Objectives The characteristics of low inertia and low damping in “double-high” (high renewable energy penetration and high power electronics application) power system pose significant challenges to grid stability, particularly in terms of frequency and voltage. Grid-forming energy storage (GFM-ES), which has the capability of frequency regulation and voltage control, is reviewed in terms of its characteristics, application scenarios, and research outlook. Methods Firstly, the main characteristics of GFM-ES are described from the aspects of the differences between GFM-ES and grid-following energy storage, as well as the control methods. Then, the main application scenarios of GFM-ES, including frequency support, voltage support, and black start, are elaborated. Finally, the research outlook is presented, focusing on the stability, optimal configuration, and practical engineering applications of GFM-ES. Conclusions The stability of GFM converters has an important impact on the operational characteristics of energy storage units, and further attention needs to be paid to the induced causes of the stability problem, parameter tuning, and switching of control and current limiting strategies. The GFM-ES configuration requires trade-offs in terms of functionality, complexity, and cost, and the hybrid configuration of grid-forming and grid-following energy storage needs to be further investigated. Coordination and interoperability between GFM-ES units should be strengthened, and technical test specifications and standards should be improved to promote their application in hybrid AC-DC grids and high-voltage transmission grids.

Objectives Magnetic confinement fusion is regarded as a critical solution to future global energy challenges. As the central component of magnetic confinement fusion devices, magnets play a crucial role in generating and sustaining plasma stability. A review of the magnetic system structures and specifications in representative magnetic confinement fusion devices worldwide was provided. Methods The technological evolution of fusion magnets was reviewed, from copper-based to low-temperature superconducting, and finally to high-temperature superconducting magnets. The structure and performance parameters of magnetic systems in various typical fusion devices were summarized systematically. Additionally, the technical challenges in magnet development were explored and an outlook on future development trend was offered. Conclusions Advances in magnet technology are vital for enhancing the performance of fusion devices and accelerating the commercialization of fusion energy. With the increasing application of high-temperature superconducting materials and continuous optimization of magnet designs, the practical realization of fusion energy is becoming increasingly feasible.

Objectives With the continuous growth of demand-side response resources, traditional energy scheduling models struggle to meet the system requirements of high penetration levels of renewable energy. To achieve the rational allocation of multiple energy sources within a community, this study proposes an energy trading strategy based on demand-side response from users, aiming to optimize energy scheduling in smart community. Methods For a residential community with multiple buildings, this study coordinates distributed photovoltaics, energy storage systems, and flexible loads. A two-stage scheduling optimization model is established using the Stackelberg game framework based on pricing interactions between community operators and user load aggregators. Results Simulation results show that, compared to the traditional heat-determined power strategy, the proposed model reduces operational costs by 40.22% and increases photovoltaic utilization by 22.57%. Compared to the conventional cost-optimal operation strategy, the proposed model results in a 29.66% reduction in operational costs and a 6.78% increase in photovoltaic utilization. Conclusions The proposed strategy demonstrates excellent performance in achieving equitable benefit distribution, mitigating power fluctuations, flexibly meeting peak-load demands, enhancing renewable energy integration, and ensuring grid operational security.

Objectives Phase change heat storage technology is one of the most concerned energy storage and management techniques in recent years, which is of great significance to realize the comprehensive gradient utilization of energy and improve the efficiency of energy utilization. Hydrated salt-based inorganic composite phase change material (PCM) exhibits significant potential for energy storage and thermal management. This review analyzes the physical properties of materials and the application in battery thermal management, to clarify the technical challenges faced in improving properties and applications. The corresponding improvement strategies are proposed to promote the application of hydrated salt-based inorganic composite PCM. Methods By analyzing the research results of hydrated salt PCM at home and abroad in recent years, the improvement effects of additives modification and reasonable encapsulation on the inherent defects such as supercooling, phase separation and liquid phase leakage of hydrated salt PCM, as well as the effects on phase change temperature, latent heat, thermal conductivity and thermal cycle stability are discussed. Results The modification technology significantly improves the inherent defects of the hydrated salt PCM, and significantly improves the thermal conductivity and thermal cycle stability of the material. Furthermore, hydrated salt-based inorganic composite PCM demonstrates excellent performance in lithium-ion battery thermal management, significantly lowering the maximum temperature and temperature differential, thereby improving overall battery performance and safety. Conclusions The large-scale application of hydrated salt-based inorganic composite PCM still faces challenges related to thermal stability and cost. Future research should further optimize the composition and structure of these materials, develop new encapsulation materials and additives, and comprehensively improve the overall performance of the materials. Particularly in the area of thermal management for lithium-ion batteries, it is crucial to strengthen the coupling research between the thermal physical characteristics of the materials and the electrochemical performance of the batteries, and provide sufficient theoretical and experimental basis for designing efficient and safe battery thermal management systems.

Objectives Weather and random factors can alter the statistical characteristics of errors. Therefore, this study considers feature extraction of various climate factors that affect wind power. To optimize the extraction of power time series features, a wind power prediction method based on multi-feature extraction (MFE), convolutional neural network (CNN), and long short-term memory (LSTM) network is proposed. Methods Firstly, 11 statistical features are extracted from numerical weather prediction (NWP) data. By extracting basic and statistical features, the original data is clustered, and prediction models are established according to categories to improve the adaptability of prediction models. Next, the network architecture of LSTM is improved. By leveraging the feature extraction ability of CNN and the nonlinear sequence prediction ability of LSTM, the historical information of wind power and NWP data is thoroughly explored. Finally, using the data from a wind farm in Xinjiang, China, the effectiveness and advantages of the proposed short-term wind power prediction method are verified by MFE and CNN ablation experiments. Results The MFE-CNN-LSTM prediction method shows a decrease in both root mean square error and mean absolute error, compared with the autoregressive integrated moving average (ARIMA), fully recurrent neural network (FRNN), MFE-LSTM and CNN-LSTM models. Conclusions The MFE-CNN-LSTM prediction method can effectively extract features, and MFE and CNN effectively improve prediction accuracy.

