This PDF file contains the front matter associated with SPIE Proceedings Volume 13159, including the Title Page, Copyright information, Table of Contents, and Conference Committee information.

For accurate evaluation of large urban power grid of distributed photovoltaic carrying capacity, the article in the grid operation characteristics of power supply, power, short circuit current, power quality, voltage deviation and loss and relay protection six aspects on the analysis of the large urban power grid characteristics of distributed photovoltaic bearing capacity calculation evaluation method, and in large urban power grid, for example, to verify its feasibility and practicability. The results show that this method can accurately evaluate the 10-220 kV bus of the power grid, which provides strong guidance for the planning, construction and operation analysis of distributed PV access to large urban power grid.

This paper tackled the issue of identifying the most suitable capacity setup for multi-energy complementary microgrids in rural regions. The microgrid model integrated solar and biogas storage within an AC/DC hybrid system. The use of biogas and solar power generation could meet the electricity load demand of users, as well as the waste heat generated by biogas power generation be used to meet the heat load demand. By integrating the two energy storage methods of electricity and heat storage, the synergistic utilization of electricity and heat energy was achieved to improve the flexibility and energy efficiency of the microgrid system. Through multi-objective optimization, the study aimed to maximize renewable energy utilization, minimize costs and reduce carbon emissions while ensuring power supply reliability. The results suggested that the optimal configuration for this rural microgrid includes a 2.6 kW biogas generator, 30.0 kW photovoltaic panels, a 2.6 kW battery pack and a 10.00 kW bidirectional inverter. This configuration minimized the total net cost and yielded favorable outcomes in terms of various evaluation indicators.

The grounding network of the substation is an important part of protecting the safety of substation equipment and personnel. Due to the underground location of the grounding network, it becomes challenging to detect and diagnose corrosion in a timely manner. This paper presents a novel approach utilizing magnetic field signals for diagnosing the corrosion degree of grounding grids. The convolutional neural network (CNN) is used as the diagnosis model. The magnetic field intensity of the grounding grid under different degrees of corrosion is obtained according to simulation and used as a template. The magnetic field signals of the grounding grid to be detected are input to diagnose the degree of grounding grid corrosion.

The current conventional unstructured data storage methods for grid edge cloud mainly use distributed storage to realize the data storage, which leads to poor storage efficiency due to ignoring the fitness between data attributes and databases. In this regard, a large-scale grid edge cloud unstructured data storage method for cross-region networking is proposed. The features of the edge cloud unstructured data are extracted utilizing data fusion. Access interfaces are designed to connect the temporal database with other types of databases to build a multi-target database with higher adaptability, and the data storage network topology is designed. In the experiments, the storage performance of the designed storage method is tested. The final results can prove that the average response time of the system is shorter when the proposed method is used to store the edge cloud data, and it has a more desirable data storage performance.

A combined CEEMDAN-LSTM-RF-RBF wind power forecast model based on incomplete data is proposed for addressing the problem of incomplete data affecting forecast accuracy due to the difficulty in obtaining wind power data in practical engineering. The wind power historical power data are decomposed using CEEMDAN and classified into high-frequency, medium-frequency, and low-frequency according to the zero crossing rate of each mode component. The forecasts are performed separately, and the final results are obtained by linear summation. The simulation shows that the forecast results of the combined forecast model basically match the trend of the actual values. Its RMSE, MSE, and MAE evaluation indexes are 6.348 kW, 40.2905 kW², and 2.7179 kW, respectively. Compared with the LSTM and RF models, the evaluation indexes of the combined model values are lower, the forecast values are closer to the actual values, and can obtain higher forecast accuracy in the case of incomplete data.

Based on biogas production, biomass thermal power generation, solid oxide electrolytic cell (SOEC), and biogas methanation, the simulation of biomass integrated power and gas system and the analysis of energy supply and demand relationship were realized by using energy and substance balance models. The results show that SOEC is coupled with methanation and thermal power generation with heat exchange, and the energy conversion efficiency of the system is 25.73%. The different utilizations of dry and wet biomass are conducive to improving overall energy efficiency. The oxygen by-product from SOEC can improve the efficiency of thermal power generation, and the decrease in the condensation temperature and pressure of spent steam benefits improving the power generation efficiency. The selection of biomass with high calorific value and low hydrogen-to-carbon ratio is conducive to improving energy conversion efficiency. Combined with energy storage, the conventional power and gas demand of residents can be realized, a negative correlation exists between the power and gas storage, and a decrease in the power storage will lead to SOEC working at high or low loads.

With the aim of enhancing the accuracy of PV power forecasting, a PV power prediction model has been presented based on the EMD-PKCA-GRU neural network. Firstly, the sequence of four meteorological factors constraining the output power of photovoltaic power generation is decomposed using the empirical model decomposition method. The nonstationary nature of the meteorological factor sequences is reduced. Next, feature extraction is conducted from decomposed sequences using kernel principal component analysis (KPCA) methods. This eliminates the redundancy of the original sequence and reduces the dimensionality of the model input. Finally, the Gated Recurrent Unit (GRU) is established, using the GRU to predict PV power generation. The validation was conducted using the 2021 data from the Trina 1B power station in the DKASC dataset from Australia. The outcome shows that the prediction accuracy is higher than the traditional model.

Solar photovoltaic (PV) generation, a keystone of renewable energy, has gained worldwide recognition and application. Conventional methods based on experience and observational data struggle to determine the specific impact of meteorological factors on daily solar PV energy generation. In this paper, the quantitative meteorological factors combined with the Pearson coefficient significance test method and multiple regression analysis model are used to analyze the impact of meteorological factors on the daily energy generation of solar photovoltaic. By using the photovoltaic power generation data collected by Shen6 gas collection station of Shenmu Second Gas Production Plant in Shenmu County, Yulin City, Shaanxi Province in 2022, combined with multiple meteorological factors such as daily maximum temperature, minimum temperature, average temperature, precipitation and weather conditions, the correlation analysis of photovoltaic daily power generation was carried out. The maximum temperature, minimum temperature and average temperature showed a moderate positive correlation with the daily photovoltaic power generation, while the precipitation showed a weak negative correlation with the daily photovoltaic power generation, in addition, the degree of sunny and the daily photovoltaic power generation showed a very significant positive correlation. The results of this study provide a certain reference for predicting the daily photovoltaic power generation.

Aimed at the distributed wind turbine integrating energy storage planning in distribution network, an optimal planning approach considering the different benefits of distribution company and distributed generation investor is proposed. Meanwhile, in view of the excellent ability of parallel processing, an improved parallel membrane computing based on non-dominated sorting genetic algorithm II is proposed to overcome the drawbacks of traditional heuristic algorithms like prematurity and easy to be trapped into local optimization. It is confirmed in study cases that, with adopting the proposed planning approach, more practical and effective schemes of the site and capacity of distributed wind turbine and energy storage can be acquired.

The accuracy of wind and solar power prediction not only affects the assessment of "two regulations" for various wind and solar power plants, but also affects the power generation revenue of wind and solar power plants in the electricity market. Regarding the power prediction of regional wind and photovoltaic power plant clusters, the traditional method of summing up the power of individual stations has low accuracy and efficiency due to different station construction times and uneven single station forecasting accuracy. Therefore, a machine learning method is used to reasonably divide the wind power plants and photovoltaic power station clusters in the region, select representative power stations, and combine historical power data to optimize and correct the numerical prediction elements. Then, a short-term power prediction framework model based on the wind power plants and photovoltaic power station clusters is established using the BP neural network method to improve the prediction accuracy and efficiency.

In the context of "carbon neutrality" and "carbon peaking", the accuracy of carbon emission control for distributed energy stations is becoming increasingly high, and it is urgent to propose a carbon flow tracking method for distributed energy stations. On the basis of power system power flow theory, this article proposes a calculation method for carbon flow on load nodes and connecting branches based on the relationship between system power flow and carbon flow. The correctness of this method is verified by an IEEE 14 node example. The results indicate that this method can clearly identify branches with high carbon flow rates and areas with high carbon emissions, and provide reasonable emission reduction planning plans.

Accurate prediction of PV power is essential to maximize solar energy utilization, enhance the performance of PV power generation systems, and ensure the dependability of power infrastructures. In this paper, an approach based on CS-LSTM neural network is proposed for short term power forecasting. The proposed approach involves processing the data using CEEMDAN, followed by the application of the CS algorithm to determine the optimal LSTM hyperparameters. To evaluate the accuracy and dependability of the model, CEEMDAN-CS-LSTM is compared to other models. Experiments show that CEEMDAN-CS-LSTM outperforms the other models in terms of prediction accuracy and stability.

Under the dual-carbon target, the dynamic monitoring algorithm of data lacks comprehensive analysis of data, which leads to a great difference between the monitoring results and the actual situation. Therefore, the design and research of dynamic monitoring algorithm of energy big data under the dual-carbon target are proposed. Combined with the characteristics of energy big data under the goal of "double carbon", the main tasks of the dynamic monitoring algorithm of energy big data are comprehensively analyzed, and the corresponding dynamic monitoring algorithm is constructed from the perspectives of energy demand and energy distribution, and the state of energy big data is corrected and calibrated from multiple dimensions. In the test results, the error between the monitoring results of different energy data and the actual situation is always stable at a low level, which shows obvious advantages compared with the control group.

To enhance the accuracy of short-term power prediction for photovoltaic (PV) power generation and ensure stable power system operations following the grid connection of PV power plants, a combined model is proposed. This model utilises an Algorithmic Optimisation Algorithm (AOA) to optimise a Deep Extreme Learning Machine (DELM) for short-term power prediction of PV power generation. The DELM model’s hyperparameters are optimised with AOA to predict the PV data’s output power. The simulation results show that for measured data of a PV power plant in the Australian region, the AOA-DELM model has greater power prediction accuracy compared to other prediction models. This verifies the efficacy and adaptability of the model.

In the process of electric power marketization, virtual power plant (VPP) has the characteristics of flexibility, economy and sustainability, and is a very high-quality electric energy resource. In order to solve the income fluctuation problem of VPP caused by resource uncertainty, a two-stage day-ahead and intraday regulation strategy of VPP for electric energy markets is proposed. In the day-ahead dispatching model, various resources such as new energy predicted output, energy storage and gas engine are integrated, and the power grid is predicted dispatched in order to maximize the market in advance. Then, on the short time scale of the day, aiming at wind-photovoltaic power output deviation, the demand-side interactive resources are introduced to correct the market capacity deviation, aiming at maximizing the actual market income, and giving full play to the flexibility of the VPP to participate in the electric energy markets. Finally, an example is given to verify the effectiveness of the proposed method.

In this paper, a multi-objective optimal scheduling model of wind-photovoltaic storage virtual power plant with demand response is established. Under system constraints, an economical and environmentally friendly virtual power plant integrated optimization scheduling model was proposed to minimize the total cost of virtual power plant and maximize the utilization rate of wind power and photovoltaic. At the same time, this paper improves MOPSO optimization algorithm, and uses nonlinear exponential decline function to obtain the algorithm inertia, which effectively improves the algorithm convergence performance and the distribution characteristics of pareto frontier. The simulation results show that the model can guide the multi-objective optimal scheduling of virtual power plant to a certain extent.

The rapid development of the power system underscores the growing significance of substation monitoring and management. Real-time monitoring and management of diverse data at substations are crucial for ensuring the stable operation of the power system. However, traditional single-architecture configuration software falls short of meeting practical demands. In response, this research proposes a hybrid architecture-based substation data configuration software design approach, incorporating both B/S (Browser/Server) and C/S (Client/Server) architectures. This approach is aimed at enhancing data presentation, addressing the need for data sharing, and facilitating the reuse of system configuration tools. Through a series of experiments, it demonstrates exceptional performance in terms of data processing speed, system stability, remote access capability, data sharing, and system configuration tool reuse. It offers robust support for technical development and applications in the power system monitoring domain.

In response to the call for "peak carbon and carbon neutrality," this study proposes a renewable energy-to-hydrogen distributed collaborative optimization strategy with the aim of fair resource allocation among different entities in a comprehensive energy system. To achieve this, the research employs a master-slave game model where the park operator acts as the upper-level leader, and energy suppliers and load aggregators serve as followers. Subsequently, the decision model for transactions within the park's comprehensive energy system, which includes hydrogen storage, is integrated into the master-slave game framework. This integration leverages an enhanced differential evolution algorithm in combination with a second-order programming-based distributed equilibrium approach to address the problem. Finally, the study validates the effectiveness of this optimization strategy through case studies, demonstrating its efficiency in harnessing renewable resources, particularly wind and solar, and improving energy storage returns.

Electric power generation is an important part of modern society, in order to meet the growing demand for electricity and improve the reliability of the grid, the accurate prediction of electric power generation power is very important. Due to the different dimensions of the influencing factors of photovoltaic power, direct input to the prediction model may lead to poor prediction effect. Therefore, it is necessary to normalize the sample data to eliminate the influence of different dimensions on the prediction of photovoltaic power. Artificial neural network has the characteristics of elastic topology, high redundancy and nonlinear operation, and can quickly find optimal solutions, so it plays an important role. This study aims to explore a wide range of generation power prediction methods based on short term memory networks (LSTM). By analyzing historical generation power data, we build an LSTM neural network model for predicting future generation power. The model takes into account the dynamics of the time series, as well as various factors related to power generation, such as weather, load and seasonal changes. Finally, we verify the LSTM model through experiments, and the results show that the LSTM model can achieve accurate power prediction in different time ranges. The results of this study have practical implications for the power industry and power system operators to help them better plan and manage electricity production and distribution.

The integration of large-scale distributed renewable energy into the power system will bring new challenges to the relay protection of the power system, which is a key issue in the transformation of new power systems. This article analyses the impact of renewable energy on relay protection, focusing on the construction goals and current situation of the new power system, and analyses the influencing factors on protection from four aspects: power generation, transmission, distribution, and energy storage. On this basis, a typical case of on-site protection malfunction is introduced and analysed, and the protective measures for renewable energy access is delivered.

