This paper investigated the effect of thermally assisted light soaking （TALS） on the performance of amorphous/crystalline silicon heterojunction （SHJ） solar cells. The experimental results confirm that light soaking （LS） effectively improves the power conversion efficiency and fill factor of SHJ solar cells by 0.23% and 0.91%, while TALS increases the gain to 0.34% and 0.98%. By measuring the dark electric conductivity of doped amorphous silicon films before and after the TALS, it is found that TALS has more significant effect on improving the electrical properties of the films compared to LS, enabling n-type amorphous silicon thin film dark electric conductivity gain ratio of 9.05. Combined with Sinton WCT-120 and Suns-V_oc measurements, it is concluded that TALS improves the field passivation performance, enhancing the built-in electric field strength of crystalline silicon and amorphous silicon heterojunction, thus reduces the recombination of minority carriers at the interface, resulting in a light-induced gain of 0.34% in the output performance of SHJ solar cells.

To mitigate the risk to secure and adequate operation of power systems under storm conditions, this paper proposes a synergistic control decision-making method aimed at enhancing adequacy and power angle stability. Firstly, the output from wind farms outside storm-affected areas is predicted using the Informer model, providing input for subsequent optimization models. Secondly, based on a “decoupling optimization, aggregation and coordination” strategy, the method decouples system adequacy optimization from power angle stabilization control. For adequacy optimization, operational risk costs are reduced by scheduling adjustable resources. For power angle stabilization, the extended equal-area criterion quantifies the transient stability margin, and preventive-emergency control synergy is applied for each fault scenario to ensure stability. A two-layer optimization model is then constructed and solved iteratively. Finally, case studies on a modified IEEE 39-node system demonstrate the benefits of the proposed model in reducing system load loss and enhancing grid security.

Under low wind speed conditions, conventional maximum power point tracking （MPPT） control strategies fail to effectively account for the dynamic influence of the pitch angle, leading to reduced wind energy utilization efficiency. Under medium and high wind speed conditions, traditional PI-based pitch angle controllers struggle to maintain stable power output. To address these challenges, this paper proposes a collaborative optimization strategy that integrates Physics-Informed Neural Networks （PINNs） with Model Predictive Control （MPC）. The PINN model accurately captures the dynamic characteristics of wind turbines, while an online training mechanism based on a stochastic weight adjustment algorithm enhances adaptability. A unified MPC strategy is developed, considering both torque and pitch angle as control variables. By dynamically adjusting the cost function, the proposed approach achieves maximum power tracking under low wind speed conditions and ensures stable power output under medium and high wind speed conditions. Experimental results validate that the proposed method significantly improves the dynamic response of the wind turbines, enhances real-time control performance, and increases wind energy utilization efficiency.

This study proposes an operational optimization framework for power-to-hydrogen systems that explicitly incorporates the influence of hydrogen price dynamics. Firstly, a fuzzy set is constructed using historical price data to simulate hydrogen price volatility. Subsequently, a seasonal production intensity forecasting mechanism is developed to coordinate hydrogen price volatility with renewable energy variability. Building on this seasonal forecasting, a distributionaly robust optimization model is formulated for the operational scheduling of power-to-hydrogen systems. Case study results demonstrate that the proposed forecasting mechanism enables rational quarterly adjustments of production intensity, and its integration with the distributionaly robust optimization model effectively enhances renewable energy consumption while reducing daily operational costs.

To mitigate the adverse effects of wind power output fluctuations, low-frequency oscillations, and multi-controller coupling in power systems, this paper proposes a coordinated optimization strategy for multi-controller parameters under diverse operating conditions. Firstly, a Simulink model of the wind-PV-thermal bundled power transmission system and its controllers is established. Typical wind power output scenarios are generated using the Voronoi partition sampling method. Subsequently, the objective function for the optimization problem is formulated based on probabilistic methods, and critical controller parameters for optimization are identified via random forest regression analysis. Finally, an improved hybrid dung beetle optimizer is employed to coordinately optimize these key controller parameters across multiple operating scenarios. Simulation results on the IEEE 4-machine 2-area system and the 16-machine 5-area system demonstrate that optimizing the extracted controller parameters effectively enhances system damping. This significantly suppresses fluctuations in active power, power angles, and other system variables caused by low-frequency oscillations, thereby validating the efficacy of the proposed probabilistic optimization approach.

