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 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 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 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 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 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 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 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 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 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 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 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 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 The photovoltaic-concentrating solar power (PV-CSP) hybrid system combines the advantages of low cost of PV and high dispatchability of CSP, but it also faces the common problems of electricity curtailment and low utilization rate of energy storage. In order to realize the utilization of PV electricity rejection as the thermal energy storage, a new type of PV-CSP hybrid system (PV-CSP-EH) integrated with an electric heater (EH) is proposed. Methods By constructing a quasi-steady-state model for the proposed PV-CSP-EH hybrid system, the annual operation characteristics of the system are analyzed at one-hour intervals. Through parametric analysis and Pareto optimization model, the performance variation law and the optimal parameters of levelized cost of electricity (LCOE) under different system configurations are obtained. Results Compared with the traditional PV-CSP hybrid system, the annual power generation and penetration of PV-CSP-EH hybrid system are increased by 8.2% and 16.2%, respectively. Moreover, the annual electricity curtailment of PV-CSP-EH hybrid system is only 2 GW⋅h, and its power recovery and conversion rates reach 94.1% and 35.2%, respectively. Under the optimal configuration, the LCOE of PV-CSP-EH hybrid system can be as low as $0.138/(kW⋅h), which is 6.8% lower than that of the traditional PV-CSP hybrid system. Conclusions PV-CSP-EH hybrid system can improve the power generation and penetration capacity, significantly reduce the electricity curtailment, optimize the problem of wind and solar curtailment in a more economical way, and contribute to the construction of a new power system.

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 The supercritical CO₂ Brayton cycle has the advantages of high efficiency, compact structure, enormous power generation potential, and strong scalability. CO₂-based mixtures as working fluids can change the critical point and improve cycle performance, which has been the focus in recent years. It is of great significance to review its research progress for basic theoretical research and engineering application. Methods A review is conducted on the research progress on the application of CO₂-based mixtures in the supercritical Brayton cycle. The types of commonly explored mixed working fluids are summarized. The research on cycle components and structure layout is discussed, and the commonly used cycle performance and operating conditions are analyzed. Conclusions The application of CO₂-based mixtures can increase or decrease the critical temperature of CO₂, but the predicted physical properties range is limited, and experimental data is lacking. The recompression Brayton cycle is a commonly investigated cycle layout, and thermodynamic performance and design conditions are widely focused. It is recommended to strengthen and deepen the theoretical and experimental research on the thermodynamic properties of mixed working fluids, conduct reasonable verification on the compatibility of component materials, and propose the novel cycle structure layout for the different thermodynamic properties of mixed working fluids. Further exploration is needed for the analysis of comprehensive performance and dynamic characteristics of the CO₂ mixtures supercritical Brayton cycle.

Objectives To address the impact of extreme high-temperature weather on the supply and demand balance of power systems, a medium- to long-term method for enhancing power system resilience is proposed. Methods First, the coupled “source-load” output characteristics under sustained extreme high-temperature scenarios are simulated, and an initial evaluation of system resilience is performed using a chronological operation simulation model. Second, based on the operation simulation results, a resilience enhancement-oriented power resource planning model is established, with the objective of minimizing the combined cost of power supply and load shedding under the constraint of supply and demand balance, thereby deriving combined measures for resilience enhancement. Finally, operation simulations of the resilience enhancement strategy scenarios are conducted to verify their effectiveness. Results Taking Guangdong Province in 2025 as a case study, compared with the scenario without the implementation of the resilience enhancement strategies for power systems, the application of such strategies under extreme high-temperature weather decreases the cumulative power shortage, maximum lost load scale, and economic losses caused by power shortage by 86.71%, 60.72%, and 91.55%, respectively. The system’s minimum power supply level increases by 11.07%. Conclusions By coordinating the expansion of power supply resources and deployment of load resources, the resilience enhancement strategies effectively improve the power system’s supply capacity under extreme high-temperature weather, while reducing the economic loss and social cost caused by power shortages.

