With the global increase in demand for sustainable energy, energy storage technology has become a key factor in achieving the green energy transition. Supercapacitors, as an important electrochemical energy storage device, have shown broad application prospects in fields such as consumer electronics, grid frequency regulation, rail transportation, electric buses, military, and aerospace, due to their excellent fast charge-discharge capability, high power density, and long cycle life. This paper mainly reviews the three basic types of supercapacitors: electric double-layer capacitors, pseudocapacitors, and hybrid supercapacitors, analyzing their energy storage mechanisms and electrode materials, with a focus on the development and classification of lithium-ion capacitors. Additionally, this paper introduces new types of supercapacitor devices and their applications, and compares the safety characteristics of lithium-ion capacitors with lithium-ion batteries, highlighting the significant advantages of lithium-ion capacitors in terms of safety.

Molten salt heat storage utilizes liquid salt to absorb heat as the temperature increases and release heat as the temperature decreases. The molten salt used for heat storage is generally a eutectic mixed salt formed by mixing two or more inorganic salts in a certain proportion, which has the advantages of wide liquid temperature range, large temperature difference, high heat storage density, and long service life. Molten salt thermal storage generally uses a dual tank liquid sensible heat storage scheme, which has the advantages of constant inlet and outlet parameters of the heat storage and release heat exchanger/electric heater, small temperature difference between hot fluid outlet and hot salt tank, and simple control. It has a wide range of application scenarios in the fields of solar thermal power generation, peak shaving system of coal-fired unit aided by molten salt heat storage, molten salt direct/heat pump thermal storage and power generation, molten salt thermal storage for heating and steam supply, compressed air energy storage and compressed heat storage, etc. It is a medium to long term energy storage technology with low-cost, large capacity and long-life. The key technologies and difficulties of molten salt thermal storage are the research and development of mixed molten salt and its composite thermal storage materials with low melting point, high decomposition temperature, low corrosion, low-cost and thermally stable, the research and development of large inlet and outlet temperature difference molten salt heat exchangers and high-voltage molten salt electric heaters, and the integrated regulation and optimization of a new energy system coupled with molten salt thermal storage. At present, more than 30 integrated large capacity thermal storage solar thermal power plants have been put into commercial operation worldwide (with a total installed capacity of over 3 million kilowatts). The longest molten salt thermal storage solar thermal power plant has been successfully operating for 18 years. In recent years, in China, Huaneng Weijiamao, Guoxin Jingjiang, Huaneng Haimen, Shandong Dezhou and other thermal power plants have successively built several molten salt thermal storage peak shaving demonstration projects. At the same time, several molten salt thermal storage heating and steam supply demonstration projects have also been built in Hebei, Beijing, Zhejiang and other places. The Liaohe Oilfield has built an electric molten salt energy storage injection test station. At present, Beijing University of Technology has successfully developed a series of low melting point, high decomposition temperature, wide liquid temperature range mixed molten salt optimization formulas with melting points between 100~160 ℃ and decomposition temperatures between 560~740 ℃, and has been widely used in molten salt heat transfer and storage engineering for a long time. Zhejiang Green Storage, Huayuan Frontline and other companies have successively developed high-voltage molten salt electric heaters.

Concentrating solar power (CSP) is endowed with the capabilities of large-scale deployment, cost-effectiveness, and high-safety long-duration thermal energy storage, and generates electricity through steam turbine generator. It can replicate the grid support and regulation functions of coal-fired power plants, demonstrating significant potential as a green, low-carbon base load power source. As an effective approach to achieve the safe and reliable replacement of traditional energy with renewable energy, CSP serves as a robust underpinning for enhancing the security and flexibility of power supply.To provide a comprehensive overview of CSP technologies and the current status of the industry, and to further advance the development of the CSP sector in China, this paper summarizes the progress of China’s CSP industry in 2025. The key aspects covered include: an overview of CSP technologies, market development status, operational performance of CSP demonstration projects, industrial chain layout, R&D advancements in CSP technologies, economic viability of CSP technologies, and carbon emission reduction benefits of CSP. Finally, targeted recommendations for the future development of the CSP industry are proposed.

Driven by the national dual carbon goals and the establishment of a new power system dominated by renewable energy, large-scale pumped storage power stations (defined as those with a total installed capacity ≥ 1 200 MW) have become critical components for integrating renewable energy into the new power system. This has led to unprecedented rapid development, accompanied by a significant transformation in the role of pumped storage technology. Technological parameters are also advancing towards higher specifications, such as larger capacity, higher head, and broader load range. This paper first provides a brief introduction to the fundamental principles of pumped storage power stations, with a focused analysis of the development status and challenges of large-scale pumped storage power stations (total installed capacity ≥ 1 200 MW) both domestically and internationally. Subsequently, it examines high-parameter pumped storage units (defined as those with a unit capacity ≥ 300 MW, head > 700 m, or load variation range > 40%), discussing research progress in key unit-level technologies (such as ultra-high head hydraulic design, large-capacity structural strength, and wide-range control strategies). The paper also identifies current research shortcomings and key technical problems that need to be addressed. Finally, considering China’s current context, it proposes key research and development directions encompassing the system integration of large-scale power stations and core technologies for high-parameter units, along with recommendations for accelerating the development of key technologies for large-scale pumped storage power stations. This aims to provide theoretical guidance for the advancement of pumped storage technology in China and support the implementation of the national dual carbon goals.

In the 1980s, Sweden pioneered the construction of megawatt-scale seasonal thermal storage systems under the framework of the International Energy Agency, making this technology popular worldwide. Empirical evidence demonstrates that seasonal thermal storage can eliminate the source-load mismatch in large-scale solar heating systems, enhance the solar fraction and heat supply stability, and reduce heat prices to levels comparable to those of coal-fired systems. For China, this technology holds strategic significance in alleviating energy supply-demand contradictions and improving the utilization efficiency of renewable energy. Since 2017, seasonal thermal storage in China has entered a period of rapid growth: a series of high-level applied basic research and technology demonstration projects have been successively implemented, driving a relatively rapid decline in thermal storage costs. This paper systematically reviews the development trajectory over more than 40 years: first, it analyzes the “heat collection-storage-release” energy chain; then, it summarizes three thermal storage methods—sensible heat, latent heat, and thermochemical—and details key materials, particularly anti-seepage materials, thermal storage media, heat exchangers, system integration, and intelligent control technologies; finally, it introduces and analyzes some representative projects. Looking to the future, long-life low-cost solar seasonal thermal storage will achieve applications on the scale of tens of millions of square meters in clean heating for northern urban areas, heat supply for industrial parks, and agricultural drying, providing a paradigm for China’s “dual carbon” goals.

