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Experimental study on co-firing of briquetted cow dung in a 350 MW coal-fired power plant
2026-05-28Experimental study on co-firing of briquetted cow dung in a 350 MW coal-fired power plant
2026-05-18Study on the effect of staggered blade twist on the performance of multistage water vapor axial flow compressors
2026-05-18Recognition of flame combustion state in garbage incinerators based on improved ConvNeXt
2026-05-18High-accuracy prediction model for thermophysical properties of CO2-H2O mixture in the low and medium density region for high-temperature applica-tions
2026-04-29Optimization of operation strategy for single-tower double-loop desulfurization system based on improved NSGA-II algorithm
2026-04-29Transient characteristics of supercritical carbon dioxide power system under variable cooling conditions
2026-04-28Multivariate analysis of S-CO2 oxy-fuel power generation system based on response surface method
2026-04-27Research on temperature characteristics of pipeline electrode type molten salt electric heater
2026-04-27Pore-scale simulation of turbulent wake flow in random ceramic foam structures
more..Journal introduction
Founded in: 1972
Headed by: China Huaneng Group Co., Ltd.
Sponsored by: Xi'an Thermal Power Research Institute Co., Ltd., Chinese Society for Electrical Engineering
Periodicity: Monthly
Standard Serial Number:CN 61-1111/TM, ISSN 1002-3364
TEL: (86-029) 82002270
E-mail: rlfdzzs@tpri.com.cn
Minute-level dynamic response-based capacity planning for alkaline-PEM hybrid hydrogen production systems
CAO Chuansheng;JIANG Cong;LI Wei;TANG Chang;HUANG Lei;WEN Chang;[Objective] Challenges such as adapting to renewable energy power fluctuations and coping with startstop mechanisms exist during the operation of electrolyzers. Capacity configuration models based on long time scales struggle to accurately capture these dynamic characteristics, which reduces the accuracy of capacity planning for hydrogen production systems. [Methods] Taking a hybrid hydrogen production system composed of alkaline(ALK) electrolyzers and proton exchange membrane(PEM) electrolyzers as the research object, this paper proposes a minute-level time-scale capacity planning method for hybrid hydrogen production. First, a minute-level electrolyzer start-stop control model is designed to accurately describe the operating states of the two types of electrolyzers. Second, considering ALK electrolyzers' poor adaptability to power fluctuations and long start-stop time, we develop a power allocation strategy that prioritizes the stable operation of ALK electrolyzers. Finally, we conduct multi-objective optimization for the capacity planning problem of the hybrid hydrogen production system, and the Pareto solution set of the model is obtained via the augmented ε-constraint method. [Results] Simulation results show that under the condition of 20 MW installed wind power capacity and 20 MW installed photovoltaic capacity, with a total system investment cost of 25 million yuan, the proposed 1-minute time-scale model increases the average daily hydrogen production by 14.7%, reduces the unit hydrogen production cost by 7.5%, and decreases the renewable energy curtailment rate by 63.6% compared with the traditional 15-minute time-scale model. In addition, with 1-minute scheduling accuracy, the ALK/PEM electrolyzer capacity ratio is gradually optimized as investment increases: when the total investment is below 25 million yuan, the proportion of ALK electrolyzers exceeds 90%; when the total investment exceeds 30 million yuan, the investment proportion of PEM electrolyzers rises to 19.4%. In contrast, for the 15-minute time-scale model, the ALK/PEM capacity ratio reaches 4:1 even when the investment is only 20 million yuan. This prematurely increased proportion of PEM electrolyzers not only deviates from practical engineering conditions but also degrades overall system performance, indicating that coarse time-scale scheduling may lead to capacity mismatch.
Multi-objective intelligent control strategy for SOEC steam inlet system based on deep reinforcement learning
WU Xin;ZHENG Minghui;XIONG Xingyu;MA Zhiyong;ZHANG Ruiyun;[Objective] The stability of the steam inlet system in solid oxide electrolysis cell(SOEC) systems is crucial for enhancing the electrolysis efficiency of the electrolytic stack and prolonging its service life. However, the nonlinear coupling between steam pressure and flow rate imposes high demands on the control strategy. [Methods] An experimental platform for the steam inlet system tailored for the 50 k W-class SOEC system was established to investigate the control of steam flow rate error and pressure fluctuation. Based on the collected operation data of the experimental platform, a double-delay deep deterministic policy gradient agent was trained. A multi-objective intelligent control(IC) strategy based on deep reinforcement learning was proposed, aiming to achieve the control goals of the system output flow error not exceeding 3% and pressure fluctuation not exceeding 1 kPa. [Results] The experimental results show that the maximum error of the output steam flow rate under the IC method is 1.4%, and the maximum pressure fluctuation is ±0.67 kPa. While under the PID control method, the maximum error of the steady-state output flow rate of the system is 3.8%, and the maximum fluctuation of the pressure is ±1.25 kPa. Compared with PID control, the IC method reduces the maximum flow rate error by 63.2% and the pressure fluctuation by 46.4%. [Conclusion] The proposed IC method demonstrates significantly superior control performance compared to the PID method.
