To address the challenges of power fluctuations and ramping demands faced by regional integrated energy systems under high penetration of renewable energy， this paper focuses on the ramping support capability of Advanced Adiabatic Compressed Air Energy Storage （AA-CAES）. A multi-timescale optimization dispatch model for regional integrated energy systems incorporating AA-CAES ramping capability is established. First， an operational model of AA-CAES is established to analyze its support capability for thermal power ramping. Second， a multi-timescale optimization dispatch strategy for regional integrated energy systems incorporating AA-CAES ramping capability is proposed. Long-timescale optimization minimizes operational costs while ensuring system power balance， and short timescale dynamic power correction is achieved using Model Predictive Control （MPC）. Simulation results demonstrate that multi-timescale scheduling， incorporating AA-CAES ramping capability， effectively enhances the system's resilience to renewable energy fluctuations， reduces thermal power dispatch requirements， lowers operational costs， and improves the integration of renewable energy. This approach provides theoretical guidance for the economic and stable operation of regional integrated energy systems.

Under the background of the "dual carbon" goals， the contradiction between the high proportion of renewable energy grid connection and the reliance on fossil energy has become increasingly prominent. To coordinate low-carbon constraints with energy security， this paper proposes an optimized scheduling model for virtual power plants （VPP） based on the collaboration of carbon capture （CCS）， electric-to-gas （P2G）， and electric vehicles （EV）. This model builds an integrated framework of "emission reduction - conversion - benefit" by aggregating distributed resources such as gas turbine units， combined heat and power （CHP）， wind power， photovoltaic power and EVs： Firstly， CCS is used to capture CO ₂ ； Secondly， through CCS-P2G， carbon dioxide is converted into methane by utilizing the abandoned wind and photovoltaic energy， and the captured CO₂ is consumed to form a carbon cycle. Finally， aggregated EVs participate in carbon market transactions and increase their profits by using the China Certified Emission Reductions （CCER） they generate. The case analysis based on MATLAB/CPLEX shows that

compared with the traditional gas-CHP system model， the model proposed in this paper can reduce carbon emissions

by 91. 3% （from 2，466. 9 tons to 214. 34 tons）， lower the cost of wind and solar power curtailage by 50，249. 30 yuan， and increase the consumption rate of renewable energy. And by selling CCER， the net cost of VPP was reduced by 8，208. 42 yuan. Ultimately， the overall net cost of VPP was reduced by 77，562. 28 yuan. The research verified the effectiveness of multi-technology collaboration in enhancing the economic and environmental benefits of VPP，providing theoretical support and practical paths for the low-carbon transformation of the new power system.

As a core domain in global energy transition and low-carbon development， hydrogen energy holds significant importance for supporting industrial innovation and talent cultivation through disciplinary development. Currently， universities and research institutions worldwide are actively exploring pathways to establish hydrogen energy disciplinary systems. Based on systematic research into hydrogen energy discipline development， this paper examines the current state of such development in China and explores practical approaches centered on talent cultivation and curriculum system construction. Analyzing aspects such as disciplinary layout， curriculum systems， research platforms， and faculty development， it elucidates achievements in cultivating specialized talent， driving technological innovation， and serving industrial growth through examining disciplinary development objectives， innovative teaching methods， and resource integration. Simultaneously， it dissects existing challenges and proposes targeted optimization strategies， aiming to provide theoretical references and practical insights for China's high-quality hydrogen energy discipline development. It identifies existing issues in current discipline development， including insufficient interdisciplinary integration， scarcity of practical resources， and room for improvement in internationalization. Recommendations are provided for the next phase of hydrogen energy discipline development and exploration in China，offering guidance for the sustainable advancement of this field and driving the high-quality development of China'shydrogen energy industry.

Mining the potential value of power load data is one of the main topics in the electric power industry. Aiming at the problem that it is difficult to extract the potential features of high-dimensional power load data by direct clustering methods， a one-dimensional convolutional autoencoder-based load feature optimization clustering method is proposed to optimize the clustering of typical load features. Firstly， a one-dimensional convolutional autoencoder is used to extract the time-series features of the user's daily load profile with the objective of optimizing the reconstruction loss to achieve the nonlinear dimensionality reduction of the data. Second， a cayley orthogonal constraint method for improving the clustering structure information is proposed to optimize the mapping of the features in the potential space and improve the clustering stability. Then the GAN-Kmeans clustering method is used to optimize the clustering center to fine-tune the coding part， so as to improve the accuracy of feature classification and identification. Finally，based on the user load dataset， three internal indicators， DBI， CHI and SC， are used to evaluate the effectiveness of the proposed method， which shows that the proposed method can effectively identify and extract the morphological features of various load curves， and it can provide a reliable support for demand response and optimal scheduling of virtual power plants.

