With the rapid development of my country's wind power industry, the service life of wind turbines has been extended, and the failure rate and maintenance costs have increased accordingly. Using artificial intelligence algorithms to mine wind power big data and achieve condition monitoring and fault diagnosis of wind turbines has important practical significance for improving the quality and efficiency of the wind power industry, and has gradually become a research hotspot in recent years. This article introduces the characteristics of the wind turbine supervisory control and data acquisition system and vibration signal data, and explains the framework of the intelligent algorithm for wind turbine condition monitoring and fault diagnosis. Relevant research results are summarized, and the challenges and development trends faced by wind turbine condition monitoring and fault diagnosis technology are prospected.

The accurate prediction of wind power is of great significance for China to achieve the goal of 'carbon peak and carbon neutrality '.Traditional wind power prediction methods often ignore the long-term dependence and spatial correlation in time series data, resulting in inaccurate prediction results. In order to solve this problem, this  paper  proposes  a  model  combining  Convolutional  Block  Attention    Module（CBAM）and Long  Short-Term Memory（LSTM）.Firstly , CBAM is used to extract the characteristics of wind power time series data and the spatial characteristics contained in numerical weather prediction. This module can adaptively learn important features in time and space. Then, the extracted features are input into the LSTM layer structure for power prediction. In order to verify the effectiveness of the proposed method, a data set of a wind farm in Jilin Province, China is used for verification. The experimental results show that compared with other power prediction methods used in this paper, the mean absolute error（MAE）of the proposed method is reduced by an average of 2.67%.The coefficient of determination（R-Square,R2）increased by an average of 23 %The root mean square error（RMSE）decreased by 2.69% on average.

In the face of the continuously increasing global energy demand and the challenge of ecological sustainability, the search for efficient energy conversion methods and the development of energy-saving technologies has become crucial. Phase change materials have become important innovative materials in the fields of energy storage, temperature regulation, and efficient thermal management due to their ability to absorb and release large amounts of latent heat during the solid-liquid transition process. Flexible composite phase change materials, due to their ability to maintain performance under deformation conditions, are considered to have significant application prospects in future smart devices and systems. This article provides an in-depth discussion and evaluation of the research progress, synthesis strategies, and functional development of flexible phase change materials. By analyzing the practical applications of flexible phase change materials in daily life, the potential and possibilities of achieving efficient thermal management and adapting to complex application environments are elucidated. This review provides a systematic theoretical guidance for the future development and further exploration of flexible phase change materials.

Compared with the conventional power system dominated by the synchronous generators, the renewable-based power system is dominated by the grid-connected inverters and features different dynamics. In recent years, a number of unidentified instability incidents have been frequently reported. The oscillation phenomena arise over a wide frequency range, with the non -linear, time-variant, and complicated dynamics. From the aspects of controller optimization, virtual impedance shaping, and other passivation methods, this paper overviews the impedance passivation methods for grid-following and grid-forming inverters, and summarizes the remaining challenges 

 Multi-load forecasting in integrated energy systems is crucial for the operation and scheduling of the system. Traditional forecasting models have not fully captured the long-term dependencies in time series or considered the coupling relationships between multiple loads, limiting improvements in forecasting accuracy. To address the challenges of multi-load forecasting in integrated energy systems, this paper proposes a forecasting model that integrates Seasonal Trend Decomposition and Crossformer. Initially, the original load data is decomposed into three sub-sequences using seasonal trend decomposition. Then, by employing a dimension-segmented embedding method and a two-stage attention mechanism, the model extracts cross-time and cross-dimensional correlations of multi-load data. Finally, a hierarchical encoder-decoder structure is utilized to generate forecasting results. Comparative experiments on real load datasets demonstrate that the model proposed in this paper has higher accuracy compared to other comparison models.

 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.

 With the increasing proportion of new energy generation and the continuous expansion of power grid scale, the capacity of DC transmission system is constantly improving, and DC power grid with different voltage levels is the inevitable trend of future development. In this context, DC/DC converter, as the key equipment of DC power network, plays an important role in multi-voltage level interconnection scenarios. In this paper, the existing modular high-voltage and large-capacity DC/DC converters are summarized, classified and compared, then the topology, working principle and characteristics of the current modular high-voltage and large-capacity DC/DC converters are expounded, and the applicable scenarios of different converters are put forward according to their characteristics. Finally，suggestions for future research directions are put forward.