Objective Ammonia plays a key role in achieving the “dual carbon” goal, but ammonia has a low combustion rate and high NO _x emission. In order to improve the combustion rate, a method of ammonia-hydrogen blending combustion was proposed. Methods The combined computational fluid dynamics and ammonia-hydrogen mixed combustion mechanism are used to numerically simulate the premixed combustion of ammonia, hydrogen and air. The effects of equivalent ratio and inlet air flow on combustion characteristic and NO _x emission are analyzed. In addition, in order to better control the emission of NO _x during combustion, the kinetic analysis of ammonia-hydrogen blending combustion reaction is carried out using Chemkin software. Results Increasing the inlet air flow rate and equivalent ratio can effectively reduce NO emission in cyclone burners, and the main way of NO formation is the oxidation of imino groups. Conclusion The results provide a reference for the characterization analysis of ammonia-hydrogen miscible combustion and the reduction of NO _x emissions in cyclone combustors.

Objectives Under the background of the “dual carbon” strategic goal, the demand for flexible regulation resources in the power system has significantly increased after the large-scale integration of new energy generation into the grid. At present, the coal-fired power is the main flexible resource on the power side with the ability to scale up peak shaving. Since 2016, the major domestic power generation companies have implemented a certain scale of flexibility transformation of coal-fired power units. Therefore, it is necessary to summarize and analyze the problems existing in the actual operation and maintenance of the unit after flexibility transformation. Methods The technical route, investment cost and actual operation of several coal-fired power units with flexible transformation in a company were statistically analyzed. Results After the flexibility improvement and transformation of the active coal-fired power generation unit, the minimum power generation output of the advanced unit can be reduced to 18%P_e (P_e is rated load) level, the load change rate with 20%P_e~30%P_e can reach 1.8%P_e/min, and an average unit capacity investment is 101 yuan/kW. In addition, under flexible operating conditions, the coal consumption of coal-fired power units after the transformation has significantly increased. Conclusions Suggestions are put forward for the operation, maintenance and further work of coal-fired power units under flexible operating conditions. The research results provide reference and inspiration for the flexibility improvement and transformation of existing coal-fired power units.

Objectives After the “dual-carbon” goal was put forward, the electric power industry has accelerated the low-carbon transition, and power generation enterprises have incorporated low-carbon transition into their corporate development plans and strategies. In order to explore the consistency between low-carbon transformation goal commitments and implementation results, important factors influencing low-carbon transformation efficiency were explored, and the intrinsic connection between textual information and low-carbon transformation efficiency was studied. Methods This paper took 31 listed power generation companies as the research object from 2016 to 2022, and adopted the text analysis technique to construct a measurement index of the level of textual disclosure of low-carbon transition, and empirically examined the relationship between textual disclosure and the efficiency of low-carbon transition by using the panel regression model with bidirectional fixed effects. Results The higher the level of textual information disclosure of low-carbon transition of listed power generation companies is, the lower the carbon emission intensity is, the better the environmental performance is, i.e., the higher the efficiency of the transition is, and the more consistent the company’s “words” and “actions” are. In addition, in companies with higher quality of accounting information disclosure and lower thermal power installed capacity, the effect of the disclosure level of low-carbon transition text on low-carbon transition efficiency is more significant. Conclusions The listed power generation companies should improve the quality of textual information disclosure of low-carbon transition, and play the role of supervision and management of annual report textual information to promote the development of low-carbon transition, and realize the consistency between textual information disclosure and transition efficiency.

Objectives In order to study the problems of increasing complexity of power balance, and increasing uncertainty of power flow distribution and increasing security and stability requirements of power grid caused by high proportion of renewable energy access, a capacity optimization configuration method of multi-microgrid system including wind power, photovoltaic, battery and hydrogen energy storage is proposed. Methods A bi-level optimization model is established under the framework of multi-microgrid distribution network. The outer objective of the model is to minimize the life cycle cost, and the inner objective is to minimize the peak-valley difference, network loss and voltage offset of the distribution network. Based on the IEEE 69-bus system, the white shark optimizer (WSO) algorithm and Cplex solver were used to solve the model, and the optimal capacity configuration scheme and planning operation results are obtained. Finally, case analysis is carried out through different energy storage combinations. Results When the capacity configuration of each component of the system is optimal, the installed ratio of the wind-solar power generation system to the hybrid energy storage system is 1∶0.27. The wind-solar-electric-hydrogen hybrid energy storage system is superior to the wind-solar-single energy storage system in terms of economy and stability. Conclusions The proposed method can not only optimize the joint optimal cost of the system, but also effectively reduce the load peak-valley difference, distribution network loss and improve the power quality.

Objectives Against the backdrop of energy resource scarcity and increasing environmental demands, distributed energy supply technologies, particularly renewable energy technologies, have attracted significant attention due to their high energy efficiency, cost-effectiveness and flexible installation. For remote areas such as plateaus, border regions and islands, which are beyond the coverage of public power grids, islanded microgrids have become the key technology to address electricity access challenges. However, optimizing energy management efficiency remains a major challenge. This paper reviews the two core objectives—economic efficiency and stability—of electric energy in islanded microgrids. Methods The current development status and research priorities of islanded microgrids, both domestically and internationally, are analyzed. The basic functions and management objectives of islanded microgrids are discussed. Additionally, the technical approaches for microgrid energy management and three types of control strategies are introduced. Finally, the paper provides a discussion of future research directions for islanded microgrid energy management systems, based on existing technological limitations and the ongoing development of new technologies. Conclusions The research results can effectively deal with the volatility and uncertainty of renewable energy and ensure the stable operation of the system. It can effectively reduce the cost of energy storage, improve the efficiency of energy utilization, and achieve higher economic benefits. It can also provide references and insights for the future integration of islanded microgrids with new technologies.

