To monitor the dynamic characteristics of the receiving AC system and assess the risk of wideband oscillation in LCC-HVDC without additional equipment, this paper presents a non-invasive impedance wideband measurement method for LCC-HVDC systems. Unlike existing invasive methods, such as single voltage/current or wideband harmonic disturbance injections, the proposed method does not inject harmonic disturbances into LCC-HVDC, thereby avoiding the potential resonance risk. Furthermore, the proposed method introduces curve similarity and a dynamically adjusted time-step sampling scheme. First, this paper discusses the interaction principles between the LCC system and the receiving AC system. Secondly, this paper establishes the harmonic state space (HSS) impedance model for LCC-HVDC, based on HSS theory, focusing on the unipolar earth loop topology. Subsequently, a non-invasive impedance wideband measurement method is proposed to observe the operating state of the system, judge the risk of wideband oscillation, and analyze the interaction and wideband oscillation characteristics between the LCC system and the receiver network through impedance analysis. Finally, an example of CIGRE standard model is used to verify the correctness and practicality of the proposed method.

Based on the HSS theory and the concept of digital modulus, this paper introduces a non-invasive impedance wideband measurement method. Firstly, the system topology and the interaction principles of the LCC-HVDC unipolar earth loop subsystems are detailed. Then, the impedance modeling of the LCC-HVDC system is elaborated, and the HSS impedance mathematical model for wideband measurement is derived based on the system's topology. Finally, the system model is constructed in MATLAB/Simulink using parameters from the CIGRE standard model, and the impedance calculation model is applied to measure the impedance of the LCC system. Combined with the measured wideband impedance curve, the interaction between the LCC system and the receiving network, as well as the wideband oscillation characteristics, were analyzed using the generalized Nyquist criterion for stability. At the same time, the Pearson correlation coefficient was introduced to capture the similarity of impedance curves with high granularity, thereby exploring the adaptability of impedance models of different orders and the factors influencing the oscillation risk. It offers theoretical support for the measurement methods used in the engineering application of impedance measurement, and appropriately expanding the permissible error range in the amplitude-frequency intersection can enhance the universality of impedance analysis methods.

The main contributions and conclusions of this paper are summarized as follows: (1) A non-invasive LCC-HVDC impedance model and impedance wideband measurement method are designed, which does not require injecting harmonic disturbances into the system. The Pearson correlation coefficient between the measured impedance curve of the 13th-order model and the fitted curve from active measurements is 0.998 3, which is very close to 1. This high value indicates that the accuracy of the measurement model is high, suggesting a close match with the actual impedance characteristics. (2) By analyzing the similarity matrix of measurement curves from models of varying orders, it is observed that the Pearson correlation coefficients for the phase measurement curves are closely matched for orders h=3, h=7, and h=11, with the highest deviation being within 0.2%. Consequently, the acceptable error margin for amplitude-frequency intersection points may be suitably broadened. Considering computational efficiency, a lower-order measurement model within the range of 3 to 13 can be selected. (3) The characteristics of the measured wideband impedance curve of the system can be observed under two conditions: adaptive adjustment of the sampling time step and variation in transmission power. Adaptive adjustment of the time step is not only conducive to accurately identifying the crossover points of the amplitude-frequency response but also reduces the computational load and minimizes storage requirements. Conversely, an increase in transmission power raises the risk of system instability.

Current electric shock detection methods are primarily designed to address faults between the live wire and the ground wire, mainly relying on monitoring changes in residual current to identify issues. However, in the case of a neutral-to-live electric shock fault, the fault circuit often does not cause a significant change in the residual current. This presents a considerable challenge for existing detection methods when it comes to identifying neutral-to-live electric shock incidents.