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 To address the challenges faced by new types of nuclear power systems in ensuring energy supply, promoting clean energy transition, and achieving “dual carbon” goals, such as structural diversification, load uncertainty, and data complexity, and to solve problems associated with the application of large language models in the nuclear power domain, such as knowledge limitations, hallucinations, and high reasoning costs, the study explores the application potential of combining large language models with knowledge graphs, especially graph retrieval-augmented generation (GRAG) technology. This combination aims to build more intelligent, efficient, and reliable information processing capabilities for nuclear power systems. Methods The research status of large language models and knowledge graphs in the nuclear power domain, their respective advantages and disadvantages, and the complementarity of their integration are analyzed. The advantages of GRAG technology over traditional retrieval-augmented generation (RAG) are highlighted, along with its specific applications in scenarios such as nuclear power risk assessment, intelligent question-answering assistance, knowledge management and decision support, and fault diagnosis and prediction. Furthermore, the technical pathway for introducing and fine-tuning large language models, constructing domain-specific knowledge graphs, and implementing GRAG enhancement is outlined. Finally, an outlook on future research is provided, covering areas such as knowledge graph construction under heterogeneous data, cognitive reasoning and decision-making of large language models, and the controllability of human-computer interaction. Conclusions GRAG technology combined with knowledge graphs can effectively alleviate the knowledge limitations and hallucination problems of large language models in specialized domains, enhancing the interpretability and reliability of the generated content. The research findings can provide references for the future optimization of knowledge graph construction in the nuclear power domain, enhancing the capabilities of large language models in complex reasoning tasks, and developing artificial intelligence agents for efficient interaction with experts in the nuclear power 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.

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 To address the issue of marine thermal pollution caused by the direct discharge of low-grade waste heat from nuclear power plants, it is imperative to explore efficient solutions to reduce thermal discharge loads. Therefore, by the characteristics of different types of waste heat sources and their potential of power generation are systematically evaluating to provide thermo-economic data basis for waste heat utilization in nuclear power plants. Methods The characteristics of different types of low-grade waste heat in nuclear power plants are summarized, and different utilization methods of waste heat and their current development status are analyzed. Three types of power generation technologies suitable for this waste heat are introduced, and their thermo-economic performance and the recovery potential of different waste heat sources are comparatively analyzed through actual case studies. Results The energy quality coefficient of the secondary loop condensate saturated water in nuclear power plants is the highest, showing the greatest potential for power generation utilization. Moreover, the organic Rankine cycle (ORC) demonstrates optimal thermo-economic performance as a waste heat power generation solution, achieving the lowest levelized cost of electricity at $0.037 6/(kW⋅h), which is 84% and 78% lower than that of the ejector organic flash cycle and the Kalina cycle, respectively. ORC achieves a generation capacity of 462.2 kW, demonstrating good feasibility. In the scenario of recovering waste heat from the low-pressure turbine exhaust, the ORC can reduce thermal discharge by 360.3 MW, showing significant environmental benefits. Conclusions Waste heat power generation technologies such as the ORC can significantly reduce thermal discharge loads into the sea, providing important references for low-grade waste heat utilization and thermal pollution reduction in China’s nuclear power plants.

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 Aiming at the problems of strong coupling, large load, and weak disturbance resistance in the firing rate to feed water ratio (FR/FW) control process of supercritical units, a new type of auto-coupling PI (ACPI) control method is proposed based on the theory of auto-coupling PID control. Methods The multivariable FR/FW system is equivalent to multiple single-variable closed-loop systems, and all complex factors such as the coupling, known or unknown internal dynamics, and external disturbances of the FR/FW system are defined as total disturbances. Then, the FR/FW control system is equivalently mapped to a linear disturbance system, and a controlled error system under total disturbance excitation is constructed. Based on this, a speed factor based ACPI controller is designed for each single variable system, and the robust stability and disturbance resistance of the control system are analyzed in the complex frequency domain theory. Results Compared with PI and active disturbance rejection control methods, the ACPI control method has fast response speed, dynamic quality without overshoot, and strong steady-state performance with strong disturbance resistance. Conclusions It is of great significance to improve the control accuracy and robustness of the FR/FW system in supercritical units.

Objectives The proton exchange membrane fuel cell (PEMFC), as a highly promising clean energy technology, has attracted much attention in the field of energy conversion. However, the high complexity and operational uncertainties of PEMFC systems pose significant challenges to state estimation and fault diagnosis, seriously affecting system reliability and safety. To effectively address these challenges, the application strategies and effectiveness of artificial intelligence (AI) technology in PEMFC state estimation and fault diagnosis are studied. Methods Current research progress on PEMFC state estimation and fault diagnosis is analyzed. In the field of state estimation, the nonlinear model characteristics of PEMFC are analyzed, AI-based state estimation technologies are introduced, and the application principles and advantages of different algorithms for PEMFC state estimation are analyzed. In the field of fault diagnosis, common fault types of PEMFC are summarized, their fault manifestations and internal causes are analyzed, and AI-based fault diagnosis technologies are introduced. Finally, the future prospects for AI-based PEMFC state estimation and fault diagnosis technologies are discussed. Conclusions With its powerful data processing and pattern recognition capabilities, AI technology can accurately estimate the state of PEMFC and effectively diagnose potential system faults, thereby significantly improving the the operational efficiency and stability of PEMFC systems and enhancing their reliability and safety. Future research can focus on areas such as AI algorithm innovation, optimization of state estimation and fault diagnosis, intelligent system development, and collaboration with other technologies.

