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.

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.

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.

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.

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.

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.

With the deepening development of new power systems, the demand for grid-forming technical equipment, such as inertia response, transient support, and rapid frequency and voltage regulation, has become increasingly urgent to address the system stability issues caused by the decreasing proportion of synchronous generator units. Flywheel energy storage, as a rotational mechanical inertia device, possesses inherent advantages for grid-forming operation. At present, flywheel energy storage can operate as an independent energy storage unit connected to the grid through power conversion devices or in combination with synchronous condensers and other equipment. The concept of grid-forming flywheel energy storage is proposed, encompassing typical technological directions such as inertial flywheel synchronous condensers, power-electronics-based grid-connected high-speed flywheel energy storage, and synchronous-machine-based grid-connected high-speed flywheel energy storage. This paper analyzes the grid-forming operation mechanisms, key technologies, and application scenarios, providing valuable references for the research and application of flywheel energy storage.

In offshore wind power via VSC-HVDC systems, the surplus power generated after an AC grid fault can lead to overvoltage in DC cables. In order to prevent the generation of DC overvoltage, a DC Chopper is generally configured at the side of the receiver converter station, which is put into consumption of surplus power under fault conditions to realize the balance of the system’s power at the sending and receiving ends. In this paper, three DC energy consumption programs, namely, switching valve section series centralized resistor, distributed resistor, and sub-module series centralized resistor, were simulated and compared, and the topologies and working principles of the three types of DC Choppers were introduced. Based on the actual parameters of the Rudong offshore wind power via VSC-HVDC project, a system model was constructed under PSCAD/EMTDC to realize the working process of the above three DC Choppers. The characteristics and FRT performance during the operation of the three DC Choppers were compared, and the advantages and disadvantages of the three DC Choppers were analyzed. The research results show that the distributed energy consumption resistor scheme provides the best control effect but at the highest cost; the series-connected centralized energy consumption resistor for switch valve sections offers the worst control effect but at the lowest cost; the submodule series-connected centralized energy consumption resistor scheme falls between the two in terms of various indicators. This study can provide a reference for the selection of DC energy consumption devices in offshore wind farm VSC-HVDC grid-connected systems.

As the construction of the novel power system continues to advance, the cooperative development of source-grid-load-storage is inevitable. However, under the background of “dual-carbon”, the existing source-grid-load-storage cooperative optimization scheduling method does not take into account enough factors such as carbon emission. It is difficult to support the low-carbon development of the novel power system. In this paper, firstly, dynamic carbon emission factors are introduced to propose a low-carbon demand response mechanism driven by both benefits and low carbon, and a low-carbon demand response model is built to accommodate the economic cost and low carbon emission. Secondly, based on the theory of carbon emission flow, a source-grid-load-storage synergistic day-ahead economic dispatch model is constructed considering the carbon flow constraints, which helps the power system to operate with carbon reduction. Subsequently, in order to deal with the nonlinearities introduced by the consideration of carbon flow constraints, a decomposition method is used to deal with them, which is solved by alternating iterations of the two subproblems: low-carbon scheduling and carbon flow calculation. Finally, through the IEEE-14 node system, we implemented the cooperative optimization scheduling for source-grid-load-storage coordination considering low-carbon demand response. The results show that the proposed model and method can effectively balance the carbon and economy, and promote the cooperative development of each section of source-grid-load-storage in the context of the “dual-carbon”.

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.

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.

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.

Aiming at the load spike problem caused by large-scale electric vehicles (EV) entering the grid, especially the fast charging behavior, this paper proposes a two-stage optimal scheduling strategy for microgrids that takes into account the characteristics of EV charging behavior. Firstly, based on the V2G characteristics and time-sharing tariff system, a function model considering EV charging and discharging costs and the peak-to-valley difference of microgrid loads is constructed, and an optimal EV charging and discharging strategy to achieve peak shaving and valley filling of loads is formulated; secondly, the output of each power generation unit or grid with the goal of minimizing the microgrid operating costs and environmental costs is optimized, and the optimal solution is determined by the entropy weighting method; lastly, the two-phase optimization model is divided into grid-connected and islanded modes for joint solution using CPLEX solver and improved MOIDBO algorithm, respectively. The simulation results prove that the strategy proposed in this paper realizes peak shaving and valley filling while guaranteeing the EV charging demand, reduces the charging cost of EV users, and improves the economy of the microgrid system containing a high proportion of EV charging loads.

