Safe Operation Of System Research Articles

Dynamic Aggregation Method for Load Aggregators Considering Users’ Deviation Electricity

Constructing a new energy-based power system is not only an important direction for the transformation and upgrade of China’s power system, but also a key to achieving peak carbon and carbon neutrality. How to fully utilize situation awareness technology to adapt to diverse and differentiated scenarios has become a crucial breakthrough point for ensuring the reliable, safe, high-quality, low-carbon, and economical operation of the new power system. Starting from the distribution network demand resources, this paper proposes a dynamic aggregation method for load aggregators considering the user deviation quantity, to deal with the current situation that the adjustable load-side resource points are multi-faceted and wide, and the operating subjects are complex and difficult to participate directly in the grid dispatch. First, considering there is subjectivity in the electricity behavior of users under the jurisdiction of the load aggregator, a deviation amount may be generated during the actual aggregation process, which reduces the profit of the load aggregator. Therefore, a load aggregator-level user deviation dynamic volume forecasting method based on the Markov chain is proposed, which is used to predict the deviation quantity of users during the dispatch cycle and achieve a dynamic status estimate on the load side of the new power system. On this basis, a dynamic aggregation model for load aggregators based on the deviation volume was constructed with the objective of maximizing the revenue of load aggregators. The examples, by comparing the aggregation results of users under three scenarios, show the proposed method can effectively guarantee the income of load aggregators, verify the effectiveness of the proposed dynamic aggregation strategy, and provide technical support for the operation situation awareness of the load side of the new power system.

Model-free control for autonomous prevention of adverse events in robotics.

Introduction: Preventive control is a critical feature in autonomous technology to ensure safe system operations. One application where safety is most important is robot-assisted needle interventions. During incisions into a tissue, adverse events such as mechanical buckling of the needle shaft and tissue displacements can occur on encounter with stiff membranes causing potential damage to the organ. Methods: To prevent these events before they occur, we propose a new control subroutine that autonomously chooses a) a reactive mechanism to stop the insertion procedure when a needle buckling or a severe tissue displacement event is predicted and b) an adaptive mechanism to continue the insertion procedure through needle steering control when a mild tissue displacement is detected. The subroutine is developed using a model-free control technique due to the nonlinearities of the unknown needle-tissue dynamics. First, an improved version of the model-free adaptive control (IMFAC) is developed by computing a fast time-varying partial pseudo derivative analytically from the dynamic linearization equation to enhance output convergence and robustness against external disturbances. Results and Discussion: Comparing IMFAC and MFAC algorithms on simulated nonlinear systems in MATLAB, IMFAC shows 20% faster output convergence against arbitrary disturbances. Next, IMFAC is integrated with event prediction algorithms from prior work to prevent adverse events during needle insertions in real time. Needle insertions in gelatin tissues with known environments show successful prevention of needle buckling and tissue displacement events. Needle insertions in biological tissues with unknown environments are performed using live fluoroscopic imaging as ground truth to verify timely prevention of adverse events. Finally, statistical ANOVA analysis on all insertion data shows the robustness of the prevention algorithm to various needles and tissue environments. Overall, the success rate of preventing adverse events in needle insertions through adaptive and reactive control was 95%, which is important toward achieving safety in robotic needle interventions.

Research on a new power system development planning model based on two-tier planning

With the rapid development of the power industry, the safe and stable operation of the power system has become an important basis for safeguarding economic and social development. In order to adapt to the trend of rapid growth of new energy sources and the demand for transformation and upgrading of the power industry, the capacity allocation scheme of the power system needs to be constantly optimized and adjusted. To address the development planning of new power systems, a new power system development planning model based on two-layer planning is constructed, taking into account the operational output results of various types of units and the interaction between the configuration schemes. Through the analysis of the evolution of the power system in a region in the next 15 years, the development pattern of the new power system in different stages under the high, medium, and low scenarios of new energy development is optimally calculated to guide the development of the power system. The results show that the model can provide a quantitative reference for decision-making on energy policies and power strategies in the medium and long term.