To address the aforementioned issues, a low-voltage neutral-to-live electric shock faults detection method based on dynamic fault characteristics and a light gradient boosting machine has been proposed. Firstly, a 1:1 prototype experimental platform for a low-voltage distribution network was established in a real system. Under various operating scenarios involving multiple household loads, experiments reproducing live neutral shock faults were conducted alongside control experiments using a sliding resistor to replace the electrically shocked body. A substantial amount of experimental samples representing both fault and normal operating states was collected, creating a comprehensive database. Secondly, the complexity of neutral-to-live electric shock faults is assessed based on the interference of load current on fault current. A fault circuit electrical equivalent model is established by considering the dynamic resistance and breakdown arcs at the dual contact points of the neutral-to-live shock, in conjunction with biological dynamic impedance. The impact of fault current on the main circuit current is analyzed. Finally, features of the main circuit current are extracted from the perspective of magnitude and high-frequency components, and the temporal changes of individual features before and after the occurrence of faults are compared. Given the difficulty in clearly distinguishing between fault and non-fault states based on individual features alone, along with the fact that these features exhibit varying sensitivity to both states, a multidimensional representation of the system state is employed. Following an ensemble computational approach, a lightweight gradient boosting machine model is developed, leveraging its uni-directional gradient sampling method and ensemble operation mechanism to accurately classify the two states.

The proposed method was evaluated on a test dataset consisting of 50 666 samples, achieving an overall accuracy of 96.82%. Specifically, the identification accuracy for 35 831 normal samples was 97.50%, while the accuracy for 14 835 neutral-to-live electric shock faults was 95.17%. The test results indicated that the proposed method could accurately distinguish neutral-to-live electric shock faults from normal operating conditions, including those in the control group with the sliding rheostat added, even when the fault information was significantly obscured by high load currents. Compared to existing methods, the proposed approach shows an advantage in accurately detecting low-voltage neutral-to-live electric shock faults.

The following conclusions can be drawn from the analysis: (1) By incorporating the time-varying impedance of biological tissues, variations in contact resistance, and breakdown arcs, the dynamic characteristics of faults were examined, revealing two effects of neutral-to-live electric shock faults on the main circuit current: changes in current magnitude and variations in high-frequency components. These findings served as the basis for constructing feature vectors. (2) The contribution of individual features to distinguishing between neutral-to-live electric shock faults and normal operating conditions is limited, resulting in significant inter-class ambiguity that can easily disrupt the sample fitting performance of traditional pattern recognition models. However, if features can exhibit a certain degree of sensitivity across different classes, the combination of multidimensional features can facilitate comprehensive discrimination. (3) Due to its inherent resilience to disturbances, the ensemble model can effectively mitigate interference caused by inter-class ambiguity and demonstrate strong generalization capabilities.

Meta-aramid (PMIA) is a unique fiber that possesses exceptional insulation strength and thermodynamic stability. It is widely regarded as an ideal material for the development of the next generation of insulation paper. However, its intrinsic thermal conductivity of 0.21 W/(m·K) is relatively low and may not meet the long-term service requirements in high-temperature environments. To enhance the thermal conductivity and insulation of the PMIA paper, AlN and BN fillers are selected for composite doping modification of PMIA paper. The surfaces of the two fillers are coated with polydopamine (PDA) and modified with a KH550 silane coupling agent to improve the dispersibility of the two fillers. By adjusting the doping ratio, AlN-BN/PMIA composite insulation paper with different concentrations was prepared. The microstructure was characterized and the breakdown strength, conductivity, and thermal conductivity were tested. The effect of two different filler ratios on the insulation and thermal conductivity of the material was studied.

Firstly, the surfaces of the two fillers are coated with polydopamine (PDA) and modified with a KH550 silane coupling agent to enhance their dispersibility. By adjusting the doping ratio, AlN-BN/PMIA composite insulation paper with different concentrations is prepared. Secondly, the microstructure of samples is characterized and the breakdown voltage, conductivity, and thermal conductivity are tested. The influence of the ratio of two fillers on the insulation and thermal conductivity of the material was studied. Thirdly, based on density functional theory, band structure calculation and analysis are conducted, and a design concept of a “stepped charge trap” is proposed. In addition, the composite breakdown model is constructed using the phase field method, explaining the inherent mechanism of performance improvement.