Objectives Due to the anisotropic nature of titanium bipolar plates, their manufacturing process is highly challenging. While hydroforming has been commercially applied in bipolar plate production abroad, research in this field remains limited domestically. In order to reveal the control relationship between hydroforming parameters and quality in bipolar plate fabrication, it is essential to investigate the hydroforming mechanism of titanium bipolar plates. Methods This study focuses on the hydroforming process of titanium bipolar plates with a serpentine flow field. It analyzes the mechanical properties of the bipolar plates during the hydroforming process and explores the effects of key parameters, including die fillet radius, flow channel width, flow channel depth, sheet thickness, and hydraulic pressure, on the final forming quality. Results The stress distribution in the corner regions of the serpentine flow channels is highly complex, making these areas most susceptible to fracture. Increasing the die fillet radius effectively reduces the thinning rate, with a recommended minimum radius of 0.2 mm. Increasing flow channel depth significantly worsens thinning, and the depth should not exceed 0.28 mm. When the flow channel width equals the rib width, the thinning rate reaches its minimum. Hydraulic pressure has a greater impact on thinning before the plate contacts the die bottom but its impact diminishes afterward. Additionally, as the plate thickness increases, the overall forming quality deteriorates. Conclusions The coordinated design of hydroforming parameters and structural parameters is critical for ensuring the forming quality of bipolar plates. These findings provide a theoretical foundation for the design and optimization of metal bipolar plate hydroforming process.

Objectives At present, most amine based CO₂ capture systems for flue gas use stainless steel filled towers. In order to significantly reduce the cost of filled towers, this paper proposes the use of polypropylene structured filler instead of high cost stainless steel structured filler, thereby reducing the investment cost of absorption towers in CO₂ capture systems after combustion. Methods Using plastic fillers instead of stainless steel fillers, the selected polypropylene material and 30% mass fraction of polypropylene grafted maleic anhydride (PP-g-MAH) particles are mixed, and then they are melt blended to form regular fillers. On the constructed testing platform for fluid dynamics and mass transfer performance of fillers, different filler properties are tested. Results The dry tower pressure drop of regular fillers of 410Y and 350Y modified by melt blending of polypropylene and polypropylene grafted maleic anhydride is similar to that of stainless steel 500Y. The effective specific surface area of modified polypropylene filler 350Y is close to that of stainless steel filler 350Y with the same nominal specific surface area. Under liquid-phase loading conditions in the CO₂ amine reaction absorption tower, the effective specific surface area of polypropylene filler of 350Y is about 90% of that of stainless steel filler of 350Y. Conclusions The modified polypropylene filler has similar performance to stainless steel filler and can replace stainless steel filler in practical applications, significantly reducing the investment cost of absorption towers in CO₂ capture systems.

Objectives Traditional power grid inspection methods suffer from high labor intensity and low efficiency. Taking Shandong Golden Power Grid as the research object, this study proposes an inspection algorithm using unmanned aerial vehicle (UAV) based on lightweight deep learning network YOLOv5-Mv3 for detecting grid insulators and foreign objects. Methods Firstly, a dataset is constructed using images captured by UAVs during power grid inspection and is trained. Then, for the grid insulators and foreign objects, Mobilenetv3 is used to replace CSPDarknet53 as the feature extraction network in order to lighten the YOLOv5-Mv3 model, reducing parameters and computational cost while maintaining accuracy and enabling real-time detection. Results The proposed detection algorithm achieves a mean Average Precision of 84.7% and 56.6 frames per second. Compared to Faster RCNN, SSD, and YOLOv4 models, the improved YOLOv5-Mv3 demonstrates higher detection accuracy and faster performance. Conclusions The proposed algorithm improves the efficiency of UAV-based power grid inspection and achieves lightweight and high-efficiency effect, fully meeting the requirements for intelligent power grid inspection.