Lead-carbon battery is a kind of new capacitive lead-acid battery, which is based on the traditional lead-acid battery, using the method of adding carbon material to the negative electrode to improve the specific capacity and charge-discharge characteristics of the battery. Lead-carbon battery solves the defects of low charge-discharge rate of traditional lead-acid battery, improves the phenomenon of negative sulfate, and has the advantages of good charge-discharge performance and long battery life. This paper firstly starts from the principle and structure of lead-carbon battery, then summarizes the research progress of lead-carbon battery in recent years, and finally looks forward to the development direction and trend of lead-carbon battery in the future.

During the "14th Five-Year Plan" period, China vigorously develops clean energy. Therefore, a large number of UHV transmission projects will be constructed as the supporting programs of clean energy bases. As an important part of UHV transmission projects, the environmental-friendly, energy-saving and low-carbon construction method of converter stations has become an important research topic. This paper analyses the installation of photovoltaic in converter stations from three aspects, which are the installation position, the photovoltaic material, and the access to station power system. Meanwhile, Zhongning ±800kV converter station was illustrated as an example. As Zhongning is a district with sufficient solar resource, photovoltaic power generation can account for 20% of the station annual power consumption. Thus, this research can introduce significant economic and ecological benefits to converter stations in similar circumstances.

The Supervisory Control and Data Acquisition (SCADA) system of the energy storage system is the main target of cyber-attacks, and its failure and its interaction with the physical system are the difficulties in the reliability evaluation of the energy storage system. To this end, for different cyber-attack scenarios of information systems, the Bayesian attack graph method is used to establish the cyber-attack process model of the SCADA system, and the average failure time of the target components of the SCADA system is calculated for different attacker skill levels. A reliability evaluation method based on Monte Carlo simulation is proposed, and a reliability evaluation model of energy storage system is established considering the failure of target components of SCADA system caused by cyber-attacks and their interaction with the physical system. The normal operation availability rate and power loss value of the energy storage system under different scenarios are calculated. The results of the analyses can provide a theoretical basis for the operation and risk management of energy storage system.

Microgrid energy storage equipment usually has a variety of operating modes, such as battery energy storage equipment can achieve charge and discharge, peak cutting and valley filling and other modes, resulting in uncertain equipment life, and power is prone to fluctuations. Therefore, a self-switching method of microgrid energy storage operation mode considering power fluctuation and energy storage life is designed. Combined with Bi-LSTM network, the energy storage life prediction model of microgrid was constructed. In the case of energy storage operation constraints, the goal is to minimize power fluctuation and maximum life. Taking the microgrid operation mode from isolated island operation mode to grid-connected operation mode as an example, the pre-synchronization control is completed by adjusting the main power supply to realize the spontaneous switching of microgrid operation mode. The experimental results show that the delay of the research method is always lower than 0.3ms, and under the application of this method, the bus voltage fluctuation of the microgrid under the state of energy storage is very small, indicating that the operation stability of the microgrid is better after the spontaneous switching of the power grid operation mode is completed by using the research method.

To achieve the goal of "carbon peaking and carbon neutrality", it is urgent to carry out research on the accounting methods of carbon emissions from energy consumption. In this paper, based on the data of 1997-2020 thermal carbon emissions, per capita GDP, total population and thermal power generation of a province in China, combined with the grey correlation prediction model and BP neural network prediction model, the thermal carbon emissions of the province in the next 15 years are accurately predicted, and the time of carbon peak is determined and analyzed.In order to improve the prediction accuracy, firstly, grey correlation was used to analyze the influence of various factors on the emission of pyroelectric carbon, and then grey prediction model was used to predict the change trend of factors strongly correlated with the emission of pyroelectric carbon in the next 15 years. The obtained results were taken as the input layer data of BP neural network, and the trend extrapolation method of BP neural network model was used to predict the emission of pyroelectric carbon.It is estimated that carbon emissions from thermal power generation will increase first and then decrease in the next 15 years.

The uncertainty impacts of the distributed generations are considered to establish a configuration planning method for the distribution systems. Based on the fuzzy theory, the active and reactive power models of wind-driven generators and photovoltaic cells are formulated. Moreover, the minimum value of the annual average operating cost and the fuzzy expectation value are formulated as objective function, and the nodal voltage, network topology constraints and tidal current balance are formulated as constraints in the planning model. In accordance with fuzzy expectation value theory and boundary value-based distribution network, the fuzzy power flow calculation method is used to improve the integer coding method by genetic algorithm. Case study shows the feasibility and practicability of the proposed method.

Accurate short-term photovoltaic (PV) power generation prediction is helpful for the allocation and management of power grid resources, and is of great significance for the stable operation and economic benefits of the power system. The historical data of PV power generation systems has characteristics such as volatility, randomness, time series, and certain periodicity. Traditional prediction methods cannot effectively mine and utilize the deep-seated features of the data, so they can no longer meet the current demand for high-precision prediction. This article proposes a WT-LSTM PV short-term power generation prediction method. Firstly, the historical time series data of the PV electric field is decomposed into high-frequency and low-frequency components through wavelet transform to improve the feature expression ability of the data. Then, a Long Short Term Memory (LSTM) network is used to predict each component, addressing the negative impact of data volatility and randomness on the prediction results caused by a single LSTM prediction algorithm. Finally, the final PV power generation prediction result is reconstructed by inverse wavelet transform.

In the context of power system planning with a high penetration of renewable energy, this study emphasizes the pivotal role of coordinated optimization of generation-load-storage flexibility resources. The primary objectives are to enhance system flexibility, foster renewable energy integration, and achieve the "dual carbon" goals. Guided by an analysis of flexibility supply and demand, we propose a multi-time scale coordinated planning approach that integrates planning decisions and operational simulations while factoring in the benefits of flexibility resources and carbon trading. To streamline the complexity and ensure holistic planning, a decomposition coordination method paired with multi-stage iterative optimization is employed. Empirical results affirm the efficacy of the proposed approach in elevating resource utilization efficiency. Concurrently, it caters to the economic and environmental aspirations of the system, bolstering the integration capacity of renewable energy.

Due to the risk of transmitting status data of lithium-ion battery energy storage power stations, it is difficult to achieve ideal safety monitoring and warning effects. Therefore, a wireless sensor network-based active safety monitoring and warning system for lithium-ion battery energy storage power stations is proposed. Set the STC12C5A60S2 chip, DS18B20 electrical acquisition device, and ATT7022 electrical data acquisition device as system hardware. A transmission mechanism based on the SimpliciTI network in wireless transmission networks has been constructed to achieve real-time monitoring of the status of lithium-ion battery energy storage power stations, and accurate warning of abnormal states is achieved through equivalent circuit models. In the test results, the monitoring error of SOC sorting power distribution during the discharge stage of the lithium-ion battery energy storage power station is the smallest, with the most stable within ± 1.5MW. The deviation of the warning results for voltage fluctuation amplitude is stable within 0.05%.

The Rural Hybrid Renewable Energy System (RHRES) holds great significance as a prospective energy system development. Ensuring its economic viability and low-carbon operation is crucial in order to fully harness its potential. Consequently, this research paper presents a multi-party cooperation operation model for RHRES, based on the Nash bargaining theory. To effectively differentiate between the various parties involved in carbon trading and guarantee the system's low-carbon operation, a ladder-type multi-party carbon trading model is formulated. The proposed RHRES cooperation operation model, grounded in Nash bargaining, is approached in two steps: determining the transaction volume between parties and resolving the transaction price through the concept of equivalent transformation. To address concerns regarding party privacy, the alternating direction multiplier method is employed for solution. The numerical findings demonstrate that the suggested cooperation operation model substantially reduces RHRES operating costs and carbon emissions, while enhancing the utilization of wind and solar power. Consequently, this model holds significant practical and promotional value.

The existing substation anti-misoperation system mainly uses logic locking to prevent errors, which has problems such as complex operating rules and susceptibility to human interference. Therefore, this paper proposes a substation misoperation prevention method based on improved convolutional neural network. By extracting the characteristics of substation wiring diagram, adjacency matrix is constructed to form a network topology model. On the basis of traditional convolutional neural network algorithms, SE channel attention has been added, and an improved convolutional neural network has been used to classify topological structure feature vectors, achieving misoperation type analysis. This paper directly realizes intelligent misoperation prevention in substations through deep learning, avoiding human factors and improving the accuracy and efficiency of misoperation prevention. This method can provide strong support for the safety production management of the power system and is of great significance for improving the safety and stability of power supply.

With the massive penetration of clean, distributed energy sources, user-side energy storage presents an increasingly complex picture, but its potential to participate in the shared energy storage market has received less attention. To this end, this paper proposes a peer-to-peer trading (i.e., direct trading of electricity between users) strategy for electricity that takes into account the energy storage at the Exchange Electricity Station (EES) and the in-situ sharing model. Firstly, a shared energy storage system based on peer-to-peer trading is constructed by combining the energy storage demand on the user's side and the operation optimization demand of Electric Vehicle Exchange Electricity Station (EVEES), with the joint participation of EVEES and users. Based on this, two parallel two-tier P2P transaction frameworks are established for electric vehicle user (EVU)-EESs and community-EESs, it improves the overall operational efficiency of the energy storage business while safeguarding user privacy protection claims. Secondly, in order to solve the problem of the difficulty of achieving the overall optimization of the scheduling framework with the participation of multiple actors (i.e., EESs, users and communities), Alternating Direction Method of Multipliers (ADMM) was introduced to design a two-layer optimized energy sharing model for community-EES-EVU. Simulation results show that the strategy proposed in this paper can guarantee the security of privacy and the economy of energy use of each subject in the Shared Energy Storage System (SESS), and also provide power support for the external grid. The paper provides a unique new perspective on the shared energy storage model.

Integrated energy microgrids, as an important means of realizing coupled and complementary energy sources, are of great significance in promoting the transformation of the energy structure and accelerating the realization of the dual-carbon goal. In the same region, the energy interaction of multi-microgrids can realize energy mutualization, improve the level of new energy consumption and reduce carbon emissions. Based on this, this paper proposes a regional multi-microgrid distributed dispatch model. A single microgrid scheduling model based on carbon trading mechanism is established. On this basis, a multi-microgrid cooperative operation model is proposed considering the energy sharing of multi-microgrids. Finally, a consensus-based ADMM algorithm is used for distributed scheduling of multiple microgrids. The arithmetic example verifies the effectiveness of the proposed model.

Smart integrated energy system is a system that combines multiple energy sources, such as coal, oil, natural gas, electricity, and heat, through the use of cutting edge physical and information technologies, as well as innovative management models, in order to facilitate coordinated planning and efficient operation between energy subsystems, as well as collaborative management, interactive response, and mutual complementation. The platform can be constructed by using energy big data, 3D digital twin, multi-energy collaborative optimization and scheduling and intelligent operation. The platform furnishes data base and decision-making assistance for Integrated Energy services, al-lowing for comprehensive management applications of energy monitoring, operation scheduling, maintenance, charge management and customer service management, supplying energy production, transmission, consumption, and control throughout the process, and achieving efficient production, flexible control, and intelligent utilization of various forms of energy. The platform has the capability to create a comprehensive energy utilization system encompassing sources, grids, loads, and storage, thereby establishing the technical groundwork for advancing the comprehensive and extensive utilization of renewable energy.

In the power market, accurate prediction of regional photovoltaic power generation has an important impact on power trading and operational decision-making. It can make reasonable arrangements for energy dispatch, energy storage management, power grid planning, and other aspects, improving the efficiency and reliability of energy systems. To this end, a short-term and medium-term prediction method for regional photovoltaic power generation based on fuzzy support vector machine is proposed. Detect abnormal data, replace abnormal data, and normalize the collected regional photovoltaic power generation data. Based on fuzzy support vector machine, calculate the short-term membership function of the pre processed the data. On this basis, the regression function of the optimal classification function of the fuzzy support vector machine model is established to achieve short-term and medium-term prediction. The experimental results show that the proposed method has high accuracy and can effectively shorten the time for predicting regional photovoltaic power generation in the medium and short term.

This paper establishes a three-layer centralized control architecture for transmission grid, distribution grid and local equipment and a multi-layer coordinated distributed control architecture for distribution system; exploring the "source-network-load-storage" comprehensive coordination and interaction mechanism of power distribution system; researching on the operation of equipment at the source, network, load and storage ends of the distribution network, and construct a coordinated centralized optimization model of "source-network-load-storage" and a zoned distributed optimization model based on the data-driven distribution robustness method, the CCG algorithm and ADMM algorithm are used to solve the problem effectively, and the method of this paper can realize the improvement of "source-network-load-storage" regulation ability.

This study addresses the challenge of accurately predicting photovoltaic (PV) power output across diverse weather conditions. To tackle this issue, we propose a novel combined prediction model that integrates Convolutional Neural Network (CNN), Bidirectional Gated Recurrent Unit (BIGRU), and Attention mechanisms. First, the Copula method is applied to identify the crucial meteorological features that impact photovoltaic output. Subsequently, K-means clustering is employed to group daily photovoltaic power generation scenarios. Following this, the CNN-BIGRU-ATTENTION model is established and applied for power prediction across various clustering scenarios. Finally, we conduct a case study utilizing photovoltaic power generation data from Australia. The results unequivocally demonstrate a significant enhancement in prediction accuracy achieved by the proposed method.

Renewable energy is a green and low-carbon energy source, which is of great significance for improving energy structure, protecting the ecological environment, addressing climate change, and achieving sustainable development. This article investigates the application of ensemble learning models in the prediction of renewable energy increment. Traditional prediction methods have problems due to their own limitations, such as imbalanced data and rough feature selection, resulting in low model prediction accuracy. This article proposes an ensemble learning based energy increment prediction model (ERLSK model). The model includes four traditional machine learning classification models: KNN, random forest, SVM, and logistic regression. The best hyperparameter of each model are obtained by cross validation and grid search. By using different feature selection and model training methods, different prediction results are obtained, and these results are weighted and fused to obtain the final classification result. The experimental results indicate that the model is effective and feasible.

