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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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。

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

The configuration of energy storage power station on the grid side can effectively increase the peak shaving capacity of the system and the amount of wind and light consumption.However,the investment cost of electrochemical energy storage power station is high.How to reasonably configure the energy storage capacity is an important basis for improving efficiency and expanding application scenarios.From the perspective of the grid side,this paper proposes an optimal configuration method of energy storage capacity considering equivalent life loss and multi-dimensional benefits.Firstly,the life parameters of energy storage battery are analyzed,and the equivalent life loss model of energy storage is established to increase the adaptability of the model to the real scene.Secondly,the annual cost function of system operation cost and energy storage investment operation and maintenance is quantitatively calculated.At the same time,the income of peak-valley price difference of energy storage on the grid side,the income of auxiliary peak regulation and the income of delaying equipment transformation are taken into account.Finally,the constraint relationship between the equivalent life loss and the energy storage operation strategy is embedded into the model to solve the optimal configuration capacity of the energy storage side and the optimal charging and discharging operation strategy of the energy storage battery.The simulation results show that the model can effectively improve the economy of energy storage power station in the application scenario of this paper.

The accuracy of wind power prediction has a significant impact on the operation of the power system.This paper proposes a wind power ultra short term prediction method based on kernel principal component analysis combined with attention mechanism using short-term and short-term memory neural networks.This method performs kernel principal component analysis to reduce the dimensionality of all features in the wind farm dataset.And the Attention mechanism is introduced,using LSTM to allocate more attention to feature elements that have a significant impact on the power at the prediction time,in order to achieve more accurate prediction results.The experimental results show that the proposed prediction model can effectively achieve ultra short term prediction of wind power,and has higher prediction accuracy compared to traditional BP neural networks and LSTM networks.

In the context of carbon peaking and carbon neutrality,various industries in China are flourishing and their dependence on energy is increasing.New clean energy is gradually replacing fossil energy with its clean and pollution-
free advantages.However,due to the power fluctuation and intermittency of new energy photovoltaic power generation, and generally working at maximum power,it is unable to provide or absorb additional energy to respond to grid frequency events.At the same time,a large number of new energy and power electronic equipment are connected to the grid,leading to a sharp decline in the system's anti-interference ability.Therefore,in order to solve the above problems,this article uses energy storage devices to suppress the power fluctuations of photovoltaic power generation, improve the utilization rate of solar power generation,and improve the traditional second-order synchronous machine control of inverters.A first-order transient voltage control is introduced to simulate the third-order model of synchronous machines,and a joint active support control strategy for photovoltaic energy storage is proposed to provide better voltage frequency support for the power grid.The superiority of active support for photovoltaic energy storage is verified through simulation.

Battery energy storage systems （BESS）with bidirectional fast response capabilities can provide dynamic frequency responses to electricity grids,alleviating imbalances caused by fluctuating renewable power outputs.In order to ensure the economic effectiveness of BESS participating in frequency response services,it is necessary to reasonably optimise BESS capacity configurations together with operating strategies.From the perspective of the UK's Dynamic Moderation frequency response service market,this paper develops a closer-to-real-time energy management for BESS through dynamic adjustment of operational baselines so as to ensure that BESS comply with the market's State of Energy Management Rules （SoEMRs）and have sufficient energy footroom and headroom to completely cope with low-and high-frequency events.Furthermore,the techno-economic operation of BESS throughout its service life is simulated and translated into an equivalent annual annuity which is maximised to determine the best BESS capacities and operating strategy variables.Simulation results show that the optimised BESS can fully respond to all the frequency events and follow the SoEMRs for the majority of the time.The paper provides deep insights into the energy storage-friendly frequency response service markets in the UK that protects the benefits of energy-limited participants,which have reference significance and practical values for the development of ancillary service markets and the operation of BESS in China.

Grid-forming virtual synchronous generator （GFVSG）has certain virtual inertia and virtual damping support capabilities by introducing the rotor motion equation of synchronous generators,but it inevitably introduces active-power dynamic oscillation problems into the GFVSG grid-connected parallel systems.As one of the important improvement measures to effectively solve the active-power dynamic oscillation problems existing in the GFVSG grid-connected/parallel systems,transient damping methods have recently attracted widespread attention and have achieved rich theoretical research as well as practical application results.This paper analyzes the working principle and main role of transient damping methods in GFVSG control,summarizes the typical implementation methods and research status of transient damping methods,and analyzes,summarizes,and discusses the research prospects,possible challenges may be faced,and key technical problems that need to be overcome in the future of transient damping methods.

With the increase of wind power penetration rate,the response ability of doubly-fed wind turbines to grid frequency gradually weakens,which has a huge impact on the frequency stability of the system.The independent frequency regulation capability of the doubly-fed wind turbines is currently insufficient to meet the frequency deviation requirements of the system,and it will result in high wind power permeability and low energy utilization rate.Therefore, energy storage equipment will be installed on the grid side of the doubly-fed wind turbines to form a wind storage joint system to participate in the primary frequency regulation of the system.In the article,a variable coefficient wind storage joint system based on the rotor speed and state of charge （SOC）is proposed to participate in the primary frequency regulation control strategy by utilizing the rotor kinetic energy of the wind turbine and the output of the energy storage equipment to jointly undertake the primary frequency regulation power demand.The doubly-fed wind turbine responds to the virtual inertia link by releasing or absorbing its own rotor kinetic energy,and dynamically adjusts the virtual inertia coefficient through its own rotor speed,andthe energy storage device dynamically adjusts the virtual droop coefficient based on its own state of charge to participate in the frequency response droop process.Finally,by building a simulation model to compare the frequency modulation effects of variable coefficient method,fixed coefficient method, and wind power independent frequency modulation under step load disturbance and continuous load disturbance,it is verified that the control strategy in the article can effectively reduce system frequency deviation.At the same time,the doubly-fed wind turbine can fully utilize its inherent rotor kinetic energy,and the energy storage equipment can also output smoothly,alleviating its overcharging and discharging problems,andimprove its service life.