According to the test results, adding BN to the AlN filler can further improve the matrix structure and fix the damage caused by the high concentration aggregation of AlN. The surface of the composite material appears relatively dense when the AlN/BN ratio is 3:7, with only a small amount of PMIA fibers and fillers precipitated. At a mass fraction of 40%, the breakdown strength of the composite gradually increases as the BN doping ratio increases. At a ratio of AlN/BN of 3:7, the composite paper exhibits its maximum breakdown strength of 186 kV/mm, which is 66.07% higher than that of the pure PMIA sample. Additionally, the conductivity of the composite is at its lowest value during this ratio. On the other hand, at an AlN/BN ratio of 7:3, the thermal conductivity of the composite is optimal, increasing by 213.6% compared to pure PMIA samples. The high aspect ratio structure of BN links it with AlN fillers to form an “thermal conductivity network”, which increases the thermal conductivity.

Energy band structure analysis based on density functional theory suggests that the wide bandgap properties of AlN and BN result in the formation of “stepped traps” at the PMIA interface. This leads to an increased energy barrier for charge transitions and limits the migration of charge carriers. In addition, a phase field simulation model indicates that the introduction of BN can further homogenize the electric field distribution, reduce the degree of local polarization, and thus enhance the insulation performance of the composite system.

Electromagnetic drive forming technology is a special forming process that uses pulsed Lorentz force to drive a high-conductivity sheet to move, thereby driving a low-conductivity sheet to cause plastic deformation, which can effectively make up for the shortage of traditional electromagnetic forming in forming low-conductivity materials. However, in the existing electromagnetic drive forming, the driver sheet also undergoes plastic deformation, which tends to lead to a serious problem of wastage of the driver sheet, and it is difficult to regulate the forming shape.

To solve this, instead of the traditional circular drive sheet, a solid copper ring with a specific thickness is employed, utilizing the strong electromagnetic force generated in the copper ring to propel it at high speed into collision with a metal sheet. This impact generates a contact force, causing the sheet to undergo plastic deformation. Additionally, an electromagnetic-structural coupling model for the copper ring electromagnetic drive forming process is developed using LS-DYNA software. A series of electromagnetic drive forming experiments are then conducted, using a TA2 titanium plate as the test material, to validate the feasibility of the proposed method. Numerical simulation and experimental results show that under a single discharge (7 kV), a metallic copper ring with a diameter of 80 mm can drive the titanium plate to deform and the forming height can reach 14 mm. Meanwhile, based on strain analysis of the forming sheet and the driven ring, the solid copper ring does not deform and can be reused. In addition, by changing the size and shape of the copper ring, the forming profile of the plate can be flexibly adjusted. For example, when the diameters of the circular driving rings are 65, 80, and 95 mm, uniformly deformed areas with diameters of 58, 72, and 87 mm are observed on the top of the sheet, which is highly consistent with the shape of the rings. Even if the forming height is increased, the forming shape of the center area of the sheet remains a flat-topped profile when enhancing the discharge voltages. On this basis, the dynamic deformation process of the sheet is further investigated through numerical methods, to reveal the deformation behavior and forming mechanism of the titanium plate driven by the copper ring, which demonstrates that the forming velocity approaching 100 m/s and the strain rate is up to 1 000 s^-1. Hence, this forming process belongs to the category of high-speed forming technology.

The obtained results indicate that, since the copper ring is a solid ring with a specific thickness, it does not experience plastic deformation during the electromagnetic drive forming process and can be reused. This effectively addresses the issue of excessive waste of the driver sheet in conventional electromagnetic drive forming. The copper ring also provides shape adjustment capabilities, allowing for the formation of sheets with circular, quadrilateral, and hexagonal flat tops. The height of the flat-topped profile can be controlled by adjusting the discharge voltage, overcoming the problem of limited shape flexibility in existing electromagnetic drive forming methods. These results are of significant practical value for advancing and expanding the applications of electromagnetic drive forming technology.

With the rapid development of the global economy, offshore wind power generation technology has been advancing towards field group scale and industrialization, becoming a research hotspot in international renewable energy. However, to reduce the economic costs associated with deep-sea wind power technology and enhance the efficient of wind energy capture and utilization, the capacity of wind turbines has been gradually upgraded to 10 MW and above. This trend towards large capacity has consequently led to increased weight and volume of wind turbines, complicating offshore transportation, lifting, operation and maintenance, which limits further development of offshore wind power technology. Moreover, the significant volatility and intermittency of offshore wind power contribute to increased grid penetration issues, difficulties in large-scale grid connections, and a notable phenomenon of wind curtailment. Furthermore, the non-stationary wind power can cause grid voltage fluctuations, flicker, frequency fluctuations, harmonics and other power quality problems, affecting the stable operation of the grid.