Cloud energy storage operators (CESO) aggregates distributed energy storage among users, which can greatly improve the utilization rate of energy storage. In order to make cloud energy storage users better carry out power trading, a cloud energy storage system architecture and operation service model are proposed, and on this basis, an internal price model based on the power supply and demand ratio is designed, and the utility cost model of user participation in demand response is proposed while considering the economy, and the optimal pricing strategy is obtained based on the Stackelberg game combined with Particle swarm optimization (PSO) algorithm. Finally, the results show that the proposed method is reasonable and effective, which can effectively promote energy consumption, achieve a balance of benefits for both parties, and improve the enthusiasm of users to participate.

In advancing renewable energy, the efficiency of photovoltaic grid-tied inverters has become crucial. This research proposes a Solar PV inverter integrated with control strategies and modulation techniques, aimed at maximizing power output and enhancing dynamic performance. Firstly, precise guidance in perturbation direction under varying conditions is proposed by employing the Incremental Conductance (INC) method for MPPT. Meanwhile, the efficiency of the boost converter with Pulse Width Modulation (PWM) control is highlighted. Secondly, Space Vector Pulse Width Modulation (SVPWM) is used to demonstrate its potential in maximizing output voltage amplitude while simultaneously minimizing voltage conversion losses. Thirdly, the synchronization with grid voltage frequency is confirmed by implementing the Phase-Locked Loop (PLL) and D-Q Transformation in system control. Finally, the significant role of neutral voltage balancing in the T-type inverter is emphasized, illustrating the system's robustness and precision. The simulation results show the distinct waveform of SVPWM's reference voltage, coupled with a 1.6% THD.

At present, the layout model of monitoring cameras in non-direct-view areas is generally target-oriented, and the coverage of monitoring cameras is limited, which leads to the reduction of the recognition extension ratio of the sensing range of monitoring cameras. Therefore, the design and research on the layout method of monitoring cameras in non-direct-view areas of substations are proposed. According to the current test requirements, the farthest identification point of the camera is set first, and the effective sensing area of the non-direct-view area is calculated. Based on this, the limitation of the surveillance camera coverage is broken by adopting a multi-target method, and an adaptive multi-target surveillance camera layout model is designed, and the camera layout is realized by adopting a "flat package vertical" correction processing method. The final test results show that: after three stages of auxiliary testing, the extension ratio of the detection range of the monitoring camera can reach above 6, which shows that the monitoring camera layout method in the non-direct-view area of the substation is more efficient, specific, targeted and has practical application value with the help of intelligent technology, predictive auxiliary structure and virtual interference processing technology.

The conventional three-dimensional route planning method for unmanned aerial vehicle (UAV) in substation inspection aims at full track coverage, and the distance to avoid obstacles is long, which affects the efficiency of substation inspection. Therefore, a blind spot-free three-dimensional trajectory planning method for unmanned aerial vehicle (UAV) in substation inspection based on digital twinning is designed. Determine the three-dimensional route cost of unmanned aerial vehicle (UAV) in substation inspection, classify the danger level of obstacles in the route, and judge whether the planned route meets the requirements of substation inspection. Based on digital twinning, a three-dimensional trajectory planning model of unmanned aerial vehicle (UAV) for substation inspection is constructed. According to the constraints of UAV, the location of substation inspection track points is optimized to avoid obstacles and find the optimal inspection path. The three-dimensional track escape force of patrol UAV is added to help UAV escape from the local minimum state, make UAV return to the track planning state, and eliminate the influence of blind spot of UAV track on substation patrol. The comparative experiments show that this method has higher planning value and can be applied to real life.

In the case of the immature auxiliary service market, there is no flexible auxiliary service rules designed to comprehensively coordinate the power required by the grid and the charge and discharge strategy of ES. a two-layer optimization model considering Energy Storage (ES) auxiliary services is proposed. The optimization objectives are the lowest operating cost of the power grid and the lowest cost of the solar power station. In order to realize the optimal allocation of ES capacity, a dynamic pricing mechanism is proposed. It is passed from the upper level to the lower level. Through the analysis of examples, the optimal results can not only allow the energy storage to fully participate in the auxiliary service market, but also maximize the interests of the upper and lower levels.

Current research on the optimal allocation of Hybrid Energy Storage System (HESS) capacity lacks consideration of both consumption and user demand. Most of the available literature only considers unilateral demand. Therefore, this paper proposes a hybrid energy capacity allocation method that considers both wind power and user two-way demand. First, a flexible load model is constructed from the user-side demand response characteristics. Second, a HESS composed of batteries and super capacitors is established, and a HESS charging and discharging strategy is proposed. Again, the capacity optimization model is constructed with the objective function of the lowest cost, the smallest wind abandonment rate, and the highest electricity satisfaction of the HESS. Finally, in order to verify the effectiveness of the model, an arithmetic example analysis is carried out.

With the increasing degree of grid integration of renewable energy sources (RES), their power uncertainty has a great impact on the safety and economy of power system operation. Energy storage can effectively mitigate the fluctuations caused by renewable energy uncertainty due to its flexible charging and discharging characteristics. In this paper, a hybrid energy storage system (HESS) optimization and regulation scheme that considers the uncertainty and correlation of wind power and photovoltaic (PV) is proposed to economically and efficiently achieve the smoothing of fluctuations in renewable energy output. Typical scenarios of wind power and PV are firstly generated using kernel density estimation and copula functions, and then the frequency components of each typical scenario are extracted based on empirical mode decomposition (EMD). Finally, a stochastic optimization model of HESS is constructed to obtain the grid-connected power of renewable energy and the regulation plan of each energy storage device.

Due to the influence of high-rise buildings, leaves and clouds, shadows will appear on photovoltaic solar panels, resulting in multi-peak power output characteristic curves. At the same time, the sudden change of the illumination environment will cause the Maximum Power Point Tracking (MPPT) process to fall into the local optimum, the tracking speed is slow, and a large oscillation amplitude will be generated during the MPPT process, resulting in serious efficiency loss. Therefore, this paper proposes an Improved Particle Swarm Optimization (IPSO) algorithm that combines dynamic inertia weight strategy, adaptive learning factor and tendency operation to improve the tracking speed and accuracy of the algorithm and sets effective convergence and restart conditions in the algorithm. Finally, the MPPT control system of photovoltaic power generation under IPSO and PSO (Particle Swarm Optimization, PSO) algorithms is simulated in the absence of abrupt multi-peak and abrupt multi-peak. The simulation results show that the IPSO algorithm has better tracking effect, faster convergence speed and smaller tracking oscillation amplitude in the MPPT application of photovoltaic power generation.

When large-scale renewable energy is connected to the power grid, its fluctuation will affect the power balance and frequency stability of the system. By connecting the Energy Storage System (ESS) on the DC side of the DC transmission system, the stability of new energy access in the power grid can be effectively enhanced. This paper first analyzes the working principle and power instruction calculation method of the DC direct-mounted energy storage system (DCDM-ESS), which participates in stabilizing the output fluctuation of new energy and supporting the power grid with peak load and frequency regulation, then explains the priority allocation principle of DCDM-ESS participating in peak load and frequency regulation under transient/steady operation of the system, proposes the multi-objective coordinated control strategy of DCDM-ESS, and summarizes the energy management method of the system. Finally, the above strategies are verified by PSCAD/EMTDC offline simulation software.

With the development of renewable energy in China, the optimal configuration of distributed energy resources has gained paramount significance. This paper comprehensively considers factors such as regional constraints, disaster impact, and system reliability to optimize the integration of Distributed Generation (DG) into the grid. Experimental results indicate that reasonable distributed energy resource configuration can enhance the grid's resilience during natural disasters and its post-disaster recovery capability. Additionally, increasing the penetration rate of distributed energy resources can bring economic benefits to the grid. This will provide valuable references for the planning and operation of actual distribution networks, aiming to achieve a more efficient and reliable power system.

Photovoltaic power generation has the characteristics of large randomness, poor stability, and great influence by meteorological conditions. A large number of grid-connected photovoltaic power system will bring random impact to the power grid, which may affect the stablility of the power grid. In this paper, at first we discuss the direct prediction method and indirect prediction method commonly used in current power prediction. Then the prediction methods including Vector Autoregressive (VAR) model and Light Gradient Bosting Machine(LGBM) model are applied to photovoltaic power prediction, the prediction simulation results of the two models are evaluated, which provides a reference for the research of photovoltaic power prediction method.

Hydrogen energy has important carbon neutral potential and long-term storage capacity, so hydrogen storage can be used as a medium - and long-term storage device in the future power grid to balance the uncertainty brought by new energy. Therefore, a hydrogen storage model is proposed for power grid planning, and constructs a power grid planning model containing hydrogen energy storage. In this paper, the sparrow search method is used to solve the model, and the method is improved to improve the local search and global search ability of the algorithm. The effectiveness of the model and method is verified by a numerical example.

The increasing global energy shortage necessitates a swift transition to renewable energy sources, with wind power emerging as a cost-effective and environmentally friendly option. However, the non-stationary, random, and intermittent nature of wind poses challenges for power grid management, leading to inefficiency and energy supply-demand imbalances. To address these issues, various computational methods have been developed, including multi-modal machine learning methods. However, existing ANN or LSTM-based methods fail to learn complex patterns from heterogeneous features efficiently, especially when NWP forecast map data is considered. Thus, this paper presents a multi-modal transformer-based deep learning approach for wind power prediction, utilizing transformer model and vision transformer model to handle both historical turbine-level time-series data and NWP forecast map data. Extensive experiments demonstrate the model's ability to handle heterogeneous features effectively, outperforming benchmark algorithms in wind power prediction.

This paper demonstrates that energy storage plays an important role in the security and stability of the distribution network, provides an inner and outer loop control strategy for energy storage converters, and lists the reference value formulas for active and reactive currents when the energy storage system carries out low-voltage ride-through. Finally, the distribution network model is established using Simulink in Matlab, and the storage model containing the control strategy of this paper is inserted into the constructed distribution network model, with a single ground fault set up, comparing the voltage at the insertion point with and without storage, to analyze the role of energy storage in the distribution network.

Bolts and anchor cables are important components of roadway support and have achieved remarkable results. Reasonable arrangement of bolts and anchor cables to create a synergistic effect can effectively exert the physical and mechanical properties of the supporting components and control the deformation of the surrounding rock of the roadway. Taking the mining roadway of the 16407 working face of Guandi Mine as the research object, FLAC^3D numerical simulation software was used to establish a model. The stress evolution law and deformation and failure characteristics of the surrounding rock after roadway excavation were studied, and a synergetic support arrangement of bolts and anchor cables arranged in the same row was proposed. Support scheme, and compare the deformation of surrounding rock between the original support scheme and the optimized scheme. The results show that the synergetic arrangement of anchor bolts and cables in the same row can maximize the physical and mechanical properties of bolts and anchor cables, effectively reduce the deformation of the surrounding rock of the roadway, and enhance the stability of the surrounding rock of the roadway.

The current substation has a large number of relatively isolated subsystems, and the data interaction is difficult, which is not conducive to the rapid development of advanced applications such as data panorama display, business linkage and trend prediction. The construction cost will be considered again for new applications developed, and the value of data cannot be fully explored. This article collects all-element data from each power zone in the substation, classifies different types of data, constructs a data resource pool, and develops a unified data calling interface on this basis to build a standardized interface data platform for the substation, providing strong support for the efficient reuse of whole-station data and the rapid deployment of application systems.

This study addresses the challenges of short and long-term power fluctuations in grid planning by establishing a composite energy storage model that considers both hydrogen energy storage and electrochemical energy storage. The paper presents a grid planning model incorporating load storage, facilitating peak shaving, and supporting operations across various time scales. The proposed model is solved using an enhanced Firefly algorithm, and case studies demonstrate the method's effectiveness in reducing overall costs and enhancing operational performance.

With the frequent extreme rainstorm and flood events at present, it is necessary to carry out flood control transformation of substation in operation to avoid its damage by flood. By identifying and evaluating risks and taking corresponding pre-control measures, the risk of substation suffering from flooding can be reduced to the greatest extent. This paper presents a method for predicting substation flood control risk. Firstly, the finite element method (FEM) is used to preprocess the historical data of substation flood control. Secondly, the improved neural network model is constructed, and part of the data is used to complete the model training. Finally, the actual substation data verification shows that the accuracy of the model is higher than that of the traditional neural network model, which is a simple and effective method.

A single prediction model is not a sufficient solution to the problems arising from the stochasticity and high volatility of PV power output. To improve prediction accuracy and mitigate the negative impact on the grid, this study proposes a short-term PV power prediction technique based on SSA-DBN. Firstly, we performed Pearson's method correlation analysis and feature screening for each meteorological feature quantity, then we used SSA to optimise the parameter settings of the DBN, and selected the optimal hyperparameters to give full play to the model performance, so as to overcome the limitations of the traditional DBN model, and ultimately compared it with a single Long-Short-Term Memory (LSTM) and Deep Belief Network (DBN) model. The experimental findings demonstrate that the methodology proposed in this paper exhibits superior predictive precision.

In order to master the output characteristics of wind-photovoltaic power generation, improve the utilization efficiency of wind and solar resources and enhance the support capacity of wind-photovoltaic power as the peak power. This paper puts forward three evaluation indexes: fluctuation characteristics, peak shaving characteristics and guaranteed output, and divides typical output scenarios such as annual, quarterly, daytime, and night peak load periods. Based on the unified regulation data of wind power and photovoltaic power generation in an eastern coastal province of China, the power generation characteristics of these two modes of power generation are analyzed. Combined with the provincial load data and based on the output coefficient, the proposed method quantifies the output capacity of wind, photovoltaic and wind photovoltaic joint power under different load levels. It provides a reference for further research on wind-photovoltaic power consumption assessment, access system, and future grid planning.