Gravity energy storage technology based on solid weights is expected to become one of the important energy storage technologies in the water-scarce areas in the future due to its advantages of independent of water resources, flexible location and abundant resources, high efficiency, and no self-discharge, and can well meet the demand of energy storage technology for new energy power system. However, due to the discreteness and non-fluidity of solid objects, power fluctuations will occur during the load/unload and acceleration/deceleration process of solid objects, and will simultaneously have a certain impact on mechanical transmission and power grid systems. In addition, the heavy-load lifting machinery is still difficult to meet the needs of energy storage systems in terms of power, efficiency and stability currently. This paper first introduces the principle and classification of solid gravity energy storage technology, and puts forward the key scientific and technical problems that need to be solved. And then, aimed at three typical gravity energy storage technologies, such as underground shaft, ground building and mountain slope, the research status and challenges of the key technologies such as heavy load lifting, automatic connection and horizontal transfer, grid connection and power smoothing are analyzed, and then the engineering application status of three technologies are given. Finally, the future development trend of three gravity energy storage technologies is forecast.

With the gradual promotion of the construction of the new power system, offshore wind power, tidal energy, and other offshore renewable energies have made considerable progress. At the same time, the demand for flexible underwater energy storage resources has become increasingly urgent. Therefore, this paper reviews the research progress and development prospects of underwater compressed air energy storage. Firstly, the basic principles and technical features of underwater compressed air energy storage systems are introduced. Secondly, the representative demonstration projects and current development status of underwater compressed air energy storage systems at home and abroad are summarized. Thirdly, the key technologies of underwater compressed air energy storage are outlined, including underwater gas storage, heat storage, anchoring, and other assistant operation technologies. Finally, based on the current technical bottlenecks, the future research direction and application research focus of underwater compressed air energy storage are clarified. This paper aims to provide reference for the research in the field of underwater energy storage and to improve the coordinated development of offshore renewable energy and energy storage technology.

Carbon dioxide energy storage technology stands out as an efficient, stable, flexible and cost-effective solution in the realm of novel energy storage technologies. It provides powerful safeguard for the new energy system construction of China, and has promising prospects for future development. The paper first introduced the overall research progress of CO2 energy storage. And then, the basic principles and advantages, engineering applications as well as improvement ideas of gas-liquid phase change CO2 energy storage system were illustrated. Finally, the future prospects of CO2 energy storage technologies were outlined, providing valuable insights and references for subsequent research in this field.

Flywheel energy storage technology as an efficient and long-lasting physical energy storage method effectively addresses the grid volatility issues caused by renewable energy sources such as wind and solar power. It is an essential part of the modern energy transition. However, the widespread application of flywheel energy storage faces technical challenges, including high costs and rotor fatigue life issues. This paper reviews the development and application of flywheel energy storage technology, with a focus on the design optimization and fatigue life analysis of flywheel rotors. To enhance energy storage density and reduce costs, significant research has been conducted on rotor shape and structural optimization, including designs for various types of disk-shaped and cylindrical structures. Frequent charging and discharging during high-speed operation cause stress variations, which in turn affect the rotor’s fatigue life. Therefore, predicting rotor fatigue life has become a key area of research. Traditional stress-strain-based fatigue life prediction methods have certain limitations under complex loading and multi-axial fatigue conditions. However, recent advancements in new prediction methods based on energy approaches, critical plane methods, and neural networks have shown stronger adaptability and accuracy. In particular, combining traditional methods with artificial intelligence technologies has greatly improved the accuracy of fatigue life predictions. In summary, significant progress has been made in materials, structural optimization, and fatigue life prediction for flywheel energy storage technology. However, challenges such as cost control and long-life design still need to be addressed in order to promote its widespread adoption in large-scale energy storage and grid frequency regulation applications.

Pumped storage is a large-scale long-term energy storage system with the best comprehensive performance, which will provide important support for the new power system dominated by new energy. However, the resources of pumped storage sites in our country are seriously insufficient, and are far from meeting the actual demand. At the same time, the seasonal output of new energy fluctuates greatly, which does not match the seasonal changes of power load, and puts forward a higher demand for the development of large-scale cross-seasonal energy storage. In view of this, research on new pumped storage systems has been carried out at home and abroad in recent years, dedicated to solving the above problems by expanding the resources and models of pumped storage. This paper defines and classifies new pumped energy storage systems, including underground pumped storage, underwater pumped storage, semi-underground pumped storage, cross-seasonal and cross-regional pumped storage, and energy storage systems based on compressed air/pumped water.

This paper conducts a comparative study on virtual impedance and virtual admittance control strategies for grid-forming static var generators (SVGs). An SVG impedance model is established using the harmonic state-space (HSS) method to analyze small-signal stability, with frequency scanning validation implemented through a simulation platform. By deriving the inner-loop transfer functions, similar equivalence conditions between the two control strategies are identified. The impedance differences of grid-forming SVGs under both control methods are systematically investigated from a small-signal stability perspective, along with their adaptability to varying grid strength conditions. Based on the stability analysis results, parameter design principles for virtual impedance and admittance are proposed. Furthermore, leveraging the fundamental differences between the two strategies, the virtual impedance control method is optimized to enhance its stability in strong grid scenarios. Finally, a hardware-in-the-loop (HIL) experiment is conducted on the RT-LAB platform to validate the effectiveness of the theoretical analysis and optimization method proposed in this study.