With the surge in the number of new energy vehicles, the stable operation of electric vehicle charging facilities has become particularly critical for grid security and user rights protection. In this study, an in-depth analysis is conducted for the fault prediction of EV charging piles. Firstly, the user’s charging behavior is studied based on kernel density estimation, and the temporal correlations of charging onset, duration, and end moments are explored, and a non-Euclidean domain data modeling method is proposed accordingly. Further, the study introduces Graph Convolutional Neural Network (GCN) and Convolutional Neural Network (CCN), develops a GCN-CNN joint deep learning model to effectively capture the complex nonlinear relationship between fault classification and data features. Through ablation and algorithm comparison experiments on real datasets, this model achieves a superior performance of 0.844 for both F1score and G-mean on the validation set, which improves the average performance over other models by 6.28% and 6.04%, respectively. This study provides an innovative solution for charging pile fault prediction, which helps to reduce O&M costs and improve detection efficiency.

Aiming at the problem of large fluctuation of wind-solar energy access to power grid, this paper studies the power distribution strategy of electro-hydrogen energy storage system based on multi-type electrolytic cell to stabilize wind-solar fluctuation, and the key role of this strategy in capacity optimization configuration. First of all, an electro-hydrogen energy storage system model is constructed. In order to enhance the applicability of the existing decomposition methods, a multi-granularity wind-solar power decomposition method considering time-of-use electricity price is proposed. Then, based on the operation characteristics of two types of electrolytic cells, a high proportion of hydrogen storage power allocation strategy based on multi-type electrolytic cells is formulated, and on this basis, an optimal capacity configuration model of electro-hydrogen energy storage system to alleviate the impact of wind and solar fluctuations is constructed. Finally, several comparative examples are analyzed. The results show that compared with the traditional single hydrogen storage power allocation method, the strategy proposed in this paper can effectively reduce the capacity and power allocation of electrochemical energy storage while strengthening the fluctuation suppression ability, improve the overall economy of the system, and highlight the leading role of hydrogen energy storage in the process of regulation and consumption.

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.

In the context of the “dual carbon” goals, to enhance energy utilization efficiency and reduce carbon emissions during system operation, a low-carbon economic dispatch model based on the carbon-green certificate linked trading mechanism is proposed for hydrogen-integrated energy systems. Firstly, the heat loss and waste heat recovery in the process of electrolysis hydrogen production are considered on the source side, and a multi-link hydrogen operation process model is established for hydrogen production from electrolyzer, hydrogen to methane, and hydrogen to cogeneration; secondly, a green certificate trading model is introduced to consider the mutual recognition between the green certificates and the carbon quota, and the carbon-green certificate linked trading mechanism is proposed, and a comprehensive demand response model that can be shifted and reduced is considered on the load side; finally, a low carbon economic dispatching model is proposed based on the cost of purchasing energy, green certificate linkage transaction cost and demand response compensation cost and minimum as the objective function to establish the optimal scheduling model. By setting up different operation scenarios for comparative analysis, the effectiveness of the model proposed in this paper is verified to realize the low-carbon economic operation of the integrated energy system.

In the grid-connected control of multiphase permanent magnet synchronous motor (PMSM) flywheel energy storage systems, maintaining stable DC bus voltage and ensuring fast response of grid-side instantaneous active power are critical requirements. However, conventional control strategies often suffer from slow dynamic response and large steady-state deviations. To address these issues, this paper proposes a grid-connected control strategy for a high-speed flywheel energy storage system driven by a six-phase PMSM. Corresponding control methods are designed for the three sequential stages of the grid-connection process: flywheel acceleration, grid-connection preparation, and grid-connected operation.During the acceleration stage, the DC bus voltage is controlled by the grid-side converter, while the flywheel speed is regulated by the motor-side converter. In the preparation and operation stages, the motor-side converter maintains the DC bus voltage, and the grid-side converter controls the power delivered to the grid. Simulation results based on a six-phase PMSM mathematical model verify the effectiveness of the proposed strategy. The system achieves stable DC bus voltage during grid-connected operation and demonstrates faster power response compared to conventional methods. Under step changes in power commands of 100 kW, 200 kW, 300 kW, 400 kW, and 500 kW, the response time is reduced by 3 ms, 9 ms, 14 ms, 27 ms, and 53 ms.