To address these problems, Hunan University's wind power generation team proposed an innovative integrated technology for hydrogen production through offshore superconducting wind power generation. This innovative system utilizes water electrolysis to locally consume offshore wind energy, with the produced liquid hydrogen being transported to land via ships or pipelines for comprehensive utilization. Additionally, a liquid hydrogen circulation refrigeration system provides a stable low-temperature environment for superconducting wind turbines, significantly reducing platform volume and weight and ensuring the reliable operation of the integrated system.

The article provides an overview of recent development in HTS wind turbine technology and offshore wind power hydrogen production technology, both domestically and internationally. It analyzes the key structures and feasibility of the proposed innovative integrated system, highlighting how it compares to traditional technologies. Additionally, the article explores recent advancements in offshore wind power generation and transmission technologies. The discussion then shifts to the benefits of the proposed innovative technology in comparison to other existing technologies and schemes. It summarizes the advantages of integrating hydrogen production and offshore superconducting wind power generation, analyzes the variability of superconducting wind turbines output power and the limitations of current converter topology control strategies, and proposes the key technologies of designing superconducting wind turbines converter topology with efficient energy transfer capability and designing a superconducting wind power system friendly control strategy.

For the future development of the integrated system, an energy island system plan that is integrated with renewable energy development is proposed. This plan is based on the operational principles of each sub-structure and aims to harness the efficient synergy of renewable energies. Research will focus on determining the appropriate ratios for various energy production and conversion devices, which will optimize the configuration of multi-energy complementarity. This approach aims to establish an integrated energy system that reduces the standby capacity required by the system’s various equipment. Furthermore, this initiative will promote the coupling of the power with renewable energy systems, facilitating the synergistic development of electric power and green hydrogen. This strategy will improve the optimized configuration of the energy supply system and establish a common technological framework for large-scale superconducting wind power hydrogen production technology.

Polymer materials are widely used in power electronics and power transmission and distribution systems because of their excellent dielectric properties. However, under the long-term coupling effect of mechanical stress, thermal effect, electrical stress and other factors inside the insulation material, it is easy to cause the growth of electrical trees, which will cause internal damage and deterioration of the material, and eventually lead to the harm caused by penetrating discharge. The numerical simulation method can provide reference for improving the insulation reliability of the system. However, some key parameters of the existing model are difficult to obtain directly from the experiment, and can only be realized through the model verification to achieve the microscopic electrical tree simulation of specific materials, and can not achieve the engineering tasks from material parameter testing to complex structure electrical tree prediction. Therefore, this paper aims to propose a method of electrical tree limb simulation with simple model and parameters that can be obtained by experiment, so as to improve the engineering applicability of electrical tree limb simulation.

The inverse power law is a phenomenological model directly based on the lifetime data of solid dielectric, which describes the physical process of the accumulation of electrical damage in solid dielectric to the generation of penetrating tree channels, and has the theoretical basis for describing the growth of electric treees. Therefore, this paper analyzes the physical relationship between the parameters of the inverse power model and the growth law of electrical trees, establishes the basic equation of local electrical damage based on the inverse power model, and establishes the electric tree simulation method based on the inverse power model combined with the electric field calculation and the material dispersion equation. Further, a sample of pin-plate electrode is used to demonstrate how to simulate the electrical tree by testing the basic parameters of the material. The experimental verification of the simulated results of electric treees is carried out, and the simulated growth law of electric treees is compared with the experimental growth law of electric treees. Finally, the difference between the proposed method and the phase-field simulation and WZ model is compared.

The final results show that the method proposed in this paper can effectively simulate the electrical trees by using the experimental material parameters. The simulated electrical trees in this paper agree with the experimental results in terms of morphology and growth law. In this method, the shape of electrical trees is correlated with voltage tolerance index and cumulative damage standard deviation, and the growth rate of electrical trees is correlated with voltage tolerance index and cumulative damage mean. Compared with the phase-field simulation and WZ model, the proposed method can simulate the gradual growth of electrical trees, and the model parameters can be obtained experimentally.