To enhance the accuracy of ultra-short-term wind power forecasting and support efficient power system dispatch, this study proposes an integrated multi-algorithm forecasting model. The variational mode decomposition （VMD） is firstly applied to suppress noise and reconstruct the original power sequence. In the modeling stage, a long short-term memory （LSTM） network is adopted to capture temporal dependencies, followed by a multi-layer convolutional neural network （CNN） to extract local features. A Self Attention mechanism is further incorporated to dynamically focus on critical time steps, resulting in a collaborative multi-module forecasting framework. To evaluate the performance of the proposed model, ablation studies, comparative experiments, and seasonal transfer tests were conducted using data from a wind farm in Shandong Province, China. The results show that, compared to baseline models, the proposed model reduces the mean absolute error （MAE） by 23.5% and the root mean square error （RMSE） by 20%, highlighting the advantages of each module in modeling complex temporal patterns. Additional validations in cross-regional （wind farms in Central and Western China） and cross-energy （photovoltaic plants） scenarios further demonstrate the model's strong generalization capability.

Under the background of global climate change, extreme weather events such as extreme high temperature, cold and freezing temperature, and strong wind and heavy rain occur frequently, which seriously threaten the operational reliability of integrated energy system （IES）. Based on this, we firstly focus on the reliability assessment methods of integrated energy system under extreme weather, and analyze the impacts of three types of extreme weather, namely, extreme high temperature, severe cold and freezing temperature, and strong wind and rainstorm, on the energy production equipment （including power generation, heat supply, and energy storage equipment） and the energy transmission network in various aspects. Secondly, we comprehensively review the integrated energy system reliability assessment methods under these extreme weather conditions, and explore the shortcomings of the existing methods in terms of meteorological data processing, system coupling modeling, multi-method fusion, toughness quantification, and multi-factor considerations. Finally, possible future research directions are proposed for the operational reliability assessment of integrated energy systems under extreme meteorological conditions, aiming to provide certain theoretical and practical references for improving the accuracy and effectiveness of IES reliability assessment under extreme meteorological conditions, so as to guarantee the safety and stability of energy supply.

To meet the inertia requirements of different voltage levels and dynamic operating conditions, this paper proposes an inertial fusion control technology of multi-voltage-level DC microgrid. Initially, a model of a multi-voltage-level DC microgrid is established, and the inertia characteristics of various levels of DC systems are analyzed. Subsequently, aiming to maximize inertia in each system level, the paper considers the involvement of battery storage and renewable energy side converters connected to the system bus. These are engaged in inertia regulation through increased output current feed-forward control and grouped adaptive control. Meanwhile, the low-voltage side use inertia control based on observation compensation with converters connected to their load side to suppress fluctuations. This approach allows converters with inertia regulation capabilities to participate in the graded fusion of inertia in the DC microgrid and designs the inertia parameters for each end. Finally, a multi-voltage-level DC microgrid hardware-in-the-loop simulation platform is constructed to validate the effectiveness of this control strategy.

Concerning the common-mode current problem and the magnetic saturation problem of energy storage inductor in the single-stage current-source inverter, a multi-input current-source inverter with bypass switches and multi-winding high-frequency transformers is adopted. Concerning the energy management and the loop decoupling problem of the multi-energy system, a one-cycle control and master- slave power energy management strategy based on power ratio is proposed, which aims to realize the stable operation of different power supply modes, smooth transformation of different power supply modes, and decoupling operation of the control-loop. The intensive theoretical analysis of the circuit structure, energy management control strategy, and small-signal model of the system is carried out, the control loop of the system is designed, and finally a 2.5 kVA prototype of single-stage current-source inverter with photovoltaic-wind dual-input is developed to validate the adopted circuit topology and energy management control strategy.

In order to study the high dimensional unsteady flow characteristics in the flow field of supercritical carbon dioxide centrifugal compressor reversed as turbine, the research object is centrifugal compressor reversed as turbine, the CFX flow field analysis software was utilized for numerical simulation, the dynamic mode decomposition（DMD） was performed on the simulated results, and the first four order modes and their corresponding spatio-temporal information are obtained. The analysis results show that the DMD method can decompose the unsteady flow field into modes with distinct energy levels and frequencies, including the basic mode with the highest energy contribution （0 Hz）, the dynamic and static interference mode with the second-highest energy （at the blade passing frequency）, and the high order harmonic characteristics of the dynamic and static interference modes with lower energy contributions（at multiples of the blade passing frequency）; The main characteristics of the basic mode are caused by the geometric parameters of the impeller, the main characteristics of the dynamic and static interference modes are caused by the dynamic and static interference between the impeller and the guide vane, and the high order harmonic characteristics of the dynamic and static interference modes show the subtle flow characteristics in the flow field; Under low-flow conditions, the first order mode exhibits a relative liquid flow angle smaller than the inlet blade angle, leading to flow separation on the pressure side of the blade inlet; Under high-flow conditions, the relative liquid flow angle of the first-order mode exceeds the inlet blade angle, resulting in flow impingement on the pressure side and flow separation on the suction side of the blade inlet. The DMD method can effectively decouple the unsteady flow field within the impeller and extract transient flow characteristics.