The high proportion of distributed photovoltaic access to the distribution network brings uncertainty risks to the operation of the distribution network. A reasonable assessment of the accessible capacity of distributed photovoltaic systems in the distribution network has become an important research topic. Therefore, this paper first proposes a method for generating scenarios of joint source-load output based on an improved generative adversarial network. After generating a large number of scenarios based on the improved generative adversarial network model, the generated scenarios are reduced through the k-medoids clustering algorithm. Then, considering the flexible resources in the distribution network, an optimal power flow model for flexible distribution networks is established and linearized. Finally, a distributed photovoltaic capacity region solving method based on multi-parameter programming is proposed to characterize the accessible capacity range of distributed photovoltaic systems at different buses. Taking the improved PG&E 69 node distribution network system as an example for verification, the research results show that the proposed method can effectively characterize the distributed photovoltaic capacity region of flexible distribution networks considering flexible resources.

With the ongoing penetration of renewable energy (RE) in power systems, power systems need to face many challenges brought by a large number of random fluctuation power access. The deep peak regulation for thermal power plants inevitably increases carbon emissions and reduces the unit lifecycle. To solve these problems, it is urgent to configure energy storage power stations at the source end of RE power stations. This can make the RE power station coupled energy storage systems have flexible characteristics in general. This paper proposes a source-end energy storage configuration method of RE power stations. The wind-solar power stations coupled energy storage systems are approximately equivalent to the thermal power unit in power and electricity.Combined with the solar, wind and load data of different provinces in China, the installed capacity of wind and solar power stations required for equivalent thermal power is determined according to the principle of intraday energy balance. On this basis, this paper combines with the operation characteristics of different energy storage power systems and optimize the energy storage capacity for the RE to generate consistent power with thermal power. Meanwhile, for the comprehensive utilization of regional wind and solar power stations, this paper considers the spatial-temporal complementarity of solar energy and wind energy. To verify the effectiveness of the method, the wind-solar power and load demands across northern regions in China are used for case analysis. The results reveal that the wind-solar power stations coupled energy storage systems can flexibly respond to the power and energy balance needs with daily load changes. Additionally, fully utilizing the spatial-temporal complementarity of solar and wind energy resources can reduce the demands for energy storage capacity of the system.

Synchronous generator is one of the core components of power grid, so to obtain its correct and accurate parameter model is the basis of power system analysis and calculation. In this paper, the RTDS model of synchronous generator is established. Then, based on the equivalent circuit diagram, flux chain equation and voltage equation of synchronous generator, the transient stator current expression of single-phase grounding short circuit is derived. The objective is to minimize the sum of squared standardized errors between the calculated values of short-circuit current. The results are obtained by comparing the single-phase grounded short-circuit current test values with the identified parameters, the migration model of Seagull algorithm is improved by adding convergence factor, and the identification method of single-phase grounding sequence impedance parameters of synchronous generator is proposed. Finally, the short circuit test of the actual synchronous generator is compared with the simulation of the identification parameters RTDS, and the validity of the proposed identification method is verified. Based on the fault current of short-circuit test and the fast convergence characteristic of the improved Seagull optimization algorithm, the accuracy of parameter identification of synchronous generator is improved by combining experimental measurement with intelligent algorithm.

Installing flexible loop-closing devices emerges as a potent solution for optimizing the power scheduling of distribution systems following the integration of large-scale, diversified distributed energy and loads. Addressing the shortcomings of power electronic loop-closing equipment represented by unified power flow controllers in terms of cost, efficiency, and reliability, this paper proposes a Three-port flexible controlled phase shifter (T-FPS), This device consists of a multi-winding transformer and a hybrid on load tap changer, which can flexibly and quickly adjust the power flow of the closed loop line. Firstly, based on the proposed flexible loop-closing controllable phase shifter topology, this article analyzes the operating principle and on load tap changer structure of the equipment, and designs the transformer winding switching timing; Secondly, under the premise of knowing the parameters of the interconnection feeder, the paper derives the three port power flow equation that takes into account the phase shifter, analyzes the power coupling relationship between the interconnection feeder, and proposes a steady-state power flow control strategy for the phase shifter; Finally, the effectiveness of the proposed flexible closed-loop controllable phase shifter control strategy under various operating conditions was verified through a hardware-in-the-loop simulation model.

This paper proposes a source-network-load collaborative planning model that considers source-load uncertainty. Firstly, an improved K-medoids method is proposed to extract typical scenarios of power grid source loads. Then, combined with the flexibility of demand response, a collaborative optimization configuration model of demand response and source network considering source-load uncertainty is constructed. Finally, a random two-level optimization algorithm based on Benders decomposition is proposed to realize the iterative solution of the optimal configuration model, so as to protect the privacy of information. Through the analysis of an improved IEEE 6-node system and IEEE 118-node system, it is proved that the proposed model can improve the system reliability and reduce the investment costs of transmission lines and conventional units.