Electric Vehicle and Photovoltaic Power Scenario Generation under Extreme High-Temperature Weather

In recent years, with the intensification of global warming, extreme weather has become more frequent, intensifying the uncertainty of new energy output and load power, and seriously affecting the safe operation of power systems. Scene generation is an effective method to solve the uncertainty problem of stochastic planning of integrated systems of new energy generation. Therefore, this paper proposes a scenario generation and scenario reduction model of photovoltaic (PV) output and electric vehicle (EV) load power under extreme weather based on the copula function. Firstly, the non-parametric kernel density estimation method is used to fit a large number of sample data. The kernel density estimation expressions of PV and EV powers under extreme weather conditions are obtained and the corresponding goodness of fit tests are carried out. Then, a variety of joint distribution models based on the copula function are established to judge the goodness of fit of each model, and the optimal copula function is selected as the joint probability distribution function by combining the Kendall and Spearman correlation coefficients of each model. Finally, the optimal copula joint probability distribution is used to generate PV and EV power scenarios. The data of extremely hot weather in a certain province were selected for an example analysis. The results show that the output scenario obtained conforms to the correlation under this extreme weather, and has higher accuracy in reflecting the actual PV output and load power in this province under this extreme weather, which can provide a reference for reliability analyses of power systems and power grid planning.

Modern numerical relays in protection of power transformer

This paper presents various protection schemes and their respective modern numerical protection relays to protect the power transformer. Proper protection is needed for the economical and safe operation of electrical power systems. The power transformer protective relay should block the tripping during an external fault or magnetizing inrush and speedily operate the tripping during internal faults. The objective of this paper is to implement protection during in-zone and outzone faults and to operate the relay with proper selective discrimination. The basic approach is to disconnect the power transformer feeder against in-zone faults and prevent disconnection of the power transformer feeder due to other operating conditions. The protection relay was able to discriminate between 2nd harmonic inrush, in-zone fault, and out-zone fault conditions. The protection scheme consists of high-speed protection relays and circuit breakers, which are required for the protection of substation equipment against abnormal conditions. A protective relay senses the fault and determines the location of the fault. Then, the protective relay sends the tripping command to the circuit breaker. Therefore, proper care should be taken in designing and selecting an appropriate relay that is reliable, efficient, and fast in operation. The PT and CT continuously measure the voltage and current of the substation and are connected to the relays to enable them to detect abnormal conditions. This paper describes numerical protection for power transformers, especially when the rating of the system is 50 MVA, 110/33 Kv, and Dyn11 at the substation. The protection relay employed in the transformer feeder is 7SR24 type (Siemens make).

Battery Energy Storage Participation in Primary Frequency Regulation Considering SOC Consistency

Abstract In recent years, battery energy storage has garnered increasing attention in the frequency regulation field due to its rapid and precise output characteristics. The focus of this paper is on the control strategy for battery energy storage that is involved in primary frequency regulation and addresses the coordination control issues of different storage units when implemented on a large scale. A control method is proposed that considers the consistency of the State of Charge (SOC) in battery energy storage, which is involved in primary frequency regulation. The control of the battery energy storage system is divided into upper-level and lower-level control. The upper-level control uses model predictive control to obtain the total frequency regulation power of the energy storage station. The lower-level control allocates the corresponding power based on the capacity of each battery energy storage unit. It introduces a proportional integral adjustment term related to the average SOC to achieve SOC consistency control for each unit. Simulation results demonstrate that, regardless of whether the capacities of various storage units are identical, the proposed method achieves good frequency regulation performance, restores SOC to the ideal range, and ensures SOC consistency among different storage units. This approach aids the efficient and safe operation of battery energy storage systems.

Современные экологические проблемы эксплуатации коллекторно-дренажной сети и пути их решения

Purpose: to determine the environmental issues that arose during the operation of irrigation and drainage systems, and the basic principles of managing the quality of drainage water from irrigated lands. Discussion. Analytical data on the condition of reclaimed lands, changes in groundwater levels during the operation of irrigation systems, the use of water resources, the results of theoretical and experimental studies of the collector-drainage flow quality in the irrigation system diversion collectors in the south of Russia are considered. The main problems are identified: land resources degradation, rising groundwater levels, resalinization and salt accumulation during irrigation, especially with irrigation water with increased mineralization, changes in the hydrological and hydrochemical regime of water bodies – collector-drainage runoff intakes. Legal problems associated with the classification of irrigation and drainage systems as objects of category III are highlighted. Ways to solve environmental problems, methods for managing the quality of drainage runoff, including the creation of local treatment facilities based on routing the collector-drainage network to irrigated areas suspended from the drainage system, in the range of 10–200 ha with construction of storage drainage sumps as places for metering flow and regulating the chemical composition of drainage flow. Conclusions. Pollution problems are regional in nature and are determined not only by economic activities, but also by natural factors. The set of irrigation canals, collector-drainage networks and water intakes of drainage-discharge waters participates in the inter-basin redistribution of surface and underground runoff. In the process of managing the quality of drainage water from irrigated lands and the environmentally safe operation of hydro-reclamation systems, three parties must certainly participate: reclamation departments, agricultural producers and government agencies.

