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Application and Challenges of Large Models in Power Industry AI Introduction
Abstract：ObjectivesWith 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.MethodsFirstly, 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.ConclusionsThe 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.Keywords：power industry;new power system;artificial intelligence (AI);large language model;knowledge services;assisted decision-making;fault diagnosis;prediction568|6366|0<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29Prospects for Future Energy Technologies AI Introduction
Abstract：ObjectivesDeveloping 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.MethodsBased 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.ResultsDriven 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.ConclusionsThe findings provide a scientific basis for promoting green and low-carbon energy transition, offering important references for researchers and policymakers in the energy field.Keywords：new-type energy system;energy transition;carbon neutrality;low-carbon;power generation technology;renewable energy;bioenergy;hydrogen energy;synthetic hydrocarbon fuels;controlled nuclear fusion320|1594|12<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29Resilience Enhancement Strategies for Power Systems Under Extreme High-Temperature Weather AI Introduction
Abstract：ObjectivesTo 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.MethodsFirst, 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.ResultsTaking 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%.ConclusionsBy 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.Keywords：extreme high-temperature weather;power system resilience;source-load output simulation;chronological operation simulation;power resource planning;hierarchical load shedding;value of lost load;resilience enhancement strategy346|1723|3<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29Optimal Operation of Residential Microgrids Based on a Robust Model Using Information Gap Decision Theory AI Introduction
Abstract：ObjectivesResidential microgrids are an important part of the new-type power system, and their optimal operation is crucial for improving the utilization efficiency of renewable energy and reducing system operating costs. However, the strong uncertainties in the output of distributed power sources such as photovoltaics, as well as in loads, pose significant challenges to the optimal operation of residential microgrids. Therefore, a robust optimal operation model based on information gap decision theory (IGDT) is proposed.MethodsFirstly, by fully considering the influence of time-of-use electricity prices, an optimal operation model for grid-connected residential microgrids is established with the objective of minimizing user costs. Then, on the basis of this model, a robust optimal operation model based on IGDT is established by comprehensively considering the influence of uncertain factors such as photovoltaic output, load, and electric vehicle charging on the optimal operation of residential microgrids. Finally, a hierarchical sequencing method combined with the entropy weight method is used to solve the model.ResultsThrough Monte Carlo simulations, the effectiveness and reliability of the IGDT-based robust model in addressing uncertainties such as photovoltaic output and load are validated.ConclusionsThis model provides a feasible solution for the optimal operation of residential microgrids. It is of great significance for improving their economic performance and the on-site consumption rate of distributed renewable energy.Keywords：renewable energy;residential microgrid;uncertainty;information gap decision theory (IGDT);hierarchical sequencing method;entropy weight method;Monte Carlo simulation;electric vehicle174|194|1<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29- Abstract：ObjectivesThe grid-connected operation of large-scale wind turbines increases the risks of sub-synchronous oscillation (SSO) in power systems, to address these issues, this study conducts an in-depth analysis of the effect of hydrogen production systems on the characteristics of SSO of wind power system.MethodsFirstly, the models of doubly fed induction generator system, series-compensated transmission system, and hydrogen production system are established. Building on this, based on the internal thermodynamic and electrochemical dynamic characteristics of the electrolyzer in the hydrogen production system, an electromagnetic transient (EMT) model of the hydrogen production device is established on the PSCAD/EMTDC simulation platform. Finally, it is integrated into a doubly fed induction generator with series compensation for external transmission to conduct time-domain simulation analysis.ResultsThe hydrogen production system participates in the SSO of the system under specific control parameters. The integration of the hydrogen production system into the long-distance transmission system of wind power—as a clean energy solution—can mitigate SSO in the grid-connected system to a certain extent, thereby offering a new approach to addressing SSO issues.ConclusionsThe integration of the hydrogen production system improves the damping of the wind-hydrogen coupled grid-connected system, leading to improved system stability.Keywords：new energy;hydrogen energy;wind turbines;hydrogen production system;sub-synchronous oscillation (SSO);doubly fed induction generator;series compensation;eigenvalue analysis;time-domain simulation109|244|0<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29