When multiple EMUs are simultaneously in a light-load starting condition within the traction network, it can lead to low-frequency oscillations in the traction network voltage. In severe cases, this may trigger traction locking, which poses a risk to the operational safety of high-speed trains. To address the issue of low-frequency oscillation, this study proposes a method based on virtual impedance for its suppression.

First, the impedance model of the EMUs-traction network coupling system is derived, and its stability is analyzed using the impedance ratio stability criterion and the Bode diagram. Second, based on the stability criterion, virtual impedance is incorporated into the EMU control strategy to correct the impedance characteristics of the load subsystem, thereby proposing a low-frequency oscillation suppression method. Third, an adaptive control method for virtual impedance is designed to handle the complex and dynamic working conditions, enabling the system to adjust the virtual impedance parameters and enhance the effectiveness of the suppression method. Finally, the proposed control strategy is compared with the traditional approach through a low-power experimental platform, validating the effectiveness of the suppression method.

The results of the system stability analysis indicate that the logarithmic amplitude-frequency characteristic curve of the system impedance ratio is significantly lower than 0 dB when only one unit (m=1) is connected to the traction network, and the absolute value of the system impedance ratio |T_sn(s)| is much less than 1, suggesting system stability. As the value of m increases, the amplitude-frequency characteristic curve approaches 0 dB, resulting in a decline in system stability. When m=6, the amplitude-frequency characteristic curve crosses 0 dB at 7.032 Hz, and the phase angle at the crossover frequency (f_c) is 182°. The absolute value of the phase angle exceeds 180°, and the system impedance ratio |T_sn(s)| does not meet the condition of being significantly less than 1, indicating an unstable state. Simulation results with the virtual impedance control strategy indicate that when six EMUs are connected to the traction network at the same time, when the virtual impedance R_p values are 10 Ω, 5 Ω, 2 Ω and 1 Ω respectively, the system voltage and current reach a steady state at 1.6 s, 1 s, 0.3 s, and 0.2 s, respectively. The suppression effect improves as the virtual impedance decreases. The simulation results with the adaptive virtual impedance control strategy show that when six EMUs are connected to the traction network, the system reaches a steady state in approximately 0.5 s. The voltage regulation time for the DC side of the EMUs is 0.46 s, with an overshoot of 37.71% and a post-stabilization voltage fluctuation of 58 V. Subsequently, one EMU with adaptive virtual impedance control is added every 2 seconds, resulting in a total of 11 EMUs. The system remains stable, and the virtual impedance value is reduced to 4.61. The comparative experimental results indicate that, after transitioning from the traditional control strategy to the adaptive virtual impedance control strategy, the system can rapidly recover from a low-frequency oscillation at approximately 7 Hz. Furthermore, the AC-side voltage stabilizes at 20 V, while the DC-side voltage remains stable at 40 V.

Based on the analysis, the following conclusions can be drawn: (1) A higher impedance ratio between the two sides of the EMUs-traction network coupling system results in decreased system stability. When the impedance ratio does not meet the stability criterion, system instability is induced, leading to low-frequency oscillations. (2) The incorporation of parallel virtual impedance in the control strategy for the EMU's four-quadrant converter can correct the impedance characteristics of the load subsystem, enhance system stability, and suppress low-frequency oscillation. (3) The adaptive virtual impedance control method can autonomously adjust the virtual impedance value based on the intensity of voltage oscillations on the network side of the EMU, thereby suppressing low-frequency oscillations under varying load conditions, improving the adaptability of the control strategy, and ensuring the stable operation of the EMUs-traction network coupling system across different operating conditions.

Frequency domain dielectric spectroscopy (FDS) is widely used for condition diagnosis of oil-paper insulated power equipment due to its high measurement accuracy and ease of operation. However, in winter in Northeast and Northwest China, the temperatures remain below -40℃ for extended periods, and rapid internal cooling of equipment during maintenance can lead to water crystallization and partial solidification of transformer oil, severely affecting accuracy of FDS results. In order to improve the accuracy of oil-paper insulation condition assessment, it is necessary to perform temperature normalization of the test results. However, traditional "master curve" methods are unsuitable for low-temperature environments as they produce significant errors in high and low frequency ranges. Existing research rarely focuses on the dielectric response characteristics and assessment methods of oil-paper insulation in extremely low-temperature environments. Therefore, this paper studies the FDS results of oil paper insulation at different temperature, and establishes a new temperature normalization model by Havriliak-Negami (H-N) model. This model improves the temperature normalization accuracy, filling the gap in the assessment of oil-paper insulation condition in low-temperature environments.