Temperature significantly influences the performance of power batteries, with an optimal operating range of 20~50 ℃. To effectively control the battery temperature rise and prevent thermal runaway, passive battery thermal management systems based on thermal energy storage technology have proven to be an effective solution. However, the low thermal conductivity of traditional inorganic or organic thermal storage materials often limits their application, making it crucial to enhance their thermal conductivity. This paper reviews the research progress on using carbon nanotubes, carbon fibers, graphene, and expanded graphite to improve the thermal conductivity of thermal energy storage materials. Among those, expanded graphite exhibits excellent adsorption properties for organic thermal storage materials and, after hydrophilic modification, significantly enhances the adsorption capability for hydrated inorganic salt thermal storage materials. This enables the preparation of expanded graphite composite thermal storage material powders. Through compression processing, continuous carbon-based thermal conduction pathways are formed within these composite materials, increasing their thermal conductivity by more than an order of magnitude compared to the original organic or inorganic thermal storage materials, which is far superior to other carbon-based materials such as carbon nanotubes, carbon fibers, and graphene. By introducing natural rubber into organic/expanded graphite-based composite thermal storage materials, a flexible insulating network can be formed, resulting in a dual-network encapsulated structure of flexible composite phase change thermal storage materials. These materials can effectively control the temperature rise of batteries and improve the temperature consistency between cells. Hydrated inorganic salt/expanded graphite composite thermal storage materials possess both phase change and chemical thermal storage capabilities, with a thermal storage density an order of magnitude higher than that of phase change thermal storage, providing a novel solution for mitigating battery thermal runaway. Looking ahead, further development of novel flexible thermal storage materials with both phase change and chemical thermal storage functionalities is required to meet the thermal management demands of batteries across a wide temperature range.

The high proportion of renewable energy integration in distribution systems under the dual-carbon strategy has introduced power quality issues such as voltage violations, harmonic distortions, and three-phase imbalances. Grid-forming converters, with their active regulation capabilities, provide a feasible technical solution to address these power quality challenges. This paper focuses on the research progress in power quality enhancement technologies for grid-forming converters in high-penetration renewable energy systems and their regulation capability evaluation. First, grid-forming control strategies for power quality improvement are analyzed from two perspectives: individual converter control and cluster operation. Subsequently, evaluation methods for assessing the regulation capability of grid-forming converters are discussed, including inertia support and other key performance metrics in renewable energy-integrated grids. Furthermore, solutions for mitigating different power quality issues using grid-forming converters are systematically reviewed. Finally, future research directions for grid-forming converters in high-penetration distribution networks are outlined, providing theoretical support for power quality enhancement in high-proportion renewable energy systems.

At present, non-invasive load decomposition studies mainly focus on residential loads, while industrial loads are rarely studied, and the temporal characteristics of active power of load operation are not considered. Therefore, this paper proposes a non-invasive load decomposition algorithm for industrial users based on TCN-CBAM-LSTM. First, a TCN-CBAM module is constructed, which uses TCN dilated causal convolution to expand the receptor field of the convolutional kernel and combines the spatial and channel attention mechanism of CBAM to achieve effective feature extraction. Then, two TCN-CBAM modules can extract device operating features from the total active power of the incoming line. Finally, LSTM is used to learn and train the relationship between the operating characteristics and the operating active power of each equipment to realize load decomposition. Compared with CNN, LSTM, TCN and other algorithms, the decomposition accuracy of the model is higher by analyzing the actual running data of a steel mill and a textile mill and HIPE data set.

The traditional fixed-parameter virtual synchronous generator (VSG) control strategy is difficult to maintain when it comes to ensuring long-term stable operation in the face of grid frequency disturbance and active output reference value disturbance. This strategy facilitates the multi-parameter adaptive optimal control of VSG by constructing a mathematical model of VSG and its adaptive control model. The exponential function is adopted to optimize the inertia and damping coefficients of VSG, and the tanh function is introduced to optimize the control of active sag coefficients. On the basis of these functions, and considering the process of angular frequency oscillation, the multi-parameter adaptive optimal control of VSG is realized. In conclusion, the simulation analysis and hardware in the Loop (HIL) experiment demonstrate the efficacy of the strategy proposed in this paper in enhancing the support of the grid frequency and ensuring the stable operation of the system.

The real-time monitoring of lithium battery core temperature in electrochemical energy storage systems is of great significance for the development of battery management systems (BMS) and the risk warning of thermal runaway. Due to cost and space constraints, as well as the limited number of temperature measurement points in the battery system, the acquisition of temperature data is not comprehensive, which affects the accuracy of thermal runaway prediction algorithms and battery management systems. The article uses the Gappy POD algorithm to reconstruct the temperature of all battery cores and multiple surface points inside the battery pack box in real-time based on a small amount of measurement point data outside the box. This article establishes a second-order RC equivalent-circuit model, identifies the model parameters using 18650PF lithium battery HPPC test and expands the equivalent circuit model identification parameter database based on proper orthogonal decomposition-radial basis function(POD-RBF) neural network to obtain a battery heating power variation curve containing more power information; A numerical model of battery modules is constructed with and without water cooling plates, and the real and abnormal heating power variation curves are imported to test the real-time temperature reconstruction ability of the Gappy POD algorithm under stable and abnormal operating conditions. The research in this article shows that the Gappy POD algorithm can achieve high reconstruction accuracy and strong trend prediction ability under complex operating conditions with only a small number of temperature measurement points, providing a foundation for the application of the algorithm in battery management systems and thermal runaway warning.

The cable joint is one of the critical components in power cable transmission lines. However, various impurities inevitably are introduced into the joint during its manufacturing process, which may cause a security problem in the long-term service of power cable. In this work, a combination of finite element simulation and experiments were carried out to study the effect of various impurities on the electrical performance of cable intermediate joints. A three-dimensional model of the 10 kV cable intermediate joints was established using the COMSOL Multiphysics software. Impurity defects such as copper particles, semiconducting debris, dust, and moisture were prepared in the cable joints for the electric field simulation calculations. Meanwhile, the intermediate joint samples with different defects were made for the partial discharge and withstand voltage tests. The results show that different types of defects will significantly affect the electric field distribution and partial discharge characteristics of cable intermediate joints. Copper particles and residual semiconducting debris exhibit the greatest impact on the electrical performance of intermediate joints, which bring about electrical breakdown of the intermediate joints in the withstand voltage test. Moisture causes a high amount of partial discharges, while dust presents a relatively small impact on the electrical properties of intermediate joints. The roles of different impurity defects on the electrical performance of cable intermediate joints have been illustrated, providing a theoretical reference for the operation and maintenance of cable intermediate joints.

