Latest ArticlesWith the popularization of co-combustion of high-alkali coal in Xinjiang, systematic studies on the difference and mechanism of alkali metal poisoning resistance between honeycomb and plate SCR catalysts remain insufficient. Most existing researches are based on the simulation of single alkali metal salts in the laboratory, whereas actual fly ash constitutes a complex mixture. The significant discrepancy between simulated conditions and real flue gas from high-alkali coal combustion results in limited engineering guidance value of relevant research findings.
In this work, actual fly ash from power plants was adopted, and typical flue gas conditions were simulated in a laboratory fixed-bed reactor. The physical and chemical property changes of fresh and aged honeycomb and plate SCR catalysts were compared. Combined with specific surface area measurement, NH3-TPD analysis of surface acidity, XPS characterization of vanadium valence states and other techniques, the deactivation behaviors of the two catalysts upon alkali metal poisoning were systematically investigated.
Although plate catalysts are more susceptible to sintering deactivation under extreme high-alkali conditions, and bulk diffusion deactivation is difficult to recover via physical or mild chemical regeneration, their denitrification efficiency decays more slowly than that of honeycomb catalysts. At the same alkali loading, the denitrification activity of honeycomb catalysts decreased by 29.5% within 4 800 hours, while that of plate catalysts only dropped by 24.6%. The vanadium valence cycle of honeycomb catalysts is more easily interrupted by alkali metals, which suppresses the “fast SCR pathway” relying on NO→NO2 conversion. Moreover, their Brønsted acid sites are more prone to neutralization by alkali metals.
Therefore, plate catalysts are the preferred choice for long-term denitrification in industrial scenarios involving co-combustion of high-alkali coal.
[Objective] To meet the increasing demand for deep peak -shaving capability of coal -fired power plants(CFPP) under the “dual-carbon” targets, this paper proposes a novel liquid carbon dioxide energy storage system (LCES) integrated with a coal-fired power plant, aiming to improve both operational flexibility and overall energy utilization efficiency.
[Methods] A 1 000 MW ultra-supercritical coal-fired power plant coupled with a liquid CO₂ energy storage system is selected as the research object . Using the EBSILON platform, a comprehensive thermodynamic and economic model is developed. During the charg ing process, compression heat is effectively recovered by utilizing condensate water from coal-fired power plant. During the discharging process, a cascaded heating strategy is adopted, in which CO₂ is preheated using both low-temperature and high-temperature feedwater to improve the turbine inlet condition and increase power output. Various coupling configurations, including different return water locations and feedwater extraction positions, are systematically investigated to determine the optimal integration scheme. In addition, the effects of key parameters, such as high- and low-pressure tank pressures, 2 throttle valve pressure drop, and mass flow rate, on system performance are analyzed in detail.
[Results] The results indicate that, under the optimal c oupling scheme, the proposed system significantly outperforms the stand -alone LCES system. Specifically, the electrical efficiency increases by 11.41%, while the energy density improves by 3.32 kW·h/m³. Meanwhile, the levelized cost of electricity (LCOE) d ecreases from 0.091 dollar/(kW·h) to 0.082 dollar/(kW·h), demonstrating enhanced economic performance and cost competitiveness. Moreover, the load regulation range of the coal -fired unit is expanded to 93.26% ~102.82%, indicating substantially improved operational flexibility and wider load adjustment capability. Among all system components, turbomachinery contributes the largest share to the total investment cost and plays a critical role in determining both thermodynamic and economic performance. Parametric analysis further reveals that the high-/low-pressure tank pressures, throttle valve pressure drop, and mass flow rate have significant and sometimes competing effects on system performance, providing important guidance for system optimization and parameter selection.
[Conclusions] The proposed coupled system enables efficient recovery of compression heat and multi-stage feedwater heating during the discharging process without relying on large-scale external thermal storage devices. By achieving effective thermal integration and cascade utilization of energy, the system significantly enhances the flexibility and efficiency of coal-fired power plants. This study provides valuable insights and practical guidance for the engineering application, system design, and large-scale integration of CO ₂-based energy storage technologies with conventional thermal power systems, contributing to the transition toward low-carbon and flexible energy systems.
[Objective] Inner Mongolia is actively promoting the development of clean energy heating to achieve environmentally friendly and sustainable winter heating goals. However, the uncertainty of wind and solar power output, coupled with fluctuations in heating demand driven by temperature variations, leads to supply -demand mismatches and challenges in power supply reliability and security. These issues have become critical bottlenecks restricting the development of clean energy heating. To enhance the economic viability and feasibility of clean 2 heating, this paper proposes an optimal dispatch method for a new energy heating system that integrates green power direct supply security constraints with the collaborative dynamic planning of supply, usage, and storage.