- Abstract：ObjectivesDue 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.MethodsThis 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.ResultsThe 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.ConclusionsThe 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.Keywords：hydrogen fuel cell;proton exchange membrane fuel cell (PEMFC);metal bipolar plate;hydroforming;thinning rate;stress distribution221|654|0<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29
New Power System
High Voltage Crossing Strategy of Wind Farm Considering Active and Reactive Coordinated Recovery AI Introduction
Abstract：ObjectivesTo address the serious high voltage ride-through (HVRT) problem faced by wind farms as a result of the large voltage surge at the grid-connected point after a DC fault, a recovery mechanism of “active priority balancing - reactive power dynamic compensation” considering the coordination of protection action time is proposed.MethodsBased on the double closed-loop control function of doubly-fed wind turbine, the dynamic characteristics of the reactive power response speed of the rotor side converter is better than that of the grid side converter are revealed. Under the load shedding mode, a HVRT strategy of wind farm considering coordinated recovery of active and reactive power is proposed.ResultsBy balancing active power and reactive power dynamic compensation, the dual objectives of voltage stabilization and energy balance during faults are achieved. Simulation results show that this strategy can effectively suppress the risk of transient overvoltage and improve the HVRT capability of wind farms.ConclusionsThe proposed strategy effectively solves the contradiction between system power imbalance and safe operation of equipment during HVRT through the active-reactive synergistic regulation mechanism, which provides a new idea for transient voltage stabilization control of power systems containing large-scale wind power.Keywords：wind power;wind power integration;DC fault;load shedding allocation strategy;high voltage ride-through (HVRT);transient overvoltage;cooperative regulation;power imbalance150|408|2<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29Research on Economic Scheduling of Active Distribution Networks With Inclusion of Data Center and Energy Storage AI Introduction
Abstract：ObjectivesTo improve the new energy consumption capacity of the power grid, reduce operational costs for power generation companies, and address issues such as increased network losses and reduced new energy consumption when large-scale renewable energy is integrated into the distribution networks, an economic scheduling strategy for active distribution networks with the inclusion of the coordinated scheduling of data center (DC) and energy storage is proposed.MethodsFirstly, by utilizing the spatiotemporal adjustability of DC and the role of energy storage devices in alleviating the imbalance between grid supply and demand, the coordinated scheduling of DC and energy storage is fully leveraged to achieve peak shaving and valley filling. Secondly, the search performance of the sparrow search algorithm is improved by incorporating Tent Chaos initialization and multi-population competition mechanisms into the algorithm. Then, considering the impact of time-of-use pricing on the demand response mechanism, the model is optimized using an improved sparrow search algorithm under constraints such as energy storage costs and operational costs, aiming to achieve the optimal matching of flexible loads and new energy. Finally, the improved IEEE 30 and IEEE 33 node systems are taken as examples to verify the effectiveness of the proposed strategy.ResultsThe proposed cooperative scheduling strategy can effectively reduce the overall operational costs of the distribution networks and improve the new energy consumption rate.ConclusionsThe strategy can provide some reference value for the related research of economic dispatch of distribution network.Keywords：energy storage devices;data center (DC);distribution network;economic scheduling;improved sparrow search algorithm (ISSA);demand response;new energy consumption201|532|3<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29- Abstract：ObjectivesTo address the operational security issues in the new-type distribution network caused by the high-penetration integration of renewable energy, this paper proposes a coordinated optimization method for intelligent