Objectives DC microgrids are prone to the issue of large-signal stability due to the low inertia and constant power load characteristics. Traditional model-based methods involve complex calculations and are difficult to solve. To address these issues, this study investigates intelligent analysis methods for large-signal stability of DC microgrids. Methods Common artificial intelligence (AI) classifiers are selected to analyze the stability of DC microgrids. A comparative analysis is conducted on three types of common AI technologies (covering six methods)-deep learning, support vector machine, and decision trees-for large-signal stability assessment in a specific DC microgrid case study. Results Comparative analysis based on specific examples shows that in the large-signal stability assessment of DC microgrids, long short-term memory (LSTM) networks outperform other methods in terms of overall performance (accuracy, real-time capability, and adaptability). Conclusions The LSTM network classifier shows high compatibility with the state-space equations of DC microgrids, making it more suitable than traditional machine learning classifiers for large-signal stability analysis of DC microgrids. Meanwhile, ensuring the performance of the classifier requires appropriate selection of parameter values.

Objectives With the rising penetration of renewable energy, it is imperative to conduct power and energy balance analysis, design planning schemes, and evaluate market mechanisms through detailed time-series operational simulation. However, due to the randomness and volatility of renewable energy and the continuous expansion of power grid, operational simulation faces challenges in balancing computational speed and accuracy. Artificial intelligence (AI), with its exceptional abilities in representation, generalization, and self-learning, provides new solutions to the current challenges. Therefore, the current status and necessity of the application of AI in accelerating the operational simulation of power systems are systematically analyzed, and the future development is prospected. Methods First, from a mathematical perspective, the concepts of acceleration methods are classified into scenario aggregation, unit aggregation, constraint reduction, and algorithm acceleration. Second, the necessity of applying AI across different aspects is thoroughly analyzed, addressing the crucial question of “why AI is needed”. Then, current AI applications in accelerating operational simulation and their advantages are systematically summarized. Finally, recommended AI application scenarios in power systems and future prospects are presented. Conclusions AI can provide effective support for accelerating operational simulation from multiple aspects. It shows particular strengths in handling non-linear correlations, substituting expert experience, and characterizing fuzzy rules.

Objectives In view of the limitations of the traditional distribution network demand response model in regulating the flexible and controllable resources on the load side, especially ignoring the unique and efficient resources such as ice storage air conditioning systems. Therefore, this paper aims to deeply optimize the load-side control object such as ice storage air conditioning, so as to significantly improve the utilization rate of renewable energy in the new distribution system and deeply explore its low carbon operation potential. Methods Ice storage air conditioning with virtual energy storage characteristics is introduced as the control object. A new distribution system multi-time scale optimization strategy is proposed for photovoltaic consumption and low carbon demand response of ice storage air conditioning groups. Firstly, the operating characteristics and low carbon demand response model of ice storage air conditioning are established. Secondly, with the minimum incentive cost for low carbon demand response in power supply companies, the maximum profit for photovoltaic manufacturers, and the minimum electricity consumption cost for air conditioning users as optimization objectives, a multi-objective optimization model for the day ahead is constructed. The non-dominated sorting genetic algorithm Ⅱ (NSGA-Ⅱ) is used to efficiently solve the model, and the optimal solution for the multi-objective optimization model is selected based on the linear programming method with multi-dimensional preference analysis. Subsequently, in order to eliminate the impact of prediction errors on the model results, a daily rolling correction optimization model is further constructed. Finally, the effectiveness of the proposed model is analyzed using test cases. Results The optimal solution obtained after intra-day rolling correction results in a cost reduction of approximately 20% for both power supply companies and air conditioning users, as well as an increase in profit margins of about 3% for photovoltaic producers. Conclusions The multi-objective optimization and control method based on multiple time scales not only ensures the interests of all parties, but also eliminates the impact of prediction errors on model results, providing important technical support for low carbon operation of new distribution systems.