[Methods] First, the K-means clustering method is employed to consolidate historical data from the heating season in Inner Mongolia into four typical energy supply and demand scenarios, with the optimal number of clusters determined comprehensively using the elbow method and the Davies-Bouldin index. Second, constrained by voltage deviation, time-series power flow simulations are conducted to determine the hourly charging/discharging schemes for energy storage batteries, capacity configurations, and forecasted wind and PV output values under different scenarios. On this basis, leveraging the green power consumption strategy of the clean heating system, a multi-scenario integrated electricity and heat energy system is constructed. Electric boilers are select ed as the core equipment for electro thermal conversion, primarily powered by supporting wind and PV stations, with gas boilers configured as a backup heat source. Finally, using the distribution network simulation results as boundary conditions, an econom ic optimization dispatch model is solved, aiming to minimize operational and environmental costs.
[Results] A case study validates the feasibility and effectiveness of the proposed collaborative heating optimization model, which integrates time -series simu lation with economic dispatch. For a high -proportion new energy system with a penetration rate of 134%, and under the established grid structure and energy flow direction, the model achieves optimized electrochemical energy storage configuration and refine d collaborative operation of electro -thermal equipment. A multi-scenario energy storage configuration scheme and operation strategy are proposed, with storage capacity accounting for 10.88% to 43.52% of the installed capacity of the wind and PV stations. Ultimately, a safe and stable heating strategy based on an unstable power supply is formulated.
[Conclusion] The proposed method can effectively suppress voltage violations, reduce voltage fluctuations, and replace traditional CHP units with a combination of "wind power/photovoltaics, electric heating, and electro-thermal hybrid storage." This significantly enhances renewable energy consumption capacity, voltage stability control, and the system's economic and environmentally friendly operational performance.
Against the backdrop of the low-carbon energy transition, catalytic gasification is a crucial approach for achieving a gentle and efficient conversion of coal, as well as reducing energy consumption and carbon emissions. elucidating the evolution of coal char gasification reactivity and the mechanisms governing its kinetic regulationis ofsignificant scientific andengineeringvalue forthehighly efficientandcleanutilization ofcoal.In this study,anthracite (YN)andbituminouscoal(TJ) wereselectedastheresearchobjects.Tocloselysimulatethe real industrial environment, the coal char was prepared by pyrolysis at a high temperature of 1500 °C in a tube furnace. CaO-loaded coal chars were prepared using direct addition and impregnation methods. Variable kinetic parameters were obtained through isothermal thermogravimetric experiments and the General SurfaceActivation Function Model (GSAFM), while structural evolution was characterized using FSEM and Raman spectroscopy. The results demonstrate that the catalytic effect of the impregnation method is superior to the physical mixing of the direct addition method, it is pointed out that the close contact between CaO and the coal char matrix is a crucial prerequisite for the catalytic effect to take place. GSAFM can well describe the catalytic gasification behaviorofcoalcharintheentireconversionrange.CaOprimarilyenhancescharreactivitybyreducingthemean activation energy; however, the stage-dependent mechanism based on variable kinetics is significantly regulated by the reaction atmosphere. Under an O2 atmosphere, the early reaction stage is dominated by the variable activation energy (EX), while the later stage shifts to being controlled by the variable pre-exponential factor (AX). Under a CO2 atmosphere, AX governs the initial reactivity difference, the decrease in EX plays the primary role in the middle stage, andthe influence of AX strengthensagain in the later stage. Undera H2O atmosphere, the initial AX is higher, and EX continuously decreases throughout the middle and later stages, ensuring the reaction rate of the catalyzed char remains consistently higher. Furthermore, FSEM reveals that CaO significantly intensifies the surface reconstruction of coal char. Raman spectroscopy indicates a decrease in the ID1/IG ratio and an increased degree of graphitization in the catalyzed char; nevertheless, the reaction rate still significantly improves. This suggests that the reaction enhancement primarily originates from the reconstruction of active sites and the alteration of surface reaction pathways. This study clarifies the stage-dependent kinetic mechanism of CaO catalysis,providingatheoreticalbasisforthedesignandmodelingofhighlyefficientcoalgasificationcatalysts.
[Objective] Under the ambitious dual -carbon goals, the new generation of coal -fired power units is undergoing a critical transition towards functioning as fundamental guarantee and system regulation power sources. Consequently, these units are required to frequently operat e at ultra -low loads (e.g., 20%–30% power load ratio conditions) for extended periods to provide deep peak -shaving services. Such severe deviation from the design conditions inevitably leads to operational bottlenecks. It is highly urgent to clarify the en ergy consumption characteristics under low-load conditions and achieve optimized energy savings.
