To realize the intelligent interconnection and interaction between the intelligent integrated energy system （IES） and the power supply system of rail transit, a synergistic optimization strategy for the hydrogen-containing IES and the power supply system of rail transit is proposed based on the dynamic ladder-type carbon trading mechanism. This strategy can reduce the cost of energy supply and utilization, while enhancing the low-carbon energy supply of the multi-flow coupling system and the low-carbon energy consumption of the rail transit system. Firstly, a dynamic ladder-type carbon trading model is designed based on the level of renewable energy and loads in IES. Secondly, according to the electric and thermal output characteristics of gas turbine （GT） and hydrogen fuel cell （HFC）, the traditional combined heat and power （CHP） unit and hydrogen energy unit are coupled by the Kalina cycle to develop a hydrogen-containing flexible energy supply unit, which can enhance the flexibility of the energy supply. Then, a hydrogen-containing IES collaborative optimization model connecting to the power supply system of rail transit is established to achieve cross-system joint optimization of rail transit energy consumption and multiple heterogeneous energy supply units, fully exploring the low-carbon economic operation potential of the system. Simulation results show that the proposed strategy can effectively balance the low-carbon economy and flexibility of multi-energy system scheduling, which can provide references for the synergistic operation of IES and power supply system of rail transit.

This paper explores the effects of attaching radiative cooling films at different positions of solar cells in different seasons on their thermoelectric performance through experiments and simulations. The results show that attaching a radiative cooling film on the front side can significantly reduce temperature, but due to the decrease in transmittance, the electrical performance is lost by nearly 70%; while attaching a film on the back side can reduce the average temperature by 5.08 ℃ in summer, and the power is approximately increased by 2%, maintaining a high photoelectric conversion efficiency while effectively reducing the module temperature, In terms of the comprehensive performance of thermal management and power generation efficiency, it performs even better. In summer, the film on the back side has a good cooling effect, and in a low-temperature environment with an average ambient temperature of -10 ℃ in winter, the temperature difference of the module decreases by 0.59 ℃. By reducing the temperature difference, the service life can be extended. At the same time, the radiative cooling effect is affected by irradiance, ambient temperature and wind speed. The cooling effect of attaching a film is more prominent under high temperature and high irradiance conditions, while enhancing convection will weaken its cooling effect.

In view of the characteristics of multi-regional correlation of full-network power flow in the new-type power system, an optimization method for coordinated electricity-carbon operation and a new full-network multi-regional cooperative optimization method for dynamically matching coordinated electricity-carbon operation are proposed. Firstly, according to the characteristics of cascade multi-source load-storage units in sub-regions of the new-type power system, the differences in operational regulation efficiency of various load-storage units within the region are analyzed, and a gradient cooperative optimization method for load-storage unit indicators is proposed. Then, aiming at the diversified characteristics of electricity-carbon regulation of load-storage units under different operating modes, a fine-tuning optimization method for load-storage unit control parameters based on complete state space and operational mode feedback is studied, and a 'multi-level multi-core' convolutional neural network model is established. Finally, a simulation model is constructed for validation. Simulation results show that the proposed multi-level indicator cooperative electricity-carbon power flow optimization control strategy can effectively enhance the electricity-carbon coordination capability and electricity-carbon regulation level of the new-type power system.

The cascaded H-bridge topology exhibits distinct advantages in the field of photovoltaic （PV） power generation with its modular design, smaller filtering inductance, and simplified layout. However, the three-phase cascaded H-bridge inevitably inherits the inherent issue of the single-phase full-bridge inverter, namely, double-line frequency voltage ripple on the DC-link. This issue leads to an output voltage deviation of photovoltaic array from its maximum power point, thereby reducing the power generation efficiency of system. To address this issue, this paper presents an innovative control method based on adaptive third-harmonic injection, which effectively avoids over-modulation risks under varying power factor angles. The method can calculate the optimal third-harmonic injection based on the real-time operating conditions of the system, thereby achieving optimal suppression of DC-link voltage ripple. Finally, a full-scale experimental platform is constructed, and experimental results confirm the effectiveness and feasibility of the proposed method.

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.

To address the high energy consumption and significant carbon reduction challenges in steel plate degreasing for industrial equipment manufacturing, this study proposes a novel PV/T-coupled ground-source heat pump heating system for degreasing （PV/T-GSHPHD） and its corresponding operational strategy. A comprehensive energy conversion model was established for system components, and an optimization configuration model was developed to enhance economic efficiency while ensuring soil thermal balance through typical scenario clustering analysis. The optimal system components and their respective capacities were determined based on these models. Results show that the optimal PV/T-GSHPHD system configuration consists of a PV/T module, a ground-source heat pump, a heat collection tank, and a thermal storage tank, with soil heat supplementation achieved via the PV/T module. Compared to a conventional electric boiler heating system, the PV/T-GSHPHD achieves 69.1% reduction in annual heating costs and 83.3% reduction in environmental costs, demonstrating significant economic and environmental advantages. The analysis of operating characteristics of the system on typical days indicates that the PV/T-GSHPHD effectively reduces grid peak loads year-round, with only 13% of its equivalent electricity sourced from the grid during summer, thereby alleviating regional electricity supply pressures. This study highlights the technical and economic viability of PV/T-GSHPHD as a sustainable alternative for industrial degreasing heating applications.