Objectives With the continuous advancement of the national “dual carbon” strategy, integrated energy services, as a new model and new form of the energy industry, are gradually becoming an important means for China to build a new energy system and create new quality productive forces in the energy field. In order to facilitate the high-quality development of China’s integrated energy service industry, the development trends and strategies of integrated energy services are studied. Methods Through in-depth analysis of the development status of integrated energy services at home and abroad, the structure-conduct-performance (SCP) analysis model is used to analyze the development and trend of the integrated energy service industry under the new situation. Conclusions From the national level, the countermeasures and suggestions of “three synergies” for the high-quality development of the integrated energy service industry are put forward, focusing on “policy-reform-planning”. From the enterprise level, the implementation path of “four advantages” for high-quality development is put forward, focusing on “layout-model-science and innovation-brand”. These strategies aim to support the healthy and sustainable development of the integrated energy service industry, providing innovative demonstrations and value contributions to the construction of China’s new power system and the implementation of the “dual carbon” strategy.

Objectives To address the uncertainties of renewable energy output and load faced by virtual power plant (VPP) when participating in electric energy and demand response markets, a robust optimal scheduling strategy considering multiple uncertainties was proposed to reduce the conservativeness of robust optimization and improve the economic benefits of VPP. Methods A polyhedral uncertainty set based on conditional value at risk (CVaR) was constructed. On this basis, considering the uncertainties of wind power, photovoltaic output and load, a day-ahead two-stage robust optimization model of VPP participating in electric energy and demand response markets was established. Then, using a column-and-constraint generation (C&CG) algorithm and Lagrangian dual theory, the model was divided into a master problem and a sub-problem that can be solved by a solver. Finally, Monte Carlo method was used to generate a large number of wind power, photovoltaic and load data. The proposed strategy was simulated and analyzed, and compared with the optimization results of other schemes. Results The proposed strategy adopting a polyhedral uncertainty set based on CVaR can make full use of historical data. Compared with the scheme using traditional uncertainty set, the total cost of VPP is reduced by about 2%. Conclusions The proposed strategy can significantly reduce the conservativeness of robust optimization results and enhance the economy of VPP participation in the market under multiple uncertainties.

Objectives As power systems evolve toward higher levels of intelligence and automation, reinforcement learning (RL), a key technology in artificial intelligence, shows great potential in the intelligent development of the power sector. Enhancing research methods for RL applications is crucial for fully exploring its potential in power system operation, control, and optimization. Therefore, the performance of RL in practical electrical applications is analyzed, and the possible research directions in the future are prospected, so as to provide assistance for the intelligent transformation of power systems. Methods This study provides a systematic review of RL applications across diverse fields of electrical engineering. It systematically introduces the fundamental principles and landmark algorithms of RL, detailing how these algorithms are applied to address practical problems in new-type power system. The study categorizes mainstream RL algorithms in current research and analyzes the advantages and disadvantages of structural improvements made to these algorithms. Results Compared to traditional algorithms, RL significantly enhances the intelligence level of new-type power system. It achieves remarkable success in various application scenarios, particularly in addressing system complexity and uncertainty. However, despite many successful cases, several urgent issues still exist in this sector, such as high computational costs, long training times, and limited generalization abilities. Conclusions Reinforcement learning provides novel solutions for the intelligent development of new-type power system. However, achieving large-scale application still needs to overcome a series of technical and practical challenges. This study provides references and insights for researchers and practitioners in electrical engineering.

Objectives Shafting mathematical model of wind turbine is the basis of torsional vibration characteristics analysis. Shafting model is very important for the reliability of analysis results. In order to study the influence of virtual inertia on the torsional vibration characteristics of transmission chain more accurately, the modeling of doubly-fed wind turbine shaft system with virtual inertia control is studied. Methods After the simulation model is determined, a damping controller is designed, which can suppress the torsional vibration of the shafting caused by virtual inertia control. Firstly, by establishing the mathematical model of various shafting, the dynamic model of fan system based on virtual inertia control is established, and the key state variables affecting the torsional vibration characteristics of the shafting are determined by modal analysis method. Secondly, the applicability of different shafting models of wind turbine is studied from two aspects : torsional vibration characteristics of wind turbine with virtual inertia control and dynamic interaction between wind turbine shafting and synchronous machine. Finally, the effectiveness of the conclusion and the damping controller is verified by simulation analysis. Results The use of virtual inertia control can effectively improve the system inertia attenuation caused by wind power grid connection, but it will also reduce the damping ratio of the system. The two-mass model has stronger observability of torsional vibration mode, and the two-mass shafting model is more sensitive to the change of virtual inertia parameters. The torsional vibration of the shafting of the three-mass model changes more than that of the two-mass model. Conclusions The two-mass shaft system model with virtual inertia control is more suitable for torsional vibration analysis of wind turbine shaft systems.

Objectives With the large-scale integration of renewable energy, new-type power systems require greater flexibility and higher prediction accuracy for prediction technology. Traditional prediction methods have limitations in handling dynamic and complex scenarios, highlighting the need for prediction technologies tailored to these systems. Large language models (LLMs), as generative artificial intelligence technologies, have capabilities in multimodal data integrating, few-shot learning, and multitask handling, enabling more intelligent and precise solutions for the prediction of power systems. Therefore, this study focuses on analyzing the current applications and advantages of LLMs in power system prediction. Methods First, the fundamental architecture, training methods, and current application status of LLMs are discussed. Then, their principles and implementations in prediction are explained, with emphasis on advantages and prospects in load prediction, renewable generation prediction, and electricity price prediction. Finally, challenges in LLM-based prediction applications are analyzed from three aspects: data quality management, privacy protection, and computational resources, and feasible solutions are proposed. Conclusions Through comparative analysis of various forecasting tasks, LLMs demonstrate superior capabilities in few-shot learning and multimodal data processing compared to traditional methods, making them more adaptable to complex and variable prediction scenarios. Effective application of LLMs can provide innovative solutions for electricity market prediction.

