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[Objective] Driven by the carbon peaking and carbon neutrality goals, coal-fired units are transitioning from baseload to flexible regulation power sources, resulting in prolonged low-load operation and a significant increase in net standard coal consumption. Scientifically allocating loads among multiple units to minimize plant-level energy consumption has become a critical issue for the thermal power industry. [Methods] This study takes six units of three types(1 000, 600 and 300 MW) from a virtual power plant as the research objects, a data-driven plant-level load optimization strategy is proposed. Grey relational analysis is employed to screen key parameters affecting the net standard coal consumption rate, and an XGBoost-based coal consumption characteristic prediction model is constructed. With the objective of minimizing the plant-level net standard coal consumption rate, an adaptive parameter differential evolution algorithm based on historical success is introduced to dynamically solve the optimal load dispatch scheme satisfying grid load commands. [Results] The results indicate that the relative error of the proposed coal consumption rate prediction model fluctuates within 0.05%. The plant-level load allocation optimization strategy proposed in this paper achieves minimized overall energy consumption under varying total load demands. The coal-saving potential exhibits a trend of first increasing and then decreasing with the total load. When the plant-level load reaches 2 600 MW, the net standard coal consumption rate under the optimized load dispatch is only 312.29 g/(k W·h). Compared to conventional dispatch modes, it demonstrates a maximum coal-saving potential of 1.92 g/(k W·h), effectively reducing both plant-wide fuel consumption and carbon emissions. [Conclusion] The proposed data-driven plant-level load allocation strategy proposed herein offers a low-cost, high-efficiency technical pathway for thermal power plants to unlock energysaving potential under flexible operating conditions. This provides support for establishing a new power system that is clean, low-carbon, safe, and efficient.
[Objective]To address the high energy consumption and poor economic performance of carbon capture systems in coal-fired power plants, this study aims to investigate the effects of the rich solvent recycle process on the absorption rate and regeneration energy consumption of the system, and to explore the technical and economic feasibility of process optimization. [Methods] A carbon capture system model based on MEA solution was established using Aspen Plus software. The effects of different return positions of the rich solvent(stages 15 to 19 of the absorber) and various recycle flow rates(100-190 kg/h) on the absorption rate and regeneration energy consumption were simulated and analyzed, and the results were compared with the original system. [Results] The results show that the closer the return position of the rich solvent is to the bottom of the absorber, the higher the absorption rate and the lower the regeneration energy consumption. When the rich solvent is returned near the bottom(e.g., stages 18 and 19), the absorption rate increases by up to 0.049 5% and the regeneration energy decreases by up to 0.002 69 MJ/kg compared to the original system. Increasing the recycle flow rate under suitable conditions further enhances system performance.[Conclusion]Implementing the rich solvent recycle process near the bottom of the absorber and appropriately increasing the circulation flow rate can effectively improve the absorption efficiency and reduce regeneration energy consumption, demonstrating promising potential for engineering applications.
[Objective] Driven by the carbon peaking and carbon neutrality target, the low-carbon transformation of the energy and power industry urgently needs a unified and standardized carbon emission and carbon footprint accounting system. The research on the carbon footprint of the power generation side and the transmission and distribution side at the provincial level needs more detailed case support. [Methods] Taking Anhui as a typical case, this paper systematically reviews the current research on the carbon footprint of six types of power generation—coal, gas, hydro, wind, photovoltaic, and biomass—as well as the transmission and distribution process. It delves into the characteristics of carbon emissions at different stages and key influencing factors, and constructs methods for calculating carbon emissions and carbon footprint factors over the entire life cycle(equipment acquisition, engineering construction, operation and maintenance, decommissioning and disposal). [Results] The results indicate that the carbon footprint factors for non-fossil energy generation are significantly lower than those for fossil energy, with hydropower(0.014 3 kgCO_2e/(kW·h)) and wind power(0.033 6 kgCO_2e/(kW·h)) having the lowest carbon footprint factors. The distribution of carbon emissions across different stages varies significantly by generation type, with coal power's operational phase accounting for more than 80% of emissions, while photovoltaic power is concentrated in the equipment acquisition phase(with the silicon material stage accounting for over 80%). The Anhui thermal power plant case shows that its full life cycle carbon footprint factor is 0.811 8 kgCO_2e/(kW·h), lower than the national coal power average in 2023, with the operation and maintenance phase accounting for as much as 98.30% of emissions, and fuel transport mainly by rail, significantly reducing emissions from the transportation phase. The case of Anhui's transmission and distribution carbon footprint shows that its full life cycle carbon footprint factor(including line losses) is 0.024 9 kgCO_2e/(kW·h). The structure of carbon emissions exhibits significant 'line loss sensitivity': without considering line losses, the equipment acquisition and construction phases are dominant; however, when line losses are included, the proportion of the operation and maintenance phase surges to over 84%, indicating that reducing line losses is the core path for the lowcarbon transformation of the transmission and distribution system. [Conclusion] This study provides theoretical support and methodological reference for precise carbon footprint calculation in the power system and the coordinated development of the electricity and carbon markets.