Accurate wind power prediction is of great significance for the safe and stable operation of the power system. In view of the problems of unreasonable cluster division in cluster prediction and difficult to effectively improve the accuracy in short-term prediction, this paper proposes a short-term wind power cluster power prediction method based on Fuzzy C-means （FCM）and I Transformer-time convolutional network （Temporal Convolutional Network, TCN）.First, divide subclusters based on FCM clustering algorithm, and then use the advantages of I Transformer-TCN model dual feature extraction to model each subcluster. Finally, this method was applied to a wind power cluster in Jilin Province, China, and the RMSE decreased by 10.8%on average compared with other methods, which verified the effectiveness of this paper.

The high proportion of renewable energy such as wind power connected to the grid is a necessary path for the green and low-carbon transformation of the power system under the "dual carbon "goal, but it also brings enormous pressure to the safe operation of the power grid. Therefore, a game theory based integrated energy system（IES）optimization scheduling strategy for carbon capture is proposed in the article. Firstly, a comprehensive energy system carbon cycle optimization model was constructed to promote wind power consumption. Then, a two-layer game scheduling strategy with Stackelberg game was constructed and proved to be balanced. Finally, the improved differential evolution algorithm is used to solve the two-layer game model to meet the convergence speed requirements of the scheduling model.

Permanent magnet synchronous motor has the advantages of high efficiency, high power factor, measurable rotor parameters and good control performance, for which it has been widely concerned and applied in academia and industry. The stator and rotor parameters of permanent magnet synchronous motor can be obtained in real time by online parameter identification technology, which can not only improve the control accuracy of the motor, but also monitor the health state of the motor online. In this paper, the research status of PMSM parameter on-line identification technology is summarized and summarized. On this basis, the key technical challenges of this technology are summarized and analyzed from the aspects of model equation rank shortage, cross saturation effect, inverter nonlinear effect and parameter identification under extreme conditions.

 The permanent magnet synchronous motor （PMSM）drive system is often affected by multi-source disturbances under various operating conditions, leading to a decrease in its control performance. Linear Active Disturbance Rejection Controller （LADRC）has been widely used in PMSM drive systems due to its simple parameter tuning and independent mathematical model. The types of multi-source disturbances present in PMSM drive systems are first explained in the paper, and mathematical models for some disturbances are established. Then, the limitations of traditional LADRC are analyzed, and new types of LADRC proposed in recent years are summarized for different types of disturbances, while algorithm expressions for some of the proposed methods are provided. Finally, the development trend of PMSM drive systems based on LADRC in disturbance rejection control is summarized.

Under the background of the "carbon peaking and carbon neutrality" goals and the construction of new power systems, the proportion of distributed energy resources in the distribution network has been increasing year by year. However, new energy generation is random and intermittent, and with the increasing peak-to-valley difference of loads, it is difficult to satisfy the demand for peak load following with only traditional generation resources. With the development of demand response technology, it has become possible to regulate flexible resources on the load side. There is an urgent need to explore the adjustable potential of controllable loads, analyze their regulation capability qualitatively and quantitatively from the perspective of the system, and establish their external characteristic models so as to be precisely dispatched. In this paper, research is carried out on inverter air conditioners on the customer side. First, the load model and control method of inverter air conditioners are clarified；then the qualitative assessment of the regulation potential of inverter air conditioner aggregation is carried out；then, according to different regulation scenarios, the external characteristic model of inverter air conditioner aggregation is established；and finally, the example verification is carried out in the active power distribution network. The result shows that the inverter air conditioner aggregation has similar external characteristics and regulation potential as the traditional generator, and can effectively participate in the optimization of the active distribution network scheduling.

