Insulated-gate bipolar transistor （IGBT） modules, as the core power components of wind turbine power converters, are crucial for achieving high operational efficiency and cost-effectiveness. Optimizing the electrical conductivity and manufacturing cost of these modules is essential. However, the demanding operational environments often cause performance degradation, and traditional design optimization methods incur high computational costs, making rapid iterative optimization difficult. To address these limitations, this study proposes a rapid iterative design optimization method for wind turbine converter IGBTs based on an improved NSGA-Ⅱ algorithm （Non-dominated Sorting Genetic Algorithm Ⅱ）. Firstly, the optimization problem is formulated with electrical conductivity and manufacturing cost as dual objectives. A parametric thermoelectric coupling model is developed to enable accurate and efficient evaluation of conductivity under varying design conditions. To further enhance the optimization process, an improved NSGA-Ⅱ algorithm based on kernel density estimation is proposed, significantly improving computational efficiency and optimization accuracy compared to traditional optimization algorithms. Additionally, a Kriging-based surrogate model is employed to construct high-fidelity mappings between design variables and optimization objectives, thereby reducing computational burdens and enabling rapid iterative optimization. Numerical experiments confirm the effectiveness and robustness of the proposed method, demonstrating reductions in electrical losses of up to 20.00% and decreases in manufacturing costs by as much as 27.63%. This study provides a practical and efficient design framework for IGBT modules, offering valuable insights into multi-objective optimization in the field of power electronics. By integrating advanced optimization algorithms with surrogate modeling, the proposed method addresses key challenges in the design and performance enhancement of wind turbine power systems.

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.

To enhance the consumption capacity of distributed photovoltaic （PV） and improve the power supply reliability of rural areas, this paper proposes a hierarchical coordinated planning method of PV and energy storage system for rural microgrids. Firstly, the medium and long-term load forecasting model based on the DenseNet-Autoformer network is proposed, which uses the autocorrelation mechanism to extract the deep time-series features, and comprehensively considers factors such as meteorology and economy on long-term monthly electricity consumption; Secondly, the Wasserstein generative adversarial network-gradient penalty method is used to expand the PV power samples. On this basis, the typical source and load scenarios are obtained via Gaussian mixture clustering; Finally, taking into account the electricity self-sufficiency and the off-grid reliability, the PV and storage configuration plan of the distribution transformer area is optimized, and the power supply reliability under extreme scenarios is quantified based on chance constraints. Meanwhile, considering the overall energy self-sustained ability of the 10 kV feeder, the feeder-level PV and storage configuration plan is optimized. The simulation results show that the proposed method can enhance the energy self-sustained ability and PV consumption capacity of rural microgrids and improve the power supply reliability in extreme scenarios.

When the grid dispatch center issues a reactive power command value, the wind farm needs to rationally allocate this command value to each wind turbine generator （WTG） to meet grid stability requirements. To achieve this objective, this paper proposes a loss-minimization-based reactive power optimization control strategy for onshore wind farms. An improved particle swarm optimization （PSO） algorithm is employed to optimize the reactive power reference value for each WTG, aiming to reduce total losses within the wind farm. These losses encompass generator losses, power converter losses, filter losses, transformer losses, and transmission line losses. To validate the effectiveness of the proposed strategy, a wind farm model with a 5×5 layout is established. Simulation studies compare the conventional reactive power control strategy with the proposed loss-minimization-based reactive power optimization control strategy under different scenarios. The simulation results demonstrate that the proposed optimization strategy significantly reduces power losses in the wind farm.

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.

By improving the forecasting accuracy of model products through wind speed deviation correction methods, reliable data support is provided for wind power generation forecasting in wind farms. Taking the Lingchuan County Wind Farm in Shanxi Province as an example, three machine learning methods recurrent neural network （RNN）, nonlinear model（NLinear）, and Transformer were employed to establish wind speed deviation correction models for CMA-WSP2.0 model products. The results show that the results of three methods are better than that of the original model products, and Transformer and NLinear perform better in improving the accuracy of wind speed at heights of 10 m meters and 100 m meters, respectively. Therefore, machine learning methods can effectively improve the quality and reliability of wind speed data, offering a more accurate data foundation for wind energy resource development and utilization, power forecasting and other fields.

