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BackgroundThere is limited data on the association between TyG-BMI and NAFLD in patients with Type 2 Diabetes Mellitus (T2DM). The magnitude of risk prediction and predictive efficacy of TyG-BMI for T2DM with NAFLD remains unclear.ObjectiveTo examine the association of TyG-BMI with NAFLD in T2DM patients and assess the effectiveness of screening using the TyG-BMI index.MethodsWe conducted a retrospective analysis of clinical data from 602 T2DM patients at an enterprise health lodge from September 2021 to November 2022. Patients were categorized into two groups: T2DM alone (n=250) and T2DM with NAFLD (n=352). The Mann-Whitney U test was used for comparing non-normally distributed continuous data between groups, while the Chi-square test was used for categorical data. Logistic regression analysis was performed to evaluate the effect of BMI, TyG index, and TyG-BMI index on NAFLD. The ROC curve was used to assess the predictive efficacy of the TyG-BMI index for NAFLD in T2DM patients.ResultsBMI predicted the development of NAFLD in T2DM patients with an area under the receiver operating characteristic (ROC) curve of 0.792 (95% CI 0.757-0.828), and the optimal cutoff value was 25.22, with 72.2% sensitivity and 71.6% specificity; The area under the receiver operating characteristic (ROC) curve of the TyG index to predict the development of NAFLD in patients with T2DM was 0.755 (95% CI 0.716-0.794), and the optimal cutoff value was 8. 945, with a sensitivity of 80.1% and a specificity of 59.2%; The area under the receiver operating characteristic (ROC) curve of TyG-BMI index to predict the development of NAFLD in T2DM patients was 0.852, (95% CI 0.822-0.882), and the optimal cutoff value was 227.385, with a sensitivity and specificity of 80.1% and 59.2%, respectively.ConclusionsThe TyG-BMI index is a significant predictor of comorbid NAFLD in T2DM patients and provides better screening performance than BMI alone. The TyG-BMI index shows promise as an early screening tool for NAFLD in T2DM patients.

Abstract Under the background of “dual carbon,” we must develop clean energy, build integrated energy systems (IES), and optimize their operation to achieve carbon neutrality. This study presents a strategy for the IES, whose goal is to reduce the carbon content of the power system, which aims to constrain the carbon emission of IES further, improve energy conversion efficiency, and reduce the carbon content of IES. First, we focus on the current situation and the improvement of the economic efficiency of the carbon trading market and introduce a tiered and hierarchical electricity carbon trading mechanism to promote IES to control carbon emissions more effectively. Then, we refine the two‐stage operation process of power to gas (P2G) and propose a P2G process. This novel approach can significantly enhance the efficiency of hydrogen energy utilization and save operational expenses. In addition, this paper proposes a flexible supply and demand dual response mechanism. On the supply side, energy efficiency is improved through cogeneration with adjustable heat and power ratio. On the demand side, the efficiency of the cogeneration system in terms of energy should be improved through a flexible supply and demand side dual response mechanism. With this mechanism, the environmental protection and economic viability of IES can be further enhanced.

Abstract The distributed generation of new energy can effectively use the local endemic clean new energy and provide green power. However, due to the small scale and scattered layout of distributed generation resources, it is difficult to participate in the economic dispatch of power systems and even the competition of the power market. Virtual Power Plant (VPP) can act as a carrier of Distributed Energy Resource (DER) to manage its internal energy, to carry out combined bidding in the day‐ahead energy market and the regulation market. When virtual power plants coordinate multiple distributed energy resources to participate in electricity market transactions, the degree of disclosure of their internal privacy information will produce different trading strategies. A Stackelberg game model between multiple virtual power plant aggregators and virtual power plant operators is constructed, and the Kriging model is combined to protect the privacy information in the transactions of virtual power plant operators. Energy management for distributed generation is carried out, and the profit situation of virtual power plant operators under different strategies is analyzed.