 For a multi-source integrated system incorporating wind, fire, and storage, the wind power output exhibits uncertainty. There is a discrepancy between the predicted and actual power of the wind turbine during specific time periods. When the actual output of the wind turbine fails to meet the scheduled power in the dispatch plan, it leads to a significant reduction in the economic efficiency of the system. To address this issue, this paper proposes a two-layer optimization strategy that considers wind power prediction errors and demand-side response. The upper-level model aims to minimize the overall operating cost of wind power, thermal power, and dispatchable loads, utilizing an Improved Particle Swarm Algorithm（IPSO）to formulate optimal scheduling strategies for thermal power and dispatchable loads. Subsequently, the Gibbs method is employed to sample the probability density function of the maximum output prediction error of the wind turbine, obtaining a certain amount of samples and determining the power deficit for each sample in the upper-level power sources. The lower-level model aims to minimize the overall operating cost of energy storage and interruptible loads. It employs linear programming to offset the power deficits from the upper-level sources, thereby formulating the lower-level model's power dispatch strategy. With a large number of sampled scenarios, the proposed two-layer optimization strategy's economic and effective nature is validated by comparing the expected value and variance of the total cost function values for each sample.

 Multi-energy virtual power plant （MEVPP）aggregates various forms of distributed energy and demand-side flexibility resources including electric and thermal energy. In order to realize the optimal scheduling of MEVPP, in this paper, a MEVPP model including power generation units, heating units, energy storage unit, air conditioning loads cluster, and demand response loads cluster is established, a deep reinforcement learning （DRL）-based optimal scheduling method is proposed for this model, corresponding reward function, state and action spaces are designed. The method is based on the proximal policy optimization （PPO）algorithm, which can regulate distributed energy and demand-side flexibility resources based on environmental information such as forecasted load, wind/light output, outdoor temperature, etc.，and obtain the strategy set of MEVPP optimal scheduling with the objective of minimizing the operating cost. The case study result proves the feasibility of DRL in MEVPP optimal scheduling and the extensibility of the strategy set.

Power-type energy storage and energy-type energy storage can be combined in a certain proportion to form a hybrid energy storage system, which can significantly enhance the power output of the energy storage system. In order to fully utilize the advantages of hybrid energy storage in participating in the primary frequency regulation of wind farms and consider economic factors, a capacity optimization configuration method based on variation mode decomposition is proposed. Firstly, a mathematical model is established with the objective of maximizing the net benefits of the hybrid energy storage system. Next, the power demand signal is decomposed into high-frequency power demand and low-frequency power demand using the variation mode decomposition method. Finally, taking a 100 MW wind farm in the Northeast as a case study, based on power demand data for a typical day, considering constraints such as energy storage charging and discharging power and state of charge, the objective model is solved using the quantum particle swarm algorithm. The results show that the optimized energy storage configuration scheme can effectively improve the economic viability of hybrid energy storage for assisting the primary frequency regulation of wind farms.

 In the context of "dual-carbon"，in order to further enhance the economic and environmental benefits of integrated energy system, the paper proposes a low-carbon optimal scheduling methodology for IES under the reward-penalty ladder-type carbon trading mechanism, taking into account a photovoltaic power plant with heat recovery device and integrated demand response. Optimal scheduling method. Firstly, the IES architecture for the joint operation of a photovoltaic power plant with heat recovery device and a cogeneration unit with carbon capture is constructed on the source side, and the operation principle of the two stages of power-to-gas conversion is analysed to establish a model of power-to-gas conversion equipment taking into account the waste heat recovery. Secondly, considering the flexible characteristics of the three loads of electricity, heat and gas on the customer side, an integrated demand response model for electricity, heat and gas is established on the load side. Finally, the carbon trading mechanism is introduced to further reduce the carbon emissions of the system,s o as to construct a low-carbon optimal dispatch model of the integrated energy system with the goal of minimising the total operating costs of the system, including the energy purchase cost, operation and maintenance cost, and the carbon trading cost, during the scheduling cycle. The results of the analyses show that the proposed method not only improves the operating potential of the units, but also effectively reduces the total system operating costs and carbon emissions.

