The high proportion of new energy grid connection leads to the reduction of power system inertia and the more complex spatial and temporal distribution characteristics of power grid frequency.It is urgent to clarify the frequency stability discrimination of power grid in new scenarios. In order to examine the frequency characteristics and frequency stability discrimination in the new scene, firstly, through the analysis of the new scene characteristics, the variables that can reflect the frequency time series characteristics are selected. After Pearson correlation analysis and feature hierarchical clustering dimension reduction, the feature quantities that are strongly correlated with the frequency and easy to measure are selected as the key response feature quantities. Through the correlation curve of its response characteristics with the frequency, the frequency is studied with the change of its feature quantities in the system stability and instability state, and the frequency criterion is constructed. The criterion can identify the fault unit while judging the stability. Finally, the effectiveness of the method is verified by an actual power grid example.

 Compared to power systems that use traditional RS485/CAN buses as transmission methods, the new power system has put forward new requirements for the radiation, efficiency, and power of information transmission methods Therefore, the article first introduces common information transmission methods, then elaborates on the principle of information power composite modulation from the essence of power electronics, analyzes the advantages and disadvantages of common digital modulation methods, and finally summarizes the current urgent problems and development prospects. The application of information composite modulation technology is quite extensive. When combined with the current flexible, efficient, and environmentally friendly distributed energy, it can not only improve energy utilization efficiency, reduce energy consumption and emissions, but also promote the development of new power systems and renewable energy, making important contributions to achieving sustainable global energy development.

The intermittency and volatility of high-proportion renewable energy access to the power grid have a significant impact on system stability. Therefore, it is crucial to evaluate and improve the stability of distribution networks with high proportions of renewable energy sources（RES）。This paper proposes a new power grid strength evaluation index, the integrated short circuit ratio （ISCR），which not only considers the interaction between RES, but also considers the impact of energy storage devices （ESD），and can more accurately identify weak links in the power grid. In addition, based on ISCR, a distribution network stability improvement method is proposed to allocate the location and capacity of RES and ESD through a two-level optimization method to enhance system strength and increase the capacity of RES and ESD. In the upper level, the location and capacity of RES are optimized to ensure the maximum ISCR value at the node. In the lower level, the location and capacity of ESD are optimized to ensure that the ISCR value is higher than the critical short circuit ratio （CSCR）to maintain system strength. The location optimization is determined by the ISCR value at the node. The capacity optimization is solved using linear programming methods. Finally, a case study verifies the effectiveness of the proposed optimization method in maintaining grid strength while increasing the capacity of RES and ESD.

Integrated Photovoltaic Power to Hydrogen and Refueling （IPp2HR）systems effectively utilize solar energy resources,providing green hydrogen for hydrogen-powered transportation and other industries.They are a promising pathway for green hydrogen demonstration.However,current research on IPp2HR systems either overlooks the operational constraints of purification or focuses solely on day-ahead scheduling.Traditional purification systems use fixed operational sequences to dry crude hydrogen,which conflicts with the flexible,variable-load operation required to accommodate renewable energy fluctuations.To address this,a bi-level energy management method is proposed to improve IPp2HR system efficiency.First,a comprehensive model covering power to hydrogen,purification,storage,and refueling is developed.The purification process is transformed into a Mixed-Integer Linear Programming （MILP）model using the Big-M method and integrated into the scheduling framework.Second,a bi-level energy management framework is designed,combining day-ahead and rolling scheduling with real-time control.The day-ahead and rolling stages determine the on/off of electrolyzers based on PV forecasts and hydrogen demand,while the real-time stage adjusts power deviations to enhance PV utilization and operational benefits.A case study based on a hydrogen refueling station in Northeast China validates the proposed method.Results show that considering the purification heating and cooling logic prevents high-cost hydrogen caused by the inability to shutdown at high temperatures.The bi-level framework effectively coordinates day-ahead,rolling,and real-time stages,improving both PV utilization and operational profitability.

Carbon Capture, Utilization, and Storage （CCUS）technology is of great significance in global climate change mitigation and achieving carbon neutrality. However, its large-scale commercial implementation is restricted by low investment returns. This paper systematically reviews the progress of research on CCUS investment benefits from both domestic and international perspectives, analyzing the impacts of technological innovation, economic costs, and policy support on investment returns. It also assesses regional differences worldwide in terms of policies, technologies, and economic benefits. The study reveals that while CCUS technology shows great promise, its economic feasibility and insufficient policy incentives are still key barriers to its deployment. By summarizing the shortcomings and challenges in current research, this paper provides directions for future studies and theoretical and practical references for CCUS commercialization and policy-making.