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.

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.

Because the health of the evaluation indicators of the transformer affects its reliability and operation, maintenance and other costs, and then affects its life cycle cost. Therefore, this paper considers an optimization method of transformer life cycle cost based on health index. Firstly, a health index calculation method based on subjective and objective evaluation results is proposed to construct a failure rate model based on multiple health indexes in transformer operation. Secondly, under the constraints of failure rate and operation life, a life-cycle cost optimization model is established considering future service life, maintenance time and maintenance degree as optimization variables and minimum annual life-cycle cost as objective function. Then, an initial population generation method based on Logistic-Tent chaotic mapping is introduced, a new exploration factor updating formula is given, and an improved Enhanced Gray Wolf Cuckoo (AGWO-CS) algorithm is proposed to optimize the whole life cycle cost of transformers. The simulation results demonstrate that, in comparison with traditional Particle Swarm Optimization (PSO), Cuckoo Search (CS) and AGWO-CS algorithms, the proposed method can further reduce the life-cycle cost of transformers. It provides a certain reference for the operation and maintenance of transformer.

Driven by the twin goals of carbon peak and carbon neutrality, the increasing penetration of renewable energy and the growing number of flexible loads introduce greater uncertainty into power system operation. How to generate accurate wind-solar-load scenarios to assist power system decision-making has become an urgent problem to be solved. However, at this stage, there lacks scenario generation method that fully considers the correlation between wind, solar, and load. In response to the above problems, this paper combines the bidirectional long short-term memory network (BiLSTM), multi-task learning (MTL) and Attention mechanism and a wind-solar-load scenario generation method is proposed. The data-driven method is used to mine the correlation between wind-solar-load historical data, extract common features, and retain differences, thereby improving the generalization ability of the model and improving the accuracy of scenario generation. The case study analysis shows that the scenario generation method proposed in this paper can greatly improve the accuracy of scenario generation, and can provide effective theoretical and method reference for medium and long-term planning of power systems.

This paper addresses the issue of inertia flywheel systems participating in grid inertia response and frequency regulation control. Firstly, it introduces the structure, mathematical model, and working mechanism of the inertia flywheel system. Secondly, it proposes a model predictive current control strategy for the doubly-fed induction motor of the inertia flywheel system based on an improved two-step prediction method. This strategy is applied to the process of inertia flywheel systems participating in grid inertia response and frequency regulation control, and a model predictive control strategy for inertia flywheel systems to participate in grid inertia response and frequency regulation control is proposed. Simulations are conducted to test the control effects of the proposed control strategy in terms of current ripple, overload capacity, and frequency regulation capability. The simulation results indicate that the proposed model predictive current control can effectively reduce the current ripple and power ripple of the inertia flywheel during normal operation compared to vector control. Meanwhile, the inertia flywheel system exhibits strong overload capacity and good inertia response capability during the grid inertia response phase.

This paper proposes an hourly robust dispatch scheme for the hybrid renewable energy generation systems with energy storage, which considers the transient stability constraint of synchronous generators, and achieves intelligent resolution of the optimization problem via a multi-agent system. Firstly, a dynamic ramp rate limit of the hourly generation plan is proposed to reduce the deviation between the actual dispatch and the rolling dispatch scheme, and to maximize the power sold to the grid while minimizing the curtailment of renewable energy; subsequently, an optimal dispatch model for the hybrid renewable energy systems is constructed, whose objective function is the ratio of the arbitrage revenue to the generation cost, taking into account the synchronous generators’ transient stability margin constraints, the energy storage system operation constraints, and the hybrid renewable energy system’s energy backup constraints. Meanwhile, the corresponding multi-agent system is established for the individual units in this optimization scheme, and the accuracy of renewable energy generation forecasting is improved via a recursive neural network and the principle of generation units clustering. Finally, the intelligent resolution of the optimization problem is realized. The effectiveness and superiority of the proposed method are verified by numerical simulation case studies under different seasons and load conditions.