 Proton exchange membrane（PEM）water electrolysis hydrogen production technology is one of the main methods for producing green hydrogen, but its hydrogen production efficiency is influenced by many factors.Considering the influence of input power on the hydrogen production efficiency of electrolyzers, this paper introduces an integrated energy system optimization model that accounts for the variability in electrolyzer efficiency. Firstly, the coupling relationship between hydrogen production efficiency and electrolyzer input power is studied. Utilizing historical data analysis, a correlation curve is established to depict the relationship between electrolyzer power and hydrogen production efficiency. Secondly, by combining the stepwise carbon trading mechanism with the green certificate trading mechanism, a carbon-green certificate trading mechanism is proposed. Subsequently, a comprehensive energy system optimization dispatch model is developed, which takes into account the fluctuation in hydrogen production efficiency and incorporates considerations regarding hydrogen energy green certificates, with the primary objective of minimizing the total system cost. Finally, through case analysis, the effectiveness of the proposed model in reducing the total operating cost of the system is verified.

With the continuous development and popularization of the park-level integrated energy system, it has become a general trend to connect it to the superior power grid and realize the interaction. In this paper, a two-layer optimization model of connecting multi-park integrated energy system to active distribution network is constructed. At the level of active distribution network, schedulable resources such as link-line power, distributed generation, energy storage equipment, flexible load and electric vehicles are taken into full consideration with the goal of minimum operation cost. At the park level, aiming at minimum total operation cost, the grid-connection situation of multi-type parks connected with each other is considered. Analytical target cascading is used to solve the two-layer model. Through the calculation examples, the proposed model has good economic characteristics and peak cutting and valley filling effect, which fully proves the economy and effectiveness of the proposed model.

 The voltage fluctuation on the low voltage side caused by the distributed generation （DG）in the new distribution network is the key factor affecting the power quality of users.  This paper aims at the voltage regulation problem of the new distribution network, proposes a hierarchical and gradual cooperative voltage regulation control strategy based on on-load regulation transformer （OLTC）and modular distribution transformer （MDT）under the condition of sufficient reactive power. In this strategy, the concept of voltage cross section quality is first proposed and the model in different conditions is given. Then the voltage fluctuation range is divided into three intervals: Interval 1 is the normal region, interval 2 is the slight over-limit region, and interval 3 is the serious over-limit region. Considering the economy and fast response of MDT, the strategy uses MDT as the main adjustment mode and OLTC as the backup adjustment means. Finally, by comparing the effect of voltage regulation with simulation, the rationality of the proposed strategy is verified.

With the large-scale wind power and photovoltaic power connected to the power grid, the problem of new energy consumption is prominent. In order to alleviate the dilemma of new energy consumption, by analyzing the time series matching relationship between new energy power generation power and regional load, a new energy power generation power quality division model was established, and the impact of new energy power generation power uncertainty on power grid operation regulation and control was quantified, and then an energy storage capacity allocation scheme that could reflect the power uncertainty law of new energy plants and stations was formulated, so as to effectively improve the level of new energy consumption. In this paper, a power quality division model for new energy power generation is proposed. The power quality of new energy power generation is characterized by two dimensions：power fluctuation and power prediction error. Analyze the time series data of new energy power generation power and grid load, as well as the prediction error data, and establish a statistical model. Determine the generation power curve that matches the grid load timing, and establish power quality indicators for new energy power generation.

Spectral beam splitting photovoltaic/thermal （PV/T）hybrid utilization technology breaks the conflict between the low operating temperature of PV cells and their high-grade heat demand and achieves efficient photoelectric efficiency and thermal efficiency. However, the preparation of suitable selective splitting media plays a crucial role in achieving reasonable energy distribution within PV/T devices. In this study, deionized water was chosen as the base liquid, and the combination of noble metal Ag nanoparticles and inorganic CoSO4 was utilized to achieve efficient regulation of the optical properties of splitting liquids. In addition, based on the energy balance method, the electrical and thermal output models of a typical PV/T prototype system were established, and the thermal and electrical output characteristics of the system before and after the optical regulation of the splitting medium were analyzed. The results show that the water-based filter has efficient infrared absorption characteristics. After adding Ag nanoparticles, the splitting medium exhibits a clear absorption peak in the visible light band, with a peak of 435 nm.
After further addition of CoSO4, the absorption peak width of the nanofluid can be expanded, and it also has certain absorption enhancement in infrared.Comparing the influence of the splitting medium on the thermal and lectrical performance of the PV/T system before and after optical regulation, it can be seen that the total absorption characteristics of the splitting medium increase, and the thermal efficiency of the PV/T system shows an increasing trend, but its electrical efficiency decreases. The total exergetic efficiency of the liquid-based splitting PV/T system also increases, exceeding 28%. Efficient regulation of the optical characteristics of the splitting media can achieve a controllable allocation of internal energy in PV/T systems.