For the future of high power electronics penetration, the stable operation of grid-forming inverters in the power system is crucial. Due to the high inertia and high damping generated by the mechanical rotor and damping winding of the traditional synchronous machine, the inverter may have obvious synchronous frequency resonance problems in the output power after grid connection due to the lack of inertia and damping and the negative damping introduced by coupling, which not only affects the waveform quality, but also leads to transient instability in serious cases. Four methods are proposed and analyzed for this problem, and finally the simulation verification is carried out by Matlab/Simulink 

In the process of achieving the goal of carbon peak and carbon neutrality, in order to achieve environmentally friendly and energy-saving development,electric vehicles have begun to take the stage.With the rapid growth of electric vehicle ownership in the market,the safety problems caused by electric vehicles have gradually begun to emerge.Spontaneous combustion and fire accidents of electric vehicles are common.They have caused serious economic losses and even life hazards to automobile manufacturers,consumers,and operators of charging equipment.The charging safety of electric vehicles has begun to restrict the development of the electric vehicle industry.In this paper, starting from the constant voltage and constant current charging method, through the analysis of the causes of electric vehicle charging faults, a data mining early warning method based on discrete point detection of Gaussian distribution is proposed.The characteristic of outlier detection method is that it can effectively distinguish the data with significant difference from a set of data sets.A large amount of data will be generated during the charging process of electric vehicles. The Gaussian distribution model of normal charging state is obtained by obtaining the data under normal charging.The outlier detection method is used to judge whether the charging of electric vehicles is in a dangerous state, and the charging fault is warned in time. The real-time monitoring and early warning of electric vehicle charging process are realized. The experiment proves that the outlier detection method has good feasibility and accuracy for electric vehicle charging safety early warning .

Large-scale cross-season heat storage technology can effectively solve the problem of winter and summer heat imbalance of new energy heating.In this paper,a new type of zonal seasonal heat storage tank is proposed,and the heat dissipation effect and annual thermal efficiency of the heat storage tank under two different storage/release modes are calculated and compared by numerical simulation.The results show that the heat storage tank can maintain high annual thermal efficiency under both storage and release modes.Mode 2 adopts the strategy of "the last one goes first"， although the thermal efficiency of each zone is abandoned,it ensures that at least half of the zones are working under a higher heat utilization rate,and the annual thermal efficiency of the whole heat storage tank can reach 88.25%.Under mode 1，the annual thermal efficiency of the heat storage tank is 87.55%，which is slightly worse than that of mode 2， but it can ensure the stability of the hot water temperature throughout the heating season.

With the transformation of the global energy structure and the improvement of environmental protection awareness, as an efficient and environmentally friendly way of energy utilization, the distributed generation system based on new energy is gradually occupying an important position in the power system. With the continuous improvement of the penetration rate of distributed generation system, the equivalent inertia of the power grid continues to decrease, which seriously affects the frequency stability of the power system. As a result, virtual synchronous control has emerged, which can compensate for inertia and equivalent damping of the grid by simulating the dynamic characteristics of traditional rotary synchronous generators. Although the small signal stability control of virtual synchronous machine （Virtual synchronous generators ,VSG）has been widely studied, the influence of its reactive power loop on the transient stability of the large signal of the system still needs to be further studied. In this paper, the influence of the reactive circuit of the grid-connected inverter on its transient stability is analyzed theoretically, and on this basis, three methods that can effectively improve the transient stability of the grid-connected inverter are listed. Finally, the simulation was verified by MATLAB/Simulink platform.

Renewable energy sources such as hydrogen, photovoltaics, and wind energy are important ways to achieve the dual carbon goals and carbon emission control goals. Due to the significant fluctuations in renewable energy generation, the dual risks of load loss and power abandonment are highlighted, which puts higher demands on the flexibility of energy systems. This article proposes a low-carbon scheduling model based on flexibility quantification for a high proportion renewable energy integrated energy system. Firstly, analyze the operational characteristics of flexible resources in multi energy coupled equipment, construct a comprehensive energy system flexible resource model, and combine the utilization and emission of carbon dioxide in system equipment to achieve deep utilization of carbon dioxide within the system；Secondly, considering the source load fluctuation of the system, combined with the quantitative difference in flexibility between the integrated energy system and the power system, the quantitative analysis of energy, power, and slope flexibility of the park system is achieved；Finally, combining the differentiated regulation characteristics of system flexibility resources with the supply-demand differences of system flexibility at different time scales, multi time scale scheduling is carried out to mitigate the flexibility requirements brought about by the uncertainty changes of renewable energy and small time scales of load. This article takes a comprehensive energy industrial park in the south as the experimental background, and uses the method proposed in this article to analyze and schedule, verifying the power and electricity balance of flexible resources at different time scales, which is conducive to improving the flexibility and low-carbon characteristics of the comprehensive energy electricity-gas-heat system.