Objectives Currently, chemical absorption CO₂ capture technology used in post-combustion is rarely applied in large-scale decarbonization projects in gas-fired power plants due to its high operational energy consumption and total costs. In order to reduce the energy consumption, particularly the regeneration energy consumption, it is necessary to carry out experimental research to improve the performance of chemical absorbents. Methods Two novel polyamine-based absorbents are developed through composition design, laboratory testing with small-scale experimental setups, and engineering validation. Results Compared to monoethanolamine (MEA), the two polyamine absorbents—19% diethylaminoethanol + 9% piperazine + 2% MEA (DT01-5) and 20% 1,4-butanediamine + 5% methyldie-thanolamine+5% 2-amino-2-methyl-1-propanol (DT02-3)—significantly improve absorption loading, absorption rate, desorption rate, and cyclic capacity. Their physicochemical properties are close to those of the 30% MEA commonly used in industrial plants. Through testing on a 2 m³/h small-scale experimental setup, the energy consumption of the two absorbents is reduced by 15.84% and 9.32%, respectively, compared to that of MEA. The 3 000 m³/h industrial test results show that the regeneration heat consumption of the two absorbents decreases by 32.89% and 39.52%, respectively, compared to MEA, while the capture power consumption decreases by 9.83% and 16.14%, respectively. Additionally, other performance indicators improve to varying degrees, resulting in total operating cost reductions of 25.95% and 34.14%, respectively. Conclusions The two novel polyamine absorbents demonstrate strong potential for commercial applications.

Objectives Arundo donax L., as a high-yield energy crop suitable for intensive cultivation, shows great potential for large-scale replacement of coal in power generation and CO₂ emission reduction. Under China’s “dual-carbon” goals and the requirements of low-carbon transformation for coal-fired power units, vigorously developing clean and efficient combustion technologies using Arundo donax L. as an energy crop offers an important low-carbon energy pathway well-suited to China’s national conditions. Understanding the current research status of Arundo donax L. combustion is crucial for guiding the utilization of its combustion in China. To this end, the research progress on the combustion and utilization of Arundo arundo is reviewed, aiming to provide reference for the large-scale utilization of biomass energy. Methods The study conducts a comprehensive investigation into the utilization of Arundo donax L. combustion through literature research methodology, systematically summarizing and analyzing existing research findings. It summarizes the fuel characteristics of Arundo donax L., pre-treatment methods and performance, the characteristics of combustion and pollutant emissions, and industrial-scale tests of Arundo donax L. combustion. Additionally, the study discusses the environmental effects of its cultivation. Conclusions Arundo donax L. shows significant potential for large-scale replacement of coal in heating and power generation. Given the requirements of low-carbon transformation for coal-fired power units in China, co-firing Arundo donax L. in power plants shows great potential.

Objectives Natural gas networks are important components of integrated energy systems, which can provide considerable flexibility for the operation of the integrated energy system. Developing an accurate gas network model is the foundation for the operation and control of integrated energy systems. However, existing mechanism models for gas networks show high complexity, with certain parameters being unidentifiable in practical engineering applications. Therefore, this study proposes a gas network aggregate modeling and identification method specifically for integrated energy system operation. Methods Taking the gas network as the research object, the concept of the gas network aggregate model is proposed, its formula is derived, and a corresponding offline parameter estimation method is designed. Since the derivation of the gas network aggregate model depends on specific operating conditions, the model parameters vary with condition changes, leading to non-negligible errors in the offline estimation model. To address this issue, a rolling calibration method based on forgetting factor recursive least squares is proposed. Results Under small gas flow variations, the offline parameter estimation method demonstrates satisfactory accuracy. However, under rapid flow variations, the offline estimation fails to accurately describe the operation status of the gas network, and rolling calibration can achieve accurate fitting of the model. Conclusions The proposed gas network aggregate model integrating offline estimation and online rolling calibration can provide a practical and efficient fluid network modeling solution for the operation analysis and control of integrated energy systems.

Objectives Under the current electricity price mechanism, it is difficult for Xinjiang pumped storage power station to fully recover the cost through the arbitrage of peak-valley price difference and participation in electric auxiliary services. Therefore, the capacity and electricity charge dredging mechanism of pumped storage power station is studied. Methods Based on the calculation of the electricity cost of Xinjiang pumped storage capacity, the problems existing in the cost dredging are analyzed. The dredging path of “market-oriented + transmission and distribution electricity price” is proposed for Xinjiang pumped storage power station. That is, the cost is recovered by transmission and distribution price before the maturity of power market construction, and the cost is recovered by marketization after the maturity of power market construction. Results Compared with the pure transmission and distribution electricity price recovery, the “market-oriented + transmission and distribution electricity price” method has higher feasibility in dredging capacity electricity price and the increase of transmission and distribution electricity price decrease by more than 90%, which is a feasible model to calm the excessive rise of transmission and distribution electricity price. Conclusions As a supplementary scheme for the operation cost recovery of pumped storage power stations, the research results will provide a broad market trading space for the pumped storage power stations.