 In this paper, a new type of bi-fluid photovoltaic/thermal roof module without cover is proposed. By combining the water-cooling duct of tube-plate with the air-cooling duct of baffle-plate, the water capacity of the component is reduced and the air outlet temperature is increased, and the supply of hot water and hot air for the module is realized. By building a performance test bench with a stable testing environment, the temperature difference between the inlet and outlet of the module and temperature of the module with solar irradiance and working fluid flow was studied, and the all-day thermal and electrical performance of the module was analyzed. The results show that the module has an obvious cooling effect. The electrical efficiency of water cooling and air cooling modes can reach up to16.80%and 17.62%.It has high all-day comprehensive performance and the all-day primary-energy saving efficiency is up to 74%.

 Conductor icing seriously affects the safety of power grid operation, the local collision coefficient is a key indicator to characterize the growth rate of transmission conductor ice cover, the traditional steel core aluminum stranded wire or its simplified cylindrical model of the growth of the ice cover characteristics of the previous analysis, the aluminum conductor composite core （ACCC），as a new type of conductor that is developing rapidly, the local collision coefficient and the growth process of the ice cover has rarely been studied. First realized the numerical simulation of ice cover growth based on the commercial finite element software Fluent, and verified the validity of the simulation method by comparing with the simulated test results. Subsequently, using the validated numerical simulation method, a comparative study of the ice cover growth characteristics of the ACCC refined model and the simplified circular conductor model was carried out to simulate the distribution of droplet collision coefficients on the surfaces of the two, and the effects of different median diameters of the raindrops and the change of the wind speed on the collision coefficients were discussed, and the icing ice shapes of the ACCC model were obtained. The results show that: the local collision coefficient of ACCC model is significantly smaller than that of round model when the wind speed is low, at the early stage of ice-covering, the ice shape is very significantly affected by the surface configuration of conductor, when the surface of conductor is completely wrapped by the ice-covering, the resistance coefficient of the round model is 15.3%lower than that of the ice shape of ACCC model as a whole, and it shows galloping instability under each wind attack angles. The results of the study are valuable for obtaining the growth characteristics of ACCC ice cover and understanding the disaster mechanism of ice cover on conductors with different surface configurations.

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.

Partial discharge detection is an important means to evaluate the insulation condition of cross-linked polyethylene power cables. In this paper, a partial discharge online monitoring system for medium voltage XLPE cables is developed, and its working principle, hardware composition, software design and implementation scheme are elaborated in detail. The system comprehensively evaluates the insulation condition of XLPE cables by monitoring the partial discharge signal in the ground wire of XLPE cables. The field operation results show that the system is stable and reliable.

New energy power generation has received more and more attention and development around the world, among which wind energy is the most common and widely used form of new energy. Wind power prediction has an important influence on the stability, reliability and economy of the power system. In order to improve the accuracy of wind power prediction, we consider the spatial dependence of wind power stations and solve the difficulty of grid prediction in the wind power grid. First, similar wind farms are divided according to their spatial characteristics, and the convergence effect of clusters is used to simplify the number of network grids. Secondly, the meteorological characteristics with the most consistent cluster power fluctuations were extracted, and the spatial characteristics of all wind farms were retained to the maximum extent. Thirdly, the input and output data form is processed to form the network format data in the spatial sense, and is trained and predicted by convolution of long and short-term memory network prediction model. Finally, the method was applied to a large-scale wind power cluster in northeast China to verify its effectiveness. The experimental results show that the proposed method is 0.24%less RMSE and 0.05%lower compared with the ungridded MAE, which effectively improves the accuracy of day-ahead power prediction of wind power cluster.