 In order to further analyze the influence brought by the increasingly obvious characteristics of power network electronization, the monitoring of power network operation condition extends to the direction of interharmonics and higher harmonics, and puts forward higher requirements for synchronous wide-frequency measurement. The windowed interpolation spectrum analysis method widely used in synchronous wide-frequency measurement has spectrum leakage, which will lead to large errors when the fundamental frequency is shifted or there are neighboring signals in the signal. To solve this problem, an improved wide-frequency phasor measurement method based on Blackman window all-phase FFT bispectrum line correction is proposed in this paper. The spectrum of wide-frequency signals is analyzed by combining all-phase FFT and Blackman cosine window, and the parameters are corrected by using bimodal spectral lines. This method can effectively improve the suppression effect of spectrum leakage, reduce the interference of adjacent spectrum, and improve the measurement accuracy of wide-frequency signal parameters in complex operating conditions of fundamental wave frequency offset or adjacent spectrum.

Thermoionic converters and thermophotovoltaic converters are two primary solid-state thermoelectric converters capable of operating at extreme temperatures with the potential for high efficiency,making them suitable for ultra-high temperature thermal energy storage applications.Both rely on the transfer of fundamental energy carriers through highly non-isothermal junctions:electrons in thermoionic converters and photons in thermophotovoltaic converters.However,the performance of both converters is constrained by factors such as the Stefan-Boltzmann law and space charge effects,preventing further enhancement.Nevertheless,when the distance between the emitter and absorber is comparable to or less than the thermal radiation characteristic wavelength,the performance of both converters can be significantly enhanced due to the photon tunneling effect generated by evanescent waves and the mitigation of space charge effects.Therefore,it holds significant importance in fields such as waste heat recovery and renewable energy utilization.This paper reviews the research progress of domestic and foreign scholars in the two types of solid-state thermoelectric converters and their hybrid systems,summarizes and analyzes potential future directions and key challenges.

In the energy transition environment, the electric-gas integrated energy system has developed rapidly. However, the increase of complexity also challenges the safe and stable operation of the electric-gas integrated energy system. In order to better evaluate the operational risk between the two, this paper presents a risk assessment method that can reflect the risk of the electric-gas integrated energy system by using digital twin technology. By monitoring and simulating the operating state of the digital twin model, the operational risk of the electric-gas integrated energy system can be evaluated effectively. This method has the advantages of high precision and reliability, and has a wide prospect in practical application. In this paper, the original data of the system during normal operation are calculated by ox method to obtain the operating state of the system voltage, pressure and power flow. Then, the integrated energy system risk index is calculated to generate a database, and finally the calculated index data is evaluated and predicted using CNN-XGBoost technology. The example results show that the method used can improve the accuracy and efficiency of safety risk assessment and prediction of electric-gas system well.

With the increasing proportion of photovoltaic power generation connected to the distribution network, voltage out of limits and fluctuations have become increasingly serious. A voltage coordination control strategy for distribution network zoning is proposed to address this issue. In terms of distribution network zoning, based on the voltage sensitivity index information obtained from day-time power flow calculation, the voltage change index suitable for distribution network zoning is constructed by calculating the changes in the voltage of each node affected by photovoltaic active and reactive power regulation. The regional division of each node is determined based on the criterion that this index meets the voltage change threshold requirements；In terms of voltage coordination control of the zone, a voltage sensitivity control strategy within the zone is adopted to pair nodes and utilize the photovoltaic active and reactive power regulation ability to control all out of limit voltages within the zone to the normal range. Based on this, an interval distributed voltage coordination control strategy is adopted to achieve interval state variable equivalence and optimize the dynamic reactive power compensation of photovoltaic systems between adjacent zones based on the alternating direction method of multipliers （ADMM）algorithm, in order to achieve optimal suppression of expected voltage fluctuations across the entire network. Taking the IEEE 33 node system as an example, numerical analysis is conducted to verify the effectiveness of the proposed strategy.

Under the background of "dual carbon"，the energy consumption in parks tends to be clean and low-carbon. The scaled development of distributed photovoltaic and energy storage can optimize the energy consumption structure and cost in parks. How to optimize resource allocation and achieve green energy consumption in parks is a current research hotspot.This paper will focus on the demand for optimizing the capacity configuration of distributed photovoltaic and energy storage in parks,and study the optimal configuration method of distributed photovoltaic and energy storage from the perspective of energy consumption cost.The goal is to minimize the annual energy consumption cost and the grid variability coefficient,and an improved adaptive weight particle swarm optimization algorithm is used to establish a two-level optimization model to propose a method for optimally configuring distributed photovoltaic and energy storage capacity for park energy consumption. An example application is carried out in a park energy consumption scenario to verify the feasibility and effectiveness of the model selection and optimization algorithm.

The use of electric hydrogen generation to consume the wind power abandoned in the integrated energy system with high percolation rate is an effective method to save energy and reduce carbon,but there exists the problem of improper power distribution of electric hydrogen generation array operation,which leads to the serious imbalance of the life span of each single tank,and greatly reduces the life span of the whole system of electric hydrogen generation, which needs to be solved urgently.The paper proposes a multi-timescale regulation strategy for the integrated energy system that takes into account the rotational start/stop of the electric hydrogen array.In the day-ahead phase,the rotational start/stop strategy of the electric hydrogen array is designed to equalise the system life depreciation；in the intra-day scheduling phase,the economic and low-carbon objective is to ensure the supply of the load demand；and in the real-time phase,the day-ahead real-time purchased power deviation is offset by the flexible use of the energy storage,so as to minimize the impact of the stochastic volatility of the lower-level integrated energy system on the power grid.The real-time phase,the energy storage is used to flexibly offset the day-ahead-real-time power purchase deviation to minimise the random volatility of the lower-level integrated energy system on the grid.Finally,engineering examples are presented to verify the economic,low-carbon and reliability advantages of the strategy.