Objectives To address issues such as insufficient multi-dimensional coordination, incomplete quantification of environmental benefits, and the lack of dynamic feedback in evaluating the benefits of electric energy substitution in regional power grids, a dynamic evaluation method based on multi-source data fusion is proposed to enhance the decision-making accuracy of electric energy substitution projects. Methods A three-dimensional evaluation index system covering “supply-side optimization, demand-side transformation, and environmental emission reduction synergy” is established, identifying 21 indicators including cross-regional consumption capacity, electric substitution penetration rate, and pollutant reduction, thereby forming a multi-level quantitative evaluation framework. A coupling weighting strategy based on the entropy weight method and an improved rank correlation method is developed to balance subjective and objective weight deviations through linear weighting. A Bayesian dynamic cloud model is proposed, optimizing the hyper-entropy parameters of the cloud model by incorporating a Bayesian feedback mechanism to achieve dynamic correction of evaluation results. Results The proposed model can effectively quantify the comprehensive benefits of electric energy substitution, accurately identify shortcomings in cross-regional consumption and terminal substitution, and improve evaluation accuracy through the dynamic feedback mechanism, enhancing alignment with practical engineering. Conclusions This evaluation method overcomes the bottlenecks in traditional benefit evaluation, such as single-dimensional indicators, inaccurate weight allocation, and significant static deviations, providing a reference for improving the accuracy of electric energy substitution benefit evaluation.

Objectives In order to improve the concentrated light interception rate of linear Fresnel collector (LFC), increase the error control threshold of the tracking system, and enhance the system’s photothermal conversion efficiency, it is necessary to study the variation patterns of light spot width on the primary reflector. Therefore, the reflected light spot width of the LFC micro-arc mirror is modeled, simulated and experimentally studied. Methods Based on the structural characteristics of the LFC, a light concentrating model of the micro-arc primary reflector is developed. Through MATLAB simulation analysis, the variation patterns of light spot width under the boundary conditions of the micro-arc primary reflector are obtained. A light spot test platform is developed, which simulates solar incidence angles of different seasons and times of the day by changing the angle of the test platform and adjusting the position of the receiver board, to verify the accuracy and effectiveness of the simulation results. Results Due to the presence of the solar angle, the reflected light spot width increases by 75.8% compared to the parallel light condition. The experimental results of rotating test platform show that the variation patterns of the measured light spot width are consistent with the simulation results. Under the influence of the solar angle, the maximum tracking error control margin of the LFC is 0.173° in winter and 0.207° in summer. Conclusions The tracking control accuracy of the LFC system in winter should be 16.4% higher than that in summer. Therefore, the focal length of the LFC micro-arc reflector should be designed based on the tracking control margin of the light spot width in winter.

Objectives During the operation of the circulating cooling water of the regulating camera, the concentration effect leads to the scaling of the heat exchanger and the cooling water packing, which affects the safe and stable operation of the regulating camera. Methods In order to solve the scaling problem of circulating cooling water, it is necessary to add various scale inhibitors to alleviate the scaling trend. In order to explore the scale inhibition effect of different formulations of scale inhibitors under different working conditions, the scale inhibition performance of PBTCA, HEDP, LQ-8531, TQ low phosphine scale inhibitors and TQ scale inhibitors was evaluated by limiting carbonate deposition method and limiting carbonate concentration ratio method. The effects of temperature, hardness and dosage on the scale inhibition performance of five kinds of scale inhibitors were studied by using self-made scale inhibition test bench. Results The comprehensive evaluation shows that LQ-8531 has the best scale inhibition performance among the five scale inhibitors, and it can maintain high scale inhibition efficiency in high hardness and high temperature environment. Conclusions The research results provide a valuable reference for the selection of scale inhibitor for circulating cooling water of heat exchange equipment in high temperature and high salt area.

Objectives As one of the core technologies of battery management system (BMS), the research on lithium-ion battery model plays a vital role in optimizing battery performance and extending battery life. In order to facilitate the selection of appropriate models in different scenarios, different types of modeling methods for lithium-ion batteries are systematically summarized and compared. Methods Firstly, the working principle of lithium-ion battery is explained, and the importance of accurate modeling is emphasized. Then, the current widely used lithium-ion battery models is comprehensively summarized according to different application scenarios, and a series of novel machine learning battery models are analyzed and discussed. Finally, the challenges of lithium-ion battery modelling techniques and future research trends are discussed. Conclusions It is found that traditional battery models all have certain limitations, while data-driven models often have more unique advantages in dealing with complex systems. Future research needs to find a balance between model complexity and usability. The research results provide a reference for application and future development of lithium-ion batteries in energy storage systems.