A full-scale simulation model of a small air-cooled PEMFC stack is established in ANSYS/Fluent software, and the solution is obtained using computational fluid dynamics （CFD） methods. The effects of different altitudes and cathode stoichiometric ratios on the output performance, temperature, and oxygen concentration of the fuel cell stack are investigated. The results indicate that when the altitude increases to 3200 m, the peak power density of the fuel cell stack decreases by 21.6%. Increasing the cathode stoichiometric ratio can compensate for the decrease in oxygen concentration caused by higher altitudes, improve the output performance of the fuel cell stack and reduce the internal temperature differences within the stack.

The issue of wind turbine icing under the mixed-phase of ice crystals and supercooled water droplets is studied. The mechanism of wind turbine mixed-phase icing is analyzed, and the numerical simulation of airfoil icing on wind turbines is carried out. A particle motion trajectory equation is established using the Lagrangian method. The solution method for the control equation and the relevant definition of the collection coefficient are provided. A new mathematical model for icing under mixed-phase conditions is proposed, and the mass and energy conservation equations for liquid water on the surface of a wind turbine airfoil under mixed-phase conditions are established. The mathematical expressions for each equation are provided, and the physical phenomena of ice crystal adhesion and erosion are analyzed. The solution method for the icing growth model is provided. The accuracy of the icing model method proposed in this paper is demonstrated through comparison with experimental results. The icing characteristics of wind turbine airfoil surfaces under mixed-phase conditions are studied, and the impact of various initial parameters on icing characteristics is investigated. The research results indicate that erosion phenomena can affect surface icing. As the erosion rate increases, the surface icing decreases. The higher the adhesion coefficient and melting ratio, the greater the amount of icing. Different temperatures can lead to the formation of various ice forms on the airfoil. Rime ice forms at higher temperatures, while glaze ice forms at lower temperatures. However, with the increase in ice crystal content and diameter, the range and amount of icing change less. The research work provides a foundation for further research on wind turbine icing under mixed-phase icing conditions and the design of anti-icing and de-icing systems.

The project boundary,baseline emissions and project emissions of fuel ethanol projects were defined. The greenhouse gas （GHG） emission of 3 generations of bioethanol projects,including corn liquid fermentation,sweet sorghum solid fermentation and cellulose hydrolysis fermentation were analyzed. By introducing a secondary emission reduction accounting system for the resource utilization of by-products, the maximum carbon emission reduction potential of each generation of bioethanol processes was systematically revealed. The results show that the sweet sorghum solid-state fermentation has the highest carbon emission reduction potential, with the maximum theoretical 5.14 ton CO₂e GHG abatement per ton of ethanol production. Cellulosic ethanol has greater carbon negative potential under the background of technological progress.

To address the issue of low-carbon operation in multi-microgrid distribution systems under conflicting interests, this paper proposes a hybrid-game based low-carbon strategy for multi-microgrid distribution system considering carbon emission responsibilities. Firstly, a carbon accounting mechanism based on carbon flow theory is employed to allocate source-side carbon emissions to the load side, thereby clarifying the carbon emission responsibilities of all parties. Simultaneously, considering the game relationships among multiple stakeholders, a hybrid game-based low-carbon operation framework for multi-microgrid distribution systems is developed. This framework incorporates both master-slave and cooperative game models to enable effective multi-stakeholder participation. In this framework, the master-slave game takes the distribution network as the leader and the multi-microgrids as followers. The distribution network aims to minimize its operation costs while accounting for carbon emissions by optimizing electricity pricing. This pricing strategy guides the demand response and operational behaviors of the multi-microgrids, leveraging their collaborative carbon reduction potential under carbon flow theory to enhance the low-carbon operational performance of the system. Moreover, using the power interaction data among microgrids obtained from the master-slave game, a cooperative game model based on Nash negotiations theory is established. This model aims to achieve balanced benefit distribution among the microgrids, promoting fairness and enhancing collaborative efficiency within the system. Finally, the proposed hybrid game model is solved using a combined of the dichotomy and the analytical target cascading approach, ensuring efficient and accurate optimization of the system. The case study analysis demonstrates the effectiveness of the hybrid game model proposed in this paper in addressing the conflicts of interest between the distribution grid and multi-microgrids, and in improving the low-carbon operation level.