With the influx of numerous distributed resources, such as electric vehicles and rooftop photovoltaics, into the substation area system, autonomous optimization scheduling can enhance the local absorption of distributed photovoltaic power and elevate the substation’s autonomous operational level by unlocking the potential for resource adjustment. This paper introduces a multi-time scale optimization scheduling method for substation systems incorporating photovoltaic storage and charging, which takes into account the flexibility of electric vehicles and the level of photovoltaic power absorption. Initially, during the day-ahead scheduling phase, the optimization aims to minimize the substation’s economic costs, reduction of load fluctuations, and maximization of user satisfaction, thereby determining the day-ahead unit scheduling. Subsequently, in the intraday scheduling phase, the objective is to minimize unit fluctuations, taking into account the real-time integration of electric vehicles and adjustments to charging strategies, fully leveraging the orderly charging and discharging flexibility of electric vehicles. Finally, simulation results indicate that the proposed multi-time scale scheduling method is effective and cost-effective. This approach helps to stabilize load fluctuations, enhance the absorption of renewable energy, and fosters autonomous optimization control in the substation area.

In light of the non-stationary and nonlinear characteristics of transformer partial discharge signals, this paper proposes a novel pattern-recognition framework that integrates variational mode decomposition (VMD), multiscale permutation entropy (MPE) and parallel dual-branch network. First, VMD is employed to decompose each partial discharge waveform into a set of band-limited intrinsic mode functions (IMFs), from which deep feature information is extracted via MPE to form the feature-vector dataset. Next, a parallel dual-branch model is constructed: Namely, branch I leverages the multi-head attention mechanism of a Transformer Encoder to capture global representations, while branch II applies a stacked one-dimensional convolutional neural network coupled with a squeeze-and-excitation (SENet) module to mine local feature details. The global and local features obtained from both branches are then concatenated through a feature-fusion strategy to enhance the discriminative power of the model. Experimental results demonstrate that the proposed method achieves an accuracy of 96.37% in transformer partial discharge pattern recognition, exhibiting high computational efficiency and substantially improving diagnostic performance, thereby providing a robust technical basis for transformer maintenance.

To facilitate the low-carbon transition of offshore oil and gas fields, utilizing marine renewable energy becomes an important development direction for the power supply of offshore oil and gas platforms. Wind power attracts widely attention owing to its advantages of rich resources and high power generation efficiency. However, the fluctuation and intermittency may cause frequency fluctuation during grid connection. The problem becomes more serious with the gradual exit of gas turbines, leading to the reduction of inertia and frequency response resource. Aiming at the problem of frequency stability and wind power utilization in the integration of high percentage wind power into offshore oil and gas grid, a collaborative configuration method of grid-forming and grid-following energy storage systems is proposed. The frequency response characteristics of system with grid-forming energy storage are analyzed. The models of grid-forming energy storage, grid-following energy storage, gas turbine generator, and wind turbine are constructed. An energy storage configuration optimization model aimed at minimizing the average daily cost is constructed, considering operational constraints and frequency constraints induced by wind power output fluctuations as disturbance power. By applying the convex hull relaxation method, constraint linearization is achieved, enabling efficient solution of the energy storage configuration. Degradation of BESS for the configuration scheme is checked. Case studies demonstrate that the proposed method can collaboratively configurate grid-forming and grid-following energy storage. The method substantially enhances frequency stability and wind power consumption, and reduces system costs.

Aiming to the problem of overhead-cable hybrid lines are open for reclosing or not, this paper proposes a fault section judge method for hybrid lines based on travelling wave energy analysis. Firstly, the propagation characteristics of fault voltage and current travelling wave in the conductor and its refraction rule are analyzed. Then the transmission characteristics of the traveling wave voltage in the overhead-cable hybrid line are studied. According to the characteristics of the discontinuous wave impedance of the hybrid line, the fault voltage and current travelling waves in different zones measured at the connection point are analyzed. Finally, a fault section judging method based on the travelling wave energy to identify the faulted section is designed. A hybrid line model is constructed in PSCAD, and simulations are carried out under different fault types, transition resistances, and instantaneous phases of faults to verify the reliability of the strategy. The strategy is applicable to all fault types by only setting measurement points at the connection to identify the faulted sections; it is also applicable to multi-hybrid lines, and there is no need to strictly synchronize sampling between the measurement points.

With the increasing proportion of renewable energy and the widespread application of power electronic interface devices, the dynamic characteristics of modern power systems are becoming increasingly complex. The rational allocation of dynamic reactive power sources with fast response capabilities is of great significance for enhancing system stability. However, their inherently complex transient characteristics pose challenges for the power system equipment planning. To reduce the computational cost associated with complex electromechanical transient models, this paper constructs a parameter-free adaptive Polynomial Chaos Expansion (PCE) surrogate model. While maintaining the global capability of PCE, this approach further improves its local prediction accuracy through a training sample selection strategy. Ultimately, the PCE surrogate model is employed to replace traditional, computationally intensive electromechanical transient time-domain simulations, thus significantly enhancing the computational efficiency of capacity optimization models. The research findings indicate that the proposed parameter-free adaptive surrogate model-based coordinated planning method for power system reactive sources not only ensures accuracy but also greatly reduces computational time, thus showing promising prospects for practical applications.