In the context of facing the increasing number of stakeholders and the physical coupling of the regional integrated energy system, an innovative scheduling strategy is proposed, which takes into account the demand response of multiple parties and adopts a multi-subject game for optimization. First, the actual characteristics of different loads in each microgrid and the response behaviors of users are studied in depth, and a corresponding multi-load integrated demand response model is established. Then, a multi-subject master-slave game model is constructed by taking the system operator as the upper layer leader and the microgrid load aggregator, centralized energy storage plant and wind farm as the lower layer followers. In the upper layer model, the goal is to maximize the system operator's revenue from energy sales, and to determine the unit price of energy sales and compensation price of each microgrid through optimization. And in the lower layer model, it is committed to minimizing the comprehensive cost of all parties, and determines the energy supply of wind farms, the energy output between multiple devices in each microgrid, and the charging and discharging strategies of energy storage plants through optimization. In order to solve this complex problem, the particle swarm algorithm is finally used to solve the upper layer subject model and the Cplex solver is used to solve the lower layer slave model, which interact with each other to finally obtain the equilibrium strategy after the game.

This article proposes a short-term wind speed prediction method based on improved adaptive noise complete set empirical mode decomposition （ICEEMDAN）and particle swarm optimization （PSO）long and short term memory neural network （LSTM）models. Use ICEEMDAN algorithm to decompose daily wind speed data and calculate corresponding marginal spectra, and screen historical data based on spectral correlation；Using PSO algorithm to optimize LSTM neural network parameters, ICEEMDAN decomposition is performed on the input data, and multiple modal components obtained are predicted using PSO-LSTM. The wind speed prediction results are obtained by overlaying the predicted values of each component. Use the proposed method to predict the wind speed of a domestic wind farm, and verify the effectiveness of the proposed method through comparative analysis.

The high proportion of renewable energy networking,represented by wind energy,has brought new challenges to the power system.Hydrogen energy storage technology is an effective way to smooth out fluctuations in renewable energy power and improve the economic and low-carbon performance of comprehensive energy systems.On the basis of analyzing the power regulation capability of a high proportion wind power interconnection system,it is pointed out that wind hydrogen coupling can reduce system wind abandonment and power shortage.The worst-case scenario cost is an important indicator for evaluating the operational status of a system under uncertain factors.Based on uncertain scenarios,a stochastic p-robust optimization method combining basic stochastic optimization and robust optimization is proposed to ensure stable operation of the system in the worst-case scenario.Taking into account both economic and environmental benefits,a unit commitment optimization model with dual objectives of expected cost and carbon trading cost was established under p-robust constraints.The results of the example show that the stochastic p-robust optimization method effectively reduces the expected cost of the system.The established unit combination optimization model can flexibly optimize the output of multi energy systems based on different objective weights,reduce abandoned wind power,and improve wind power utilization.

To improve the accuracy of low-voltage DC cable location,an improved empirical wavelet transform（IEWT）method and an improved double-end traveling wave criterion method are proposed.This method first uses the double-end traveling wave method to obtain the current signals at both ends of the faulty cable,and then decomposes and reconstructs the current signal through IEWT to obtain processed intrinsic mode function （IMF）components, which are screened to effectively avoid mode mixing in the decomposition of complex noise signals.Finally,the selected IMF components are detected using kurtosis rules,and the position of the fault point is determined by using the improved double-end traveling wave fault criterion.Simulation results show that this method has high accuracy and meets the requirements of engineering practice for location.

How to achieve efficient and precise control of multi-band radiation properties is a common scientific challenge in military camouflage,aerospace,solar energy and other fields.Conventional radiation property control often uses inefficient trial-and-error optimisation of functional groups or micro-nanostructures,which is time-consuming, laborious and difficult to obtain the best radiation properties.The emergence of machine learning has overturned the traditional optimisation methods and greatly improved the efficiency of radiation property optimisation and design by simulating the brain's learning and thinking.In this paper,machine learning algorithms in radiation property regulation are discussed in detail,and their advantages and challenges in terms of accuracy,scalability and efficiency are evaluated；the advanced results of the fusion of machine learning and radiation property directional regulation are summarised in a systematic way,including forward radiation response prediction and material directional optimal design；and finally,the hot spots of the research on the combination of radiation property regulation and machine learning and the direction of future development are explored.By reviewing the existing literature,this paper provides a reference for the design and application of radiation property directional regulation and machine learning algorithms, and makes suggestions for further optimisation and innovation of radiation property directional regulation.