[Objective]Flue gas flow measurement is a critical component in the online monitoring of CO2 emissions, and its accuracy directly determines the reliability of carbon emission accounting. This study aims to experimentally investigate the measurement deviation characteristics of the multi-point averaging method and the ultrasonic transit-time method under different flow field conditions, providing technical support for optimizing flow measurement techniques in carbon emission online monitoring systems.[Methods] A modular flue duct model was used to simulate different lengths of straight pipe sections. Experiments were conducted to analyze the measurement deviation characteristics of the multipoint method and the ultrasonic method under various flow field distributions, offering technical support for optimizing flow measurement techniques in online carbon emission monitoring. The experiment was conducted in the return-flow wind tunnel with a 4 m × 3 m × 24 m test section, where a flue model was placed. The flue model had a square crosssection of 50 cm × 50 cm. Three hot-wire flowmeters, designed as insertion-type, were installed on a 50 cm × 50 cm × 50 cm module. Additionally, two pairs of ultrasonic flowmeters, arranged in a crossed configuration, were mounted on a 50 cm × 50 cm × 100 cm module.[Results] For the multi-point averaging method, the relative deviation showed weak correlation with average flow velocity and flow field distribution, primarily depending on the number of measurement points. Under the several flow field distributions involved in the experiment, the relative deviation for 4-point measurements ranged from 5% to 10%. For 6-point measurements, the deviation was generally controllable within 4%, and for 15-point measurements, it was generally controllable within 2%. For the ultrasonic transit-time method, the measurement deviations of two acoustic path configurations were studied. The two paths were arranged in a crossed layout: for Path 1, the upstream transducer was installed on the inner arc sidewall after the bend of the duct model, and the downstream transducer on the outer arc sidewall; for Path 2, the upstream transducer was installed on the outer arc sidewall after the bend, and the downstream transducer on the inner arc sidewall. Analyzing the measurement deviations of the two paths separately showed that under short straight pipe conditions, Path 1 measurements were lower than the true value, with the relative deviation increasing as the average flow velocity increased. Path 2 measurements were higher than the true value, with the relative deviation decreasing as the average flow velocity increased. When the average flow velocity exceeded 5.5 m/s, the measurement deviation of Path 2 was smaller. Considering the actual flue gas velocity in thermal power units, the path type corresponding to Path 2 is more suitable for application in flue gas flow monitoring. [Conclusion]The results of this study provide an experimental basis for the design and optimization of flow measurement techniques in CO2 emission online monitoring systems.
[Objective] High-precision CO2 emission data is served as a crucial foundation for the effective operation of carbon markets and international recognition. Carbon emission reduction in coal-fired power units is effectively guided by obtaining high-precision CO2 emission accounting methods and data. [Methods] Based on over 3 000 pieces of historical data from the 600 MW coal-fired power unit, the comparative analysis of the carbon emission accounting results and relative deviations of the emission factor method, carbon balance method, and online monitoring method is conducted under different time scales, and the impact of carbon oxidation rate on carbon emissions is discussed under different time scales in this paper. [Results] The deviations of carbon emission accounting results between the emission factor method and the carbon balance method, and between the emission factor method and the online monitoring method are 0.95% and 21.14% respectively, indicating a relatively large deviation in CO2 emission accounting results between the accounting method and the online monitoring method. Under the same time scale, the optimal carbon emission accounting results with the smallest relative uncertainty is provided by carbon balance method, which is 1.40% at the daily scale and 1.70% at the hourly scale, both lower than the 8% requirement of Tier 3 in IPCC. The accuracy and reliability of data is improved by reducing the time scale. The scientific regulation of CO2 emissions is more conducive to accounting based on daily-scale data. The accuracy of carbon emission accounting results is greatly affected by the time scale minor change. The average daily-scale carbon oxidation rate is 93.6%, which is 5.4 percentage points lower than the recommended value of 99%. [Conclusion] The CO2 emission accounting results are significant deviations under different accounting methods. The feasible high-precision CO2 emission accounting method is advanced based on daily-scale monitoring data, which the adverse impacts of factors such as methodology, timeliness, and carbon oxidation rate is effectively reduced under current technical conditions.