When the robot joint motor runs under high overload conditions, the short-term high temperature rise at the end winding will endanger the insulation of the motor winding, which will bring hidden dangers to the operation performance and reliability of the permanent magnet motor. In this paper, a transient equivalent thermal network modeling method based on lumped parameter thermal network method (LPTN) is proposed to solve the transient short-term high temperature rise of a permanent magnet motor with epoxy potting structure at the winding end under high overload conditions. The method considers the change of epoxy specific heat capacity with time and the equivalent heat capacity of each node, and deduces the iterative calculation formula of transient temperature by finite difference method. Based on the proposed model and calculation method, the instantaneous temperature rise characteristics under natural convection and epoxy encapsulation of end-winding were calculated respectively for the short-time operation of the prototype under 4 times overload, and the simulation results were compared with the Computational Fluid Dynamics(CFD) method. In addition, the effects of the thermal conductivity and overload time ratio on the temperature characteristics of epoxy were analyzed by using the established model. Finally, through the overload temperature rise experiment of the prototype, the rationality of the transient thermal network modeling method and the accuracy of the transient temperature calculation method are proved.

When the amplitude of grid voltage drops, the traditional PI algorithm will lead to even harmonics on the DC side of the rectifier and odd harmonics on the AC side of SWISS rectifier. Instantaneous Active Power Control (IAPC) can suppress fluctuations of active power and harmonics of grid side current effectively, but the particularity of SWISS topology poses boundary problems on the application of IAPC method. Therefore, this article deeply investigates the features of the current and the voltage of SWISS rectifier based on IAPC method firstly, and then calculates the operating boundary of the SWISS rectifier under this control strategy under two typical situations, that is, single-phase and two-phase voltage drop in grid. Finally, the performance of IAPC method and traditional PI control under various operating conditions is compared in the experiment, and the results show that the IAPC method can effectively suppress grid side current harmonics and fluctuations of active power within the operating boundary compared to traditional PI control, but it will lead to an increase in the THD of grid side current and the oscillation of active power if it exceeds the operating boundary.

Overheating caused by electrical contact failure of tulip contacts is a common fault in switchgear. Traditional temperature detection techniques are difficult to directly observe and measure the hot spot temperature of the tulip contact in switchgear by going deep into the equipment. It is of great significance to use mechanical contact pressure to predict the hot spot temperature of the tulip contact. This article calculates the contact resistance through the contact pressure of the tulip contact, constructs a pressure-electromagnetic-heat-flow multi-physics coupling simulation model, and analyzes the effects of fault current, ambient temperature, and contact pressure on the temperature rise of the tulip contact. Finally, a reasonable orthogonal experiment is designed to generate a sample database of tulip contacts in switchgear. Multiple physical field coupling simulation data is used as training samples to establish a support vector regression model for predicting the hot spot temperature of tulip contacts. The research results can provide theoretical basis and technical support for the accurate prediction of the hot spot temperature of the tulip contacts of circuit breakers in KYN type switchgear and the intelligent operation and maintenance of equipment.

The inherent fluctuations and low inertia characteristics of current renewable energy generation systems lead to degraded frequency stability in power systems. Conventional DC capacitance inertia-synchronization control methods, which lack damping components, tend to cause system oscillations under disturbances. To address this issue, this paper proposes a feedforward damping strategy suitable for grid-forming converters in flexible DC receiving-end systems. By introducing DC-side voltage deviation and damping coefficients to construct feedforward damping, this strategy can compensate for the missing damping components in control systems. Through comparative analysis of small-signal models with/without damping components can regarding inertia, synchronization coefficients, and damping characteristics, we demonstrate that this strategy can effectively provide system with damping. The mathematical model of feedforward damping aligns with the physical principles of synchronous machines, overcoming the interpretability limitations of existing research methods while facilitating parameter matching and ensuring full-frequency applicability, thereby enhancing model universality. Simulation models and experimental platforms for flexible DC grid-connected systems were established, with results confirming that the proposed feedforward damping control strategy effectively suppresses system oscillations under various disturbance conditions.

The Grid-forming converter(GFM) face a contradiction between transient stability and power quality under asymmetric faults. The currently dominant fault ride-through (FRT) method based on command switching fails to effectively address the issues of suppressing power double-frequency components and reducing current asymmetry. To resolve these problems, this paper proposes a multi-objective optimal control method. First, by analyzing the modification mechanism of power loop control parameters, it is clarified that the reasonable design of commands and control variables under faults is critical to achieving smooth FRT. Second, the active power reference, voltage reference, and damping parameters of GFM under symmetric faults are integrally adjusted to enhance their transient stability. Next, the impact of asymmetric faults on the transient characteristics of GFM is analyzed. Building on the symmetric FRT method, smooth FRT under asymmetric faults is realized by additionally applying the integral feedback of angular frequency deviation upon detecting negative-sequence components. Meanwhile, a multi-objective strategy for suppressing power double-frequency components and balancing current asymmetry is designed using fuzzy control to select the current reference in the negative-sequence loop. Finally, simulation results verify that the proposed method enhances FRT performance and improves the flexibility in power quality.

As the core equipment of the power system, the temperature rise of the transformer is an important representation of its health status. The internal temperature monitoring of the transformer is of great significance to the safe and stable operation of the power grid. Aiming at the problem that it is difficult to arrange distributed sensors inside the transformer for direct temperature monitoring, this paper proposes an inverse method for measuring the internal temperature based on multi-point inverse transformation mapping of transformer surface temperature. This method constructs the matrix inverse problem model of the internal and external temperature mapping of the transformer, solves the inverse matrix through the electromagnetic heat flow coupling finite element simulation data of the transformer, and then realizes the internal temperature inversion by combining the temperature data of the outer measuring points. In this paper, the validity of the multi-point inverse transformation model is verified by multi-physical field simulation and the D-800/35 scaled transformer model. The results show that the average error of the internal temperature inversion error of the transformer is less than 1 K under the condition of less temperature input of the measuring point on the surface of the transformer, and has higher accuracy and lower computational complexity. This method can inverse the internal temperature of the transformer quickly and without sensor intrusion, and can provide theoretical support and technical support for the temperature monitoring and fault diagnosis of the transformer.