Objectives The application of high-temperature superconductivity (HTS) magnet system is an important technical route for future tokamak devices. However, the magnetic field anisotropy of the HTS tape and the complex conductor structures greatly increase the complexity of the simulation in the electromagnetic design stage of the HTS magnet system of the fusion device. It is necessary to carry out the corresponding research on the electromagnetic simulation and simplified methods. Methods The finite element simulation software COMSOL was used to establish the overall electromagnetic simulation model and various simplified models of the HTS magnet system, and the related electromagnetic properties were simulated and compared. Results The calculation results of the central magnetic field and the maximum ripple of plasma are mainly controlled by the magnetic field generated by the toroidal field (TF) coil. However, the TF coil has little influence on the magnetic field distribution on the central solenoid (CS) coil. When the detailed structure of the conductor is combined and the magnetic field anisotropy of the HTS tape is considered, the calculated results of the vertical magnetic field show a large difference compared with the overall model. Conclusions The TF coil can only be considered in the calculation of the relevant electromagnetic parameters in the plasma region, and the TF coil can be ignored in the analysis of the magnetic field on the CS coil. In addition, by combining the average current distribution of the conductor structure, the simulation error of the vertical magnetic field of the HTS tape can be effectively reduced.

Objectives In response to global climate change, China has committed to achieving carbon neutrality by 2060, which will inevitably bring strategic transformations across various industries and present an opportunity to develop new quality productive forces. This paper analyzes the potential transformations driven by China’s carbon neutrality goal from the perspective of technological innovation. Methods This paper investigates the main challenges China faces in achieving carbon neutrality, discusses the relationship between carbon neutrality and the development of new quality productive forces, and highlights research directions in zero-carbon energy supply, fossil resource utilization, and CO₂ capture and utilization. Conclusions Achieving carbon neutrality in China requires large-scale development of new energy and advanced transformation and upgrading of traditional industries, especially the fossil resource utilization. It also requires continuous scientific and technological innovation and application, as well as breakthroughs in disruptive technologies. The successful realization of carbon neutrality also relies on CO₂ capture and utilization.

Objectives The wake of wind turbine affects the overall power output of wind farm. The analysis of wake characteristics is the key to study the influence of yaw control on wake. Therefore, the effects of different yaw angles of the upstream wind turbine on the wake characteristics (such as turbulence integral scale, power spectral density, etc. ) and the eddy current motion in the wake were studied, to understand the wake characteristics of the wind turbine under yaw conditions. Methods The wind tunnel was used to study the evolution law of the downstream wake of the wind turbine at 0°, 15° and 30° yaw angles. The turbulence integral scale and power spectral density were used to describe the wake characteristics, and the correlation between different feature points was analyzed. Results The yaw angle has little effect on the horizontal wake, and the turbulence integral scale does not change much. The turbulent integral scale in the vertical direction has large fluctuations at different yaw angles. Through the analysis of the power spectral density, the contribution of eddy current motion to the turbulent kinetic energy of the wake is further quantified. The incoming current at 0° yaw is less disturbed by the rotation of the wind turbine than that at 15°. However, the increase of yaw angle does not always lead to an increase in the damage degree of wind turbines to the incoming current, and the decoupling effect on the incoming vortex structure at 30° is smaller than that at 15°. Conclusions There is a significant difference in the influence of yaw angle on the horizontal and vertical directions of wind turbine wake. The power spectral density analysis provides a quantitative basis for understanding the contribution of eddy current motion to the turbulent kinetic energy of the wake, which is helpful for in-depth study of the energy transfer and flow mechanism in the wake. There is not a simple positive correlation between the yaw angle and the disturbance and decoupling of the wind turbine on the incoming stream, which provides a reference for the optimal operation of the wind turbine and the layout of the wind farm.

Objectives Selective catalytic reduction (SCR) denitrification is widely used in denitrification of boiler flue gas due to its high denitrification efficiency and reliable operation. However, under high-ash arrangements, the denitrification flue gas has not yet been de-treated, and fly ash in the flue gas will block the catalyst and cause wear on it. In order to reduce the wear and clogging of fly ash particles on SCR catalysts, an SCR external flue is designed and studied to trap and separate fly ash particles in flue gas. Methods Based on simulation software and its user defined function, numerical simulation of fly ash deposition characteristics and flow field optimization for SCR external flue is carried out. Results When the flue gas flow rate increases from 1 to 2 m/s, the fly ash deposition rate decreases from 24.0% to 1.88%. Therefore, as the increase of flow rate, the fly ash deposition rate decreases rapidly. It is difficult to trap fly ash particles below 50 μm. However, with the increase of the fly ash particle size, the fly ash deposition rate increases rapidly, and the fly ash is trapped by the SCR external flue. The flow velocity distribution at the inlet of the denitrification reactor is extremely uneven. Based on the flow field analysis, an optimization plan for the flow field at the inlet of the denitrification reactor is proposed. The flow field distribution is improved through the optimized design of the guide plate, and the velocity unevenness coefficient at the inlet of the denitrification reactor is reduced from 93.7% to 14.7%. Conclusions The research results may provide reliable theoretical guidance for the field operation of SCR external flue.

Objectives With the continuous advancement of China’s photovoltaic (PV) industry, the industry is transitioning from being heavily reliant on government policy subsidies to achieving grid parity, which places higher demands on the economic benefits of PV power stations. Consequently, a more comprehensive and detailed study on the investment decision and economic evaluation of photovoltaic power stations is conducted. Methods Investment decision guidance for a 4.45 MW distributed PV power station project is provided from multiple aspects, including site solar resource analysis, solar module selection, and inverter selection. By calculating the construction investment and revenue, economic analysis of the project is conducted, and the effects of construction investment, operating costs, power generation price, and power generation output on the project’s economics are studied. Results The economic analysis reveals that the project achieves an after-tax financial internal rate of return (FIRR) of 7.43%, with an after-tax investment payback period of 11.39^{a. For equity capital, the FIRR is 11.43%, and the payback period is 10.38a. Conclusions The project offers favorable economic benefits and the project economics is most sensitive to changes in electricity prices. Furthermore, the project demonstrates strong resilience to risks. The research results can provide theoretical reference and engineering guidance for the research and application of PV projects.}