The industrial control systems of new energy power plants are becoming increasingly complex and diversified, and there are also protective isolations between various industrial control systems, resulting in insufficient overall network security monitoring and defense capabilities of new energy power plants. In order to solve this problem, this paper studies a network security situational awareness solution for new energy industrial control systems. This solution is an industrial control security protection method for new energy power plants based on probabilistic attack graphs. First, knowledge graph technology is used to semantically associate security metadata such as devices, vulnerabilities, and topologies, and a cross-domain attack graph generation algorithm is used to automatically generate a cross-domain attack graph; then, based on the cross-domain attack graph, the basic attributes of vulnerabilities and The time-varying characteristics of threats are incorporated into risk propagation probability calculations, thereby enabling quantitative analysis of cross-domain dynamic security risks in new energy power plant industrial control systems. Experimental results show that this method realizes automated cross-domain dynamic security risk analysis of new energy power plant industrial control systems, and effectively improves the overall network security protection capabilities of new energy industrial control systems.

With the rapid development of water conservancy and hydropower in our country, the construction of dykes has also developed rapidly. However, since some DAMS have been built for a long time and have been damaged by river water immersion, rain corrosion, natural weathering, and other special hydrological environments for long time, dam leakage or dam break will always occur in every flood season. The flow field method is to detect the flow direction of water according to the distribution of current field potential. Emission electrodes are placed on both sides of the DAMS. Due to the existence of leakage, the resistivity of the leakage site is lower than that of the non-leakage site, and the leakage site will become the main channel through which the current flows. The simulation results of LABVIEW demonstrate the feasibility of the flow field method in dam leakage.

Smart energy services are an important support platform for the development of a comprehensive energy business. Based on CPS, they connect with energy consumption scenarios such as communities, commercial buildings, industrial enterprises, and parks, achieve massive data acquisition of energy equipment, energy conservation, and consumption reduction, and smart regulation of flexible loads. They are also built on cloud platforms and microservice architectures. Distributed task scheduling technology is widely used in massive data acquisition, data archiving, data supplementary acquisition, data quality, data calculation, energy consumption analysis, energy efficiency analysis, energy efficiency diagnosis, monitoring alarm, instruction encryption, and issuance, energy efficiency report generation and push and many other scenarios for processing. There are various shortcomings in the existing distributed task scheduling under the microservice architecture mode. In order to fully leverage the important role of distributed task scheduling in smart energy services based on microservice architecture, this article analyzes the characteristics of microservice architecture and the shortcomings of existing distributed task scheduling and applies the design concepts of distributed application coordination services, balanced sharding, and microservice architecture to study a distributed task scheduling oriented towards microservice architecture, effectively supporting the widespread application of smart energy in distributed task scheduling.

The energy flow relationship of the heat exchange process in No. 5 United Station of Northwest Oilfield is complex and there is energy waste. The original heat exchange network is analyzed by using the problem table method of pinch analysis technology to find out the unreasonable points and optimize the transformation in combination with pinch analysis. After optimization, 7 heat exchangers are added. The minimum utility heating load is 924.87 kW, the minimum utility cooling load is 101.70 kW, the maximum energy-saving potential can reach 1134.3 kW, and the energy-saving potential can reach 55.08%. This optimization has excellent energy-saving effects.

The multiple loads of the integrated energy system have the characteristics of complex coupling, strong volatility, and strong randomness, and accurate prediction is the foundation and guarantee for optimizing the scheduling of the integrated energy system. An integrated energy system scheduling method based on SSA-BI-GRU multivariate load forecasting is proposed. Firstly, in order to explore the relationships between data more fully, the SSA-BI-GRU algorithm is proposed. Random forest regression model and cross-telecommunication validation are introduced into the data processing module. Then, we construct an integrated energy system optimization scheduling model to incorporate system reliability into the objective function. Finally, the load prediction results of the SSA-BI-GRU model are used as input to solve the IES scheduling problem using the improved MOGWO algorithm. The results show that SSA-BI-GRU improves the accuracy and speed of load forecasting, and the established multi-energy flow coupling optimization scheduling model achieves the economic, efficient, and reliable operation of IES.

The integration of building energy-saving measures during early architectural design and the energy-efficient retrofitting of existing buildings holds paramount importance in achieving carbon neutrality. This study focuses on five representative climate zones in China to assess the sensitivity of building energy consumption to ten key external envelope parameters. Leveraging two research prototypes from 18 high-rise mixed-use office buildings in the hot-summer and warm-winter climate zone (Hong Kong), the DeST platform is utilized to simulate the impact of various design parameters on the annual building load. Employing the Sobol method for quantitative analysis, the study examines the contribution of each parameter to building energy consumption. Findings reveal distinct sensitivities of building envelope parameters concerning heating load, cooling load, and total load indicators, contingent upon diverse climate regions and building types. Under the total load indicator, the heat transfer coefficients of glass and roof, along with the window-to-wall ratio on the west and south facades, emerge as the most sensitive design parameters, while the solar radiation absorptance of walls and roofs show lower sensitivity. This research offers valuable insights for energy-efficient design and building renovations, paving the way for a more sustainable and environmentally conscious future.

In this paper, the process of confined combustion of a gas mixture with a molar ratio (4:1) of hydrogen to oxygen is simulated and analyzed. The formation of deflagration to detonation transition is analyzed in it, and the process under other different operating conditions is also simulated. The maximum temperature and the maximum pressure reached during the combustion process were monitored and comparatively analyzed to demonstrate that the hydroxide-oxygen molar ratio is an important influencing factor in the deflagration to detonation transition formation of hydroxide-oxygen gas mixtures. The results indicate that increasing the hydroxide molar ratio is beneficial to inhibit the generation of deflagration to detonation transition, and no instances of deflagration to detonation transition occur when the hydroxide molar ratio is about 8:1. This study provides a theoretical basis for the safe utilization of hydrogen energy.

With the application of multi-stage hydraulic fracturing technology in horizontal wells of shale gas, the number of perforating clusters in each section has gradually increased. How to use temporary plugging and diverting technology to improve the opening efficiency of each perforation cluster is crucial to fracturing effect. This paper analyzes the temporary plugging and diverting effect in multi-cluster fracturing by combination of geological characteristics of shallow shale gas in Taiyang structure and perforation hole size inversion from ultrasonic imaging logging data. The analysis result shows that low formation stress is conducive to opening efficiency of perforation cluster. If the density of the temporary plugging balls is large, it is easy to appear unstable plug and sometime fall off the perforation holes. And it is more inclined to plug the holes at the lower perforating section. This aggravates the appearance of non-uniform fracturing and "super hole". The four-parameter method of treating pressure increase, overlaying positive pressure difference, fracture indication (or pressure drop from abrasion) and stop pump pressure increase is proposed. It can provide a more comprehensive and accurate basis for the analysis and evaluation of temporary plugging effect. It is suggested that "variable density temporary plugging balls + temporary plugging agent" or line-connected temporary plugging balls could further improve the temporary plugging effect. This paper have guiding significance for optimization of multi-cluster temporary plugging fracturing scheme in horizontal wells.

As the global community shifts from fossil fuels, the demand for efficient electric vehicles (EVs) intensifies. Among the EVs, Battery Electric Vehicles (BEVs) predominantly powered by lithium-ion batteries (LIBs) have marked their prominence due to their high efficiency. This paper aims to offer a thorough analysis of the several lithium-ion battery types used in electric vehicles, emphasizing their performance metrics, safety protocols, lifespan, and overall health. The study underscores the evolution of LIBs, comparing early adaptations to recent advancements such as lithium-sulfur battery, lithium iron phosphate battery, lithium-air batteries, and ternary lithium batteries. Despite individual research on these batteries, a holistic comparison has remained scarce. The presented evaluations consider key parameters, including energy density, safety implications, and environmental impact. Furthermore, this study introduces innovative health assessment and safety detection models for these batteries. Targeted at a diverse audience from automobile manufacturers to policymakers, this paper seeks to bridge knowledge gaps, offering a robust reference guide for using lithium batteries in new energy automotive.

The oilfield adheres to the principle of "valuing new energy as much as oil and gas production", and firmly explores a new path of low-carbon transformation in the oilfield based on wind and solar energy resources and supported by CCUS. At the same time, the oilfield is also facing increasing uncertainty in key development indicators such as energy exploitable resources, new displacement methods and economic risks. The influencing factors of these development indicators include the diversity of development form. The poor resource quality, the gradual change and difference of development fields, and frequent fluctuations in international oil prices. This article studies and analyzes various influencing factors of development and geology in oilfield development indicators, and applies operational research optimization theory and methods to establish a strategic optimization mathematical model and solution method for new energy and oilfield production planning in the oilfield development process. It explores the medium and long-term multi-objective development planning and strategic planning of the oilfield. At the same time, in order to improve the decision-making level of oilfield development and planning, this article explores new methods for the practical application of mathematical optimization theory.

In this paper, the geochemical characteristics of Yan9 crude oil from west Jingbian Oil Production Plant were analyzed by means of crude oil assay, gas chromatography and gas chromatography-mass spectrometry. The physical properties, group components, saturated hydrocarbon fractions and biomarkers of Yan9 crude oil were systematically studied. The results show that the Yan9 crude oil of Yan 'an Formation has the characteristics of medium-low density, low viscosity, medium-low freezing point, high saturated hydrocarbon content, high saturation aromatic ratio, low aromatics, non-hydrocarbon and asphaltene content.

The electric bag filter of a unit is retrofited, by means of numerical simulation, the design of the inlet guide vane and the internal stepped structure of the filter should be optimized to improve the internal flow field of the filter greatly, the flow deviation can be controlled less than ± 5% , and the rising speed between the filter bags is less than 2.5 m/s, so the resistance of the system can be reduced and the efficiency of the dust removal system can be improved.

The online monitoring devices for dissolved gases in insulating oil play a significant role in monitoring the operating status and pre-diagnosis of latent faults in oil-filled electrical equipment such as transformers and reactors. To quickly evaluate the reliability and long-term stability of products from different manufacturers with different detection principles and process designs, and to effectively utilize the devices’ online monitoring capabilities, this article proposes a method for accelerated degradation testing of such equipment based on comprehensive research and analysis. Additionally, a corresponding on-site reliability testing platform has been established using a retired transformer at a certain substation. The method and platform proposed in this article can be used to conduct fully automatic short-cycle continuous operation (time compression), intensive stress event impact (event compression), and other accelerated degradation tests on online monitoring devices for dissolved gases in insulating oil, automatically generating various reliability indicators using statistical methods.

Gas extraction wellbore and surface pipelines are prone to wall thinning, puncture leakage, and fracture under the erosion of sandy extraction fluid, which seriously affects the safety of gas extraction. In this paper, using the discrete numerical method of particle-containing multiphase flow, we establish an erosion damage prediction model of a gas extraction pipeline and calculate the erosion rate of pipeline wall under different production rates for the oil pipeline at the wellhead of gas extraction wells, and the pipeline and elbow on the surface, to get the amount of damage. The calculation results reflect the oil pipeline and ground pipeline wall with the thinning amount, the local bending wall damage rate, and the formation of thinning distribution values. The results can provide a reference for controlling the gas extraction flow rate of gas wells and evaluating pipeline conditions.

The pressurizer safety valve is a nuclear primary over pressure protection device in the reactor coolant system, and is one of the important equipment to ensure the safe operation of nuclear power plants. This article analyzes the functional principles, structural components, and other key technologies of spring type pressurizer safety valves and pilot type pressurizer safety valves widely used in nuclear power plants, and deeply analyzes the surge mechanism of spring type pressurizer safety valves, as well as the cold and hot state schemes of pilot type pressurizer safety valves, The relevant conclusions can be effectively used for the research and design analysis of pressure regulator safety valves, providing important references for the selection and design, production research and development, and design optimization of pilot operated pressure regulator safety valves in nuclear power plants.

With the proposal of achieving the "3060" targets in China, as well as the demand to advance the construction of a newtype power system, a profound analysis of the driving factors behind carbon emissions in the power industry holds significant strategic importance for China. This study comprehensively employs the LMDI to investigate the impact of various factors, including carbon intensity, thermal power fuel consumption structure, thermal power generation efficiency, energy structure, power dispatch, transmission and distribution losses, electricity consumption-economic intensity, economic development, and population density, on carbon emissions in China's power industry and 30 provincial regions from 2005 to 2020. The results reveal the following findings: (1) Economic development level emerges as the primary driving force behind carbon emissions growth, followed by population density. Thermal power fuel consumption structure, thermal power generation efficiency, and electricity consumption-economic intensity play crucial roles in curbing carbon emissions growth. (2) Energy structure, power dispatch, and transmission and distribution losses contribute to restraining carbon emissions growth. (3) Population density acts as a suppressor of carbon emissions growth in provinces with continuous outflow of population. In contrast, electricity consumption-economic intensity and thermal power generation efficiency act as promoters of carbon emissions growth in provinces with dominant heavy industries. The influence of power dispatch and transmission and distribution losses on carbon emissions varies significantly among different provinces. Finally, this study provides policy recommendations for carbon emission reduction efforts. This study contributes to the formulation of more effective emission reduction strategies by the government and relevant authorities.

Configuration of energy storage is conducive to the advantages of new energy resource-rich areas, to achieve large-scale consumption of clean energy, hydrogen energy storage is a new type of energy storage in the power system, with clean and non-polluting, large storage capacity, high energy density and other advantages. Adopting the hybrid energy storage method of battery combined with hydrogen energy storage can ensure the short-term demand of the load as well as longterm storage of a large amount of backup energy. In the planning and design process, determining the topology of the system, reasonably analysing the operation of each unit and establishing relevant mathematical models are the basis for the research on the optimal allocation of system capacity. In this paper, the structure and model of wind-hydrogen storage system are studied, the topology diagrams of off-grid type and grid-connected type are given, the principles of wind turbine, photovoltaic battery, storage battery and hydrogen storage are introduced, and the mathematical model is constructed, which lays the foundation for further research on the optimal configuration of the system.