In order to achieve high-precision ultra-short-term prediction of wind power,this study conducted cross-domain feature selection based on Wasserstein distance and Random Forest （RF），and combined it with Evolutionary Bagging （EvoBagging）。A new method for ultra-short term wind power prediction is proposed.Firstly,the Local Outlier Factor （LOF）algorithm is used for outlier detection,and K-Nearest Neighbors InterpolationK-NNI is used to replace outlier points in the original data.Secondly,the data after outliers were decomposed by Empirical Mode Decomposition （EMD）algorithm and statistically calculated to build the reconstructed data,and Wasserstein distance and RF cross-domain feature selection were used to reduce the feature dimension of the reconstructed data.Finally,in order to combine the advantages of each model to improve the prediction accuracy of the model,It is constructed with Deep Belief Network （DBN），Deep Neural Networks（DNN），Light Gradient Boosting Machine （LGBM）and eXtreme Gradient EvoBagging ensemble learning ultra-short term wind power prediction model based on Boosting（XGBoost）learner.It is proved that the prediction error of this model is reduced by 5%on average compared with that of a single model,and it can achieve high precision prediction of ultra-short term wind power.

A high proportion of new energy power sources connected to the grid will lead to power system voltage with volatility and randomness,thus reducing system voltage stability.As a key reactive power compensation device in new energy stations,distributed synchronous regulators play a significant role in enhancing system voltage stability.However,in the current new energy power system,how to configure these distributed regulators in an economically reasonable and reliable way needs to be explored in depth.To this end,a new distributed regulator siting and capacity-setting method focusing on voltage stability constraints is proposed in the paper.First,the static voltage stability index of the system is evaluated to guide the selection of the optimal installation location of the regulator to ensure that the selected location can most effectively improve the system voltage support capability.Subsequently,on the basis of site selection,the capacity configuration of the regulator is reasonably planned with the objectives of minimizing the investment cost and maximizing the operational reliability of the system.Finally,through the analysis of actual cases, this method is not only economical and efficient,but also provides a strong guarantee for the stable operation of the new energy power system.

Based on the unpredictable AC-DC intrusion caused by human error or detection equipment failure in the terminal of secondary screen cabinet of substation relay protection, a new intrusion signal detection method based on generative adversarial network and principal component analysis is proposed to solve the problem of poor quality and small quantity of data samples. Gaussian kernel smoothing is used to preprocess the terminal data to reduce noise interference, and then the cleaned data is amplified through the generated adversarial network （GAN）to meet the subsequent principal component analysis processing and fault detection and identification of AC and DC intrusion signals. The identification accuracy of the proposed method reaches 95%，which realizes the accurate detection of the small sample fault data of the terminal of the secondary screen cabinet.

Aiming at the lack of system operation flexibility caused by the low utilization rate of flexibility resources during the operation of virtual power plant cluster （VPPC），this paper proposes an optimal scheduling strategy for VPPC that takes into account the mutual benefit of flexibility resources.Firstly,based on the trading relationship between VPPC and the electricity spot trading market,a VPPC trading model is constructed in which VPPC counts and flexibility resources are mutually beneficial.Under this trading model,the supply-demand relationship between VPPC operating flexibility under the source-load uncertainty scenario is further analyzed with respect to the flexibility shortage phenomenon,and the VPPC flexibility mutual aid service fee settlement model is established.Secondly,the impact of participation in flexibility mutual aid service on the energy interaction between the lower virtual power plants and the economic operation of the upper cluster is studied in depth,and a two-layer optimization model of master-slave game is constructed to take into account the interests of multiple subjects,and a master-slave game equilibrium algorithm based on the Kriging meta-model is used to simulate the operation of each virtual power plant and reasonably formulate the price of mutual aid for the energy of virtual power plant clusters；lastly,the proposed solution can not only enable the cluster operator to earn a profit but also provide the cluster operator with a reasonable price for the energy of the cluster.Finally,it is verified through case studies that the proposed scheme not only enables the cluster operator to earn profits from the mutual aid service,but also reduces the power supply pressure on the power grid,lowers the operating costs of each virtual power plant,significantly improves the overall operational flexibility of the system and reduces the operating costs of all parties involved in the service.

Short-term power load data are characterized by complexity and uncertainty, which often have an uncontrollable impact on the prediction results of the data. When short-term electricity load data are clustered and analyzed using traditional clustering methods, the prediction results will be biased due to characteristics such as the uncertainty of electricity load. In addition, considering that global regression forecasting methods are unable to use different modeling approaches for different parts of the data in the modeling stage, which limits the problem of adaptive capability for different distribution regions or different subsets of features. In this paper, we adopt the density peak clustering algorithm with K-nearest neighbor and weighted similarity to classify the features of short-term electricity load data, and propose a locally weighted linear regression model using K-nearest neighbor to forecast short-term electricity load. The advantages of this model are that it avoids the influence of Euclidean distance on the selection of cluster class centers, reduces the negative influence of global data on local data, avoids the centralized effect of cluster class division, and improves the generalization ability of the model. By comparing with fuzzy C-mean clustering and traditional global regression prediction methods, the model proposed in this paper is more superior for the prediction of real power data.