soft open points and multiple types of shared energy storage, by integrating these flexible devices while considering the risk of flexibility deficiency.MethodsFirstly, the flexible regulation mechanism of flexible resources, including intelligent soft open points and multiple types of shared energy storage, is elaborated, and a flexibility deficiency risk indicator based on conditional value at risk is constructed. Secondly, considering flexibility, safety, and economy, a coordinated optimization model for intelligent soft open points and multiple types of shared energy storage in the new-type distribution network is established. Finally, the proposed method is tested on an improved IEEE 33-bus system to verify its effectiveness.ResultsThe proposed coordinated optimization method for intelligent soft open points and multiple types of shared energy storage, considering flexibility deficiency risk, can fully cope with the volatility of renewable energy output and the randomness of load demand, effectively reduce active power loss of the new-type distribution network, and enhance its flexibility, safety, and economy.ConclusionsThe proposed flexibility optimization model provides significant practical reference value for flexible scheduling of new-type distribution systems.Keywords：renewable energy;new-type distribution network;intelligent soft open point;multiple types of shared energy storage;flexibility;conditional value at risk116|541|2<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29
Research on Three-Phase Imbalance in Distribution Networks Considering Coordinated Photovoltaic Phase Switching and Flexible Load AI Introduction
Abstract：ObjectivesConsidering the impact of single-phase distributed photovoltaic integration into distribution networks, this study proposes a mitigation method for three-phase imbalance in low-voltage distribution station area by coordinating flexible load response with photovoltaic regulation.MethodsThe proposed mitigation method comprises a flexible load response mode and a photovoltaic regulation mode. The flexible load response mode has the optimization objectives of minimizing network losses and load regulation costs, while the photovoltaic regulation mode has the objective of minimizing the number of photovoltaic phase-switching operations. The particle swarm optimization algorithm with different encoding strategies is used to solve the two adjustment modes respectively. Finally, the proposed method is validated using a real low-voltage distribution station area in North China.ResultsThe results show that the method reduces the number of photovoltaic phase-switching operations and load response regulation costs. In addition, by analyzing the influence of weighting coefficients on the objective function, a recommended range for these coefficients is provided.ConclusionsThe method effectively addresses three-phase imbalance violations and reduces mitigation costs.Keywords：power system;distributed photovoltaic;flexible load;low-voltage distribution network;three-phase imbalance;commutation switch;three-phase power flow;coordinated optimal operation157|758|2<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29Photovoltaic Output Prediction Based on Improved Long Short-Term Memory Network Using White Shark Optimization Algorithm AI Introduction
Abstract：ObjectivesIn order to ensure the safety, stability, and economic operation of the power system after photovoltaic integration, a photovoltaic power prediction model based on improved long short-term memory (LSTM) network using white shark optimization algorithm is proposed, and uses the white shark optimization algorithm (WSO) to optimize the prediction model parameters.MethodsTo fully consider the environmental variables that affect photovoltaic output power, this study first uses the variational mode decomposition (VMD) method to decompose the environmental factor sequence, in order to reduce the non stationarity of the sequence. Next, the kernel principal component analysis (KPCA) method is used to extract the feature sequences of the main influencing factors and obtainthe optimal meteorological feature sequence. Finally, a LSTM network is used to predict multivariate feature sequences, and WSO is used to optimize the parameters of the LSTM to achieve accurate prediction of photovoltaic output.ResultsCompared with traditional photovoltaic power prediction methods, this model significantly improves the prediction accuracy of photovoltaic power generation.ConclusionsUsing WSO to optimize the number of hidden units, maximum training period, and initial learning rate in LSTM can effectively improve the efficiency and accuracy of optimization, providing a reference for the stable operation of high proportion photovoltaic systems after integration.Keywords：solar energy;photovoltaic power generation;photovoltaic output prediction;kernel principal component analysis;variational mode decomposition;long short-term memory network;white shark optimization algorithm143|203|3<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29Power Coordinated Control Strategy for Grid-Connected DC Microgrid Considering State of Charge AI Introduction