Urban rail transit is rapidly developed, but the key electrical equipment DC circuit breakers (DCCB) still rely on imports, because its technical difficulties have not yet been overcome in our country. In forced current-zero DCCB, the commutation parameters are designed based on short-circuit current. However, the rail transit system needs frequent interruptions of the rated current, and its lifetime requirements reach up to 200 000 cycles. Using huge reverse current to interrupt small current will damage the DCCB. The inductor switching forced current-zero topology can realize different reverse current inputs to solve the problem of vacuum forced over-zero interruption of small current difficulties. Firstly, the equivalent circuit model is constructed and the values of peak current and interrupting frequency under different capacitance and inductance are calculated, to select suitable parameters for the experimental verification. Then, the forced-over-zero DC interrupting experimental platform is constructed to verify the feasibility of the interrupting performance of bi-directional currents. Results show the commutation circuit can interrupt 3.6 kA overload-current and 9.6 kA short-circuit current in the reverse direction, and 3.6 kA overload-current and 8.5 kA short-circuit current in the forward direction within 3 ms. The rated mode current drop rate is only 36~48 A/μs and the interrupting condition is good, which can meet the frequent interrupting rated demand and promote the localization and application process of DCCBs for rail transportation.

To address the instability and insufficient detection accuracy of density peak clustering (DPC) caused by manual setting of the cutoff distance, this study proposes an anomaly detection method combining improved grey wolf optimization and DPC. An improved chaotic mapping is employed to uniformly initialize the grey wolf population, and a nonlinear decaying convergence factor is designed to dynamically balance global exploration and local exploitation. The Davis-Bouldin index is used as the fitness function to automatically optimize key DPC parameters, achieving parameter adaptivity. Case studies on electricity consumption datasets from multiple regions and comparative experiments against mainstream anomaly detection algorithms show a notable improvement in AUC relative to the baseline, validating the method’s significant advantages in detection accuracy and stability.

In order to improve the quality of power supply and the ability of stable operation of power system, the method of additional damping is usually used to suppress the harmonic resonance generated by the system. Compared with passive damping, the active damping strategy not only has well harmonic suppression effect, but also has the advantages of low loss and easy realization, so it has been widely used in recent years. However, the traditional active damping strategy mainly adopts the fixed control parameter design, which may cause the harmonic resonance problem when the working condition changes greatly. To solve this problem, based on the compound control strategy composed of harmonic compensation and resonance suppression in hybrid compensation system, an adaptive additional active damping composite control strategy is proposed in this paper. Firstly, we establish the parallel resonance equivalent model of the hybrid compensation system, and analyze the shortcomings of the traditional compound control strategy. On this basis, the harmonic voltage at PCC point is extracted by the second-order generalized integrator-frequency lock loop (SOGI-FLL), and the harmonic resonance suppression in the power supply system is designed and realized by adapting the parameters of the resonant suppression controller with the method of harmonic content limitation. Finally, the feasibility and effectiveness of the proposed method are verified by simulation experiments.

A prediction model for dissolved gas concentration in transformer oil based on MVMD and IFA-TCN addresses the issue of low prediction accuracy in traditional single-variable prediction models. Firstly, gases with high correlation to the target gas are selected as input features, and a Multivariate Variational Mode Decomposition(MVMD) is employed to decompose multiple input features simultaneously to overcome the frequency imbalance issue of different sequences. Secondly, a Time Convolutional Network(TCN) is utilized to predict each sub-sequence and extract temporal features of the gases. Finally, the predictions from all sub-sequences are aggregated to reconstruct the final prediction result. The results of the calculation example demonstrate that, compared to other models, this model possesses distinct advantages in prediction and exhibits strong transferability.

To enhance the accuracy and reliability of photovoltaic power prediction, this paper proposes a short-term photovoltaic irradiance prediction model based on the improved grey wolf optimizer (GWO), variational mode decomposition (VMD), particle swarm optimization (PSO), and long short term memory (LSTM). First, LGWO optimizes the key decomposition parameters K and α1 of VMD to construct a parameter optimization model. Then, LSTM predicts each mode parameter decomposed by the optimized VMD. Simultaneously, the improved PSO, using root mean square error as the objective function, optimizes the neural network parameters, including the number of LSTM hidden layers, LSTM iterations, and LSTM learning rate. This ultimately improves the prediction accuracy and reliability of the model. Finally, the output modeling and prediction analysis of a photovoltaic power plant in Gansu Province are conducted. The results show that, compared with the traditional LSTM prediction model, the proposed algorithm significantly improves the prediction accuracy.

特约主编肖立业研究员致开篇词：

以光伏和风电为主的可再生能源替代化石能源，是实现国家能源独立和“碳中和”目标的必由之路。2021年10月24日，《中共中央  国务院关于完整准确全面贯彻新发展理念做好碳达峰碳中和工作的意见》提出：到2060年，我国绿色低碳循环发展的经济体系和清洁低碳安全高效的能源体系全面建立，非化石能源消费比重达到80%以上。综合多方面的分析和预测，到2060年，我国电力总装机将达到100~110亿kW；其中，火电（含煤电、气电、生物质/垃圾焚烧发电）12~15亿kW、水电约6亿kW、核电约2亿kW、太阳能热发电1~2亿kW、风电15~20亿kW、光伏装机60~65亿kW，总发电量将达到22~23万亿kW·h，电力占能源生产的比重将超过80%，而最大电力负荷（储能系统用电除外）将达到约30亿kW。

   风电和光伏出力具有随机性、波动性和季节性的特点，光伏发电的出力还具有昼夜周期性。根据中国电力科学研究院郭剑波院士提供的数据，各省、各区和国家电网公司经营范围内，风电光伏某天最小日平均出力水平分别为3.6%、8.0%和10.7%；3天内最小日平均出力水平分别为5.9%、9.1%、11.6%。这就是说，即使考虑较大范围内的风光时空互补性，其最小置信容量也只有装机容量的10%左右，即2060年我国全国范围内风电光伏总的最小日平均出力仅为7.5~8.0亿kW。与此同时，风电和光伏的出力还具有强烈的短时不稳定性。例如，在出现快速云层遮挡时，某些光伏电站的出力可以在1 min内下降90%；风力观测数据表明，单个站点的风功率1  min内的波动值最大可达20%、10 min内的波动最大值达到了100%。此外，由于风电和光伏装机都是通过电力电子装置并网，因而不具备同步惯量。由此可见，以新能源为主体的新型电力系统中，新能源资源的不确定性、低惯量或无惯量支撑将对电网的电力平衡与灵活调节（调频或调峰）和安全运行带来巨大挑战。