The research aims to explore the new construction and renovation strategies for energy-saving transmission conductors.By comparing and analyzing the application of five types of conductors in practical engineering,including aluminum-clad steel-cored （high-conductivity）aluminum stranded wire,aluminum alloy-cored （high-conductivity）aluminum stranded wire,medium-strength aluminum alloy stranded wire,steel-cored high-conductivity aluminum stranded wire,and aluminum alloy-cored aluminum stranded wire,combined with the principle of energy-saving conductors,the investment and operating costs of transmission conductors under different voltage levels （+800kV DC，500kV AC，220kV AC，110kV AC）and their energy-saving effects are evaluated.The research results show that the aluminum alloy core -type aluminum stranded wire has the best economic performance,and can cover the reconstruction cost and new construction cost through energy-saving benefits in a relatively short period of time.Further analysis shows that line length,annual utilization hours,and carbon price are key factors affecting the cost-benefit balance point.Therefore,using aluminum alloy core-type aluminum stranded wire for energy-saving transmission transformation can not only effectively reduce power loss and carbon emissions,but also bring significant economic benefits and promote the sustainable development of the transmission system.

Application of hydrogen-enriched combustion in natural gas generator unit could reduce CO2 emission and promote transformation development for low-carbon in Chinese electric power industry.In this paper,a simplified model of combined cycle using hydrogen-enriched natural gas was established for a 9FA gas-steam combined cycle generator unit.Variations of performance,carbon emission and their influences on power generation economy were all investigated under different hydrogen-enriched ratio and different ambient temperature.The results showed that：Hydrogen-enriched combustion in combined cycle lead a decrease of cycle efficiency and a significantly reduction of CO2 emission.When hydrogen ratio was increased from 0 to 30%，the revenue of generator unit decreased by 52.98%under computational conditions,which significantly affected the economy of combined cycle unit.Cycle performance was better at a higher ambient temperature,and the changing trend was maintained after the introduction of hydrogen.

Aiming at the uncertainty of distributed photovoltaic （PV）power generation and the overall low utilization rate of the equipment,which leads to the problem of rising cost faced by distribution network planning,a grid planning method considering the utilization rate of distributed PV equipment is proposed in the paper.By using information entropy to extract scenarios from PV output data,a set of typical scenarios is obtained.Based on these scenarios,a joint optimization model of distributed PV storage operation-planning is developed in the paper:at the upper level,the distributed PV and storage are selected and sited with the objective of minimizing the investment and construction cost and maximizing the utilization rate of distributed PV；at the lower level,the distributed PV power and storage are optimized with the objective of minimizing the cost of discarded light,network loss,operation and maintenance,and purchased power,and the planning model solves the optimization problem.In the lower layer,the distributed PV power and storage charging/discharging power in each time period are optimized with the objective of minimizing the abandoned light cost,network loss cost,operation and maintenance cost,and purchasing power cost,and a modified particle swarm algorithm is used as a method for solving the planning model.Finally,the IEEE 33 node system is used as an example for scenario analysis,and the results show that the proposed method can improve the utilization rate of PV equipment,improve the stability of distribution network operation,and reduce the comprehensive cost.

This paper proposes a capacity configuration method for a photovoltaic hydrogen storage coupling system that takes into account the flexibility constraints of distribution network operation,in response to the problems of high proportion of photovoltaic access leading to voltage exceeding limits,branch power imbalance,and high curtailment rate in the distribution network.Firstly,based on the improved K-means clustering algorithm,load scenarios are divided, and on the basis of considering voltage and power factors,as well as constraints such as Distflow's flow model and second-order cones of line voltage and current,distribution network flexibility indicators are established from both spatial and temporal perspectives；Secondly,taking into account constraints such as flexibility and power balance,an optimization configuration objective function is constructed with the goal of minimizing the cost of electricity per kilowatt hour；Then,an optimization operation strategy for the photovoltaic hydrogen storage coupling system based on net power conditions is proposed,and an improved particle swarm optimization algorithm is used to solve the optimization configuration model.Finally,the effectiveness of the optimization configuration method proposed in this paper is verified through a case study of the actual grid structure of the glass Kezi substation area in Jiaohe City,Jilin Province.

In order to mitigate the impact of extreme events on the integrated energy system, restore various loads as soon as possible, and improve system flexibility. This paper proposes a fault repair strategy that considers the coordinated operation of the regional electric and gas system （IEGS）at the level of transportation and distribution networks. Firstly, a traffic flow allocation model considering the real-time recovery index correction of the system is established through the cellular transmission model to predict the traffic flow of the transportation network. Secondly, under the premise of fault pre allocation, a fault recovery model for regional IEGS is established to coordinate and optimize resources such as maintenance teams, distributed power supplies, and distribution network structures, to reduce outage losses. Then, using the minimum travel time matrix of the maintenance team and the road modification parameters of the traffic network obtained from the real-time recovery indicators of the system as transfer variables, at the time point when the maintenance team starts moving, the subsequent repair plan and the operation of the traffic network are recalculated, and the multi time section optimization is performed. Finally, the effectiveness of the proposed strategy is verified through a numerical simulation comparison. And analyze the impact of mobile energy storage devices on fault recovery.