Abstract：ObjectivesExisting research on decentralized coordinated control strategies mainly focuses on power distribution within DC microgrid, with limited consideration given to the operation of grid-connected DC microgrids. To address this, a power coordinated control strategy for grid-connected DC microgrids, considering the state of charge (SOC), is proposed.MethodsThe proposed strategy treats the interconnecting converter as a dispatchable resource. It regulates the output power of the energy storage unit and interconnecting converter based on the DC bus voltage while keeping the bus voltage and SOC of the energy storage unit within allowable limits.ResultsThe proposed strategy can achieve decentralized power coordination between the energy storage unit and the interconnecting converter, as well as SOC balancing among energy storage units. It also prevents overvoltage and overcharge/overdischarge conditions for the energy storage units.ConclusionsThe proposed control strategy ensures reasonable power distribution in grid-connected DC microgrids and guarantees the stable operation of the system.Keywords：DC microgrid;coordinated control strategy;decentralized control;power distribution;energy storage unit;SOC balancing138|1365|2<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29Dynamic Power Decoupling Control of Virtual Synchronous Generator Based on Improved Virtual Impedance AI Introduction
Abstract：ObjectivesWhen a virtual synchronous generator (VSG) is connected to a medium and low voltage distribution network, the line impedance exhibits resistive and inductive characteristics, resulting in strong coupling in the VSG output power. This coupling can cause power oscillations and even deviations in the internal power control, affecting power quality and transmission stability. To address these issues, an improved power decoupling strategy based on virtual capacitance is proposed.MethodsBy simplifying the VSG grid-connected model and conducting small-signal modeling, the power coupling mechanism is analyzed to identify key factors contributing to coupling. Based on this analysis, the concept of virtual capacitance is introduced, and an improved control method utilizing virtual capacitance is proposed. Finally, simulations are conducted on the MATLAB/Simulink platform to verify the effectiveness of the proposed strategy.ResultsThe capacitive characteristics of virtual capacitance not only enhance the system’s operational stability, but also correct the output voltage reference, effectively mitigating oscillations exacerbated by power coupling.ConclusionsThe proposed control strategy reduces the interference caused by active power command changes or load switching on reactive power, suppressing strong power coupling during the dynamic process of the system.Keywords：virtual synchronous generator (VSG);power coupling;virtual capacitance;microgrid;line impedance;virtual impedance;power decoupling;distributed energy resources183|708|3<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29Techno-Economic Analysis of Photovoltaic-Concentrating Solar Power Hybrid System for Thermal Energy Storage of Electricity Curtailment AI Introduction
Abstract：ObjectivesThe 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.MethodsBy 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.ResultsCompared 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.ConclusionsPV-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.Keywords：photovoltaic (PV);concentrating solar power(CSP);energy storage;new power system;electrical heater;electricity curtailment;Pareto optimization;levelized cost of electricity (LCOE)150|501|2<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29
Key Technologies for Large\-Scale Renewable Energy Integration and Operation Control
Research on Power Generation Technologies for Low-Grade Waste Heat in Nuclear Power Plants AI Introduction
Abstract：ObjectivesTo 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.MethodsThe 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.ResultsThe 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.ConclusionsWaste 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.Keywords：nuclear power;waste heat power generation;thermal pollution prevention and control;low-grade waste heat recovery;organic Rankine cycle (ORC);ejector organic flash cycle (EOFC);Kalina cycle (KC);thermo-economic feasibility analysis457|1147|1<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29Study on Numerical Simulation of Dynamic Deposition and Shedding Characteristics of Zhundong Coal Ash AI Introduction