There are numerous energy equipment and systems within networked intelligent buildings and parks, involving multiple energy types and energy interactions and coupling relationships between multiple buildings. This has led to an increase in the complexity and uncertainty of the integrated energy system between networked intelligent buildings and parks, making coordinated planning more difficult. To this end, a meta heuristic algorithm based coordinated planning method for networked intelligent buildings and integrated energy systems in parks has been proposed. Using a master-slave game, design a coordinated planning framework for networked intelligent buildings and integrated energy systems in the park. On this basis, a coordinated planning model for networked intelligent buildings and the comprehensive energy system of the park is constructed. Based on the simulated annealing algorithm in the metaheuristic algorithm, the coordinated planning model between networked intelligent buildings and the comprehensive energy system in the park is solved to achieve coordinated planning between networked intelligent buildings and the comprehensive energy system in the park. The experimental results show that the coordinated planning effect of the networked intelligent building and the comprehensive energy system in the park proposed in this paper is good.

Using Maxwell 3D to simulate the measured values of wind farms is susceptible to distortion, resulting in mismatches between the analyzed thunderstorm field strengths of different main towers and the actual thunderstorm field strengths. Therefore, it is necessary to design a new simulation and analysis method for thunderstorm activity patterns in wind farms. That is, to correct the thunderstorm data of the atmospheric electric field instrument, combined with the fitting of lightning current amplitude and lightning probability, to establish a simulation model for the thunderstorm activity law of the wind farm, and complete the simulation analysis of the thunderstorm activity law of the wind farm station. The example analysis results show that the thunderstorm electric field strength of different main towers analyzed is in good agreement with the actual thunderstorm electric field strength, which has good analysis results, reliability, and certain application value. It has made a certain contribution to reducing the thunderstorm risk of wind farm stations.

With the development of industrial production, product demands are gradually changing from simple static demands to complex dynamic demands. The steel industry is a high-energy-consuming industry, which is usually accompanied by intensive energy consumption and emissions. Gas systems are an indispensable component of the steel industry, and almost all steel enterprises are equipped with gas systems to meet the demand for gases in the steel production process. In the gas supply and demand system, if the supply of gas is imbalanced with the consumption, it will cause the phenomenon of gas leakage or even device alarms, thereby affecting the normal production of the steel industry and causing a waste of resources. Therefore, enterprises urgently need to reasonably arrange equipment operations through scheduling and timely response to demand changes with the goal of cost reduction and efficiency improvement, to reduce leakage while reducing production energy consumption and improving economic benefits. In order to facilitate modeling and calculation, this paper ignores the nitrogen and argon parts and only considers the oxygen gas system. This paper takes the actual industrial air separation unit as an example to instantiate the scheduling model and establishes a nonlinear optimization problem of multi-product joint scheduling with minimum oxygen leakage amount and maximum economic benefits as the goal. By comparing the artificial scheduling results with the optimization problem-solving results, the feasibility of the decision is verified. This model can provide a theoretical reference for the optimization problem of multi-gas system scheduling in steel enterprises.

This study proposes a risk analysis method for balancing positive and negative reserve gaps in distributed new energy grids based on historical load peak valley values and big data. This method analyzes historical load data, explores potential laws and correlations in distributed new energy grid data, and combines ARIMA model and hidden Markov model to describe the operation of distributed new energy grids, obtaining the evaluation results of distributed new energy power supply capacity. Based on the evaluation results, calculate the positive and negative backup gaps and obtain the risk indicator values at each time point. Analyze and make decisions based on risk indicator values to achieve risk analysis. The experimental results indicate that this method can accurately evaluate the risk of positive and negative backup gaps in distributed new energy grids, and provide reasonable decision-making basis for grid managers. It is expected to play an important role in practical applications.

The accurate measurement of dissolved gas concentration in transformer oil is directly related to the safe operation state of transformer. In order to realize real-time and automatic monitoring of dissolved gas in transformer oil, online monitoring method has been developed and applied, but the accuracy of online monitoring method remains to be investigated. Therefore, this paper evaluates the accuracy of two online monitoring methods, i.e., gas chromatography and photoacoustic spectroscopy, that have been used in the market. The results show that the test results of two online monitoring methods can meet the requirements of Class A standard. Among them, the online monitoring method using gas chromatography has less error and higher accuracy, but its operation is relatively complicated and requires carrier gas. The online monitoring method using photoacoustic spectroscopy does not need carrier gas and the test speed is fast, but its test error is relatively large, and the accuracy is relatively low, indicating further research is still needed to improve the test accuracy.

During fuel transport, oil sloshing due to sudden braking in emergency situations can result in cracking in the fuel tank. The head of the fuel tank is the most impacted during sloshing and plays a greater role in bearing the sudden force that arises in such a situation. The shape of the head and the oil filling rate significantly influence the strength and occurrence time of the maximum impact force, fluid state in the tank, and pressure distribution during sloshing. Therefore, minimizing the impact of sloshing is important for the sustainable transport of fuel. In this study, we conducted a sloshing simulation using the FLUENT model for the two tank structures (i.e., longitudinal and transverse) with four different head shapes (flat bottom, elliptical dome, butterfly 1, and butterfly 2) to determine the effect of oil sloshing during braking on the pressure-bearing capacity and structural reliability of the tank structure. Based on those results, we determined the optimal head styles for longitudinal and transverse tank structures, providing a basis for the stability calculation of an oil storage unit.

At present, the constraint structure of variable-time scheduling of power supply load in hybrid power grid is mostly set as one-way structure, and the efficiency of scheduling is low, which leads to the increase of the average error percentage. Therefore, the research on variable-time scheduling of power supply load in hybrid power grid considering wind-solar correlation is put forward. Firstly, the basic data of power grid load variable-time scheduling is preprocessed, and the multi-level mode is adopted to improve the efficiency of scheduling, and the multi-level scheduling constraints are set. Based on this, the power grid power supply load variable-time scheduling model under wind-solar correlation is constructed, and the normal distribution correction processing is adopted to realize variable-time scheduling. The test results show that, for the selected six test cycles, the average error percentage of the load variable-time scheduling task of the hybrid power grid is well controlled below 1.1, which shows that the designed scheduling method is more flexible and stable, and the scheduling effect is better, which has practical application value.

The significance of perovskite materials in the field of renewable energy is becoming increasingly prominent, with their photovoltaic conversion efficiency emerging as the focal point of research. Traditional research and assessment methods are not only time-consuming but also labor-intensive. However, in recent years, machine learning technologies have provided new insights into addressing such challenges. Drawing upon a review of prior research, this paper categorizes the photovoltaic conversion efficiency of perovskite materials into high, medium, and low grades. Subsequently, we employed six mainstream machine learning algorithms, including Logistic Regression, Support Vector Machine (SVM), Decision Tree (DT), AdaBoost, XGBoost, and Long Short-Term Memory (LSTM) for training and prediction. Comprehensive evaluations revealed that SVM outperformed in overall accuracy, while XGBoost showcased superior classification performance based on the AUROC metric. Notably, the DT model excelled in terms of the F1 score. In contrast, both Logistic Regression and LSTM showed slightly inferior performance across various evaluation metrics, particularly in the F1 score and AUC. This study not only furnishes an efficient evaluation methodology for the photovoltaic conversion efficiency of perovskite materials but also offers valuable insights for research in related fields. We anticipate that as technology advances, we can provide more accurate and efficient solutions for real-world applications.

Driven by the dual-carbon targets, the application of integrated energy stations is becoming increasingly widespread. A core aspect of these stations is the utilization of various energy sources and storage devices to meet a variety of load demands. This study delves into the characteristics of an integrated energy microgrid, encompassing aspects such as energy storage properties, time-of-use electricity pricing, and electric-thermal load. Under the premise of fully considering demand response, we construct a dual-layer operation optimization model, implemented via edge computing technology. An integrated energy station equipped with a combined heat and power system, an electric boiler, a fuel cell, and electricity and heat storage systems is used as a simulation example to study the response models of different electricity and thermal energy demands. We analyze and compare four different application scenarios, and perform simulation calculations of the economic benefits of the model. The research results reveal the superiority and effectiveness of the demand response model based on edge computing in achieving real-time demand response. This validates the feasibility and rationality of executing the comprehensive energy demand response on the edge side.

In order to further reduce the curtailment of wind power and reduce the operating costs of the integrated energy system, a method for optimizing the dispatch of integrated energy systems that considers supply and demand coordination and promotes wind power consumption is proposed. First, in order to improve the flexibility of energy supply, highly efficient heat pumps, Rankine cycle units and Power-to-gas waste heat recovery devices are introduced on the source side to optimize the electrical-thermal coupling relationship; secondly, the time dimension response of electricity, gas, and heat loads is considered on the demand side. At the same time, the substitution relationship between the three loads is considered; finally, an optimal dispatch model is constructed with the minimum energy purchase cost, operation and maintenance cost, wind curtailment cost, and demand response cost as the objective function, and the original problem is transformed into a mixed integer programming problem , through case simulation analysis, the source-load coordination optimization effect of heat pumps, rankine cycle, power-to-gas and demand response is considered. The results show that the proposed optimization dispatch method can improve wind power accommodation capacity, reduce system operating costs, and improve system flexibility.

To achieve quality evaluation of hydraulic oil, establish oil parameter conversion models and comprehensive evaluation models respectively. Compare the oil indicators with industry standards and obtain online monitoring plans for some indicators. Combining the improvement of deterioration function with dynamic weight to improve the overall evaluation plan of hydraulic oil, and conducting comparative analysis of the plans. The analysis results show that the method used in this article is significantly superior to the other two single improvement schemes, and the scheme used in this article has a stronger fit with the oil quality, which can better reflect the operating status of the oil.

Reverse electrodialysis can convert salt difference energy into electrical energy by mixing two water bodies with different salt concentrations. However, in recent years, it has been found that when using actual water bodies instead of sodium chloride solution as the feed solution, the electricity generation efficiency of reverse electrodialysis will significantly decrease. Compared to sodium chloride solution, actual water bodies are more complex in composition and contain various high valent ions. By adding potassium ions, magnesium ions, calcium ions, and sulfate ions as feed solutions to sodium chloride solution through experiments, the contribution of common trace ions in concentrated seawater and seawater to the performance of the stack was studied. Through experiments, it was found that divalent ions can reduce the performance of the stack. The concentration of magnesium and calcium ions in seawater and seawater has the greatest impact on the performance of the stack, significantly increasing the internal resistance of the stack and thereby reducing the power density output of the stack. The greater the salt difference energy between the feed solutions, the smaller the internal resistance, and the higher the open circuit voltage and power density. Increasing the flow rate of the feed solution can improve the output performance of the stack. However, as the flow rate continues to increase, the internal resistance of the stack will become unstable. Increasing the temperature of the feed solution can improve the performance of the stack. Therefore, a suitable temperature should be maintained for the feed solution based on the properties of the ion exchange membrane

A testing platform for studying the corrosion characteristics of grounding electrode materials was established in response to the corrosion phenomenon of pure copper grounding electrodes caused by DC interference current. Firstly, the variation law of grounding potential and corrosion behavior of copper grounding materials in typical soil of Shenyang were studied. Secondly, SEM, EDS, XRD and other techniques were used to observe the surface morphology of the corroded sample, analyze the composition of the corrosion products, and finally, a linear correlation fitting was performed between the corrosion rate calculated using interference current and the grounding potential. The results showed that there was a linear relationship between the initial and stable potential offsets and the corrosion rate measured using the weight loss method. The experimental results can provide a certain reference basis for engineering practice.

In a liquid desiccant dehumidification system, the setting of solution recirculation can enable the reuse of a portion of the solution, resulting in good dehumidification and energy-saving effects. The concentration difference between the concentrated and dilute solutions determines the dehumidification effect when the system is in equilibrium. Therefore, the influence of different dehumidification-regeneration solution recirculation ratios (C_s,deh, C_s,reg) on the equilibrium concentration difference of the system is crucial, but there is still a certain gap in this area of research. To fill this gap, this paper constructs a liquid desiccant dehumidification system with a solution recirculation structure. Based on the characteristics of the recirculation structure, solution recirculation is divided into three models: DR model - only dehumidification solution recirculation (C_s,reg =0), RR model - only regeneration solution recirculation (C_s,deh =0), and DRR model - simultaneous setting of dehumidification-regeneration solution recirculation. A mathematical model is established to parameterize the equilibrium concentration difference and COP of the system. The results show that compared with the single-cycle model (NR model: C_s,deh= C_s,reg =0) of traditional liquid desiccant dehumidification systems, setting up a solution recirculation structure can significantly improve the COP of the system: The maximum COP under DR model, RR model and DRR model are 6.6 times, 7.0 times and 12.1 times that of NR model respectively.

This study focuses on the impact of uncertainty in renewable energy output on the regional power system balance in Jiangsu Province, China. It highlights that the high-level integration of renewable energy sources brings forth a series of challenges, including increased uncertainty, seasonality, and output fluctuations. The paper summarizes the seasonal and time-of-day characteristics of renewable energy output in Jiangsu Province and outlines output patterns. Furthermore, it conducts an in-depth analysis of the influence of renewable energy on power system balance. The study provides valuable insights into understanding the characteristics and patterns of renewable energy output in Jiangsu Province and summarizes the roles and challenges of high levels of renewable energy integration in power system balance. It emphasizes the importance of adopting a balanced, flexible, and sustainable approach to power supply to promote power system equilibrium.