Selection and estimation of multi-point feature wind speeds for large-scale wind turbines
SHEN Shanshan;HU Yang;WANG Jiheng;SONG Ziqiu;[Objective] With the continuous development of the wind power industry toward high power and large capacity, the soaring unit capacity and expanding blade radius of large-scale wind turbines have resulted in increasingly complex spatial distributions of the inflow wind field in front of the turbine and significantly enhanced vertical wind shear effects. The conventional method of characterizing wind conditions using single-point wind speed at hub height can no longer fully reflect the wind speed distribution differences and dynamic patterns within the ultra-large rotor swept area, which is prone to causing issues such as wind power prediction deviations and inadequate adaptability of operation control strategies. To address these challenges, this study proposes a multipoint feature wind speed selection and estimation method for large-scale wind turbines, which can accurately capture key wind speed information in the rotor swept area, overcome the limitations of single-point feature wind speed, and provide data support for the optimal operation of wind turbines. [Methods] To achieve the aforementioned research objective, this study adopts a step-by-step technical approach for systematic investigation. Firstly, based on high-precision grid data of the inflow wind field in front of the turbine, a two-stage stepwise feature selection algorithm is proposed, which first carries out preliminary selection with random forest and then implements refined selection via Boruta(RF-Boruta). The Boruta algorithm is employed to conduct significance tests on the feature importance scores output by the random forest model, thereby eliminating redundant and irrelevant wind speed grid points and realizing stable and accurate selection of feature wind speed points within the ultra-large rotor swept area. Secondly, for the selected feature wind speed points, the extended long short-term memory neural network(xLSTM-Mixer) algorithm is introduced, combined with an embedded feature engineering strategy that accounts for input-output delay orders. This strategy fully exploits the temporal correlation and spatial correlation of wind speed sequences, and constructs a unit dynamics-driven ultra-short-term multi-step dynamic estimation model for multi-point feature wind speed points. Finally, to verify the effectiveness and superiority of the proposed method, large eddy simulation(LES) of a 10 MW wind turbine is performed on the SOWFA platform. Meanwhile, 7 typical wind conditions covering the full wind speed range specified in the IEC standards(including complex wind conditions such as shear wind and turbulent wind) are configured for numerical simulation and flow field data collection. The feature selection performance and speed estimation accuracy of the proposed method are comprehensively validated based on the collected high-fidelity data. [Results] The numerical simulation and verification results demonstrate that four representative feature wind speed points, including the hub center, are identified via the RF-Boruta stepwise algorithm. These feature points are arranged at a radius of 50～60 m with an angular interval of 120°, which can effectively cover the key regions of the rotor swept area and fully characterize the spatial distribution features of the inflow wind field. The constructed xLSTM-Mixer model exhibits excellent performance in the multi-point feature wind speed estimation task: the relative error of 80-step-ahead(second-level) prediction for multi-point wind speeds is ≤2.8%, achieving second-level high-precision estimation. Statistical characteristic analysis shows that the Kolmogorov-Smirnov(KS) statistic between the model estimation results and the actual wind speed data is ≤0.2, and the structural similarity index(SSIM) is ≥0.96, indicating a high degree of consistency in both distribution characteristics and structural features between the two datasets. Comparative experiments with mainstream time-series prediction models such as the conventional LSTM and Transformer reveal that the estimation accuracy of the xLSTM-Mixer model is improved by approximately 10%, with distinct advantages in wind speed distribution matching and spatial structure capture capabilities. [Conclusion] The multipoint feature wind speed selection and estimation method proposed in this study effectively breaks through the limitations of conventional single-point feature wind speed, realizing accurate selection and efficient estimation of key wind speed information within the ultra-large rotor swept area. The high-precision multi-point wind speed data provided by this method can reliably support wind power prediction, operation control optimization, and power generation evaluation of wind turbines, helping to enhance the operational stability and energy utilization efficiency of wind turbines. It holds important theoretical significance and engineering application value for promoting the high-quality development of the wind power industry.