Objectives The construction and application of new-type power systems driven by China’s “dual carbon” goals have demonstrated unprecedented complex dynamics characteristics, featuring a high-proportion integration of renewable energy and power electronic devices, posing new challenges to the safe and stable operation of power systems. Intelligent computing in power systems leverages new-generation artificial intelligence technologies—especially large models and pre-training techniques—to achieve the integration of physical models, data-driven models, and knowledge models, thereby developing a novel calculation and analysis method for power systems. This study reviews and discusses the applications of intelligent computing methods and technologies in the analysis, optimization, operation, scheduling, and control of new-type power systems. Methods First, the concept and key technologies of intelligent computing in power systems are introduced. Technical roadmaps are discussed through case studies on intelligent processing in key areas such as time-series prediction, security region optimization, and multi-energy microgrid collaborative optimization. Finally, the prospects of potential applications are explored. Conclusions Intelligent algorithms outperform traditional methods across multiple evaluation indicators, significantly enhancing prediction accuracy and control efficiency. The results validate the potential of intelligent computing in calculation and analysis for power systems, providing an effective intelligent pathway for new-type power systems and holding substantial significance for the development of intelligent power systems.

Objectives In the context of “dual-carbon” goals, achieving low-carbon economic operation of integrated energy system (IES) is of paramount importance. However, existing IES models incorporating power-to-gas (P2G) technology often overlook energy losses during hydrogen production. To address this issue, this study proposes a day-ahead low-carbon economic scheduling model for an IES that considers diversified utilization of hydrogen energy. Methods First, a mathematical model of a cross-regional IES is established, with carbon capture power plants and P2G as the main energy coupling technologies. Second, given the clean and efficient nature of hydrogen energy, mathematical models are developed for hydrogen-blended combined heat and power generation, P2G systems, hydrogen fuel cells, and hydrogen storage tanks. To avoid energy waste, a heat recovery device is integrated into the two-stage conversion process of P2G. Third, a mathematical model for demand response and green certificate-carbon trading mechanism is established and incorporated into the system’s low-carbon economic scheduling strategy. Finally, the optimization objective is set to minimize the total cost, including expenses on green certificate trading, carbon trading, coal-fired power generation, electricity procurement, and natural gas purchases. Results The proposed model reduces the total system cost by 55% and achieves the full utilization of wind power. Conclusions This scheduling model effectively mitigates energy losses during system operation and significantly enhances the low-carbon economic efficiency of IES.

Objectives With the growing integration of renewable energy, the complexity of new power system has significantly increased. The power industry requires the integration of large-scale multi-source data, more complex analysis and decision-making processes, and more intelligent approaches to enhance system flexibility and adaptability. Large models represented by large language models have attracted significant attention due to their robust natural language processing capabilities and reasoning abilities across various complex tasks. Based on this, the study reviews implementation technologies for applying large language models in the power industry and summarizes relevant achievements to inform future applications. Methods Firstly, key technologies for implementing large language models are introduced, including prompt engineering, retrieval-augmented generation, model fine-tuning, and the development of intelligent agents. These technologies enhance the accuracy and practicality of large language models in real-world applications and broaden their range of use cases. Secondly, the study outlines the research progress of large language models in areas such as power knowledge services, assisted decision-making, equipment fault diagnosis, and power system prediction. Lastly, the challenges in applying large language models in the power industry are analyzed. Conclusions The application of large models in the power industry is currently focused on the cenarios based on large language models, which are relatively mature. In contrast, applications involving multimodal models, time-series models, and large-small model collaboration remain in the exploratory and rapidly evolving stage.

Objectives Pumped storage units have gray start potential. Integrating this capability with the multi-energy complementary advantages of an integrated energy system (IES) makes it suitable for system recovery under extreme events. To investigate the post-disaster recovery mechanism of IES, this paper proposes an optimization scheduling model for a cold-heat-electricity integrated energy system (CHEIES) under pumped storage gray start. Methods First, stochastic scenario optimization is employed to address the uncertainties in wind, solar, cold, and thermal power. Latin hypercube sampling is used to generate a large number of random wind-solar-cold-heat scenarios, and a probability distance-based rapid reduction method is applied to reduce the number of scenarios. Then, for CHEIES under gray start, pumped storage serves as the gray start power source to provide startup power for the combined heat and power unit. A single-objective optimization scheduling model is established with gray start benefit as the core consideration, incorporating cold-heat-electricity power balance constraints to ensure the stable operation of IES under various load conditions. Finally, simulations are conducted to solve the model, and optimization scheduling strategies and economic benefits under different operation schemes are analyzed. Results CHEIES with pumped storage ash start-up shows high flexibility and operation efficiency in response to extreme natural disasters. Compared with the scheme without pumped storage ash start-up, the system operation cost is reduced by 12.14%. Conclusions The proposed method fully explores the reliability, economic efficiency, and flexibility of CHEIES under emergency conditions, providing strategic support for the rapid recovery of IES after extreme events.

Objectives Aiming at the low-carbon transformation of micro-grid, an optimal scheduling method for electric-hydrogen hybrid energy storage micro-grid was proposed to address the scheduling challenges under different business models. Methods Firstly, a mathematical model of micro-grid with electro-hydrogen hybrid energy storage was developed. Based on two typical business models of multi-party cooperative energy supply and multi-party independent energy supply were analyzed. Based on these models, the corresponding multi-objective optimization scheduling models and these constraints were constructed. Then, the non-dominated sorting genetic algorithm II (NSGA-II) was introduced, which was combined with variance crowding distance method and normal distribution crossover operator to improve optimization efficiency and solution accuracy. Finally, the simulation experiments were conducted using an operational electro-hydrogen hybrid energy storage micro-grid system in a southeastern coastal area to validate the effectiveness of the proposed method. Results Compared with before optimization, the economy of the multi-party cooperative energy supply business model is improved by about 4.1%, the wind-solar energy abandon rate is reduced by about 19%, and the annual carbon emission is reduced by about 47.42 t. Conclusions The multi-party cooperative energy supply business model aligns better with the current power market conditions of China’s, and the optimized system performance is significantly enhanced. This study demonstrates that the proposed optimization scheduling method effectively supports the low-carbon transition of electro-hydrogen hybrid energy storage micro-grid under various business models.