Off-grid hydrogen production using offshore wind power is an economically effective way to utilize offshore wind energy. In offshore wind power off-grid hydrogen production projects, multiple alkaline electrolyzers often form clusters to absorb the wind turbine power. The power distribution strategy can significantly affect the energy efficiency and overall system lifespan. This paper proposes an adaptive power distribution strategy for offshore wind hydrogen production clusters, considering the degradation of individual units. This strategy broadens the operating power range of the hydrogen production cluster by flexibly managing the start and stop of units. It achieves optimal system energy efficiency by ensuring equal marginal output across units. The priority queue method, weighted by the number of starts and stops, determines which units to start or stop, ensuring consistency in the degradation levels across the hydrogen production cluster, thereby improving the overall system lifespan. Simulation examples with five 1 000 Nm3/h alkaline electrolyzers demonstrate that the proposed adaptive power distribution strategy can increase total hydrogen production by 2.1% and 4.2%, compared to average and stepwise power distribution strategies, respectively. Additionally, the number of starts and stops for each unit in the cluster is the same, effectively reducing the inconsistency in degradation levels caused by frequent power fluctuations in traditional power strategies.

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.

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.

Aiming at the influence of inductor parameter mismatch on current prediction accuracy in the traditional model prediction control system for magnetic bearings, this study proposes a model prediction control strategy for magnetic bearings that considers a parameter mismatch with magnetic bearings with permanent magnet bias as the research object. First, the three-level modulation principle of the drive circuit for magnetic bearings is analyzed. Then, the model predictive controller for magnetic bearings is designed based on the mixed logic dynamic model, and a sliding mode observer is introduced to calculate the current prediction errors corresponding to all switching states in one control cycle. Finally, the prediction current errors are further calibrated to correct the prediction model in the event of parameter mismatch. Then, the current prediction accuracy is guaranteed in the event of parameter mismatch. The simulation and experimental results show that the current prediction error of the current mismatch state corresponds to the current prediction error. The simulation and experimental results show that compared with the traditional model prediction control strategy, this study presents a lower current ripple and a higher current prediction accuracy.

Traditional repetitive controller can only suppress harmonics at a fixed frequency, and cannot meet the vibration control requirements of high-speed flywheel energy storage magnetic bearing system in a wide range of operating frequencies. For this reason, this paper proposes a variable speed repetitive controller based on the variable sampling frequency method and is compared with the repetitive controller based on the fractional order delay method to demonstrate the superiority of the variable sampling frequency method. Meanwhile, in order to improve the suppression ability of the repetitive controller on harmonic current, a proportional differential repetitive control method is proposed. Based on this, a new variable speed repetitive controller combining the variable sampling frequency method and the proportional differential repetitive control method is proposed, and the new controller solves the multi-frequency vibration control problem of the flywheel energy storage operating in a wide frequency range. Finally, numerical simulations and experimental tests show that the proposed repetitive controller is capable of suppressing about 66% or more of the harmonic components and is suitable for the multi-frequency vibration control of magnetic bearing systems.

The district heating network is a crucial component of integrated energy systems. Its modeling and simulation process involves abstracting its actual structure and physical characteristics into appropriate mathematical models and solving them efficiently to simulate the operation of real systems and evaluate their performance. To address the inherent contradiction between error control and computational efficiency in traditional time-domain discretization methods, this paper focuses on the quality-regulated heating networks and innovatively proposes a semi-analytical modeling and simulation method based on time-space two-dimensional power series embedding, suitable for online applications. This method aims to meet the dual requirements of computational accuracy and efficiency for operational analysis. The case study results show that the proposed semi-analytical solution can significantly reduce analysis complexity and demonstrate remarkable advantages in accuracy and efficiency compared to conventional difference methods. Under similar computational costs, the simulation error of the semi-analytical solution is 0.55% (single pipeline scenario) or 0.004 2% (network scenario) of that of the difference method.

Online tuning of PID controller parameters is critical for optimizing the operational performance of active magnetic bearing (AMB) systems. However, existing parameter design methods often ignore the control delay introduced by the controller response period and the inductive effect of the bearing coil, resulting in PID parameters calculated on the basis of the closed-loop transfer function failing to ensure system stability. Therefore, this paper investigates the operating characteristics of the AMB system that characterize the control delay. The control parameter calculation model of the AMB system considering the control delay is constructed based on the PID algorithm. Furthermore, a numerical model of the AMB system considering the control delay is established. And the experimental platform of the AMB rigid rotor system is built. The operating performance of the active magnetic bearing under different rotor frequencies and PID parameters is investigated experimentally and simulated. Meanwhile, the mapping relationship between PID parameters and system performance under control delay is analyzed. Accordingly, an adaptive PID parameter tuning strategy based on rotor frequency is proposed. The experimental test results show that the proposed control strategy is able to achieve stable operation of the rotor in the full speed range with less vibration.

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.