[Methods] Taking a typical 660 MW supercritical coal-fired power unit as the targeted research object, a comprehensive system off-design analysis model was established to simulate the thermal performance across the entire load spectrum. Through this rigorous mathematical model, the decay laws of the extraction steam pressure differential were quantitatively obtained, revealing the underlying thermodynamic mechanism of drainage blockage induced by the insufficient driving force. Furthermore, based on the principle of energy cascade utilization, three targeted drainage retrofit configurations were proposed and systematically evaluated: Scheme A (rerouting the drains of the high-pressure heaters directly to the deaerator); Scheme B (rerouting the drains of the low -pressure heaters to the condenser); and Scheme C (installing variable-frequency low-pressure drain pumps). A comprehensive techno-economic evaluation model was also formulated to assess the life -cycle viability.
[Results] The comparative simulation results effectively demonstrate the varying thermodynamic benefits of the proposed configurations. For the high-pressure regenerative system, Scheme A effectively rebuilds the necessary drainage driving pressure differential, guaranteeing the stable 2 directional flow of the drains. More importantly, it successfully recovers the high -grade thermal energy, subsequently reducing the net coal consumption rate by a subst antial 3.10 g/(kW·h) under the extreme 20% THA (Turbine Heat Acceptance) condition. Regarding the low -pressure regenerative system, although Scheme C introduces a minor amount of mechanical pump work consumption, the exergy analysis reveals that it fundamentally eliminates a significant portion of the irreversible mixing exergy loss at the cold end. Consequently, compared to the conventional bypass method in Scheme B, Scheme C can further decrease the net coal consumption rate by an additional 0.20 g/(kW·h) .
[Conclusion] Comprehensive techno -economic evaluation indicates that the initial investment and operational costs of the active pumping strategy are well justified by the long-term fuel savings. It is concluded that Scheme A and Scheme C serve as the opt imal retrofit configurations for resolving the drainage blockage problems in the high -pressure and low -pressure regenerative systems, respectively. These optimized solutions excellently balance the unit's operational stability with life-cycle thermal economy.
[Objective] To align with the national policy on low-carbon retrofitting of coal-fired power plants and tackle the dual challenges of cow dung pollution and underutilization of this resource in the animal husbandry sector of northern China, this study carried out a test on briquetted cow dung co-combustion in coal-fired power units, to explore a low-cost, easy-to-implement technical solution for both coal power decarbonization and livestock manure resource utilization.
[Methods] This study selected a 350 MW coal-fired power plant in Inner Mongolia as the research subject and conducted briquetted cow dung co-combustion experiments adopting the "single-mill co-firing" 2 technology, with no retrofitting required for the existing equipment. Under co-firing ratios of 0%~20%, this study systematically investigated the effects of co-combustion on the adaptability of the pulverizing system, combustion characteristics of the boiler, pollutant emission characteristics, and performance of the dust removal system.
[Results] The results show that when the co -firing ratio exceeds the range of 10% –15%, the mill current, specific power consumption, and pressure difference across the mill exhibit significant variations, with maximum increments of 3.96 A, 0.64 kWh/t, and 0.59 kPa, respectively. Furthermore, the fineness of pulverized coal decreases, with the two screen residue indices (R200 and R90) registering maximum increments of 1.95% and 6.53%, respectively. After co-firing briquetted cow dung, the boiler maintains stable combustion performance, with only slight fluctuations observed in furnace and flue gas temperatures. The CO concentration remains stable at 60.00 mg/Nm3, the carbon content of ash shows negligible variations, and boiler thermal efficiency undergoes a marginal decline with a maximum reduction rate of 0.20%. This co-firing technology exerts no significant influence on pollutant emissions and the operating parameters of the dust removal system. Under the pure coal combustion condition, the NO x concentration at the SCR inlet is 264.86 mg/Nm 3, and the SO 2 concentration at the desulfurization inlet is 2771.36 mg/Nm3. After co-firing, NOx generation shows a slight increase with a growth rate ranging from 3.94% to 8.19% (equivalent to an absolute increment of 10.45 –20.69 mg/Nm 3); SO 2 emissions experience a minimal reduction and remain essentially stable. Meanwhile, the current and voltage parameters of the electrostatic precipitator stay relatively stable, and the pressure difference across the bag filter section presents no obvious variation, with a maximum increment of only 72.87 Pa.
[Conclusion] The findings demonstrate that utilizing the existing equipment of the unit enables the efficient co-combustion of cow manure, offering a replicable and scalable new technical paradigm for the resource utilization of livestock and poultry manure waste.
Challenges such as adapting to renewable energy power fluctuations and coping with start-stop 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.
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.
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.
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.
An experimental platform for the steam inlet system tailored for the 50 kW-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.
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%.
The proposed IC method demonstrates significantly superior control performance compared to the PID method.
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 multi-point 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.
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.
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.
The multi-point 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.
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.
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