As the core component of the new energy system， the new power system serves as a crucial cornerstone for China to achieve the "Dual Carbon" goals. The integration of complex source-grid-load-storage elements introduces significant system complexity. With the increasing penetration of renewable energy in large-scale power systems, traditional balancing frameworks and layered analysis methodologies are becoming increasingly inadequate. Against this backdrop， theoretical advancements and research technologies related to hierarchical-zonal balancing architectures in new power systems are systematically reviewed to provide innovative insights for architectural design exploration. First， the adaptability requirements of hierarchical-zonal architectures in new power systems are analyzed. Focusing on hierarchical control and zonal strategies， the concepts and methodologies of hierarchical-zonal division are systematically categorized， followed by detailed discussions on existing strategies. The integration mechanisms of hierarchical-regionalized frameworks are then elucidated， with critical analysis of current limitations in data and modeling methodologies. Finally， leveraging developments in large language model technology and artificial

intelligence， future opportunities for hierarchical-zonal balancing frameworks are prospected.

 The construction of new power system requires Hybrid Microgrids (HMG) to have inertia support capability, the application of Virtual Synchronous Generator (VSG) technology makes the sub-grids present different inertia characteristics, and the interlinking converter (ILC) makes the electrical characteristics of AC and DC buses coupled with each other during load fluctuation, resulting in ILC transmission power oscillation, which affects the dynamic stability of the system, therefore, the ILC transient power compensation is proposed to take into account the difference in inertia of sub-grids. During load fluctuation, the interlinking converter (ILC) couples the electrical characteristics of the AC and DC buses, which leads to the oscillation of the ILC transmission power and affects the dynamic stability of the system, therefore, the transient power compensation control of the ILC that takes into account the inertia difference of the sub-networks is proposed. A normalized equivalent VSG model of the AC-DC subnetwork is established to evaluate the equivalent inertia level of the subnetwork. The subnetwork equivalent inertia is further introduced into the transient power compensation controller to inject transient compensation power into the ILC to suppress the oscillation of the transmitted power and reduce the risk of overrun of the bus electrical quantity change rate. A simulation model is built based on the Matlab/Simulink platform, and the effectiveness of the proposed control strategy is verified under various operating conditions. Compared with the existing control strategy, the proposed control strategy can realize the overall power coordination and load sharing of the system, the drop of the AC/DC bus electric quantity change rate is mitigated, the ILC transmission power is smoother, and the dynamic performance of the system is improved.

The development of wind power generation is a critical measure for achieving the “dual carbon” goals. The safe and reliable operation of wind turbines is the foundation for ensuring their sustainable operation. As key fastening components of wind turbines， connection bolts are subjected to alternating loads and environmental corrosion over long periods， making them prone to loosening，fatigue fractures，and other safety risks that threaten the operational safety of the turbines. To address this issue， this study proposes a stress state detection sensor device  for tower and blade root bolts based on magnetic field signals. On this basis, a dedicated experimental testing system was established to quantitatively investigate the correlation between magnetic memory signals and bolt stress. Experimental results demonstrate that stress-induced magnetic signals can be effectively detected on the bolt surface. Under tensile loading， the magnetic memory signals exhibit a clear linear response to stress variations， enabling reliable stress state monitoring through magnetic measurements. Furthermore， the influence of bolt material on the relationship between stress and magnetic signal variation is relatively minor. In contrast， significant differences are observed in the slopes of the magnetic signal–stress curves for bolts of different strength grades， indicating the necessity of prior calibration. The test analysis in this paper can provide the necessary experimental foundation and reference data for engineering applications such as remote monitoring and early diagnosis of wind turbine bolt conditions.

In response to the challenge of early warning for abnormal oil sump temperatures in wind turbine gearboxes， a fault warning method based on supervisory control and data acquisition （SCADA） data is proposed to enhance the operational reliability of the turbines. Firstly， by integrating wind speed-power distribution characteristics，an outlier detection approach utilizing the interquartile range and longitudinal filtering based on data dispersion is employed to eliminate anomalous power points. Subsequently， key input features influencing oil sump temperature are identified using a random forest algorithm， leading to the development of a temperature prediction model based on categorical boosting （CatBoost）. The hyperparameters of this model are optimized using tree-structured parzen estimator （TPE）. Finally， dynamic warning thresholds are established through statistical process control based on residual distributions. In a practical case study from a specific wind farm， this model issued effective warnings approximately 5 hours prior to gearbox failure； notably， the time points at which residuals exceeded control limits closely aligned with the progression of faults. The proposed method demonstrates significant efficacy in identifying abnormal conditions related to oil sump temperatures and possesses strong early warning capabilities along with substantial engineering application value.