Under the context of "dual carbon," an optimization scheduling model considering carbon emission flow and staged carbon trading mechanism is proposed in this paper to promote the lowcarbon economic operation of distribution networks. Firstly, the participation of distribution networks in the carbon trading market is taken into account, and the theory of carbon emission flow is introduced to determine the carbon emission status of each node within the distribution network. Subsequently, the stochastic states of electric vehicles are determined using the Monte Carlo algorithm, and the carbon quota of power generation equipment is obtained based on the entropy weight method. Simultaneously, a carbon quota model for electric vehicles is constructed, and a staged carbon trading mechanism is applied to model electric vehicles, photovoltaic units, wind power generation, and thermal power units. Finally, the system is optimized using an improved particle swarm optimization algorithm, with the objectives of minimizing the system operating cost and maximizing the system carbon income. The proposed model is verified through case studies conducted on an improved IEEE33 node distribution network system, where four operating scenarios are set. The research results demonstrate that the proposed model reduces carbon emissions by 539.43 tons, and the amount of wind and light discarded is reduced by 555.27 kW·h, which also makes the system's carbon revenue increase by 79.627 9 yuan.

To address the issue of optimization in the consumption of renewable energy in a distributed and flexible resource setting, a model based on opportunityconstrained crossregional grid renewable energy integration is proposed. This paper discusses the fundamental architecture of the twostage optimization model and analyzes the solving strategies for each stage. The twostage optimization model is established, comprising an upperlevel optimization model with opportunity constraints and a lowerlevel economic dispatch model. The paper proposes the use of a balancing constraint solving algorithm and strong duality theory to solve the model. Finally, the effectiveness of the proposed model is demonstrated through simulation analysis, and the conclusion discusses the relationship between the proportion of renewable energy consumption and grid output, load levels, and carbon societal costs.

The article focuses on corn stover as the research subject and prepares black titanium dioxide (RTiO₂) photocatalyst using the sodium borohydride reduction method. Through comparisons of different pretreatment results, the alkali photocatalytic pretreatment technology was selected. With the yield of reducing sugar and the main components of corn stalks as evaluation indicators, the study explores the impact of various factors, including photocatalyst concentration, reaction time, H2O2 concentration, and NaOH concentration, on the pretreatment effectiveness. The FTIR, XRD, and SEM analyses show that, compared with other pretreatment methods, the physical properties and microstructure of corn stover undergo significant changes after alkaline photocatalytic pretreatment, the lignin removal rate is effectively improved is effectively improved, which favors the subsequent enzymatic hydrolysis of corn stover.

With the development of photovoltaic(PV) industry, high capacity ratios have gradually become popular in the PV power station. To find the optimal charging/discharge strategy of energy storage (ES) in PV subarray with a high capacity ratio, one operation strategy based on working mode recognition was proposed to coordinate two competitive objectives—load shifting and smoothing. Furthermore, a capacity configuration model with the objectives of life cycle net present value maximization and output fluctuation minimization was constructed considering the generation income, ES cost, fluctuation characteristics, and typical day type. Moreover, taking a 1 MW subarray with 1.8 capacity ratio in a northeast utilityscale PV power station as a case, the optimal capacity of 700 kW·h was obtained. The simulation results under different typical days verified the feasibility and the effectiveness of the proposed ES operation strategy as well as the configuration model.

China's shallowsea development potential is 1 730 GW, and deepsea development potential is 1 830 GW, making China the country with the largest offshore wind power development potential in Asia. In recent years, with the continuous breakthroughs in floating wind turbines and concerning technologies, offshore wind power is gradually moving towards the deep sea. The development and delivery costs will drop rapidly. According to calculations, by 2030, the LCOE of floating wind power will drop to 0.28 ¥/(kW·h), and by 2050 it will drop to 0.14 ¥/(kW·h). The use of VSCHVDC for deepsea floating wind power, by 2050, the transmission costs of offshore 100 km, 150 km, and 200 km are estimated to be 0.050, 0.059 ¥/(kW·h)and 0.068 ¥/(kW·h) respectively in the basic scenario. Under the scenario of rapid technological progress, the estimated results are 0.033,0.040 ¥/(kW·h) and 0.046 ¥/(kW·h) respectively.