An APT Attack Detection Method of a New-type Power System Based on STSA-transformer

Background: Complex structures such as a high proportion of power electronic equipment has brought new challenges to the safe and stable operation of new-type power system, increasing the possibility of the system being attacked, especially the more complex Advanced Persistent Threat (APT). This kind of attack has a long duration and strong concealment. Objective: Traditional detection methods target a relatively single attack mode, and the time span of APT processed is relatively short. None of them can effectively capture the long-term correlation in the attack, and the detection rate is low. These methods can’t meet the safety requirements of the new-type power system. In order to solve this problem, this paper proposes an improved transformer model called STSA-transformer algorithm, and applies it to the detection of APT in new-type power systems. Methods: In the STSA-transformer model, the network traffic collected from the power system is first converted into a sequence of feature vectors, and the location information and local feature of the sequence, is extracted by combining position encoding with convolutional embedding operations, and then global characteristics of attack sequences is captured using the multi-head selfattention mechanism of the transformer encoder, the higher-frequency features of the attention are extracted through the self-learning threshold operation, combined with the PowerNorm algorithm to standardize the samples, and finally classify the network traffic of the APT. Results: After multiple rounds of training on the model, the expected effect can be achieved and applied to the APT detection of a new-type power system. Conclusion: The experimental results show that the proposed STSA-transformer algorithm has better detection accuracy and lower detection false-alarm rate than traditional deep learning algorithms and machine learning algorithms.

Safety assessment of technical systems based on limit values

Ensuring the safe operation of complex technical systems is an urgent task. Complex technical systems include lifting, mobile, agricultural machinery, etc. Absolutely every complex system fails during operation. At the same time, failures can have different effects not only on reliability indicators, but also on the degree of safety. The degree of safety of operation of complex equipment is directly influenced by the probability and nature of the occurrence of failures. Thus, in order to increase operational safety, it is necessary to reduce the risk of a negative event, which is defined as the possibility of an adverse event occurring, that is, a combination of the degree of negative consequences with the possibility of its occurrence. The article provides an example of calculating the risk of failure; a method for calculating the parameters of the three-parameter Weibull distribution is proposed; an algorithm for determining three parameters of the Weibull law for a sample is given; the results of parameter calculations are presented; Proposals have been put forward for ways to increase the level of reliability and safety, respectively.

Аспекти інтеграції відновлюваної розподіленої генерації в систему енергозабезпечення України

The paper examines the strategic and practical aspects of integrating renewable distributed generation into the energy supply system of Ukraine, which faces challenges related to increasing the reliability, safety, and efficiency of the energy system in the context of global decarbonization trends and military threats. The key role of distributed renewable energy generation in increasing the stability of the energy system and reducing dependence on centralized energy sources has been established. An analysis of world experience has been carried out, which emphasizes the economic benefit and increased flexibility in the management of energy resources due to the implementation of distributed generation. Examples of successful implementation of distributed renewable energy generation projects in different countries and analysis of the potential for the development of renewable energy sources in Ukraine, in particular, solar, wind and small hydropower, are given. The completed review allows to draw a conclusion about the need for an integrated approach to the development of distributed renewable energy generation. Special attention is paid to the study of technical, economic, legal and regulatory aspects that determine opportunities and limitations for attracting investments in distributed renewable energy. Emphasis is also placed on the need to develop and implement innovative management systems and the latest technological solutions that will allow maximum use of the potential of distributed generation to ensure reliable, safe and efficient operation of the power system. Aspects of integration of energy storage systems are taken into account, allowing to smooth out peak loads and ensure the stability of system operation. It was concluded that it is necessary to improve financial support mechanisms that stimulate the development of small producers of electricity from renewable energy sources. Particular attention is paid to the study of international experience and its adaptation to Ukrainian realities, which allows to take into account the specifics of the national energy market and maximizing the effectiveness of the introduction of the latest technologies.