As distributed power sources access the distribution network in a wide range of multiple points,the power quality problems of the distribution network are highlighted.Distributed power sources are connected to the distribution network through grid-connected inverters,and the optimal control of grid-connected inverters can enhance the source-side improvement effect of power quality and fully exploit the regulation potential of the remaining capacity of the inverters.Therefore,this paper proposes a multi-objective optimisation model of distribution network power quality based on the control of grid-connected inverters of distributed power sources.Firstly,the quantitative model of the adjustable capacity of distributed power inverters is analysed to ensure the quality of power generation；then,according to the results of the power quality assessment of distribution network zones,the optimization model of grid-connected inverter control parameters is established,and the multi-objective optimization method of power quality based on inverter control is proposed；finally,the effectiveness of the proposed method is verified through simulation examples.Finally,the effectiveness of the proposed method is verified by simulation examples.The results show that,with the method proposed in this paper,the harmonics and unbalance of the distribution network are obviously suppressed, and the compensation effect is obvious,which is an effective means for the future optimisation of the power quality of a new type of distribution network.

The production of "green hydrogen"by electrolyzing water from offshore wind power is an important technological direction for promoting the consumption of new energy and achieving deep decarbonization in the power and chemical industries.With the shift of offshore wind power hydrogen production from nearshore hydrogen transmission to offshore hydrogen transmission,utilizing existing offshore oil and gas platforms and pipelines for centralized hydrogen production from offshore wind power is one of the main directions for obtaining"green hydrogen"in the future.However,the design and application of centralized hydrogen production equipment for offshore wind power are constrained by problems such as small insulation margin and difficult optimization design of medium voltage and high-frequency transformers.The article proposes an intermediate frequency isolated offshore wind power centralized hydrogen production equipment based on Modular Multilevel Matrix Converter （M3C）to address the above issues.The equipment uses an M3C converter in the front stage and a 12 pulse thyristor rectifier in the rear stage to achieve intermediate frequency isolation and avoid insulation design difficulties.The high current stress of the thyristor enables high-power hydrogen production,and its key parameters are optimized.Finally,a simulation platform for the proposed hydrogen production equipment was built using MATLAB/Simulink simulation software to verify its effectiveness.

In order to adapt to the "dual carbon"transformation goal of future electric power development in Jilin Province,considering the role of multiple influencing factors under the new situation,it is necessary to integrate social and carbon emission related influencing factors to improve the accuracy of power demand forecasting.In the current context,the existing models are still facing challenges in terms of stability and accuracy of electricity demand forecasting.In order to address these challenges,firstly,multiple factors affecting power demand are analyzed through system dynamics model.Based on rigorous correlation analysis,key indicators that have a significant impact on power demand are further screened.Six strongly related indicators,namely permanent population,industrial added value,total energy consumption,low-carbon index of energy consumption structure,per capita GDP and GDP,were determined, and the introduction of carbon emission indicators was increased,highlighting the innovative attention in the "double carbon"aspect.Then,Particle Swarm Optimization （PSO）was used to optimize the key parameters of the Support Vector Machines （SVM）model,and the PSO-SVM power demand prediction model was constructed.The problem that the existing model is easy to fall into the local optimal solution is overcome.The effectiveness of the PSO-SVM model is verified by comparison with the traditional SVM model,BP model and the optimized PSO-BP model.In power forecasting,the model not only has high accuracy,but also shows a faster training speed.Finally,the forecast model is applied to the power demand forecast of Jilin Province from 2023 to 2028，which provides a strong support and reference for power planning and decision-making.

In order to meet the real-time requirements of the new power system for transient stability emergency control,a transient stability generator tripping control method based on generator current and angular frequency response characteristics is proposed.Firstly,the relationship curves between current and angular frequency are drawn when the system is stable and unstable,and the relationship between current and angular frequency and transient stability is studied.The key characteristics of power angle stability and power angle instability are extracted,and the transient power angle stability criterion based on I-ωresponse characteristics is constructed.Secondly,the slope characteristics of the I-ω relationship curve are studied,and the relationship between the slope of the relationship curve and the transient stability under different proportion of generator tripping control is explored.Based on the generator rotor motion equation,the relationship between the slope of the relationship curve and the amount of generator tripping control is derived,and a calculation method of emergency generator tripping control based on the slope of the relationship curve is proposed.After that,the power angle instability criterion is used as the starting criterion of emergency control,and the generator tripping index considering the influence of generator power angle and kinetic energy contained in the rotor is defined.The fast selection of the generator tripping control location is realized, and a reasonable allocation method of generator tripping control quantity is given.Finally,the proposed method is simulated in the classical second-order one machine infinite bus system and the New England 10-machine 39-bus system with wind turbines,and the effectiveness of the proposed method is verified.