Firstly, starting from the physics of dielectrics, derive the effects of temperature on the relaxation processes. to obtain the formula for temperature normalization parameters. Samples of oil paper insulation with different moisture contents (0.41%~3.91%) are prepared, and an experimental platform for high and low temperature dielectric response testing is set up. By measuring the frequency dielectric spectra (1 mHz~5 kHz) of samples with different moisture contents at various temperatures (-40~30℃), it is found that as the moisture content gradually increases, the dielectric loss values also increase. Additionally, the decrease in temperature tends to make the high-frequency FDS results more consistent the relaxation peaks less distinct, which increases the difficulty of assessing moisture content. In order to understand the changes in the internal water morphology of oil-paper insulation at low temperatures, the distribution of moisture within insulation paper is studied using isothermal adsorption experiments, revealing a substantial amount of free water attached to cellulose fibers. At low temperature, this part of water crystallizes and precipitates, which affects internal polarization processes of oil paper insulation. Using thermally stimulated depolarization current (TSDC), it is discovered that concentration polarization, interfacial polarization, and dipole polarization are the main three polarization processes in oil-paper insulation. Based on the extended derivative method, it is found that as the temperature decreases, the intensity of concentration polarization gradually weakens, and the relaxation time of interfacial polarization decreases.

In order to study different polarization processes separately, the improved Havriliak-Negami (H-N) model is used to decompose FDS results, extracting characteristic parameters of each relaxation process. It is discovered that as temperature decreases, concentration polarization diminishes and disappears below 0℃, temperature only changes the relaxation time of interfacial polarization without altering its strength, and dipole polarization intensifies due to reduced molecular thermal motion. Moreover, the conductivity process, influenced by both ionic and electrophoretic conductivity, gradually decreases and stabilizes. At the same time, temperature normalization parameters for each relaxation process are extracted.

Finally, a new temperature normalization method is proposed based on the characteristics of each polarization process. Compared to the traditional “master curve” method, this method has higher accuracy in low temperature environments and in conditions with high moisture content. In low temperature, this method maintains high accuracy with a goodness of fit of 0.975 7, compared to 0.952 6 with the traditional method, In samples with high moisture content, the goodness of fit is 0.982 2. At the same time, 5 to 7 more frequency data points are added and full-frequency range correction is achieved, solving the issues of large low-temperature correction errors and insufficient frequency data in the “master curve” method.

As electric vehicles (EVs) achieve higher penetration, their potential as mobile energy storage systems for auxiliary frequency control becomes increasingly evident. However, uncertainties in EV user behaviors, such as irregular charging patterns and diverse preferences, present challenges to fully utilizing their frequency regulation capabilities. This study proposes a power boundary description model and frequency support strategy for EVs, integrating user-specific characteristics and preferences to address these issues.

The research begins with a detailed analysis of uncertainties related to EV user behaviors, battery capacities, and charging/discharging rates. A Gaussian mixture distribution method is employed to model these uncertainties, capturing the probabilistic variability inherent in user behavior. To further refine the model, a Logit framework predicts the schedulability of EVs, accounting for user willingness to participate in grid services based on factors such as charging convenience and state-of-charge (SOC) preferences.

Building on this foundation, the study develops a dynamic EV regulation boundary model that reflects user preferences and behavior characteristics. By adjusting the upper and lower limits of power fluctuations, the model defines flexible boundaries tailored to individual user needs. This approach ensures an upward trend in users’ SOC during participation in grid services, preventing excessive battery depletion and enhancing user satisfaction. The regulation strategy dynamically adjusts to user-defined constraints, enabling effective participation in grid frequency control while respecting user autonomy.