[Objective] The plant-level electric load distribution mode of coal-fired power plant affects its economy. In order to reveal the difference of energy efficiency characteristics of multiple ultra-supercritical units in a power plant and the influence of electric load distribution on the economy of the whole plant, the optimal electric load distribution strategy is proposed to realize the energy saving and efficiency increase of the whole plant operation. [Methods] In this paper, the 2 × 1 000 MW ultra-supercritical single-reheat unit in the first phase and the 2 × 1 000 MW ultra-supercritical doublereheat unit in the second phase of a power plant are taken as the object. Based on the Ebsilon software platform, the thermal system models of the first and second phase units are established. The variation of coal consumption and heat consumption rate of the first and second phase units is simulated under the condition of pure condensation with 5 % step size and 30%THA ～ 100%THA, a total of 15 load points. [Results] The results show that when the electric load is between 300 MW and 1 000 MW, the coal consumption rate of the first stage unit is 277.1 ～ 310.9 g/(kW·h), and the maximum difference is 33.8 g/(kW·h). The coal consumption rate of the second phase unit is 266.8 ～ 297.6 g/(kW·h), and the maximum difference is 30.8 g/(kW·h). At low load, the influence of unit electric load change on coal consumption rate is more significant. The coal consumption rate of the first-stage unit decreases more under low load; under high load, the economy of the second-stage unit is better. Under the total load of 2 200 MW, the heat consumption rate of the first-stage multi-load scheme is 30.26 kJ/(kW·h) lower than that of the second-stage. [Conclusion] The load distribution of the unit needs to be dynamically optimized. When the total load is low, it is preferentially borne by the first-stage unit. As the total load increases, the load ratio of the second-stage unit needs to be gradually increased. The high-load period is dominated by the second-stage unit, which can significantly reduce the coal consumption of the whole plant.
[Objective] The heat pipe heat exchanger has the effect of saving energy and reducing consumption in coalfired power plants, but how to obtain the optimal efficiency with the least manufacturing materials is an engineering application problem. At present, for the structural optimization of heat pipe heat exchanger, it is necessary to break through the problems of no basis for algorithm selection, dependence on experience for parameter setting and difficulty in landing theoretical solutions, find out the parameter sensitivity law in the optimization process and establish a new perspective of "theory-engineering" evaluation. [Methods] In this paper, the gas-gas heat pipe heat exchanger selected for the air preheater of a 90 t/h CFB boiler is taken as the research object, and a multi-constraint optimization model with the goal of reducing the metal consumption is constructed. Three algorithms, constrained genetic algorithm(CGA), particle swarm optimization(PSO) and linear population size reduction success-history based adaptive differential evolution(L-SHADE), are selected. The system comparison is carried out under the same conditions. The coupling effects of population size, iteration times and randomness on performance are quantitatively analyzed innovatively, and the post-evaluation mechanism of ' rounding the theoretical optimal solution to the engineering standard specification ' is first proposed to simulate the real manufacturing constraints. [Results] Studies have shown that all three algorithms have optimal parameter intervals. Although CGA algorithm can effectively deal with constraints, its convergence speed is not as fast as PSO algorithm and L-SHADE algorithm. PSO has the highest computational efficiency under the premise of ensuring the quality of the solution. After rounding the theoretical continuous solution according to the manufacturing standard, the metal consumptions calculated by CGA, PSO and L-SHADE algorithms are 0.592 m3, 0.586 m3 and 0.588 m3, respectively, which are 24.30%, 25.06% and 24.81% lower than the initial design. Although the performance of the algorithm in the theoretical space is different, the three final schemes tend to be consistent after the engineering specifications are rounded, which reveals the performance convergence between the ideal model and the actual constraints. [Conclusion] Although the final engineering schemes are similar, PSO has become the first choice due to its excellent efficiency and stability. The method described in this paper significantly improves the engineering applicability of the optimization design.