Abstract：ObjectivesPower plant boilers often experience severe fouling and slagging issues on the heating surfaces when burning Zhundong coal. Therefore, the deposition and shedding characteristics of Zhundong coal ash on the heating surface were studied.MethodsA deposited ash erosion model based on particle adhesion energy is established, and numerical simulations are then conducted to examine the dynamic deposition and shedding processes of Zhundong coal ash on a single tube.ResultsThe model incorporating the coupled erosion mechanisms has a deviation of only 3.3% compared to the experimental results, demonstrating high accuracy. Besides, the shedding rate of the deposited ash increases initially but then levels off. However, within the simulated timeframe, only 25% of the deposited ash can be removed. In addition, the accumulation of deposited ash leads to a gradual increase in the heat transfer loss rate of the heating surface, with the average heat transfer loss rate during a 6-hour period being 1.76 times that of a 1-hour period. Furthermore, the adhesion energy of the deposited ash after 6 hours is 6.11 times that after 1 hour, and the corresponding mass flow rate of air at the soot-blowing outlet needs to be increased by 1.85 times to effectively remove all the deposited ash.ConclusionsThese findings enhance the understanding of the dynamic deposition and shedding processes of Zhundong coal ash and offer important research data and theoretical support for optimizing soot-blowing operations in industrial applications.Keywords：coal-fired electricity;Zhundong coal;ash deposition;erosion and shedding;average adhesion energy;numerical simulation;alkali metal;soot blowing153|120|1<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29Multi-Objective Optimization Analysis of Gas-Steam Combined Cycle Power Generation Systems AI Introduction
Abstract：ObjectivesTo improve the efficiency of gas-steam combined cycle power generation units, an optimized design of thermodynamic systems is required. Therefore, a multi-objective optimization modeling method for combined cycle power generation systems, simultaneously considering thermal efficiency, operating cost, and carbon emission is proposed.MethodsThe gas cycle is modeled incorporating blade cooling, while the steam cycle is modeled based on a triple-pressure reheat heat recovery steam generator. The constructed mixed-integer nonlinear mathematical programming model is solved using GAMS software to optimize the generation units, explore optimal design parameters of the combined cycle power generation system including cycle pressure ratio, turbine inlet temperature, and bottom-cycle steam parameters, and analyze trade-off relationships among multiple objectives.ResultsTurbine inlet temperature has the most significant effect on cycle efficiency, total cost, and carbon emissions. When conducting multi-objective optimization that balances combined cycle efficiency, total system cost, and carbon emissions, the combined cycle efficiency should be 65%.ConclusionsThe proposed method can more effectively optimize the thermodynamic parameters of large-scale generation units to achieve energy saving, emission reduction, and improved energy efficiency.Keywords：combined cycle generator sets;multi-objective optimization;carbon emission;thermodynamic calculation;waste heat boiler;cycle pressure ratio;simulated analysis188|595|1<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29Integrated Modeling Study of Desulfurization in Circulating Fluidized Bed Boilers Under Wide Load Conditions AI Introduction
Abstract：ObjectivesWith the proposal of the “carbon peaking and carbon neutrality” goal, the proportion of clean energy generation in China continues to increase. In order to promote the consumption of new energy generation, thermal power units frequently adopt the wide load peak shaving operation mode. Therefore, it is necessary to establish a SO2 emission concentration model for coal-fired circulating fluidized bed (CFB) boilers that can adapt to wide load operation conditions to achieve ultra-low SO2 emission of CFB units.MethodsThrough in-depth research on the dynamic characteristics of SO2 generation and removal, a dynamic lumped parameter model of SO2 emission concentration at the furnace outlet is established using mechanism analysis method combined with mass conservation equation. A comprehensive dynamic model for the entire desulfurization process from coal supply and air supply to SO2 emission at the outlet of the desulfurization tower is established by reasonably simplifying the input of the model. The simulation verification is carried out by using the steady-state and dynamic operating condition data segments from the actual measurement data of CFB boilers.ResultsThe established dynamic model can predict the dynamic trend of net flue gas emissions approximately 240 seconds in advance.ConclusionsThe model can reflect the dynamic characteristics of the desulfurization process in the furnace, and has certain applicability under wide load conditions.Keywords：coal-fired power generation;thermal power unit;circulating fluidized bed (CFB);SO2 emission characteristics;dynamic modeling;wide load integration;pollutant emission168|668|0<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29
Power Generation and Environmental Protection