To validate the feasibility of the proposed method, simulations are conducted under various scenarios. The results demonstrate that the regulation strategy significantly improves frequency stability metrics. Compared to conventional methods, the proposed approach reduces maximum and minimum frequency deviations by 13.91% and 29.27%, respectively, and decreases the root mean square frequency deviation by up to 29.59%. The method also shortens the duration of extreme frequency deviations by 42.69%, showcasing its ability to enhance grid frequency stability while minimizing disruptions to user operations.

This study also examines the broader implications of integrating user-specific characteristics into EV frequency regulation. By ensuring a balance between grid stability and user satisfaction, the proposed strategy highlights the potential of EV fleets as flexible and reliable grid resources. The findings emphasize the role of EVs in supporting renewable energy integration, mitigating the challenges posed by the variability of wind and solar power. In conclusion, the study provides a comprehensive framework for characterizing EV power boundaries and developing frequency support strategies. By incorporating user behavior and preferences into the control process, the proposed method offers a practical solution to the challenges of large-scale EV integration. These results contribute to the advancement of smart grid technologies and provide valuable insights for policymakers and grid operators aiming to maximize the benefits of EV participation in modern power systems.

Under the background of the dual carbon goals, the regional integrated energy system (RIES) can achieve interconversion between heterogeneous energy sources due to its multi-energy coupling characteristics, providing new technical support for energy-saving and efficient operation of modern energy systems. Due to the differences in the flow of heterogeneous energy sources in transmission pipelines, existing research usually adopts convex relaxation techniques or linearization methods to model and solve the RIES for multi-time-scale, and relies on high-precision source-load forecasting results and equipment mathematical modeling to improve the reliability of scheduling decisions. However, the increasingly complex internal energy coupling structure of the RIES has increased the difficulty of its refined mathematical modeling and solution, posing challenges to the real-time scheduling decisions and safe optimal operation of the RIES. therefore, this paper proposes an improved distributed bi-layer proximal policy optimization (DBLPPO) deep reinforcement learning scheduling model. This model can achieve multi-time-scale optimization management of various energy networks in the RIES and avoid the optimization difficulties caused by non-convex nonlinear model structures in scheduling solutions.

Firstly, the power output, storage, and transformation of internal energy in the RIES are constructed into a high-dimensional space Markov decision process mathematical model. Secondly, based on the improved distributed proximal policy optimization (DPPO) algorithm, a sequential decision description is made for it, and a control model of the internal bi-layer proximal policy optimization (PPO) is constructed. the local network adopts the "coupling first, then decoupling" solution approach to carry out multi-time-scale optimization decision-making for the cold-heat system and the power system. In the early stage of long time scale, the inner and outer models perform coupled solutions, and the RIES cold-heat system and power system achieve coordinated optimal operation. In the remaining short time scales, the inner and outer models perform decoupled solutions and carry out short-term flexible regulation of the power system. the inner and outer models interact with each other and fluctuating convergence towards the reward maximization direction, eventually achieving multi-time-scale optimization scheduling of the RIES cold-heat system and power system.

This paper conducts simulation experiments with a cold-heat-electric RIES as the scheduling scenario, and compares the scheduling results of the DBLPPO scheduling model with those of a single time scale scheduling model (PPO, DPPO). the results show that the DBLPPO scheduling model can flexibly regulate the system's adjustable resources in the short time scale, meet the power fluctuation requirements of electricity, heat, and cold loads in the short time scale, and has the lowest comprehensive operating cost, which is 24.47% lower than that of the DPPO scheduling model and 28.54% lower than that of the PPO scheduling model. In addition, simulation experiments are conducted with the DBLPPO scheduling model and the bi-layer PPO scheduling model in the same scenario, and the results show that the distributed structure of the DBLPPO scheduling model still has a significant advantage in improving model training efficiency, which can effectively shorten the training time, 10.01% shorter than that of the dual-layer PPO scheduling model.

Through case analysis, it is verified that the proposed scheduling model can achieve coordinated optimal management of various energy networks in the RIES at different time scales, accelerate the optimal decision-making speed of the multi-time-scale scheduling model, and by virtue of the fast adaptability of the deep reinforcement learning algorithm, efficiently solve random optimization problems in complex RIES scenarios, and improve the economic benefits of system operation. The next step of work will be to improve the model to enhance the environmental awareness ability of the inner model, so that its decision-making scheme is always the optimal scheduling decision in the long time scale.