 In order to promote the consumption of wind power, reduce carbon emissions of thermal power units, and solve the problem of low-carbon economic operation of Integrated Energy System （IES），variable oxygen-doped oxygen-enriched combustion technology is introduced in this paper to transform the gas unit. Combined with Liquid Air Energy Storage （LAES），which uses the cold energy of Liquefied Natural Gas （LNG），an optimization strategy of low carbon economy for electric heating gas cooling IES is proposed. Firstly, the IES architecture of variable oxygen-enriched combustion Gas unit, LAES using LNG cold energy, Power To Gas （P2G）equipment, central air conditioner and lithium bromide chiller is constructed, and the mathematical models of each equipment are established. Secondly, the cascade carbon trading mechanism is introduced, and the low carbon economic scheduling model of electric and gas-cooled IES is established with the goal of minimizing the system operating cost. Finally, MATLAB is used to invoke GUROBI solver to solve multiple scenarios, which verifies that the low-carbon economy optimization scheduling strategy proposed in the paper can improve the system's wind power consumption, effectively reduce system operating costs, and achieve carbon emission reduction.

In recent years, China has made great efforts to develop new energy power generation mainly based on wind power, especially in the "three North" regions rich in wind resources in China. However, the load demand in these regions is often small and it is difficult to match the installed capacity of wind power. Therefore, there is a large amount of wind abandoning in the "three North" regions of China. In order to solve this problem, this paper introduces a high load scheduling strategy for improving wind power consumption capacity. The load involved in dispatching is two kinds of high load capacity load, namely electrolytic aluminum load and ferroalloy load. According to the operation characteristics and regulation characteristics of the two kinds of high load, the two kinds of high load are classified and the mathematical model is established. Then, according to the different characteristics of the two kinds of load, the mathematical model of day-ahead scheduling and intra-day scheduling is established respectively, and the effectiveness of the proposed scheduling strategy is verified by an example.

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 

Aiming at the problem that the cost of electricity sales companies increases under the deviation power assessment mechanism, a cost control strategy for power sales companies under the transfer of deviation power is proposed. First, the AHP-Logit model is used to analyze the market share of electricity sales companies from two aspects：price factors and non-price factors. Secondly, construct the electricity purchase cost model of the electricity sales company based on the negotiation and bargaining model. Then, a transaction model between power sales companies considering the transfer of biased electricity is established, and the impact of negotiated compensation and transfer of biased power on the cost of power sales companies is revealed, so as to reduce the cost of power sales companies.Finally, the rationality and effectiveness of the method proposed in this paper are verified by an example analysis.

With China vigorously developing green economy and promoting energy conservation and carbon reduction, the mutual utilization of new energy and train braking energy in electrified railway system has been paid more and more attention. In this paper, an electrified railway energy storage allowable scheduling method considering photovoltaic and energy scheduling device is proposed. By integrating photovoltaics into the traditional traction substation, the energy storage and energy dispatching devices cooperate with each other to improve the utilization rate of regenerative braking energy of trains and reduce the electric energy delivered by the grid to the traction power supply system. At the same time, the controllability and flexibility of power supply between source and charge are strengthened. The numerical simulation results show that after the optimization of the proposed method, the power obtained from the traction power supply system to the grid is reduced by 2 500 kWh, which verifies the effectiveness of the proposed method.

 PIES is a typical complex energy system with complex energy supply equipment and diversified energy coupling mechanism. In order to realize the low-carbon economic operation of PIES, increase the consumption of wind power and solve the problem of low energy utilization efficiency caused by the unreasonable energy use structure of the system, the paper established an electric heating demand response model, fully considering the "quality" and “quantity" of energy, and established a comprehensive energy efficiency model with strong constraint on the carbon emission of the system based on the first and second laws of thermodynamics. According to the principle of cascade utilization, a cascade utilization model containing multiple energy coupling devices is established. Finally, a multi-objective optimization scheduling model of the integrated energy system of the park is established based on the economic cost objective of the system and the comprehensive energy efficiency objective of the system, and the output scheduling of each equipment in the system is realized. The example analysis shows that the optimized scheduling scheme proposed in this paper can not only improve the system's wind power consumption rate and operation economy, but also take into account the low carbon and efficient operation of the system.

