membrane fuel cells.

The tracer gas dilution method can address the issue of significant measurement errors in flue gas flow caused by the complex flow field in large-diameter stacks of power plants. The method is traceable and operates on a measurement principle different from the conventional velocity-area method, making it a promising candidate for on-site calibration of flow measurements. To this end, this paper employs numerical simulation to analyze the feasibility and accuracy of the tracer gas dilution method for measuring flue gas flow in power plant stacks. On this basis, it studies the influence of the tracer gas dilution ratio and injection cross-section on measurement results. In addition, different tracer gas sampling schemes were designed to evaluate the stability of the measurements. The results demonstrate that, at a height of approximately 9D (where D is the stack diameter), the tracer gas achieves full mixing with the flue gas; both excessively high and low tracer gas dilution ratios can negatively affect the mixing efficiency; injecting the tracer gas at the flue section can effectively reduce flow measurement errors. Under 80% load rate, when the tracer gas is injected into the stack, the measurement errors vary considerably across different sampling schemes. However, the three-point sampling method demonstrates a stable and accurate performance, with measurement errors of only -3.59%, -0.69%, and -1.05% at the 3D, 8D, and 12D cross-sections, respectively. When the tracer gas is injected into the horizontal flue, the flow measurement errors for all sampling schemes remain within ±10%. Specifically, with three-point sampling, the errors at the 3D, 8D, and 12D cross-sections are 0.98%, -0.52%, and 0.21%, respectively—all within ±1%. These results demonstrate the feasibility and accuracy of the tracer gas dilution method for flue gas flow measurement in large-diameter stacks.

The district cooling system (DCS) belongs to a class of centralized air-conditioning loads and has frequency regulation potential. This paper proposed an auxiliary frequency regulation control strategy of DCS based on model predictive control (MPC) with terminal constraints, which controls the power consumption of the DCS by adjusting the chilled water flow rate and the number of chiller shutdowns. Firstly, the study established a dynamic model of DCS and traditional units considering the relationship between chilled water flow rate and chilled water outlet temperature, and constructed the state space expression of the system. Then, based on MPC with terminal constraints, it established a joint frequency regulation control model for DCSs and traditional units, with the objective function of minimizing frequency deviation, building temperature deviation from human comfort temperature, chilled water flow’s control instructions, and traditional unit’s control instructions. The terminal constraints include terminal cost function and terminal set. Moreover, it was proved that the MPC problem with terminal constraints is asymptotically stable by constructing the Lyapunov function of the system. Finally, simulations on a 10-unit 39-bus system and an actual power system were carried out. The results verify that adding terminal constraints can improve system stability, and the use of DCS to assist in grid frequency regulation can help the system to quickly restore the rated frequency and improve regulation performance. In addition, the participation of DCSs in grid frequency regulation have no significant impact on comfort.

For the thermal management of high power consumption modules in 5G communication base stations, this study proposed a phase change heat transfer module with roll bond aluminum vapor chamber, in which the evaporation chamber of the module is interconnected with the flow channels of all vapor chambers. By constructing a performance testing platform, experimental studies were conducted to investigate the heat transfer performance of the module under different filling ratios. The impacts of the boiling state and the flow distribution of the working fluid on both temperature uniformity and heat dissipation efficiency of the module were analyzed. Additionally, the variation in surface temperature distribution of the heat source under different lateral inclination angles was also explored. The research results indicate that under the condition of an input power not exceeding 400 W, as the filling ratio increases, the total thermal resistance of the phase change heat transfer module initially decreases and then increases, reaching its minimum at a filling ratio of 15.0% with the lowest total thermal resistance being 0.211 6 ℃/W. Appropriately reducing the filling ratio induces boiling of the liquid working fluid at the bottom of the vapor chambers, thereby promoting the balanced distribution of gaseous working fluid among different vapor chambers and enhancing both the heat dissipation efficiency and temperature uniformity of the phase change heat transfer module. At input powers of 350 W and 400 W respectively, reducing the filling ratio from 30.0% to 15.0% leads to a decrease in the standard deviation of temperatures among the vapor chambers by 40.92% and 34.04%, resulting in a significant improvement in temperature uniformity. When the tilt angle of the phase change heat transfer module changes, the liquid level in the evaporation chamber shifts, leading to uneven temperature distribution on the heat source surface. This adverse effect becomes more pronounced with increasing inclination. At a tilt angle of 10.0° (under the same power conditions), the maximum temperature difference on the heat source surface increases to more than 11.7 times that under horizontal placement.

Anomaly detection of energy consumption in building lighting and socket systems can effectively improve energy efficiency. It holds significant importance for the implementation of building energy optimization measures and the realization of energy-saving management and control. Since the energy consumption of building lighting and plug load systems is heavily influenced by the random behavior of building occupants, and given the challenges posed by noisy time-series data and difficulty in feature extraction, this study proposed an unsupervised anomaly detection method that integrates operating condition classification with deep learning, aiming to enhance the accuracy and robustness of energy consumption anomaly identification. First, the decision tree algorithm was employed to classify the energy data based on attributes such as working days vs. non-working days and working hours vs. non-working hours. Then, for each identified condition, a long short-term memory autoencoder (LSTM-AE) model was constructed to detect anomalies. This model learns to reconstruct normal data and calculates the reconstruction error. By setting differentiated thresholds, it enables energy consumption anomaly detection under unlabeled data conditions. Using 578 days of hourly lighting and socket energy consumption data from an office building located in a hot-summer and warm-winter region, the study conducted model training and hyperparameter optimization experiments. Results indicate that the number of iterations, the number of neurons, and the activation function have significant effects on the model’s performance. Energy data during working days demonstrate greater stability than those on non-working days, resulting in higher detection accuracy. The proposed method achieves average precision, recall, and F₁ of 91.23%, 90.87%, and 90.80%, respectively, across four typical operating conditions, demonstrating its effectiveness in detecting energy anomalies in building lighting and socket systems.