Ultra-short-term forecasting model of power load based on fusion of power spectral density and Morlet wavelet.

An accurate ultra-short-term time series prediction of a power load is an important guarantee for power dispatching and the safe operation of power systems. Problems of the current ultra-short-term time series prediction algorithms include low prediction accuracy, difficulty capturing the local mutation features, poor stability, and others. From the perspective of series decomposition, a multi-scale sequence decomposition model (TFDNet) based on power spectral density and the Morlet wavelet transform is proposed that combines the multidimensional correlation feature fusion strategy in the time and frequency domains. By introducing the time-frequency energy selection module, the "prior knowledge" guidance module, and the sequence denoising decomposition module, the model not only effectively delineates the global trend and local seasonal features, completes the in-depth information mining of the smooth trend and fluctuating seasonal features, but more importantly, realizes the accurate capture of the local mutation seasonal features. Finally, on the premise of improving the forecasting accuracy, single-point load forecasting and quantile probabilistic load forecasting for ultra-short-term load forecasting are realized. Through the experiments conducted on three public datasets and one private dataset, the TFDNet model reduces the mean square error (MSE) and mean absolute error (MAE) by 19.80 and 11.20% on average, respectively, as compared with the benchmark method. These results indicate the potential applications of the TFDNet model.

Power disassembly equipment for high efficiency heat transfer plate heat exchangers

Plate heat exchangers are realized by means of a heat transfer mechanism, in which heat is naturally transferred from the hot substance to the object with a lower temperature according to the laws of thermodynamics. Two liquids of different temperatures flow on the wall, heat transfer on the wall and convection of the liquid on the wall, thus promoting heat transfer between the two liquids. Under the same flow rate and power consumption conditions, its heat transfer coefficient is three times that of shell and tube heat exchangers, which is a key and efficient new equipment for effectively using effective resources and saving and developing new energy. However the heat supply plate heat exchanger has a tiny circulation surface and is easily obstructed. Regular maintenance and cleaning, troubleshooting, and plate replacement all necessitate frequent heat exchanger disassembly and installation. The requirements and challenges in dismantling and assembling the heat exchanger are very high, and manual disassembly is wasteful, making consistent force difficult to achieve. The current methods of disassembly and assembly are inefficient and incorrect. The intelligent mechanization of disassembly and assembly equipment is realized in this paper by driving, clamping, automatic control, distance measurement, sensing, and other systems. The problems of uneven force, low efficiency, and precision in plate heat exchanger disassembly and assembly are solved. Our power disassembly equipment for high efficiency heat transfer plate heat exchangers not only enhances disassembly efficiency and precision, but it also ensures the safe operation of the plate heat exchanger heating system, and the heat transfer efficiency and heat exchange efficiency are improved. Furthermore, it has a wide range of applications in the petroleum, chemical, and other industries.

Few-shot bearing fault detection based on multi-dimensional convolution and attention mechanism.

Bearings are critical components of industrial equipment and have a significant impact on the safety of industrial physical systems. Their failure may lead to equipment shutdown and accidents, posing a significant risk to production safety. However, it is difficult to obtain a large amount of bearing fault data in practice, which makes the problem of small sample size a major challenge for bearing fault detection. In addition, some methods may overlook important features in bearing vibration signals, leading to insufficient detection capabilities. To address the challenges in bearing fault detection, this paper proposed a few sample learning methods based on the multidimensional convolution and attention mechanism. First, a multichannel preprocessing method was designed to more effectively utilize the information in the bearing vibration signal. Second, by extracting multidimensional features and enhancing the attention to important features through multidimensional convolution operations and attention mechanisms, the feature extraction ability of the network was improved. Furthermore, nonlinear mapping of feature vectors into the metric space to calculate distance can better measure the similarity between samples, thereby improving the accuracy of bearing fault detection and providing important guarantees for the safe operation of industrial systems. Extensive experiments have shown that the proposed method has good fault detection performance under small sample conditions, which is beneficial for reducing machine downtime and economic losses.