Experimental study on the properties of fly ash-steel slag composite phase change heat storage materials
SU Yanan;XIONG Yaxuan;LI Meng;YIN Meichao;HE Miao;WU Yuting;ZHANG Cancan;DING Yulong;In response to the escalating energy crisis and the mounting pressure associated with industrial solid waste disposal, the development of efficient and stable composite phase change heat storage materials is of paramount significance. This study uses solar salt as the phase change medium, with steel slag and fly ash employed as porous skeleton materials. A novel composite phase change heat storage material is synthesized via the cold pressing and hot sintering process. Through systematic optimization of material composition, the optimal mass ratio is determined as fly ash: steel slag: solar salt equal to 25:25:50. The characterization results demonstrate excellent chemical compatibility among the composite components, with no formation of new phases. The composite exhibits superior thermal energy storage performance, with a phase change latent heat of 57.96 J/g, a heat storage density of 291.968 J/g within the temperature range of 100～400 ℃ and a thermal conductivity of 0.952 W/(m·K). Mechanical property testing reveals a high compressive strength of 55.0 MPa. Crucially, the material maintains stable phase change behavior and structural integrity after 3 600 thermal cycles, with a mass loss rate below 0.05%. This research not only facilitates the high-value-added utilization of industrial solid wastes but also provides novel insights into the material design for medium and high-temperature thermal energy storage systems.
Optical path optimization and performance analysis of solar full-spectrum utilization system based on beam splitter
ZHANG Shunqi;FU Kangli;LIU Qingfan;WANG Yingcheng;HAN Wei;WANG Fengnian;ZHANG Kezhen;YAO Mingyu;JING Dengwei;[Objective] Full-spectrum solar energy utilization through spectral splitting offers an effective pathway to improve overall solar energy conversion efficiency by allocating different wavelength bands to suitable energy conversion devices. Linear Fresnel lens-based systems are particularly attractive due to their structural simplicity and scalability. However, the optical efficiency and optical distribution uniformity of such systems are highly sensitive to structural parameters and tracking deviations. The objective of this study is to enhance the optical performance of a linear Fresnel lens-based full-spectrum solar splitting system by optimizing the installation configuration of the photovoltaic(PV) module and by systematically evaluating the influence of incident angle deviations on system performance. [Methods] An optical ray-tracing model of the proposed system was established using TracePro software. The model incorporated the geometric configuration of the linear Fresnel lens, spectral splitting characteristics, PV module positioning, and reflective components. To ensure model reliability, a prototype system was constructed, and experimental measurements were conducted under controlled conditions. The simulation results were validated against experimental data by comparing optical efficiency values. Subsequently, a parametric study was performed to investigate the influence of PV module installation height and tilt angle on the optical distribution uniformity and total optical efficiency. In addition, the effects of lateral and longitudinal incident angle deviations, which represent practical solar tracking errors, were quantitatively analyzed. Key performance indicators included total optical efficiency and optical distribution uniformity on the PV surface. [Results] The comparison between simulation and experimental results showed a relative error within 1%, confirming the accuracy and validity of the established optical model. Parametric optimization revealed that when the PV module installation height was set to 540 mm and the inclination angle was 135°, the system achieved optimal optical performance. Under these conditions, the PV surface attained a maximum optical distribution uniformity of 0.86, and the total optical efficiency reached 80.1%. The sensitivity analysis demonstrated that optical performance is significantly affected by incident angle deviations. When the lateral deviation angle increased from 0° to 3.0°, the total optical efficiency decreased from 80.1% to 67.1%, while the optical distribution uniformity declined from 0.86 to 0.79. The influence of longitudinal deviation was even more pronounced. As the longitudinal deviation angle increased from 0° to 30.0°, the total optical efficiency sharply decreased from 80.1% to 26.4%, and the optical distribution uniformity dropped from 0.86 to 0.74. These results indicate that longitudinal tracking errors have a more severe impact on optical performance than lateral deviations, highlighting the importance of precise solar tracking in practical operation. [Conclusion] This study establishes and experimentally validates an accurate optical model for a linear Fresnel lens-based full-spectrum solar splitting system. The results demonstrate that appropriate configuration of PV installation parameters can significantly enhance optical distribution uniformity and overall optical efficiency. Furthermore, the system exhibits strong sensitivity to incident angle deviations, particularly in the longitudinal direction, which must be carefully controlled in engineering applications. The findings provide theoretical support and quantitative guidance for the structural design, parameter optimization, and operational control of full-spectrum solar splitting systems, contributing to the advancement of high-efficiency solar energy utilization technologies.
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