   采用直流电网模式，虽然可以避免频率稳定性问题，但以新能源为主的直流电网的电压稳定性问题将成为新的挑战，且在无储能支撑情况下，直流电网内的潮流分布波动频繁，加之电力电子装置的频繁控制和快速响应，可能导致系统出现新的电磁振荡现象。此外，将交流系统全部改造为直流系统的代价也很大。因此，未来电力系统仍将以交流同步电网为主，直流电网虽有可能因地制宜地存在于电网之中，但仍将需要交流电网作为基础与支撑。

   为应对上述问题，迫切需要发展多种形式的储能系统。为解决不同时间尺度的功率和能量调节需求，一般需要发展长周期能量转移型（周内、月内、跨季节储能）、短周期能量转移型（日内削峰填谷）、短时间尺度功率型（短时调频）三种类型的储能系统。特别是，当光伏发电成为主要电源后，日内调峰将成为储能系统最重要的任务之一。

   长周期能量平衡，一般可以通过备用化石能源或可再生能源制备燃料来应对。可再生能源制备燃料主要包括生物质制燃料、电解水制氢、电解水制氢加CO₂制备清洁碳氢燃料、人工光合作用等方式，这种类型的储能属于化学储能领域，是解决大规模能量的跨周、跨月、跨季平移问题的主要方向。

   随着可再生能源占比不断增大，惯量支撑（毫秒至秒级）、一次调频（数秒至分钟级）或二次调频需求（分钟至十分钟级）、小时级功率和能量调节、日内调峰、数日内（2~3天）调峰的储能需求量十分巨大。仅从功率响应特性和能量平移需求角度来讲，电池储能在这些方面均可发挥作用。然而，电池储能也存在明显的不足。首先，对于惯量支撑或一次调频的应用场景来说，需要储能系统频繁充放电，从而严重影响其使用寿命。在日内调峰或数日内调峰应用方面，与抽水储能系统、储热发电、压缩空气储能等物理储能相比，电池储能的综合性能（提供旋转惯量、环保性、安全性、经济性等）仍有较大差距。目前，锂离子电池是使用最多的电化学储能，但存在较大安全隐患，锂资源也较为有限。综合多方面的分析认为，电池储能在紧急功率支撑、小时级储能（1~4h）或分布式储能方面具有较大的优势。

   物理储能包括抽水蓄能、压缩空气储能、重力储能、飞轮储能、超级电容器储能、超导储能、显热储热、相变储热等。物理储能的共同特点是使用寿命长、环境友好、报废后处理简单容易。在惯量支撑或一次调频应用场景，飞轮储能和超级电容器储能都是优选方案。

   抽水蓄能具备惯量支撑、黑启动、调频、调相、调峰等多方面的综合优势，是建设以新能源为主体的新型电力系统中最为理想的储能方式之一。然而常规抽水蓄能资源不足，发展地下抽水蓄能等新型抽水蓄能，可以大大拓展抽水蓄能的站点资源，且具有与常规抽水蓄能基本相当的经济性，因而将成为抽水蓄能的重要发展方向；同时，高水头抽水蓄能电站、大规模跨季节抽水蓄能电站也日益受到关注。

   在日内调峰方面，压缩空气储能也是较好的储能系统。当前，依靠盐穴等天然洞穴作为储气室的压缩空气储能具有良好的经济性，已经得到了较为广泛的应用。为拓展压缩空气储能的资源，基于人工硐室的压缩空气储能近年来也开展了应用示范。基于高水头的恒压压缩空气储能，可有效提高储气硐室的储能利用率和系统能量转化效率，近年来也日益受到关注。此外，作为压缩空气储能的相近技术方案，压缩二氧化碳储能、液态空气储能也取得了良好的发展。

   随着储能需求的快速增长，人们在不断探索各种新型储能的同时，重力储能这类传统机械储能系统近年来也重新受到广泛关注。依托垂直竖井、斜坡和人造建筑物形成落差的各种重力储能系统的研究和示范项目越来越多。

   热能在终端能源中的占比超过50%以上，当前主要依靠化石能源来供应。随着碳中和目标的实施，供热用热低碳化也是物理储能的重要发展方向。为此，加快热储能技术的研究发展也成为当前的重要任务。热储能形式多样，电加热储能（熔盐储热、固体储热）、相变储热、冰蓄冷、跨季节储热采暖等都得到了较快的发展。其中，电加热熔盐储热还可与火电机组（煤电、气电、燃气-蒸汽联合循环、太阳能光热发电等）有机结合起来，或直供汽轮机组发电，是实现日内调峰和数日内调峰的重要储能技术方向。

   诚然，解决可再生能源规模化并网与消纳利用，需要有机融合多种形式储能系统的功能特点，使混合储能系统耦合新能源发电系统具备等效为常规火电机组所具有的各种特性，从而新能源电力系统面临的各种问题提供解决方案，但物理储能将以其良好的安全性、环保性、经济性、长寿命、可用资源可拓展性、可为电网提供机械惯量等综合优势，必将在新能源和新型电力系统建设中发挥关键作用。

   以上就是开设物理储能专刊的原因。

                                          

                                                                                                          专刊特邀主编

             肖立业

            2025年8月22日

This paper addresses the practical problems of slow speed and low efficiency in traditional voltage regulation in transformer areas, and proposes a multi-time-scale voltage feedback correction control and its optimization strategy aimed at the comprehensive management of voltage over-limit, fluctuation and imbalance. Firstly, a priority evaluation mechanism for adjustable resources in transformer areas based on voltage sensitivity was established. The voltage sensitivity of reactor groups, energy storage, and flexible interconnection devices was derived and analyzed. Considering multi-dimensional weight characteristics, a comprehensive evaluation of the regulation priority of adjustable resources was conducted. Based on this, for the key nodes of the transformer area, combined with the proposed multi-scenario equipment adjustment scheme, the multi-time-scale voltage feedback correction control strategy was analyzed from three modules: data input, voltage prediction and instruction output. Further considering the influence of measurement errors and unknown disturbances, based on the design of the optimal prediction window, corresponding optimization strategies are proposed.