The investment of multi-circuit transmission lines on the same tower has solved the problem of land occupation by transmission lines, but it has caused the harm of induced electricity. In order to analyze this kind of induced power problem and make suggestions for maintenance personnel, this paper uses EMTP software and distributed parameter method to accurately calculate and model 500kV / 220kV four-circuit transmission lines on the same tower. The calculation results show that: when the grounding switch at both ends of the maintenance line is grounded, the electromagnetic induction current is 90.122 A, which is much larger than the safety limit. Therefore, despite the double-ended grounding of the line, workers still need to wear protective clothing to ensure safe operation.

In response to the lack of publicly available datasets for solar panel defects and the challenges in applying general recognition models, this study presents an algorithm tailored for small-sample solar panel defect detection. The algorithm leverages the strengths of deep learning models and the R-CDT transformation, initially employing Faster R-CNN for defect localization and segmentation, followed by the application of the R-CDT algorithm for defect classification. This approach effectively mitigates human-induced factors and reduces the risk of overfitting in small-sample scenarios. Experimental results demonstrate its high effectiveness as a small-sample recognition method, surpassing popular models (Faster R-CNN and YOLOv5) in terms of recognition accuracy.

The core feature of the new power system is that new energy replaces traditional thermal power as the main power source of the power system, and the basic development positioning is clean, low-carbon, safe and efficient. To analyze the low-carbon contribution of photovoltaic power generation to the power system, a method for analyzing the impact of uncertainty in photovoltaic injection power on the carbon emission flow of the power system is proposed. Firstly, by modeling the output of photovoltaic units and the power of system nodes, a functional expression between light intensity and the total carbon flow rate of system nodes is established. Secondly, the randomness of photovoltaic output is transferred to the balanced unit. Based on the carbon flow rate correlation matrix between the balanced unit and the system node branch, the impact factor of photovoltaic injection power on the node and branch is calculated, and the uncertain characteristics of carbon emissions in the power system under photovoltaic injection power are obtained. Finally, the correctness of the proposed method is verified through case analysis results, and the low-carbon contribution of photovoltaic access to the power system is quantitatively analyzed.

For new energy and DC to replace a large number of conventional units, the system's moment of inertia is reduced, and the burden of traditional frequency modulation method is increased. In this paper, VSG inertia and damping coefficient adaptive strategy is designed, and the adaptive parameters are adjusted. According to the characteristics of the wind storage system based on three dynamic frequency indexes, the day-ahead scheduling control strategy is designed, and the adjustable standby capacity of the wind storage system is obtained. Finally, when the frequency deviation exceeds the threshold value, real-time scheduling analysis of frequency parameters, considering three frequency dynamic indicators, the frequency adjustment coefficient and frequency modulation capacity of each part of the Wind energy storage system are optimized, and the optimal allocation results are obtained. In IEEE4 computer system, the adaptive inertia forward real-time scheduling control strategy designed in this paper is compared with other frequency control strategies, and the superiority of the proposed strategy is verified.

China's hydrogen energy industry has ushered in a period of rapid development, but it shows the characteristics of small scale and scattered sources of hydrogen production. It relies on hydrogen storage and transportation technology to achieve large-scale collection and then make use of it. Limited by the level of technological development, the cost of hydrogen storage and transportation is high. It accounts for about 30% to 40% of the total cost of hydrogen, and the stability of hydrogen supply can not be guaranteed. Through the hydrogen demand analysis of hydrogen terminal and the economic evaluation of different hydrogen storage and transportation modes, the research on the hydrogen supply scheme of different hydrogen storage and transportation modes is of great significance to the large-scale development of hydrogen energy in China.

Electro-ceramic materials are widely used in the construction of power grids in China due to their good insulating properties. The high-strength porcelain casing of electric porcelain-type electrical equipment is the weak link in seismic action, and has high fracture susceptibility. In different loading rates, especially in the middle and high loading rates the mechanical properties of electric ceramic materials have large differences, clearing the differences in the mechanical properties of electric ceramic materials under different loading rates can provide a basis for the seismic design of electrical equipment. In this paper, from the quasi-static and dynamic response experimental research of electric ceramic materials, including experimental techniques, strain rate effect, etc., the mechanical response characteristics of electric ceramic materials under different strain rate loading are reviewed systematically; the dynamic constitutive model is elaborated; the dynamic constitutive model of electric ceramic materials considering the strain rate effect can be used as a reference for revealing the dynamic damage mechanism of electric ceramic materials under different strain rates.

Reverse electrodialysis (RED) is a method of power generation that converts the energy of salt difference between two solutions of different concentrations into electrical energy. There is a lot of NO^3- in the denitrification wastewater produced by the power plant, and by using RED, the denitrification wastewater can be used as a feed solution for power generation. However, the impurity ions in the wastewater will have an impact on the power generation efficiency. In this paper, the impurity ions in denitrification wastewater provided by a power plant were measured, and the current, voltage and internal resistance at different temperatures were obtained by adding different salt solutions to the measurement. The order of opencircuit voltage at the same temperature is denitrification wastewater + NaCl > denitrification wastewater + KCl > denitrification wastewater + Na₂SO₄ > denitrification wastewater + MgCl2 > denitrification wastewater + CaCl₂, and the order of the effect on the internal resistance of the membrane stack is denitrification wastewater + NaCl ＜ denitrification wastewater + KCl < denitrification wastewater + MgCl₂ ＜ denitrification wastewater + Na₂SO₄＜ denitrification wastewater + CaCl₂. The order of maximum output power density is denitrification wastewater + NaCl > denitrification wastewater + KCl > denitrification wastewater + Na₂SO₄ > denitrification wastewater + MgCl2 > denitrification wastewater + CaCl₂By adding salt solution to increase the denitrification wastewater of different impurity ion content on the open-circuit voltage, internal resistance, power density of the comparison of the impact of impurity ions on the power generation efficiency of the unfavourable effects of the order of Ca²⁺>Mg²⁺>SO₄ ²->K⁺>Na⁺ , so in the use of denitrification wastewater for reverse electrodialysis power generation, it should be given priority to the removal of Ca²⁺, Mg ²⁺, and other bivalent ions.

To make sure the security of energy data in cross-domain flow, this paper designs an energy data cross-domain flow traceability system based on blockchain. On the basis of a blockchain model with master-slave dual-chain, this study construct the overall architecture of the system. According to the characteristics of energy data in production and transportation, the data in cross-domain flow is categorized into basic data and process data, and then the content structure of traceable data is designed. Subsequently, in conjunction with practical business scenarios of energy data cross-domain flow, the application framework and key mechanisms of the traceability system are designed. Finally, the performance of the system in terms of transmission delay and throughput is verified through simulation experiments.

The MgAl-LDOs was synthesized in this work to adsorb the COD and heavy metals in accident oil pool of substation synchronously. The MgAl-LDOs was synthesized by SEM, XRD, FTIR, BET, indicating its abundant surface –OH and rich channel structure, benefiting for its excellent dual-effect adsorption properties for COD and heavy metals removal. By using MgAl-LDOs, the COD removal efficiency of oily wastewater from simulated accident oil pool can achieved to be 81.78%, while the removal efficiency of Fe (10ppm) and Cr(10ppm) can reach more than 98%. Besides, the adsorption capacity of Fe and Cr by MgAl-LDOs can reach 158.49 and 149.94mg/g, respectively, whose adsorption isotherm conforms to the Freundlich model and the adsorption process is multi-molecular layer adsorption. Such a synchronous control method for COD and heavy metals can provide a useful strategy for multiple pollutants control in accident oil pool of substation.

The purpose of this article is to investigate optimization methods for engine intake manifolds to improve air intake efficiency and reduce pressure losses. By using AVL BOOST software for one-dimensional simulation modeling, the influence of different intake manifold structure parameters on engine performance can be quickly evaluated, providing guidance for optimization in actual production. Firstly, a one-dimensional simulation model of a dual-fuel engine was established using AVL BOOST software, and the model was initially set using bench test report data. Then, the simulation accuracy under different load conditions was verified to ensure an error of less than 5%. Based on this, the structure parameters of the engine intake manifold were optimized to obtain the best air intake performance. Through simulation result analysis, we found that the optimal intake pipe length is 160mm, the optimal pipe diameter is 150mm, and the optimal intake manifold volume is 110L. These optimized parameters significantly improved major performance indicators such as cylinder torque, volumetric efficiency, and mechanical efficiency. The optimized dual-fuel engine showed improved performance in major parameters and a reduced brake specific fuel consumption, indicating significant optimization effects of the engine intake manifold. Therefore, studying one dimensional simulation modeling of engines provides important theoretical and practical guidance for optimization in actual production processes.

Aiming at the problem that the power of industrial servo equipment is heterogeneous and random start and stop, and the distributed energy storage capacity of multi-type parks does not match the energy consumption period of equipment, this paper constructs a multi-level energy management system of servo equipment based on the ' end-edge-network-cloud ' IoT system. Based on the portable characteristics of container applications, the application service scalability of multi-level energy management system is improved. Considering the power balance of park equipment and multi-level power constraints, the collaborative matching of manufacturing container and energy container in multi-level container architecture was carried out, and the bidirectional collaborative energy consumption level of energy storage and servo equipment was analyzed. The results showed that the multi-level energy management system effectively improved the flexibility and economy of the park production plan, and effectively reduced the park's abandoned wind and light power.

Proton exchange membrane fuel cells use hydrogen as fuel and have the advantage of being safe and efficient, important for the research and development of future energy systems. This paper addresses the problem of constant efficiency of PEMFC in most region energy systems, an optimization model considering the partial load characteristics of proton exchange membrane fuel cells is proposed. First refine the modeling of PEMFC in Sinmulink, obtain the power generation efficiency and waste heat recovery efficiency functions at different load rates, then the optimized operation model is established with the lowest typical daily operating cost and carbon emission cost as the objective function, Finally, the solution is performed by particle swarm algorithm. The algorithm shows that the proposed optimization model can effectively reduce operating costs as well as carbon dioxide emissions, which is more economical and environmentally friendly.

The Business Park Integrated Energy System (BPIES) is one of the important carriers for achieving energy conservation and emission reduction in the park. In order to achieve the low-carbon economic operation of BPIES, it is necessary to consider the comprehensive impact of both carbon trading and flexible loads. Therefore, we proposed a low-carbon economic optimization operation strategy with carbon trading market and flexible loads coordination., and established a BPIES model incorporating carbon trading and flexible loads. Through the analysis of operational strategies in different scenarios, the results indicated that under the carbon trading system, incorporating flexible loads in system scheduling not only achieved low-carbon operation but also ensured economic efficiency. This approach better achieved the low-carbon economic operation of BPIES.

Against the backdrop of global consensus on protecting the ecological environment and reducing carbon emissions, an increasing number of the Building Integrated Photovoltaic (BIPV) projects have been built around the world. Taking a high-speed railway station as an example, this paper quantitatively analyses the operating parameters and benefits of the project’s photovoltaic system based on predicted and measured data, and then obtains the actual operating effects of the photovoltaic system, thereby verifying the good benefits of BIPV technology in economic and social aspects.

Industrial park resources are diverse, with high energy coupling and a variety of energy consumption types, offering significant potential for carbon emission reduction. This paper proposes an economic optimization approach for the energy system in industrial parks, considering energy behavior guidance. Considering the economic operation needs of industrial parks, the study first constructs a integrated energy system(IES) model for a typical industrial park. Then, it establishes a Integrated demand response model incorporating price-based demand response, incentive-based demand response, and heat-sensitive demand response. Subsequently, considering equipment operation costs and different types of demand response costs, the economic operational objectives for the park are designed. The Cplex commercial solver is used to solve the model, obtaining the optimal system operation results. Finally, a case study is conducted using the energy system of an industrial park in Shandong Province, demonstrating that the proposed method effectively enhances the economic efficiency of system operation and reduces operational costs.

This paper presents a bi-level collaborative optimization model for multi-source load complementarity based on the coupling characteristics of various source-load equipment. The upper layer conducts global optimization with the objective of minimizing the annual net present value cost to determine the optimal planning scheme and capacity of the multi-source load complementarity system. The lower layer builds the optimal operation model of the multi-source load complementarity system with the objective of minimizing the cost of new energy consumption. The case study shows that the proposed planning model can increase the proportion of wind and solar power in the system and reduce the construction costs, demonstrating the effectiveness of our model and method.

Negative sequence current and three phase voltage balance are the most important power quality problems in the traction power supply system of high speed railway Compared with ordinal electric locality, the traction power required by high speed trains is significantly increased, and the negative sequence current injected into the power system is significantly increased At the same time, the high speed train has the characteristics of high load rate and long power receiving time, which makes the negative sequence current generated by the high speed railway has the characteristics of long duration This paper introduces the power characteristics and load characteristics of high speed rail traction power supply system, studies the calculation of its negative sequence current and three phase voltage balance, and puts forward a new three phase voltage balance compensation device and its matching algorithm, Which is suitable for the treatment of negative sequence current and three phase voltage balance in high speed rail traction power supply system Finally, the expected effect is verified by simulation, which provides its feasibility and correctness.

With the development of oil and gas exploration and technology, China's oil and gas development has shifted from conventional reservoirs to low-permeability and difficult-to-utilize unconventional reservoirs, and reservoirs with thin layers and poor physical properties account for a larger portion of low-permeability reservoirs. In order to develop these types of reservoirs, it is necessary to explore production enhancement methods that can effectively exploit the reservoir. In this paper, numerical simulation was used to study the application effect of radial water jetting-deep acidizing combination measures. The field trial proved that this combination of measures has the effect of increasing oil production. The results show that this combination measure can improve the recovery degree of reservoirs with thin thickness and poor physical propertie, and that the method can change the seepage field around the wells, increase the spreading volume during the water drive process, and reduce the degree of interference between the wells; there is an optimal half-length of the water jetting borehole, and the half-length of the borehole is affected by the permeability of the reservoir. The ideal borehole half-length in the 10×10-3μm2~80×10-3μm2 range is 20~60 m. The results of this paper provide technical support for the effective development of thin thickness and poor physical reservoirs.