Abstract The rapid development of new energy technology can make this type of energy the primary power source in the future. However, new energy power generation is considerably affected by the environment, resulting in intermittent and fluctuating characteristics. Given the shortage of power generation in urban distribution networks during extreme weather, a study on power supply restoration strategy considering flexible resource regulation ability is proposed in this study. First, the typical adjustable flexible resources, air conditioners, and electric vehicles in urban distribution networks are modeled, and their adjustment ability is evaluated. Second, on the basis of the evaluation results, the optimal power supply recovery path is solved to ensure the power supply of essential loads in the distribution network. Last, the IEEE 33‐bus system proves that this method can quickly find the optimal path of power supply recovery and effectively reduce the power loss rate of essential loads, thus improving the toughness of urban distribution networks.

The reliability of wind turbines (WTs) is directly related to the safe and stable operation of wind farms. However, existing data-driven fault detection methods are challenging in coping with the complex operating conditions of WTs, which affects the ability to distinguish fault samples. To this end, a k-nearest-neighbor (kNN) fault detection method based on domain feature discrimination is proposed. A clustering-based data screening method is adopted, deep metric learning (DML) is introduced to extract discriminative features, and a potential generalized feature data mining method based on generative adversarial network (GAN) is proposed and introduced into the kNN-based fault detection framework, which enhances the model’s ability to describe the complex working conditions. Through experimental verification of 10 common faults in megawatt-level WTs, the results show that the proposed method reduces the average false alarm rate and missing alarm rate to 0.48% and 1.28%, respectively, which is an overall decrease of 6.17% and 4.73% compared to traditional methods.

Fault detection based on $k$ -nearest neighbor (FD- $k$ NN) is one of the most widespread fault detection techniques for industrial processes under complex working conditions, owing to its characteristic of local modeling. However, its state separation ability tends to worsen when the operating data is heterogeneous distribution. To tackle this challenge, a weighted $k$ -nearest neighbor fault detection method based on multistep index and dynamic neighbor scale is proposed. The multistep nearest neighbor index is defined to evaluate the state separation ability, and a weighted $k$ -nearest neighbor fault detection framework is formed by the assigned weights obtained from kernel principal component analysis. On the basis above, a dynamic neighborhood scale correction method and a dynamic threshold setting strategy are proposed to deal with the heterogeneous distribution of operating data and track the abrupt change of the operation state. 10 common faults of wind turbines with complex operation conditions are used to verify the effectiveness of the proposed method.

After the large-scale wind power airport is connected to the power grid, the wind farm cannot actively provide inertia support to the system, resulting in a significant decline in the inertia of the power system and weakening the frequency regulation ability of the power system. In order to explore the measurement and evaluation of inertia constant after large-scale wind farm is connected to the power grid. This paper first introduces the composition of inertia of power system at the present stage, then expounds the response process of inertia to disturbance after large disturbance of power system, and then deduces the power system model with large-scale wind power access through the analysis of traditional inertia measurement. Finally, the simulation model is used to compare the traditional measurement estimation with this paper. The maximum error is 9.18%, the minimum error can reach 2.4%, and the minimum error of the traditional estimation method can also reach 18%. Compared with traditional methods, it can be concluded that the error between the theoretical value of inertial time constant and the estimation of inertial time constant of the method described in this paper is significantly reduced.

To improve the accuracy of the evaluation of the equivalent inertia of a power system, a method for evaluating the equivalent inertia level of a power system based on an intelligent optimization algorithm is proposed. The proposed method constructs the objective function based on different degrees of system perturbation, ignores the frequency characteristics of the load, constructs the objective function with the generator swing equation, solves the inertia time constant of each generator using the intelligent optimization algorithm, and then evaluates the equivalent inertia of the whole system, eliminates the influence of inconsistent frequency change rate under perturbation on the system equivalent inertia evaluation, and thus obtains a more accurate system equivalent inertia evaluation, experimental result shows the evaluation accuracy can be improved by 10.79% and 3.48% respectively compared with other two comparison method.