To flexibly address the issues of unreasonable integration of distributed photovoltaic （PV） systems and the temporal fluctuations of PV and loads, while improving voltage quality and operational efficiency in distribution networks, a bi-level planning method is proposed under typical source-load temporal scenarios considering demand response. Firstly, the SOM-KFCM clustering algorithm is introduced. This method combines the topological preservation characteristics of self-organizing maps （SOM） and the nonlinear clustering capability of kernel fuzzy C-means （KFCM） to enhance clustering accuracy, and typical scenarios are derived using the probability-weighted Pearson correlation coefficient. Then, by incorporating demand response and reactive power optimization, the active and reactive power of the distribution network are jointly regulated, forming a bi-level planning model. The upper model optimizes the PV planning scheme with the goal of minimizing total annual costs and maximizing voltage stability indices, while the lower model aims to minimize daily operational costs for each scenario. To effectively solve this model, an improved whale optimization algorithm with multiple strategies is adopted to enhance global search capability and escape from local optima, and it is combined with second-order cone programming for solving, achieving a globally optimal and locally coordinated planning solution. Finally, simulation analysis through case studies demonstrates that the proposed model and planning scheme enhance both the economic benefits and stability of the distribution network.

To investigate the dynamic response of the multi-bucket jacket foundation of offshore wind turbines under storm surge load, this paper conducts a series of 1 g model tests using the motor servo random dynamic loading equipment. Then, the influence of different depth-to-diameter ratios of the suction bucket on the soil and structure is further investigated through numerical simulations. The results indicate that the extreme horizontal displacement of the structure rises as wind speed increases under storm surge conditions, and the amplification coefficient of horizontal displacement follows a trend of “increasing initially before decreasing”. The peak value of the Fourier amplitude spectrum for the nacelle position acceleration increases with the load, while the peak frequency exhibits a pattern of “decreasing first and then increasing”. As the depth-to-diameter ratio of the suction bucket increases, both the extreme horizontal displacement and the amplification of the structures coefficient corresponding decrease. A depth-to-diameter ratio of L/D= 0.5 is the least favorable, as it cannot withstand storm surge loads. The difference between depth-to-diameter ratios of 1.0 and 1.5 is negligible. Taking into account reliability and economy, a depth-to-diameter ratio of 1.0 is the optimal type. Different depth-to-diameter ratios also have the influence onthe rotation center of the SBJ foundation.

To address the problem of poor voltage stability of wind-solar-energy storage DC microgrids in distributed renewable energy systems, an optimized nonlinear active disturbance rejection control strategy based on the SAC algorithm （SAC-ADRC） is proposed. Firstly, the wind-solar-energy storage system is modeled with nonlinear ADRC control. Then, the gain parameters of the nonlinear ADRC are reconstructed by using linear/nonlinear ADRC switching to improve its internal parameters which are more difficult to tune and analyze. Finally, the analysis establishes a mechanism for SAC intelligence to learn interactively with the microgrid environment, enabling the adjustment of non-linear ADRC parameters. Comparative analysis using algorithm convergence curves and simulation of various classical working conditions confirms the superiority of the SAC-ADRC control strategy in terms of interference performance. Thus, it is shown that the organic integration of nonlinear ADRC and deep reinforcement learning improves the stability of the microgrid bus voltage.

An integrated energy system coupled with solar energy and biogas is designed in this study, and a joint optimization method of capacity and operation is proposed. Firstly, a thermodynamic model of biogas production is introduced, and a capacity-operation two-layer optimization model is constructed. The upper layer optimizes the size of the biogas digester and the capacity of energy conversion and energy storage equipment to minimize the annual total cost. The lower layer takes the upper-layer results as constraints to optimize the operation plan of energy conversion and energy storage equipment, minimizing the operation cost. Secondly, by integrating the nonlinear programming method into the genetic algorithm, the model is solved to determine the optimal capacity configuration and operation scheme of the system. Finally, five simulation scenarios are used to verify the effectiveness of the proposed system and the joint optimization method. The results show that the integrated energy system optimized by the proposed method has the best economic performance, and the energy consumption scheme is efficient and reasonable.