A Multistage Algorithm for Estimating Electromechanical Modes of Power Systems

Low-frequency oscillation modes of electromechanical origin, when they have low damping rates, can affect the stability of power systems. The Identification of these electromechanical modes is essential to preserve the continuous and safe operation of power systems. This paper proposes a multistage algorithm to determine the electromechanical modes of signals collected by Phasor Measurement Units (PMUs) in power systems. The proposed algorithm is composed of four processes and at each stage an electromechanical mode is estimated and evaluated. Case studies of a real PMU signal were conducted and discussed.

Enhancing Human-Robot Collaboration in Industry 4.0 with AI-driven HRI

Human-robot interaction (HRI) is an important consideration in mechatronic design to ensure safe and intuitive operation of robotic systems. With advancements in artificial intelligence (AI), new opportunities have emerged to enhance HRI through learned models that can adapt to human behavior and preferences. This paper provides a comprehensive review of techniques to integrate AI into HRI for mechatronic systems. An overview is first provided of challenges and objectives in integrating intelligence into robotics for effective HRI. Modern approaches utilizing neural networks, reinforcement learning, and graph neural networks are then discussed for robotic perception, decision-making, motion control, and interaction adaptation. Additionally, hybrid approaches combining rule-based methods with learned models are highlighted. Guidelines are provided for collecting human interaction data, evaluating integrated system performance, and considering adjustability, explainability, and safety. Multiple tables summarize key studies on AI-enhanced user interfaces, interactive task learning, socially aware navigation, bio-inspired sensorimotor control, and personalized robots. Finally, open issues and future outlook are discussed. This paper aims to support mechatronic designers through an structured analysis of the emerging field of intelligent HRI with insights into current best practices for integration.

Design, Simulation, and Prototype of an 18-Wheeler Electric Vehicle with Range Extension using Solar PV and Regenerative Braking

In response to California's initiative to propel the advancement of hybrid and electric vehicles toward achieving zero emissions, this study undertakes the design of a hybrid 18-wheeler aligning with the state's stringent standards. The motivation stems from the imperative need to address the research and development in the domain of hybrid/electric class 8 vehicles, specifically focusing on the pivotal segment of 18-wheelers. The research team developed a realistic theoretical design, verified it using MATLAB Simulink simulation, and ultimately built a prototype. The truck's novel features, namely, 60% electric power, regenerative braking, and solar PV for range extension, are included in the design and partially implemented in the prototype. Two motors, a DC and an AC, are used as the prototype drives where the system control is mostly automatic. Simulation of the electric 18-wheeler design shows that the model works properly. It follows the speed command closely while the acceleration and deceleration behaviors are normal. Testing of the prototype shows that it functions appropriately. The DC motor speed can be regulated over a wide speed range while the AC motor can run at two different speeds as designed. The prototype microcontroller logic is followed, ensuring safe operation of the solar PV system and the battery, and effective control of the motors. Overall, the project succeeded in achieving a harmonious blend of simulation, design, and physical implementation. It can be used as an engineering and public education tool. Further, by exploring cutting-edge technologies such as regenerative braking and solar power for truck range extension, the project contributes to raising vehicle efficiency and finding sustainable transportation solutions, which make the transportation sector more friendly to the environment.

Nonlinear autoregression-based non-destructive evaluation approach for railway tracks using an ultrasonic fiber bragg grating array

The safe operation of railway systems relies on rapid and accurate non-destructive damage detection and evaluation techniques to help rule out potential threats brought by rail track damages. In this paper, an innovative fiber Bragg grating (FBG) array-based sensing system is developed for rail defect inspection by integrating the ultrasonic testing technology, and a nonlinear autoregressive neural network with exogenous inputs (NARX) is proposed for rail damage detection based on measured ultrasonic signals. The developed sensing system can realize the high-speed and multiplexed ultrasonic signals demodulation. The NARX is proposed to process the ultrasonic signals obtained by the developed sensing system, and a specified probabilistic damage sensitive feature (DSF) is tailored to indicate rail health status, derived from the probability density function (PDF) of NARX prediction errors. To verify the proposed sensing system and damage identification algorithm, experimental studies considering four rail conditions (i.e., intact, cracked, bump, and welded conditions) are conducted in this study. First, baseline NARX models are priorly trained using ultrasonic signals acquired on an intact rail segment; then, DSFs corresponding to each condition are calculated enabling effective rail damage diagnosis. To optimize the training parameters of NARX models, three different training functions are utilized to select the most appropriate parameters that are sensitive to rail damages. Last, large-scale testing is conducted, further proving the applicability and reliability of the proposed rail damage evaluation approach. Through this study, it is suggested that integrating high-performance sensing technologies with effective data processing tools would maximumly enable efficient and robust non-destructive testing, especially on important infrastructures such as rails.