To comprehensively investigate the impact of demand-side adjustability on carbon emission reduction, this paper introduces a park-level integrated energy system(PLIES), integrating considerations for both the carbon trading mechanism and demand response. Initially, demand response is classified into price-based and alternative-based categories based on load characteristics. Subsequently, the paper establishes a price-based demand response model incorporating the price elasticity matrix, and an alternative-based demand response model considering the interconversion of multiple loads like cooling, heating, and electricity. The baseline method is then applied to allocate carbon emission allowances for the system, accounting for actual emissions from the gas turbine, gas boiler, and absorption refrigeration unit. The developed model is suitable for the PILES, encompassing a carbon trading mechanism that considers actual emissions from various equipment. Finally, a low-carbon dispatching model for the PLIES is formulated, aiming to minimize the sum of energy purchase costs, carbon trading costs, and operation and maintenance costs. The paper validates the model through four typical scenarios, analyzing the impacts of different carbon trading prices, operation costs, and carbon emissions. The key conclusion emphasizes the importance of establishing a reasonable carbon trading price to achieve synergy between system economy and low carbon.

In order to assess the fire hazard of two new transformers natural ester insulating and cooling oils, an experiment was conducted to examine the combustion characteristics of pool fires using rapeseed oil and soybean oil in transformer room. The results show that, compared with rapeseed oil, soybean oil is more likely to ignite under the same conditions. The vigorous combustion stage of a soybean oil pool fire lasts 30% longer than that of rapeseed oil. Additionally, the flame height and flame area of a soybean oil pool fire are larger, resulting in a more intense burning. The average temperature at the fire plume centerline of the soybean oil pool fire was approximately 50 °C higher than that of the rapeseed oil fire plume. The maximum thermal radiation intensity of the soybean oil pool fire was 14.7 kW/m² , which was similar to the rapeseed oil pool fire's intensity of 15.3 kW/m² . However, the duration of the thermal radiation phase with high intensity of the soybean oil pool fire was 27.3% longer than that of the rapeseed oil pool fire. Overall, the fire hazard of indoor combustion is greater for transformer soybean oil than for transformer rapeseed oil.

The main insulation material of the capacitance-graded bushing's capacitance core is composed of composites made from epoxy resin-impregnated paper (ERIP). The insulation property of ERIP composites is dependent on the fluctuations in environmental temperature. Therefore, the influences of a high-low temperature environment on the dielectric response properties of ERIP composites were studied in the paper. Firstly, the cycles test of ERIP samples is carried out in a high-low temperature environment from 20°C to 120°C, and ERIP samples are tested for frequency-domain dielectric spectroscopy (FDS) at each cycle. Then, the dielectric loss factor (tanδ) and the real and imaginary parts of complex capacitance at different cycle numbers and frequencies are all analyzed in the paper. Finally, the features are extracted from the frequency domain of tanδ, and the relationship between features and period is established. The test results and analysis indicated that as the number of cycles increases, the value of the tanδ and the imaginary part of the complex capacitance has an increasing trend, the value of the real part of the complex capacitor decreases with the increase of the number of cycles. As the number of cycles increases, the rate of change of the power tanδ, the imaginary and real parts of the power complex capacitance are 46.79%, 39.73%, and 4.26% respectively. The tanδ at 1Hz, 0.46Hz, and 0.22Hz are the selected features, all of which have a linear relationship with the cycle times. The conclusion can provide a data basis for the insulation state of the ERIP composites within the capacitance-graded bushing.

Lamaidean oilfield is a sandstone oil reservoir with low porosity, low permeability and low abundance, with extremely broken structure, many faults, deep burial, poor physical properties and complicated oil-water relationship, which makes the development extremely complicated. After the production is put into operation, the production decreases rapidly, and there is the development contradiction of not injecting and not extracting in the lower part of the side, how to improve the development effect of the oilfield and supplement the formation energy as soon as possible is an important issue for the stable production of the oilfield, which is of great significance for controlling the decrease of the production of the oilfield and improving the recovery rate of the crude oil. Based on the well development, reservoir prediction, tectonic/fault development, etc., the drilling sequence was formulated to flexibly vary the well locations while following the well placement principles. The research results show that the water-driven recovery rate in the pre-distribution area of the calculated wells is 22.37 %, with a combination of water injection at low parts of the tectonics and flexible water injection at points, the ratio between the number of oil and water wells is 3.25:1, the production hourly rate is 300 days, the daily oil production of a single well is 3.0 t on a stable basis, and the capacity of the wells can be built up to 0.81×10⁴ t.

The new energy multi-field short-circuit ratio (MRSCR) indicator reflects the static voltage stability of new energy field station grid connection points, the smaller the indicator, the more likely to occur static voltage instability risk, so the new energy output needs to be analyzed and controlled according to the new energy multi-field short-circuit ratio indicator to connect to the power grid. Taking a high proportion of new energy power grid as an example, the calculation of new energy multi-site short-circuit ratio is carried out in two cases of night-time pure wind power mode and daytime wind and light co-generation mode, to analyze the impact of PV start-up on the overall new energy multi-site short-circuit ratio calculation results, and the areas with low short-circuit ratios of new energy multi-site short-circuit ratios are respectively used in the areas of "differential control" and "homogenization control". The two schemes of "differentiated control" and "uniformized control" are adopted to control the new energy output respectively for the areas with low short circuit ratio of new energy multi-field stations, and the advantages and disadvantages of the two schemes are compared. Finally, for the problem that controlling new energy output reduces the level of new energy consumption in the power grid, the enhancement measures of strengthening the grid framework and installing regulating cameras are proposed, and the effects of the two measures on improving the short circuit ratio of new energy multi-site stations are analyzed.

In order to improve the management efficiency and comprehensive level of field operation of new energy power generation production, a real-time monitoring method of field operation of new energy power generation production based on mobile Internet is proposed. Establish the corresponding risk influencing factor system, identify the risk influencing factors of new energy power generation production site operation, build the overall architecture diagram of real-time monitoring according to the identification results, calculate the data distribution density, screen out duplicate data, realize data deduplication, and obtain the monitoring results of data outliers according to the deduplication results. The experimental results show that the fitting values of this method are low, all below 0.2%, and the monitoring results are more consistent with the actual situation, with higher processing speed.

The substation area line loss rate is an essential economic and scientific indicator for power companies. As distributed power grid-connected scale continues to expand, it's crucial to accurately compute the line loss rate in substation areas that include distributed power sources. This paper presents a method for calculating the substation area line loss rate based on PCA and GRNN, taking into account the elementary operational identities and grid integration identities of PV power. The factors affecting line loss in regions with photovoltaic distributed generation are first extracted, followed by dimensionality reduction using PCA. The reduced features are then used as input for training the GRNN, Meanwhile, the error between actual line loss and the training results is analyzed. In the end, the algorithm's effect to identify high line loss areas is tested. The findings indicate that the effect of the GRNN is better, resulting in small calculation errors in line loss rate and high accuracy in identifying high loss areas.

This paper introduces a combined pump-driven thermosyphon loops (CPDTLs) system that consolidates chip-level and room-level heat dissipation pathways for efficient heat removal from both CPUs and other heat-generating components within servers in data centers. The study elucidates the system’s underlying principles and demonstrates its application in a single-cabinet data room with a 10 kW IT full-load power capacity, followed by a comprehensive performance evaluation. The experimental computer room is located in Xiangyang City, Hubei, China, with server full-load testing initiated in February 2022. Data from typical days during transitional seasons, summer, and winter are examined. The findings reveal that the average daily heat dissipation power usage effectiveness (PUE) of the computer room is 1.06 in winter, 1.10 during transitional seasons, and 1.18 in summer. Employing interpolation and extrapolation techniques, the study estimates the heat dissipation performance of the computer room under varying ambient temperatures based on the collected data. Furthermore, the annual hourly energy consumption of the computer room is predicted using typical weather day data from Harbin, Beijing, Kunming, and Guangzhou. These predictions indicate that the PUE values for the four cities are 1.085, 1.1, 1.087, and 1.139, respectively.

The digital model for the typical faults of low voltage DC system is analyzed, and the analyzation system is developed to offline calculate and pre-analyses whether the fault results in mis-action of the low voltage DC system. Firstly, the digital model for typical faults of low voltage DC is analyzed based on the analytical method, and the mis-action power for typical faults is found. Secondly, the software for verification of the above analysis is developed, and typical cases are used to further verify the correctness and effectiveness of the developed software. In the tested case, the developed software can flexibly pre-analyses the mis-action of low voltage DC system for the targeted typical faults.

Biodiesel can effectively replace traditional fossil fuel. In this paper, a three-dimensional combustion model of biodiesel engine is constructed using the relevant geometric parameters. The changes of pressure and temperature in the cylinder of biodiesel engine under different injection duration and injection timing are analyzed. An injection time of 10 degrees before top dead center and a fuel injection duration of 7 degrees were found to be more suitable.

There is a substantial demand for more efficient methods of reusing waste heat from manufacturing operations. Vapor compression technology is used to transform low-grade heat sources into high-temperature hot water, and subsequently water vapor compression technology is used to generate high-temperature steam. To achieve the required water temperature for the vapor compression system, a high-temperature cascade heat pump system is designed in this article. To achieve these requirements, the state point approach is used to design the system's operational parameters under maximum operating circumstances, and a suitable refrigerant is chosen in line with the performance coefficient of the cascade system. The high-temperature cascade heat pump system has a fin heat exchanger and an air heat exchanger on its low-temperature side. The low temperature side of the cascade system is designed to use a dual system mode to address the issue of the negative effect that system defrosting has on heat generation. This is done so that the desired demand for hot water may be met regardless of the actual temperature in the room. During this period, the theoretical calculation analyzes how changing the output water temperature and the surrounding temperature affects the rest of the system's characteristics. This provides strong theoretical backing for the smooth operation of the system.

The carbon flow tracking model is adopted to compute the carbon flow and each node carbon potential, puts forward the principle of user-side dynamic factor establishment, establishes the user-side dynamic carbon emission factor (DCEF) model, and then gives the definition of the user-side DCEF. Guided by the low-carbon demand response mechanism, this paper conducts an empirical test in Inner Mongolia Autonomous Region as an example. The results show that compared with the traditional static carbon emission factor, the use of DCEF can significantly improve the carbon reduction and carbon reduction benefits, thus effectively promoting the active low carbon emission reduction and measuring the emission reduction benefits of users.

Wave energy is the potential ocean energy resource. The hydraulic energy storage and generating system (HESGS) has been applied in wave energy converter (WEC) for its high reliability and smooth power fluctuation. WEC often works in greatly changed wave conditions to absorb wave energy as much as possible. The output power also varies in a wide range. To deal with the wide range and various wave conditions, a cascade hydraulic energy storage and generating system is proposed in this paper. The optimized adaptive control strategy is studied. Units are divided into different levels, which start and stop depending on the control pressure of valves. Gravitational Search Algorithm has been used for the calculation of the control pressure according to wave condition. The simulation has validated that the cascade HESGS with an adaptive control strategy has improved the stability and flexibility of the generating system of WECs.

In recent years, countries around the world have been advocating green development and gradually implementing new energy-based power grids. Due to the contradiction between the randomness of new energy generation and the growing demand for residential electricity, the stable operation of power grids is facing a huge challenge. Air conditioning load accounts for a large proportion of residential electricity consumption, so it is of great significance to monitor it. Through understanding the electricity consumption of air conditioning load, residents can be guided to use electricity reasonably, ensure the stable operation of the power grid, and provide a decision-making basis for demand response to alleviate the contradiction between supply and demand. At present, there are relatively few air conditioning load monitoring works, most of which are carried out as part of non-intrusive load monitoring. The selected input data mainly consider generality and seldom consider the operation rule of air conditioning and the influence of meteorological factors. Therefore, the accuracy of air conditioning monitoring is still insufficient. Based on this, a real-time monitoring method of air conditioning based on electrical characteristics and a twin-tower neural network is proposed in this paper. Based on Long Short-Term Memory (LSTM), the sequential branch was constructed to mine the sequential characteristics of air conditioning operation. Based on Back Propagation (BP), an electrical feature branch was constructed to capture the operation rules of air conditioning. Meanwhile, the experimental results on one year of real data from 20 users show that compared with the traditional LSTM model, this model can realize the accurate identification of air conditioning load and provide support for the demand response potential assessment of air conditioning load.

Active jet technology has great advantages for heat prevention and drag reduction in hypersonic vehicles and is becoming a hot region of research. In real flight missions, hypersonic vehicles fly at different angles of attack, so it is of strong practical interest to investigate the heat protection and the drag reduction effects of jet technology at different angles of attack. This paper investigates the effect of the combined model of the jet model on the drag reduction and the heat protection performance at the angle of attack from 0°to 15°under the free flow at Mach number 6, using the compressible Reynolds averaged Navier Stokes equation and (SST) k- ω turbulence model. The results indicate that the best drag reduction and heat prevention effect of the model with a combined jet configuration at 0°angle of attack. The drag reduction reached 27.1% and the peak heat flux density was reduced by 38.3%. As the angle of attack increases, the drag reduction and the heat prevention effects of the combined model gradually decrease. However, it still outperforms non-jet configurations.

IT equipment, air conditioning systems, electrical equipment, and lights make up the majority of the data center's energy use. Nearly 40% of the energy used by the entire data center goes toward the air conditioning system's energy use. This paper simulates the energy consumption of the air conditioning system of a hypothetical data center located in Guangzhou. It is assumed that the annual cooling load of the data center is constant at 500kW, and the air conditioning system uses the evaporative condensing fluorine pump air conditioning system. The research shows that the energy saving effect of the system in the cooling process of the data center is obvious.