Objectives Deflectors can modify flow distribution within wind farms, and their integration into existing wind farms is an effective method for improving wind energy capture by wind turbines. To quantify the effect of deflectors, various operating conditions of deflectors are established to obtain the velocity distribution within the wind field and the wind turbine output power under different conditions. Methods A three-level orthogonal combination of three factors of deflector inclination angle, length, and distance between the deflector and the wind turbine is conducted. Numerical simulations based on the Reynolds averaged Navier-Stokes (RANS) method are performed to maximize the power output of the wind turbine. Results Different deflector inclination angles, lengths, and distances between the deflector and the wind turbine have varying degrees of effect on inflow wind velocity and wind turbine output power. Among these influencing factors, the inclination angle has the most significant effect on the inflow wind velocity and output power of the wind turbine, followed by the deflector length, and finally the distance between the deflector and the wind turbine. Conclusions The findings provide valuable guidance for improving wind turbine power output and optimizing the design and application of deflectors in wind energy utilization.

Objectives The onboard hydrogen supply system serves as a core component of hydrogen fuel cell vehicles. How to achieve complete hydrogen gas recycling and improve hydrogen utilization efficiency have become one of the key bottlenecks limiting the development of hydrogen fuel cell vehicles. Therefore, an analysis of the current development status of onboard hydrogen supply systems is conducted. Methods This paper introduces the basic working principles of onboard hydrogen supply systems, and summarizes the structure, working principles, and development status of six types of hydrogen-recycling supply systems and two types of non-recycling supply systems. The advantages and disadvantages of different hydrogen supply system modes are compared. The technical status of key components in onboard hydrogen supply systems, such as hydrogen storage cylinders, hydrogen refueling modules, and combination valves is reviewed. Additionally, the development status of core valves such as pressure relief valves, overcurrent valves, and manual valves is analyzed. Finally, the development trends and research directions for onboard hydrogen supply systems are discussed. Conclusions The recycling mode of hydrogen supply systems is the main development direction for the future. Advanced information technologies, automatic control technologies, and intelligent decision-making algorithms will be gradually applied to onboard hydrogen supply systems. The components of onboard hydrogen supply systems will gradually evolve towards lightweight, high reliability, low cost, standardization, and modularization.

Objectives The coverage of power systems continues to expand, and the structure of integrated energy systems is becoming increasingly complex. This trend leads to a significant decline in the accuracy of fault location in the distribution network that is a critical component of the energy system. To address this, a fault location method for distribution network based on an improved binary particle swarm optimization (BPSO) algorithm is proposed. Methods During each iteration of the binary particles, an adaptive mutation operation is first performed on the position of the particle. Furthermore, an adaptive method is introduced into the setting of inertia weight, establishing a BPSO algorithm with dual adaptive characteristics. Results In the standard radial distribution networks and those incorporating distributed generation, the improved BPSO algorithm can accurately pinpoint fault sections. Conclusions Compared with the traditional BPSO algorithm and genetic algorithm, the improved algorithm demonstrates stronger robustness in convergence ability. It remains unaffected by differences in fault types and has greater reliability. Therefore, it is more suitable for fault location tasks in complex and dynamic distribution network environments.

Objectives In order to truly reflect the damage characteristic of fusion neutron irradiation, it is necessary to carry out research on high-flux fusion neutron sources. As the first experimental device in the world that can realize cascaded magnetic compression with high compression ratio of field-reversed configuration plasma, the preliminary research device of magnetic confinement deuterium-deuterium fusion neutron source has important scientific significance. As one of the core components of the device, the magnetic compression magnet was designed to increase the magnetic induction intensity of the central magnetic field from 0 T to 7 T within 500 μs. Therefore, a design idea for the conductor part of the magnetic compression magnet was proposed. Methods Based on the conductor design of magnet, the conductor stress was analyzed by finite element simulation software from the three aspects of conductor material selection, conductor turn distance and conductor radial thickness. The influence of conductor material conductivity, conductor turn distance and conductor radial thickness on conductor stress was obtained. Results The design idea of the next magnet is determined. That is, the conductor material with low conductivity is appropriately selected within the allowable range of ohmic loss, the conductor turn spacing is appropriately increased by increasing the thickness of the insulation layer within the allowable range of axial space, and the radial thickness of the conductor is appropriately reduced within the allowable range of material stress to reduce the construction cost. Conclusions With the further development of the preliminary research device of neutron source project, the design ideas proposed in this paper can provide optimization direction for the design of magnetic compression magnet device.

Objectives In order to support the testing requirements of the international thermonuclear experimental reactor AC/DC converter system and apply to high-precision control standards for future fusion magnet power supplies, a real-time control system suitable for high-power DC test platforms was developed. Methods Choosing the QNX real-time operating system as the core platform and combining it with reflective memory technology provided by GE company in the United States, a network architecture for real-time high-speed data exchange was built. The system was designed with multiple operation modes to cope with different testing scenarios, while implementing different levels of safety interlocking mechanisms to ensure equipment safety. This system possessed the capabilities of setting converter operating parameters, fault identification, equipment status monitoring, and millisecond-level real-time control and safety protection functions. Results Experimental validation has demonstrated the system’s stability and reliability in high-power environments, achieving precise control of 120 kA steady-state current and 500 kA pulse current. Furthermore, the system not only meets the basic requirements of real-time control but also ensures safe interlocking and continuous stable operation of the equipment during multi-mode operation. Conclusions The designed real-time control system for the high-power DC test platform achieves efficient multi-module synchronous management and fully complies with strict millisecond-level control cycle requirements.