In the context of dual carbon,technologies such as Carbon Capture and Storage （CCS）and Power-to-Gas（P2G）are the key to realizing low-carbon economic operation of the Integrated Energy System （IES）。important means.In order to take into account the low-carbonization and economic benefits of IES,this paper proposes a low-carbon IES optimal dispatch model including CCS-P2G.First,at the technical level,IES combines a carbon capture power plant with a solution storage and a two-stage power-to-gas device,and analyzes the linear relationship between carbon dioxide mass and solution volume and CCS operating energy consumption and total output to further simplify the CCS model.At the same time,the conventional hydrogen storage tank model is improved,reducing the computational burden required.Secondly,at the market mechanism level,the paper introduces a carbon trading mechanism and a wind power trading mechanism.Finally,an optimal scheduling strategy based on minimizing the total operating cost of IES as the objective function is proposed.Different scenarios were set up for comparative analysis to verify the effectiveness of the scheduling plan.

The pipeline transportation system in thermal power plants is a critical infrastructure for the conveyance of liquids or gases,and the safety of the system's pipelines is of paramount importance.This paper,in conjunction with the current state of research both domestically and internationally,provides a comprehensive review of the hardware and software methods for detecting leaks in energy supply pipelines.Hardware methods for pipeline leak detection rely on sensor measurements,which are suitable for detecting small flow leaks but are costly and cannot provide continuous monitoring.Software methods for pipeline leak detection utilize computer analysis,which are more cost-effective, capable of rapid leak detection,but require a substantial amount of historical data and real-time sensor data.This paper also summarizes and analyzes the technical limitations of various pipeline leak detection methods and provides an outlook on future research directions.To enhance the efficiency and accuracy of leak detection,it is necessary to integrate multiple technical methods.Furthermore,the development of leak detection technology should be directed towards integration,intelligence,and automation,improving the stability of real-time data transmission and the precision of computational accuracy.

As an effective mechanism to achieve China's dual carbon goals,the impact of carbon emission rights allocation has become a crucial factor that needs to be considered in power quantity balancing.To address the issue of ineffective allocation of carbon emission rights resources under existing methods in the power industry,this study proposes an uncertainty power quantity balancing model under a carbon emission rights allocation scheme based on data envelopment analysis.Firstly,an analysis of carbon emission rights allocation policies is conducted,and a carbon emission rights allocation method based on data envelopment analysis is proposed.Secondly,considering the uncertainty introduced by the high proportion of renewable energy grid integration,a power quantity balancing model that incorporates carbon emission rights allocation is developed.The model is transformed into a solvable deterministic model using robust optimization methods based on multi-segment interval uncertainty sets.Finally,simulation results on the HRP-38 system demonstrate that the carbon emission rights allocation method based on data envelopment analysis can reduce carbon emission costs and effectively allocate carbon emission rights resources.This research provides technical support for formulating carbon emission rights allocation schemes in power quantity balancing analysis.

Because the scheme of energy storage unit inside the wind turbine has the advantages of flexibility and controllability,it is widely used in the field of fault ride through.However,the difference of energy storage unit is not considered in the previous analysis,which may cause power imbalance during the fault period and endanger the safe and stable operation of the system.To address this issue,a permanent DC fault ride-through coordinated control strategy based on precise control of wind farm energy storage is proposed.Firstly,the structure of the system,the energy storage control strategy,and the configuration of energy storage capacity are explained.Then,considering that one pole of the bipolar MMC-HVDC system still has the ability to operate normally when the other polar exits,the fault is divided into self-absorption and non-self-absorption cases according to the size of the unbalanced power.In the case of self-absorption,the unbalanced power is transferred to non-fault poles.In the case of non-self-absorption,the unbalanced power is precisely distributed to the internal energy storage units of the wind turbines based on the different State of Charge （SOC）levels.This ensures power balance in the system while reducing the differences between energy storage units.The capacity constraint of the energy storage units is addressed by the stepwise cut-off method,which ensures that the power balance is maintained when an internal energy storage unit is removed from operation.Finally,the effectiveness of the proposed fault ride-through scheme is verified through model simulation using the PSCAD/EMTDC platform.

Based on the inception mechanism of lightning upward leader from wind turbine blade,the critical inception height （CIH）is defined.Around obtained CIH and proportion of CIH,an assessment method of triggered lightning ability of multi-wind turbines based on CIH is proposed.Physical simulation model of lightning strike of wind turbine is established,the assessment method of triggered lightning ability of multi-wind turbines was applied to assess the ability of multi-wind turbines under different numbers,sizes,and thundercloud heights.The research shows that proposal of relevant assessment method provides important support for exploring triggered lightning ability of multi-wind turbines.Under different numbers and sizes of wind turbines,compared to single wind turbine,triggered lightning ability of multi-wind turbines shows a weakening trend.When height of thundercloud drops to a certain value,triggered lightning ability of multi-wind turbines will be stronger than that of single wind turbine when height of thunderclouds is higher.In addition,in scenario of multi-wind turbines,a decrease in number of wind turbines or an increase in size of wind turbines will lead to an enhancement in triggered lightning capability of multi-wind turbine.At the same time, lower height of thunderclouds is,greater enhancement of triggered lightning ability of multi-wind turbines is.The relevant conclusions will provide theoretical support and necessary reference for assessment of triggered lightning ability of multi-wind turbines under different conditions.