As a clean energy conversion technology, hydrogen fuel cell has great development prospect in the field of sustainable energy. Scientists have been working on the design and optimization of hydrogen fuel cells to make the efficiency and performance higher. This article introduces the main directions and methods of designing and optimizing hydrogen fuel cells. First, the design of hydrogen fuel cell mainly depends on the which catalyst materials you choose and catalyst active sites. For example, precious metal catalysts, transition metal catalysts, and catalysts of nanostructured materials all make a huge impact on battery performance. Improving the activity and stability of catalyst and reducing the cost is one of the key points of this design. In addition, optimizing of the electrolyte film to improve the efficiency and battery life is also key. By taking the above factors into consideration and using advanced materials and technologies, hydrogen fuel cells can achieve high efficiency, high performance, and long life, and promote their commercial application.

Due to the large internal volume of the switch cabinet, when the characteristic gas is generated due to partial discharge and other faults, it will show different distribution in each position of the switch cabinet. In this paper, by building a real switch cabinet model, the characteristic gas diffusion process of a specific gas generation position is simulated. By setting the gas product ratio and boundary conditions, the dynamic diffusion process of NO₂ and O₃ in the switch cabinet at different times is analyzed, and the phenomenon is explained and analyzed. Through the summary of the diffusion law, combined with the switch cabinet structure and gas distribution, the optimal detection position of the gas sensor is obtained, which provides theoretical and data support for the experiment.

Due to some design defects, the economic indicators of the early 600MW units put into operation did not meet the design guarantee values under actual operating conditions. Taking an supercritical 600MW steam turbine produced by shanghai steam turbine company as an example, We have implemented technical improvements in the flow passage of the steam turbine by replacing the internal cylinders, bearings, sealing components, and other components of the high, medium, and low pressure cylinder bodies of the original steam turbine. The number of high-pressure flow passages has increased from I+11 to I+13, the number of intermediate pressure flow passages has increased from 8 to 9, and the number of low-pressure flow passages has increased from 2×2×7 increased to 2×2×9. After improvement, the heat consumption rate of the unit can be significantly reduced. The performance test results of the steam turbine after technical improvement show that the efficiency of the high-pressure cylinder has increased by 3.85 percentage points, the efficiency of the intermediate pressure cylinder has increased by 2.78 percentage points, and the efficiency of the low-pressure cylinder has increased by 4.79 percentage points. The heat consumption rate of the unit at THA has decreased by 307.3 kJ/(kW · h), equivalent to a reduction of 11.5 g/(kW · h) in coal consumption. The research results can provide technical reference for energy-saving management and energy-saving reconstruction of large-scale thermal power units.

To accelerate the transformation of energy in the power industry and satisfy the requirements of a low-carbon economy, it is necessary to comprehensively analyze the low-carbon development level of the power supply, power grid, load, and energy storage that links to the urban power system, and establish a multi-layer evaluation method for the "SourceNetwork-Load-Storage" (SNLS) integrated power system. First, according to the characteristics of the new power system, from the perspective of cleaner energy production, efficient energy allocation, electrification of end consumption, and flexible network load interaction, a multi-layer key indicator method of SNLS in the city is established in this paper. Second, the analytic hierarchy process and entropy weight method are used to determine the subjective and objective weights. Then, the subjective and objective consequences are optimally combined based on game theory to determine the best comprehensive weight. Finally, the model is applied to a city SNLS integrated power system in Zhejiang Province to verify the effectiveness of the model.

At present, the lithium-ion battery has become an important part of new energy vehicles and energy storage systems and its performance plays a vital role in the healthy and rapid development of new energy vehicles and energy storage industry. Among them, electrolyte, as an important part of the lithium-ion battery, plays a key role in battery performance, but the leakage of liquid electrolyte will lead to lithium-ion battery fire, combustion and even explosion. Therefore, the development of gel electrolytes with high safety performance is favored by researchers. In this paper, the preparation method of PVDF-HFP gel electrolyte was introduced based on the characteristics of the polymer material matrix. The modification method of PVDF-HFP gel electrolyte was studied from three aspects: polymer matrix, inorganic filler and additive. The results showed that the modification of the polymer matrix was used to introduce the advantages of other polymers to improve the performance of the target matrix and the modification of inorganic filler can accelerate the ion migration and improve the conductivity of electrolyte by adding filler. In addition, additive modification is to achieve specific functions by introducing additives into the electrolyte.

Winter heating methods in rural regions of Northern Jiangsu, China are relatively outdated, and measures are needed to take promptly. This research used DeST to get the heat load of a 190 m² building, and dynamic meteorological and building structural parameters were taken into the calculation. This research designed a physical model of Solar Assisted Ground Source Heat Pump (SAGSHP) and used an instantaneous energy system simulation software (TRNSYS), to simulate the dynamic performance of the system. It was found that solar heating was unstable, and solar heating system only started when the intensity of solar radiation reached a certain value. Solar system was mainly affected by the intensity of solar radiation. The addition of the solar system allowed the SAGSHP system to maintain a high energy efficiency, and the number of days with an energy efficiency of more than four was close to 61%.

Under the dual-carbon target, the power industry needs to promote the reform of the power trading system to reduce carbon emissions, and it is very important to build a carbon market, green certificate market and electricity market coupling trading mechanism. This paper proposes a System dynamics carbon-green card-electricity market transaction coupling mechanism to construct a new low-carbon trading system from the macro-modeling level. Firstly, the carbon trading mechanism based on baseline method and the green certificate trading mechanism considering historical completion degree data are modeled respectively. Then, from the perspective of System dynamics theory, a modeling method of the carbon-green certificate-electricity market coupling trading mechanism is proposed. Finally, the proposed mechanism is simulated using data from Fujian, China, and the results prove the effectiveness of the proposed mechanism.

Solid electrolyte is a new kind of material which has been widely used in battery field in recent years. In recent years, due to the extensive progress of battery technology, various air-stabilized solid electrolytes have been developed, and this stable transport characteristics enables solid electrolytes to be applied to transistor devices. In this paper, the solid electrolyte (LAGP) Li_1.5Al_0.5Ge_1.5(PO₄)₃ was prepared by magnetron sputtering method, the capacitance of LAGP was studied, and the electric double layer effect of lithium-ion solid electrolyte LAGP was used as gate dielectric of metal oxide transistor (MOS transistor), and Si was used as the substrate. In₂O₃ is a semiconductor layer, Al is used as the electrode, and the bottom gate top electrode structure is used to make a good switching ratio (<10⁴ ), good transfer curve characteristics of the transistor. These results demonstrate that Li-ion solid-state electrolytes can be used as advanced thin film materials to replace transistor gate dielectric.

In the context of high investment in electrical power generation, electrical equipment operates under high-frequency and high-voltage conditions for extended periods, leading to overheating and component damage, resulting in frequent accidents. Real-time monitoring of electrical equipment is essential to fulfill its preventative, reliability, regulatory, realtime, and security functions. This paper elucidates the principles of infrared temperature measurement and summarizes the current strengths and weaknesses of temperature measurement technologies. It introduces the principles of fluorescence optical fibers, fiber Bragg gratings, semiconductor optical fibers, and distributed optical fiber temperature measurement technologies, analyzing their advantages and disadvantages while emphasizing the stability of optical fiber temperature measurement. The paper concludes by summarizing and prospecting the main temperature measurement methods in power systems.

The cooling chain system in nuclear power plant is designed to efficiently export residual heat and discharge sensible heat. However, its current operation primarily focuses on meeting the peak cooling demand, without comprehensively examining the effects of real-time changes in cooling demands, seawater temperature, and ambient temperature on the system’s performance. In this paper, we analyze the operation optimization of cooling chain system in different scenarios and consider coupling characteristics between subsystems in cooling chain system. We propose a Nonlinear Programming (NLP) model for the scheduling problem and employ the differential evolution algorithm to solve it. Numerical simulations reveal that the proposed method effectively reduces the energy consumption and ensure the system safety.

The analysis of the dissolved gases present in the transformer oil is a crucial aspect in understanding the transformer's operational status and identifying potential issues within the transformer. The analysis of dissolved gas in transformer oil is a crucial ability for transformer operators. This article presents a standard device for recycling transformer oil. It can extensively purify the used transformer oil and transform it into standard transformer oil, with a recycling rate exceeding 95%. When used to treat reclaimed transformer oil, the device's degassing efficiency for the recovered oil is higher than 90%. Additionally, the difference between the configured transformer standard oil and the preset value is less than 10%, and the component loss rate after 168 hours of storage is less than 5%. In practical training, the per capita fuel consumption was reduced to 0. 2L, and the training cost was cut by 82%. Furthermore, by reducing the purchase, storage, and transportation of transformer oil, he safety of the training process has been significantly improved.

The transcritical CO₂ cycle mode has become one of the most popular research topics in the refrigeration field due to its advantages such as environmental friendliness, high-temperature heating performance, and adaptability to low environmental temperatures. This article discusses the thermodynamic principles and research progress of DMS technology for transcritical CO₂ refrigeration heat pumps, providing reference for academic research and engineering applications. The research results indicate that the dedicated mechanical subcooling technology (DMS) for the transcritical CO₂ cycle has broad development prospects in the field of refrigeration and heating. DMS technology can increase the COP of the transcritical CO₂ cycle by more than 20%, and in some scenarios, it can already achieve the effect of replacing artificial synthetic refrigerants.

The self-cleaning property of superhydrophobic makes the coating applicable in the anti-pollution flashover aspect for electrical external insulation. To date, in the studies on the anti-pollution flashover capability of superhydrophobic coatings, only inorganic dirt is used to artificially pollute the superhydrophobic coatings, and the effect of organic pollutants on the anti-pollution flashover capability of the coatings is not taken into account. In this paper, methyl-silica superhydrophobic coatings with insulating properties were prepared, and the coating samples were artificially polluted by both organic and inorganic pollutants, and the surface leakage current amplitude of the polluted coating was tested under humid environment to reflect the anti-pollution flashover capability of the coating, and the influence of different degrees of staining, electric field strength and surface dirt wetting time on the anti-pollution flashover ability of the coating were analyzed.

In order to improve the accuracy of the equivalent circuit model, on the basis of the second-order RC model, the effect of temperature on the hysteresis electromotive force is investigated and a temperature influence coefficient is added to the single-state model; the effect of temperature on the actual usable capacity of the battery is analyzed, and a correction coefficient considering the capacity loss is proposed. Finally, the dynamic current working condition is simulated and verified by experiments using the constructed model. The results show that the established model can fit the operating characteristics of the battery well, and the error with the experimental voltage is only about 20 mV, which is about 2.48% higher than that of the traditional second-order RC model.

Heat source tower heat pump systems are receiving more and more attention because they take into account cooling and heating, have no frost problem, and are not limited by geographic and geological conditions. In this paper, the heating energy consumption and thermodynamic economy models of heat source tower heat pump and air source heat pump are established, and then the performance of heat source tower heat pump and air source heat pump is compared by simulation experiment. The experimental results showthat the heat source tower heat pump system saves 12.88% of the operating cost compared with the air source heat pump system. In summary, the energy saving and thermal economy of the heat source tower heat pump system are better than that of the air source heat pump system

Disc suspension insulators are common equipment in power transmission and distribution systems, and may be invaded by various foreign objects during long-term operation, leading to equipment failures and power accidents. In order to improve the detection effect and efficiency of foreign objects in disc suspension insulators, a real-time semantic segmentation based infrared detection method for foreign objects in disc suspension insulators is proposed. By using an infrared thermal imager, infrared images of disc suspension insulators are collected, and the grayscale values of the infrared images of disc suspension insulators are calculated for denoising, and foreign object features of disc suspension insulators are extracted. On this basis, based on real-time semantic segmentation technology, infrared detection of foreign objects in disc suspension insulators is achieved. The experimental results show that the proposed method has good infrared detection performance for foreign objects in disc suspension insulators, and can effectively improve the infrared detection efficiency of foreign objects in disc suspension insulators.

With the promotion of energy conservation and emission reduction in China, lithium-ion batteries as a clean energy source have been widely used in military, aerospace and other fields due to their own characteristics. However, due to the possible hazards of lithium batteries, a reasonable prediction of lithium battery RUL is necessary. Traditional prediction methods have lower prediction accuracy and stability due to the capacity regeneration problem existing in lithium batteries. In this paper, the discrete wavelet algorithm is used to decompose and reconstruct the data. After better studying the data features, the battery RUL is predicted using the GRU neural network. Through the lithium battery data slet provided by NASA, the prediction results of the DWT-GRU model are compared with the GRU model, and the results show that the prediction accuracy is improved by nearly 30%, which proves the accuracy and robustness of the method.

In the rapidly advancing fields, particularly in electric vehicles, addressing voltage imbalances in series-connected battery packs is crucial for optimizing battery performance. This paper introduces a novel approach using a leakage magnetic selfexcitation driven virtual parallel battery equalization circuit to rectify these imbalances. The circuit design is based on a unique configuration founded on leakage magnetic self-excitation, enabling virtual parallel interconnection of battery packs through leakage magnetic induction. This innovative strategy achieves automated voltage equalization. To address the issue of voltage imbalances within serially connected battery packs, the study examines the complex structure and high costs associated with conventional voltage equalization circuits. The introduction of a leakage-type self-excitation driven virtual parallel battery equalizer circuit is shown to be imperative. This circuit aims to simplify complexity, reduce costs, and enhance battery pack performance. The exposition provides a comprehensive explanation of the architecture and operational principles of the leakage-type self-excitation-driven virtual parallel battery equalization circuit. The circuit design incorporates a leakage coil into the battery pack, utilizing the principle of leakage magnetic induction to achieve virtual parallel battery interconnection through self-excitation. Fine-tuning of circuit parameters leads to the automated attainment of voltage equilibrium among the batteries, providing significant application value and growth potential. The proposed approach is validated through simulation and analysis, offering not only novel insights but also innovative methodologies for advancing battery management technology.