A smallscale physical model test was conducted in sandy soil to investigate the bearing characteristics of a monocolumn composite bucket as a new type of offshore wind turbine foundation. In the experiment, a displacement controlled horizontal monotonic loading method was used, and the loading speed and pressure were selected as variables to obtain the horizontal bearing capacity load displacement curve. The experimental results show that the ultimate bearing capacity of the foundation is positively correlated with the loading speed and ballast mass; Summarized the variation law of pore water pressure in different compartments of the foundation during the loading process.

Wind turbine blades are subjected to various loads during operation, which can cause deformation, and yaw can make the force situation of the blades more complex. To investigate the dynamic flapping deformation of blades under yaw conditions, experimental research was conducted using digital image correlation (DIC) technology to explore the influence of changes in yaw angle, wind speed, and speed on the dynamic flapping deformation of blades. The results show that the dynamic flapping deformation of horizontal axis wind turbine blades varies in a sinusoidal pattern, and the higher the wind speed, the greater the dynamic flapping deformation. The higher the rotational speed, the greater the dynamic flapping deformation, and at the same time, the time it takes to reach the maximum value is also shorter, with more variation cycles experienced; The existence of yaw angle changes the force situation of the blade. The larger the yaw angle, the greater the trend of dynamic flapping deformation. The shorter the time it takes to reach the peak, the earlier the peak position is. At the same time, the proportion of positive dynamic flapping deformation decreases, and the proportion of negative dynamic flapping deformation increases. The most obvious trend is at a 30° yaw angle. The research results of this article can provide experimental data support for effective control of blade deformation and subsequent research on the impact of blade deformation on the aerodynamic characteristics of wind turbines.

In the context of new power systems, represented by renewable energy sources such as wind and solar, low system inertia and high uncertainty have led to prominent issues with grid frequency stability. While new energy sources with virtual inertia control have improved frequency stability to some extent in lowinertia grids, they have simultaneously increased the difficulty of inertia assessment in the grid. Addressing the challenge where traditional online inertia monitoring methods struggle to accurately estimate synchronous machine rotational inertia alongside virtual inertia from new energy sources, this paper proposes a comprehensive estimation method for rotational and virtual inertia in power systems based on multiimportance sampling and Bayesian inference without requiring any linear assumptions. This approach utilizes local measurements from PMUs (Phasor Measurement Units) within a Bayesian inference framework and employs multiimportance sampling algorithms to sample from the nonGaussian posterior distribution of inertia parameters, ensuring the accuracy of inertia estimation. Simulation results demonstrate that this method exhibits high precision in online inertia estimation for both synchronous and asynchronous generators and can be widely applied in novel electric power systems dominated by new energy sources.

As a clean and lowcarbon energy system, the integration of wind, solar, and hydrogen microgrids plays an essential role in facilitating the transformation of energy structures and enhancing energy utilization efficiency. This paper investigates the capacity configuration issues of wind, photovoltaic, and hydrogen storage microgrid systems. A model of the windsolarhydrogen microgrid system has been developed, taking into account the uncertainties in wind and solar outputs. Based on historical data, typical daily scenarios are selected using an improved Kmeans clustering algorithm, and the uncertainty probability distributions are jointly constrained by both the 1norm and infinity norm within a confidence set. This study proposes a twostage distributionally robust model for the capacity configuration of windsolarhydrogen microgrids. The first stage determines the capacity of each component with the goal of minimizing investment costs, while the second stage aims to minimize operational costs. The solution to the model is derived through the application of the ColumnandConstraint Generation (C&CG) algorithm. The results indicate that the model can achieve a rational configuration of capacity, and it enhances the energy utilization efficiency and economic performance of the windsolarhydrogen microgrid.

Given the increasing proportion of new energy power generation, the deepening of electricthermal coupling, and the high carbon emissions of coalfired units, a multitimescale scheduling model of multienergy system including carbon capture power plants is established based on new energy optimal consumption and electricthermal demand response. First, the carbon capture coalfired power plant model with integrated flexible operation mode is established. It can reduce the carbon emissions of the system and improve the flexibility of coalfired units to cooperate with new energy sources. Second, the different demand response resources are applied to the load demand of different time scales, which can reduce the load peaktovalley difference and cooperate with the optimal consumption strategy of new energy to explore the lowcarbon characteristics of carbon capture based coalfired power plants. Third, considering multiple types of power and heat source equipment and taking the minimization of system operating costs as the objective function, a dayahead intraday realtime multi time scale scheduling model is established for sourceload coordination. It can optimize the load distribution and unit output plan under different time scales, and improve the new energy consumption capacity. Finally, the effectiveness and feasibility of the model are verified by experimental simulation results.