Permanent magnet synchronous motor （PMSM）is often operates at high speed due to its inertia or external propulsion when the power supply is interrupted during operation. It is necessary to estimate the initial position/speed before flying start. The fixed action time and interval time of the traditional zero voltage vector pulse method often reduce the estimation accuracy and even cause overcurrent. Therefore, an adaptive triple zero voltage vector pulses method is studied in this paper, which can adaptively adjust the action time according to the amplitude of the current vector excited by the zero voltage vector pulses and adjust the interval time according to the initial speed estimation twice successively, so as to automatically apply the reasonable zero voltage vector pulses for different PMSM parameters or operating states. The simulation results show that the proposed adaptive triple zero voltage vector pulses method can adaptively adjust the reasonable action time and interval time, so as to improve the reliability of initial position/speed estimation and flying start.

 Combined cycle （CC）units have been more and more widely used in the power system due to their high efficiency, low emission, and flexible operation, and have actively participated in the electricity market. In order to study the bidding behavior of the CC unit in the day-ahead electricity market, a bi-level bidding model for the CC unit is proposed in the paper. The upper-level model is the bidding model for the mode-based CC unit. The lower-level model is the day-ahead electricity market clearing model. The CC unit and the independent system operator （ISO）in this model influence each other on the locational marginal prices （LMP）obtained through electricity market clearing. Using the Karush-Kuhn-Tucker （KKT）condition and duality theory, the proposed bi-level optimization model is transformed into a mathematical program with an equilibrium constraint （MPEC）problem. The results show that the bidding strategy proposed in the paper increases the profit of the CC unit in the electricity market by 12.28%and reduces the operating cost of the market by 25.37%，which verifies the effectiveness of the model proposed in the paper.

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.

The interconnection of multiple energy sources has gradually become the direction of future power system development, and the optimization and scheduling of multi energy interconnection systems is an important means to cope with the large-scale consumption of new energy connected to the grid. We have established a cost model for the wind solar water fire pumping and storage interconnected system that takes into account the cost of deep peak shaving for thermal power units, and a day ahead optimization scheduling model for the wind solar water fire pumping and storage interconnected system that takes into account the initiative of peak shaving. A hierarchical optimization scheduling model solving method is proposed for the complex structure of multi energy coupled systems. The upper level model aims to minimize the fluctuation of net load and obtain the equivalent load curve；The lower level model, based on the scheduling results of the upper level model, takes the economic optimization of peak shaving operation and the minimum amount of new energy waste as the objective function, taking into account the initiative of peak shaving, to solve the economic scheduling problem of a new energy system that considers the deep peak shaving coal consumption characteristics of thermal power units. The simulation examples show that the constructed model achieves complementary operation of multi energy interconnected systems, improves the deep peak shaving ability of thermal power units, reduces the abandonment rate of new energy and system operating costs.

Aiming at the problem of poor current loop immunity in the high-speed operation of permanent magnet synchronous motor （PMSM），a harmonic suppression strategy for flux weakening with voltage phase angle regulation is proposed in the paper. Firstly, the small-signal model of the control system is established through linearisation, and then the closed-loop transfer function of the system is obtained to analyse the stability of flux weakening. In order to overcome the problems of low damping coefficient and poor system stability during acceleration, a proportional differential compensation strategy is introduced into the straight-axis current regulator to realise the inner-loop feedback in order to improve the system damping ratio and suppress the system resonance peaks so as to reduce the current oscillations during high-speed operation. Finally, the key parameters of the compensator are determined based on the principle of equivalent internal mode controller and frequency domain analysis.The experimental results show that the method can effectively improve the stability of the current loop and the anti-interference performance of the control system, and significantly reduce the harmonic content of the motor current.

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.

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.

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.