Long-stator linear motors typically operate with sectionalized power supply, requiring rapid power transfer between stator units during linear motion. However, frequent switching induces abrupt load variations that generate current surges, threatening the reliable operation of both the motor and the power supply system. This paper proposes a fast and smooth power switching strategy integrating time-optimal control with PI regulation. The approach establishes a mathematical model of the linear motor during switching transitions and derives time-optimal turn-off/on voltage vectors alongside their corresponding minimum switching durations through time-optimal control theory. By applying the time-optimal turn-off voltage for rapid decay of the outgoing segment’s current while combining the time-optimal turn-on voltage with a PI controller output reuse mechanism for precise current establishment in the incoming segment, the proposed strategy eliminates current overshoot while compressing the switching process to 2 electrical radians. Finally, a computer simulation model validates the proposed approach, with results conclusively demonstrating the effectiveness of the switching strategy.

For the quantitative analysis of system strength of receiving end system, the formula of short circuit ratio about the network structure parameters is obtained by deducing the Thevenin equivalent circuit of receiving end system. The power stability of Thevenin equivalent circuit is analyzed. It is shown that the value of short-circuit ratio is related to the equivalent impedance angle, the phase angle difference between grid-connected voltage and Thevenin equivalent potential, and the critical short-circuit ratio under some limit conditions is further discussed. The phase angle difference index is proposed to evaluate system strength of receiving end system, and the standard of dividing system strength of receiving end system by the phase angle difference index is given. The relationship between the phase angle difference index and the potential phase in the generator is analyzed. A simulation example is given to verify the validity of the phase angle difference index in evaluating system strength of receiving end system.

Harmonic currents significantly increase the harmonic losses and temperature rise in transformer windings. Quantifying the impact of harmonic currents on transformer temperature distribution and thermal aging life is crucial for accurately modeling transformer lifespan. This paper investigates the temperature distribution and thermal aging life of transformer windings under the influence of harmonic currents. The study is structured as follows: First, a simulation model for an 110 kV oil-immersed AC transformer, incorporating electromagnetic, thermal, and fluid flow fields, is constructed to analyze the temperature distribution characteristics of the windings under different load currents and harmonic currents. Second, a quantitative calculation model for winding hotspot temperature, considering the influence of harmonic currents, is developed and experimentally validated. Finally, an improved thermal aging life model for oil-paper insulation, accounting for harmonic currents, is proposed to quantify the impact of harmonic currents on thermal aging life. The results indicate that the winding hotspot temperature increases exponentially with the load factor and decreases exponentially with the inlet flow rate. The maximum error percentage between the simulated and measured temperatures under power frequency currents is 1.06%, while the maximum relative error between the simulated and calculated hotspot temperatures under harmonic currents is 0.855%. The thermal aging life of the windings decreases exponentially with the increase in harmonic current frequency and content. When the 250 Hz harmonic current content increases from 5% to 30%, the reduction percentage in the thermal aging life of the oil-paper insulation reaches 97.02%. The findings provide a scientific basis for addressing temperature rise detection and aging life assessment of transformers under the influence of harmonic currents.

The gearbox of a wind turbine is the core component for energy transmission in wind turbine units. Vibration signals, serving as a critical data source for fault diagnosis of the gearbox, can accurately reflect the internal dynamic characteristics of wind turbines. However, during actual operation, internal vibration signals of gearboxes are susceptible to multi-source compound noise interference, causing traditional signal denoising techniques to face bottlenecks in effectively separating noise. There is an urgent need to develop adaptive denoising methods for wind turbine gearboxes operating in complex service environments. This paper proposes a hybrid denoising method integrating complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), genetic algorithm (GA), and wavelet threshold denoising (WTD). By constructing three-dimensional time-frequency-energy evaluation metrics under unknown ground truth conditions and establishing a parameter space mapping model, the method achieves multivariate collaborative optimization of wavelet basis functions, decomposition levels, threshold rules, and correlation coefficient thresholds. The system overcomes the limitations of single metrics through complementary validation of smoothness, spectral entropy, and residual energy ratio. Experimental results demonstrate that this method elevates the signal-to-noise ratio (SNR) to 12.16 dB in simulated signals, and achieves a root mean square error of 0.78×10-2, significantly outperforming traditional algorithms in SNR and transient feature preservation. Through the hierarchical noise elimination mechanism combining CEEMDAN modal decomposition and WTD, along with GA’s global parameter optimization capability, it breaks through the bottlenecks of traditional methods that rely on empirical settings and struggle with coupled parameter optimization. This provides a high-fidelity signal preprocessing solution for early fault diagnosis of wind turbine gearboxes, holding significant engineering implications for enhancing the reliability of wind turbine condition monitoring systems.

Traditional transformer expansion techniques commonly involve increasing the physical capacity of transformers to cope with the continuous and steady growth of electrical loads. However, this expansion method struggles to address short-term load spikes, requiring additional investment costs and often leading to idle transformer capacity. To solve these issues, this paper proposes a transformer capacity expansion scheme based on energy storage devices, leveraging the advantages of additional power support from energy storage systems and the maturity of traditional transformer control and protection technologies to provide diversified ancillary services for future smart distribution networks. Firstly, this paper proposes an annual average cost model for the collaborative configuration of transformers and energy storage systems. Subsequently, a collaborative optimal configuration model for transformers and energy storage systems is established, with the objectives of minimizing load curtailment indices and comprehensive economic cost indicators. To evaluate the effectiveness of capacity expansion, three comprehensive benefit evaluation indicators are introduced: namely, average effective utilization rate of transformers, peak shaving and valley filling capability, and comprehensive economic cost. Finally, simulation results demonstrate that this optimal configuration scheme can alleviate transformer overload levels in the overall power grid, balance short-term transformer overload costs with energy storage unit construction investment costs, and provide a reference for developing more scientific and reasonable application practices for the collaborative configuration of transformers and energy storage systems in future scenarios.