Objectives In the context of carbon peaking and carbon neutrality, the energy saving and efficiency improvement of thermal power units is the key to the low-carbon transformation of the power industry. Moreover, the implementation of cold end optimization to improve the operating economy of the unit is one of the effective methods, Therefore, it is necessary to optimize the cold end system with wide load. Methods This paper took the cold end system of the 1000 MW unit of Ninghai power plant as the research object, the calculation model of the wet natural ventilation cooling tower was established, and the corresponding software module was developed, which can realize the coupling calculation of the cooling tower-circulating pump group-condenser-steam turbine unit according to the ambient temperature, humidity and atmospheric pressure data. Taking the actual environmental parameters and operating load as variables, the operation curve of the cooling tower, the comparison between the constant frequency and the frequency conversion operation of the circulating water pump group, and the comprehensive optimization of the cold end of the unit were carried out. Results The maximum energy saving rate of pump frequency conversion is close to 50%. When the ambient temperature is about 31 ℃, the coal consumption rate can be reduced by adjusting the circulating water flow. The lower the ambient temperature is, the greater the coal consumption rate benefit is, the average coal consumption rate can be reduced by more than 2.0 g/(kW⋅h). However, the coal consumption income of different working conditions varies greatly. Conclusions The research results can provide reference for optimizing energy conservation at the cold end of the unit.

Objectives As clean energy sources and chemical raw materials, green hydrogen, green ammonia, and green methanol play an important role in achieving the “dual carbon” goal. With the increasing demand for clean energy, wind-solar-hydrogen-ammonia-methanol integrated industry has emerged as an important new field of interest. However, the industry in China is still in its early stages of development, with numerous challenges in various links of the industry chain. Therefore, it is necessary to explore the technologies related to wind-solar-hydrogen-ammonia-methanol integration and to analyze the future direction of its development. Methods Firstly, the technological routes of producing green hydrogen, synthesizing green ammonia, and synthesizing green methanol through water electrolysis are introduced, along with an overview of technologies addressing the instability of wind and solar power. Next, the economy of the wind-solar-hydrogen-ammonia-methanol integrated industry are analyzed, highlighting the implementation of key projects and related policies in recent years. Additionally, the electricity and water consumption for producing green ammonia and green methanol are analyzed and estimated, and the production costs of green hydrogen, green ammonia, and green methanol are predicted. Based on this, suggestions for industry development are proposed, including technological innovation, industry chain coordination, and policy support. Finally, the future development directions of the wind-solar-hydrogen-ammonia-methanol integrated industry are analyzed. Conclusions In the future, the integrated development of wind-solar-hydrogen-ammonia-methanol will show a trend of technological integration, diversified applications, regional coordination, and cost-effectiveness, making it one of the core pathways to achieve the “dual carbon” goals and transform the energy system.

Objectives Photovoltaic arrays operating under complex outdoor conditions encounter various fault types with varying degrees of severity. To accurately assess the working status of photovoltaic arrays, a fault diagnosis method based on an improved grey wolf optimized extreme learning machine (IGWO-ELM) is proposed. Methods Firstly, nine fault simulation output characteristics are analyzed, and a five-dimensional fault feature vector is established, consisting of short-circuit current, open-circuit voltage, maximum power point current, maximum power point voltage, and fill factor. Secondly, to address the limitations of the grey wolf algorithm, such as uneven distribution of initial position and imbalance between global search and local exploitation, Circle mapping and nonlinear convergence factors are incorporated. An improved grey wolf optimization algorithm is then proposed, which optimizes the input layer weights and hidden layer node biases of the extreme learning machine to improve performance. Finally, simulation models and experimental platforms are developed to collect fault data, which are divided using K-fold cross validation. The data are input into the IGWO-ELM model for accuracy verification and compared with other algorithms. Results The IGWO-ELM model demonstrates high recognition rates for various fault types in photovoltaic arrays, achieving classification accuracy of 98.32% and 95.48% for simulation and experimental data, respectively. Conclusions The fault diagnosis method based on IGWO-ELM offers high accuracy, requires fewer iterations, and achieves fast convergence speed, effectively judging the working state of photovoltaic arrays.

Objectives Hydrogen energy, as a clean energy source with high energy density and zero carbon emissions, is an important component of future energy systems. For park-level hydrogen-electric coupling systems (HECS), a two-stage robust optimization scheduling model that considers demand response and tiered carbon trading is proposed. Methods First, a park-level HECS is established, consisting of wind and solar power generation units, backup generation units, energy storage systems, and hydrogen-electric conversion devices. Then, demand response and tiered carbon trading are integrated into the model, aiming to minimize the total costs of system energy procurement, operation and maintenance, and carbon emissions, thereby establishing a deterministic optimization model for the system. Finally, a source-load uncertainty set is incorporated into the deterministic optimization model to mitigate the effect of source-load uncertainty on scheduling results, forming a two-stage robust optimization model. This model is re-established using a master-slave framework and solved using the column-and-constraint generation method. Results With source-load uncertainty coefficients of 12 and 6, demand response loads with an adjustable ratio below 0.5 can reduce the system's operating costs by 1.6%. The introduction of tiered carbon trading can reduce carbon emissions by 604.9 kg. Conclusions The proposed model can improve the risk resilience of park-level HECS, and the integration of demand response and tiered carbon trading can ensure the economic and low-carbon operation of HECS.

Objectives The preliminary research device of magnetic confinement deuterium-deuterium fusion neutron source is a novel neutron source preliminary research device based on field-reversed configuration (FRC) cascade magnetic compression. It aims to leverage the experiences from the first-phase construction to enhance system design, significantly improve plasma parameters, and further expand research on magnetic compression fusion, laying the foundation for achieving a large-volume high-flux fusion neutron source in the third phase. Methods The preliminary research device control system optimized and reconstructed the control framework, provided safety interlocking, pulse control and comprehensive data services, coordinated and integrated each service into the automated discharge process through integrated control, and added a number of resources to expand applications and DevOps tool. Results Through the reconfiguration design, the comprehensive performance of the control system in terms of safety, stability and efficiency had been significantly improved. The safety interlock system ensured the safety of personnel and equipment during the experiment process, the pulse control system achieved high-precision timing control, the comprehensive data service provided full process support from data collection to analysis, and resource expansion applications and DevOps tools further improved the system flexibility and operation and maintenance efficiency. Conclusions By optimizing the control framework and introducing advanced operation and maintenance tools, the design can better meet the needs of complex device structure and precise discharge flow, and provide an efficient control system construction plan for the subsequent long-term cooperation construction of the magnetically confined deuterium fusion neutron source preliminary research device.