In order to solve the problem of poor economy of series voltage compensation device designed for low voltage at the end of rural power grid, a low voltage control method based on series voltage compensation control is designed. Considering the influence of static characteristics of load voltage, the two indicators of line active power loss and average voltage deviation of each node are normalized and weighted as the objective function, and the method for selecting the location and capacity of compensation devices is proposed. The low voltage line at the end of an actual power grid is selected for simulation test, and the test results show that the method can effectively solve the low voltage problem at the end of rural power grid.

为给电力系统规划提供精准数据支持，提出一种改进自适应噪声完备集合经验模态分解（Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise,ICCEMDAN）和庥雀搜索算法（Sparrow Search Algorithm,SSA）改进长短期记忆神经网络（Long-Short Term Memory neural network,LSTM）的空间负荷预测方法。首先，应用箱形图离群点检测法对电力地理信息系统中Ⅰ类元胞历史负荷的离群点进行检测及校正；其次，运用ICCEMDAN技术将校正后的I类元胞负荷时间序列分解为不同频率和幅值的模态分量；然后，针对每个模态分量分别建立各自的LSTM模型，并利用SSA对各LSTM模型的参数进行优化，将得到的所有模态分量结果线性重构，得到目标年的基于I类元胞空间负荷预测结果；最后，应用空间电力负荷网格化技术，转化I类元胞的空间负荷预测结果为Ⅱ类元胞预测结果，并以幅值大小和空间分布两个维度评价预测精度。算例分析结果表明，文中所提出方法能够提升空间负荷预测精度。

With the rapid development of new energy,the difficulty of solving voltage stability problems in new power systems has increased,and traditional evaluation methods are no longer able to meet real-time and accurate requirements.Based on the Q-V curve theory,a sensitivity index for voltage stability evaluation of a new type of power system connected to the wind turbine grid has been established in the article.Firstly,the power system flow equation is used to calculate and analyze the changes in reactive power and voltage amplitude of the load,and sensitivity indicators are classified according to the results.Then,a weighted average algorithm is introduced to fit the sensitivity data over a period of time,and the fitted values are used to represent the sensitivity of the load during that period.Finally,the DIGDILENT simulation platform was used to conduct simulation verification on the IEEE 10 machine 39 node system.By comparing the sensitivity index and reactive power margin under different operating conditions,the effectiveness of this index in evaluating the voltage stability of the new power system was verified.

Under the goal of "dual carbon"，virtual power plant is an effective vehicle for optimizing multi-regional resource allocation and increasing renewable energy penetration.Against this background,the paper proposes a coordinated and optimal scheduling strategy for virtual power plants that considers the participation of waste incineration under the stepped carbon trading mechanism.First,a new power system structure including multiple power plants and multiple energy storage is constructed from the system structure.In order to fully explore the potential of power generation and gas production in waste incineration power plants,an analytical study is carried out for dry and wet waste electrical cogeneration,and a mathematical model of waste incineration power plants is established.Secondly, the joint operation mode of power-to-gas and carbon capture is adopted,and a carbon capture-power-to-gas-hydrogen fuel cell subsystem model is constructed to formulate the joint operation strategy of multiple power plants and multiple energy storage.Again,the carbon trading mechanism is introduced,and a laddered carbon trading calculation model is constructed and analyzed for the price base price,interval length and price growth rate in the model.Finally, with the objective function of minimizing the sum of thermal power cost,purchased energy cost,carbon emission cost, equipment maintenance cost and scenery cost,the coordinated optimal scheduling model of virtual power plant is established,and the model is optimally solved by using the CPLEX solver of Matlab software in multiple scenarios.

With the rapid development of mobile energy storage systems （MESS），their importance in power system dynamic response,renewable energy integration,and emergency power supply has become increasingly prominent.However,challenges such as multi-objective resource allocation,path optimization under transportation-grid coupling constraints,and planning-scheduling coordination remain unresolved.Traditional methods struggle to address these issues due to computational complexity and insufficient dynamic adaptability.In contrast,artificial intelligence （AI）algorithms,leveraging data-driven technologies like reinforcement learning （RL）and graph neural networks （GNN）， have achieved breakthroughs in dynamic scheduling,collaborative optimization,and security-economy trade-offs.This paper systematically analyzes AI applications in MESS planning and scheduling,summarizes the advantages of deep reinforcement learning （DRL）in uncertainty decision-making and swarm intelligence （SI）algorithms in distributed coordination,and identifies research gaps in battery state of charge （SOC）modeling and cross-domain collaborative scheduling.Furthermore,an AI-empowered innovative framework for MESS planning and scheduling is proposed, offering theoretical foundations and practical pathways to enhance the resilience,economy,and low-carbon transition of power systems.