Object and Subject of Cri¬minal Offenses in the Field of Use of Electronic Computers, Systems and Computer Networks and Telecommunication Networks

The article deals with the object and subject matter of computer criminal offenses. The purpose of the article is to study the object and subject matter of computer criminal offenses, to analyze the current state of legal regulation of computer criminal offenses, and to provide suggestions and comments to the legislation.
 It is proved that due to the development of scientific and technological progress at the end of the twentieth century, a new type of social relations in the field of computer information circulation emerged in the State, which were subsequently taken under the protection of criminal law. The information security (cybersecurity) of the state has become one of the components of Ukraine's national security. Unlike other scholars, we believe that the direct object of computer criminal offenses is social relations to ensure the integrity and safety of computer information and the safe operation of information systems. The foundation of an information system is a set of information
 resources in the form of computer information, computer technologies and equipment, as well as related computer infrastructure, including telecommunication networks. Not all computer information requires criminal legal protection and, accordingly, can be the subject of computer criminal offenses, so the legislator should amend the dispositions of Articles XVI of the Special Part of the Criminal Code of Ukraine and replace the term "information” with "information protected by law”. It seems that by limiting the ways of influencing computer information in the law, the legislator has provided opportunities to avoid criminal liability to persons who otherwise cause damage to the owners of computer information.
 The complexity of protecting legal relations in the field of computer information lies not only in the evolution of technologies for its registration and transfer, but also in the evolution of legal relations themselves and the ways of influencing them. The backwardness of the articles of Section XVI of the Criminal Code of Ukraine and the problems of qualification of acts under these articles which arise in modern judicial practice cannot be solved by changing the dispositions of these corpus delicti only.

Transformer fault diagnosis method based on TLR-ADASYN balanced dataset

As the cornerstone of transmission and distribution equipment, power transformer plays a very important role in ensuring the safe operation of power system. At present, the technology of dissolved gas analysis (DGA) has been widely used in fault diagnosis of oil-immersed transformer. However, in the actual scene, the limited number of transformer fault samples and the uneven distribution of different fault types often lead to low overall fault detection accuracy or a few types of fault misjudgment. Therefore, a transformer fault diagnosis method based on TLR-ADASYN balanced data set is presented. This method effectively addresses the issue of samples imbalance, reducing the impact on misjudgment caused by a few samples. It delves deeply into the correlation between the ratio of dissolved gas content in oil and fault type, eliminating redundant informations and reducing characteristic dimensions. The diagnostic model SO-RF (Snake Optimization-Random Forest) is established, achieving a diagnostic accuracy rate of 97.06%. This enables online diagnosis of transformers. Comparative analyses using different sampling methods, various features, and diverse diagnostic models were conducted to validate the effectiveness of the proposed method. In conclusion, validation was conducted using a public dataset, and the results demonstrate that the proposed method in this paper exhibits strong generalization capabilities.

Short-term wind farm cluster power prediction based on dual feature extraction and quadratic decomposition aggregation

The intermittency and uncertainty of wind energy affect the accuracy of wind power prediction, which is not conducive to the safe and stable operation of the power system. Aiming at this problem, a short-term power prediction method for multi-Stacking wind power clusters based on dual feature extraction and quadratic decomposition aggregation is proposed. Firstly, a graph network matrix is built based on the spatial relationship of wind farm clusters to extract spatial features, and then a subset of features is filtered using a feature selection method combining filter and wrapper. The original signal is then decomposed using a quadratic decomposition method combining variational modal decomposition and swarm decomposition to reduce the volatility of the signal; the wind power signals are reconstructed using multiscale permutation entropy, and finally the reconstructed signals are fed into multi-Stacking models for information modelling depending on the frequency to predict rolling multi-step power of wind farm clusters. A real wind farm cluster arithmetic example is used for validation. The experimental results show that NRMSE, NMAE, and MAPE are improved by 15.8 %, 8.99 %, and 6.79 % compared with the traditional model. The method has important reference value for both grid scheduling and stable operation.