Objectives As an essential sustainable energy technology, renewable energy-powered water electrolysis for hydrogen production has attracted widespread attention due to its advantages in environmental protection and low carbon emissions. However, conventional water electrolysis technologies for hydrogen production face challenges in terms of efficiency and cost, the rapid development of artificial intelligence (AI) provides an effective way to solve the difficult problems of hydrogen production technology through electrolysis of water. To address this, this study aims to explore the key applications and development prospects of AI for optimizing the efficiency and economic performance of water electrolysis systems for hydrogen production. Methods Common AI tools such as MATLAB, Python, and SimuNPS are employed for algorithm development, deep learning model training, and multi-physics simulation in water electrolysis systems for hydrogen production. By integrating AI technologies, applications such as output prediction, system capacity optimization and scheduling, and fault diagnosis are implemented to improve system performance and stability. A comparative analysis of performance of different AI models in various real-world scenarios is conducted to explore their specific roles and implementation methods in enhancing system performance and controllability. Conclusions AI technology offers new avenues for enhancing the efficiency and intelligent scheduling of renewable energy-powered water electrolysis hydrogen production systems. Future research should focus on the application of AI in output forecasting, scheduling optimization, and fault diagnosis, promoting deep integration between AI and system operation. Moreover, innovative applications of AI in intelligent monitoring, automatic control, and multi-source coordination should be explored to provide strong support for the development of efficient, stable, and low-carbon hydrogen energy systems.

Objectives Desulfurization tower mist eliminator is widely used in coal-fired power plants due to its simple structure and good fog removal effect. However, due to the limitation of its own gas-water separation principle, it is prone to scaling up and blocking on the plate surface, which seriously affects the output power of the unit. Therefore, it is necessary to solve the problem of frequent scaling and clogging of the wet desulfurization tower mist eliminator and the resulting excessive resistance loss. Methods The method of optimizing the flow field of desulfurization tower and its inlet flue using deflector plates was proposed, and the simulation calculations and engineering application verifications of the Z-shaped desulfurization tower and its L-shaped inlet flue before and after optimization were carried out. Results The simulation results indicate that under rated boiler load conditions, the relative standard deviation of the velocity at the outlet section of the L-shaped inlet flue decreases from 27.57% to 19.99% after optimization. The relative standard deviation of the velocity at the inlet section of the mist eliminator in the Z-shaped desulfurization tower decreases from 45.66% to 40.24%. Meanwhile, the mass flow rate of the slurry droplets at the mist eliminator inlet section drops from 441.136 kg/s to 368.498 kg/s, indicating that the optimization scheme effectively reduces the workload of the mist eliminator. Experimental results show that prior to the modification, there were regions with a velocity of 0 m/s at the mist eliminator inlet section, which are improved to a velocity of 1-5 m/s after the modification, consistent with the trends observed in the simulation. Data from 180 days of operation after the modification indicate that the pressure drop before and after the mist eliminator does not exceed 200 Pa. On-site measurement results during maintenance show that the scaling thickness on the mist eliminator plate is reduced from over 1 cm before the modification to about 0.1 cm, eliminating severe scaling and blockage phenomena. Conclusions The proposed flow field optimization method significantly improves the uniformity of the flue gas flow field in the desulfurization tower, reduces the workload of the mist eliminator, and effectively slows down the fouling problem of the mist eliminator, which has great engineering application value.

Objectives Developing future energy is both a current focus of international competition and an essential pathway for China to build a new-type energy system and advance Chinese-style modernization with high-quality development. Therefore, this study explores the categories, research status, and future development trends of energy technologies, aiming to provide references for research and policy making in related fields. Methods Based on reports from international authoritative institutions and data from typical global projects, multidimensional analysis methods are used to systematically introduce the global R&D status of green low-carbon power sources, bioenergy, synthetic hydrocarbon fuels, and hydrogen energy. Through the technology readiness level framework, combined with economic indicators, policy support, and commercial case studies, this study focuses on analyzing the key breakthroughs, application bottlenecks, and synergistic potential of different technological pathways, and establishes a systematic cross-technological roadmap for energy transition. Results Driven by technological advancements and policy support increase, the proportion of renewable energy in the power structure has significantly increased, accelerating the transition toward becoming the main power source. Additionally, the development of emerging technologies including bioenergy, hydrogen energy, synthetic hydrocarbon fuels, and controlled nuclear fusion will provide new solutions for energy transition. Conclusions The findings provide a scientific basis for promoting green and low-carbon energy transition, offering important references for researchers and policymakers in the energy field.

Objectives Low-temperature weather poses challenges to the operation of power systems with a high proportion of new energy, such as wind power. Improving the accuracy of short-term wind power prediction under low-temperature conditions will provide effective decision-making information for power system scheduling and operation. To address this, a wind power prediction method considering the clustering of unit operation status under low-temperature conditions is proposed. Methods The fuzzy C-means (FCM) clustering algorithm is used to cluster wind turbines based on their operation status and protection control information. Then, a prediction method based on support vector machine is proposed to predict whether the wind turbines are in normal operation status. The LightGBM algorithm in ensemble learning is employed to predict the power output of wind turbines under normal operation. Based on the prediction results of both operation status and power values, the overall wind power output of the wind farm is determined. Finally, a case study of a wind farm in northern Hebei is conducted to validate the effectiveness of the proposed method. Results By fully utilizing the characteristics of wind turbine protection control behaviors under low temperatures, the proposed method accurately predicts the critical shutdown time of wind turbines and provides the shutdown capacity. It effectively fits the variation patterns of wind power curves,which improves the prediction accuracy of the wind power to more than 90%. Conclusion The proposed method can provide reliable prediction information for power scheduling and control. Additionally, it can provide a reference for short-term wind power prediction under other extreme weather conditions, such as strong winds.