Aiming at the problems of insufficient power system regulation capacity and lack of flexible resources caused by source-load uncertainty, the flexible resource planning of power systems is studied with the goal of improving flexibility. Uncertainty and flexibility are combined in this article, and a probability index of an insufficiently flexible supply-demand ratio is proposed based on the probability characteristics of flexibility. A bi-level programming model of power system flexibility resources considering the probability of an insufficiently flexible supply demand ratio is constructed. Optimal economics is used as the objective function of the planning layer, and the proposed minimum probability index of the flexible supply-demand ratio is used as the objective function of the operations layer. Economics and flexibility are studied, taking the power system in a certain area in Northeast China as the research object. A flexible resource planning scheme that meets different flexibility expectations is obtained, and the scheme is discussed in detail from the aspects of system flexibility, economic cost, and new energy consumption capacity. The effectiveness of quantitative indicators and planning methods are verified.

The cleanliness of charging power determines whether electric vehicles can fully utilize their low-carbon properties. This paper, taking into account the impact of temperature on the energy consumption of electric vehicle air conditioning, uses the Monte Carlo algorithm to calculate the typical daily charging load of electric vehicle clusters in different seasons. Secondly, based on the Shapley value carbon responsibility allocation method, a reasonable range of carbon emission responsibilities for different electric vehicle agents is calculated, and a tiered carbon pricing method is proposed accordingly. Then, using carbon emission flow theory, we calculate the carbon emissions generated by the different agents’ charging amounts and corresponding carbon emission costs. Finally, a low-carbon incentive guidance model is constructed with the signal of tiered carbon prices and the goal of minimizing operating costs to re-optimize the charging load distribution of electric vehicles. Case studies showcase that the proposed method is effective in reducing power system carbon emissions and electric vehicle charging costs.

The interval prediction of wind speed is crucial for the economic and safe operation of wind farms. To overcome the probability density function parameter optimization and long-term correlation of time series problems in an interval prediction method, a hybrid model based on the beta distribution of an allele real-coded quantum evolutionary algorithm (ARQEA) and a shared weight long short-term memory (SWLSTM) neural network is proposed for predicting the interval of short-term wind speed, which is beta–ARQEA–SWLSTM. Input variables are determined via autocorrelation functions, and the shape and position parameters in the beta distribution function are optimized by the ARQEA algorithm. An interval-divided multi-distribution function aggregation is proposed to deal with the fluctuation of wind speed series. Lastly, case studies are provided to demonstrate the effectiveness of the proposed method.

To reduce the curtailment of renewable energy and improve energy efficiency, the integration of multi-energy systems, which is proven to be effective in reducing energy loss, has been extensively studied all over the world in recent years. Among all the coupled systems, the integrated electricity and heat system is comparatively mature and has been large-scale applied. However, the widely use of combined heat and power (CHP) units limits the flexibility of integrated systems due to the coupling of power and heat. This paper proposes a bi-level programming model to take full advantage of the flexibility of the heating system. The heat pumps (HP) are utilized to decouple the strong coupling constraints introduced by CHP units. The upper-level problem is the optimal power flow of power system which is composed of coal-fired units, wind turbines, and electric loads. The bottom level decision-maker is the district heating network (DHN) which contains CHP units, HP and heat loads. The upper and lower level problem are linked by price signals sent by the power system operator (PSO). A quantum optimization-based solution method combined with single-level reduction is proposed for solving the bi-level model. The simulation results show that the proposed model can reduce the curtailment of wind power and increase the profit of power system significantly according to the price signal.

Research on fault identification for wind turbines (WTs) is a widespread concern. However, the identification accuracy in existing research is vulnerable to uncertainty in the operation data, and the identification results lack interpretability. In this paper, a data-driven method for fault identification of offshore WTs is presented. The main idea is to improve fault identification accuracy and facilitate the probabilistic sorting of possible faults with critical variables so as to provide abundant and reliable reference information for maintenance personnel. In the stage of state rule mining, representative initial rules are generated via the combination of a clustering algorithm and heuristic learning. Then, a multi-population quantum evolutionary algorithm is utilized to optimize the rule base. In the stage of fault identification, abnormal states are identified via a fuzzy rule-based classification system, and probabilistic fault sorting with critical variables is realized according to the fuzzy reasoning of state rules. Ten common sensor and actuator faults in 5 MW offshore WTs are taken to verify the feasibility and superiority of the proposed scheme. Experimental results demonstrate that the proposed method has higher identification accuracy than other identification methods and thus prove the feasibility of the proposed probabilistic fault analysis scheme.