System Of Thermal Power Plant Research Articles

Modeling and dynamic characteristics analysis of concentrating-receiver-heat exchanger system of sCO2 solar thermal power plant

In a supercritical carbon dioxide (sCO2) solar thermal power plant system using solid particle as the heat absorption and transfer medium, the concentrating-receiver-heat exchange coupled system composed of a heliostat field, solar particle receiver, and particle/sCO2 heat exchanger is one of the most important subsystems in the power plant system, and its dynamic characteristics directly affect the performance of the whole system. In this paper, taking the 200kWe sCO2 solar thermal power plant as the research objective, firstly, a discretized lumped parameter model of the coupled system was established to study the dynamic performance and operational characteristics based on the TRNSYS platform, and the following characteristics of some key parameters such as particle and sCO2 outlet temperature to the DNI variation were deeply studied without taking any control measures. The results indicate that the particle and sCO2 outlet temperature are positively correlated with the DNI variation and are highly sensitive, and the efficiency of the particle receiver is basically maintained at around 0.7 whether it is sunny or cloudy; Secondly, in order to achieve the rated outlet design parameters, the dynamic characteristics on June 13 and 15, 2023 with a PID control strategy were also studied. The results show that the particle outlet temperature can be maintained at 800 °C by controlling the particle inlet mass flowrate in the particle receiver, while the sCO2 outlet temperature can be guaranteed at 550 °C by adjusting the sCO2 inlet mass flowrate in the heat exchanger with the condition of mild DNI variation. Besides, it is also found that, the PID control is unlikely to be effective under severe DNI changes. Therefore, the addition of buffer particle tanks is essential to compensate for the intermittency of the DNI in the system design process.

IG-ENT:A innovative ensemble approach for the flow prediction of main steam system in thermal power plant

The main steam flow is a crucial indicator for monitoring and controlling industrial operations, directly affecting the accuracy of calculations for key metrics such as heat consumption rate and coal consumption rate. Traditionally, main steam flow monitoring relies on the Flügel formula for calculations; however, the predictive accuracy of this method is limited. Alternatively, installing traffic monitoring devices increases throttling losses. To address these challenges, we propose a data-driven integrated framework and establish an innovative ensemble model. In the data processing phase, we employ the mRMR method for feature selection. Furthermore, we use genetic algorithms to optimize the ensemble model. To enhance predictive performance, we propose ensemble models based on quantiles. Finally, we apply various approaches to validate the model's performance. Comparative results show that, compared to baseline models, integrated models, and baseline models integrated with quantile intervals, our model demonstrates superior predictive performance.

Mathematical modeling and transient analysis of boiler system in thermal power plant

Mathematical modeling and transient analysis of boiler system in thermal power plant

Numerical study on the influence of plugging rate on the performance of adjustable steam ejector

During the operation of thermal power plant systems, severe challenges to the quality and safety of the heat supply arise owing to the effects of peak regulation. Steam ejectors, widely employed in the district heating sector, often exhibit poorer performance under off-design conditions as a result of their fixed structure. This study analyzes the effects of varying plugging rate, primary pressure, and induced pressure on the performance of adjustable steam ejectors. As the plugging rate increases, the mass flow rate of the primary fluid decrease, potentially falling below 32%, and that of the induced fluid initially experiences a minor increase, followed by a rapid decrease. Concurrently, the critical back pressure also decreases with an increase in the plugging rate, potentially falling by up to 65%. By contrast, the entrainment ratio surges with increasing plugging rate, with the maximum increase reaching up to 4.75 times. Under the same primary steam pressure and induced steam pressure, there exists an optimal entrainment ratio. Furthermore, both the critical back pressure and the optimal entrainment ratio increase as the primary steam pressure and induced steam pressure increase.

Using Risk Points in Risk Management System of Thermal Power Plants

Using Risk Points in Risk Management System of Thermal Power Plants

Fuzzy fault tree analysis method of denitration system in thermal power plant

Fuzzy fault tree analysis method of denitration system in thermal power plant

Artificial Intelligent Based Energy Saving System in Thermal Power Plant

Artificial Intelligent Based Energy Saving System in Thermal Power Plant

Performance modelling and availability analysis of the boiler furnace system in thermal power plant

Performance modelling and availability analysis of the boiler furnace system in thermal power plant

Fuzzy fault tree analysis method of denitration system in thermal power plants

Fuzzy fault tree analysis method of denitration system in thermal power plants

Artificial Intelligent Based Energy Saving System in Thermal Power Plant

Artificial Intelligent Based Energy Saving System in Thermal Power Plant

Digital twin modeling and operation optimization of the steam turbine system of thermal power plants

The increasing deployment of renewable energy sources necessitates peak regulation services from thermal power plants, impacting their energy efficiency. Central to these plants, the steam turbine system significantly influences their operational efficiency. A digital twin model of this system was developed, integrating mechanism-driven and data-driven modeling methods. The neural network data-driven approach was specifically utilized for parameters such as feedwater pump speed and steam flow rate to the pump turbine. Other parameters were modeled with mechanism data hybrid driven modeling method. This model computes vital metrics such as low-pressure turbine exhaust steam enthalpy, work done and heat absorption per unit mass of steam, system efficiency, feedwater mass flow rate, and water-coal ratio—key for evaluating and enhancing the system's energy efficiency. An investigation into a reference case showed a decline in efficiency below design levels due to aging. By optimizing the live steam pressure and the cold-end system, relative improvements in energy efficiency of 0.35 % and 0.14 %, respectively, were achievable.

Performance Assessment of Single Flash PLTP LMB 1 After First Year Inspection Using ASME PTC 46:1996 Method

PLTP LMB 1 performs a First-Year Inspection after operating for one year to inspect and repair equipment after the operation. After these activities, a performance test needs to be carried out to determine the effectiveness and success of these activities. ASME PTC 46: 1996 is a widely-used standard for analyzing the performance test of thermal power plant systems, which includes plant heat rate, power output, specific mass consumption, and more. The purpose of the testing is to evaluate the results of system improvements. Calculation and comparative analysis are performed to determine the system's condition during warranty, commissioning, and testing, as well as after one year of operation and post-first year inspection. After testing, the thermal performance of PLTP LMB 1 was obtained as follows: The corrected net plant heat rate (NPRH) was 18923 kJ/kWh, the corrected net power output was 56,417 MW, and the net specific mass consumption (NSMC) was 6,881 tons/MW. Based on the data guarantee, the thermal performance value of PLTP LMB1 is still below the required value, so it can be concluded that the thermal performance value of PLTP LMB1 is in good condition. The isentropic turbine efficiency value decreased by 2.1% from its design value of 85% to 82.9%, which was caused by suboptimal heat energy absorption by the turbine above the design pressure of 5 bar.

Energy Optimization Model of Multi Energy Interaction in Thermal Power Plants with Wind Power, Photovoltaic, Hydrogen Production and Hydrogen Fuel Cell System

It is proposed that an energy optimization model of multi energy interaction in thermal power plants with wind power, photovoltaic and hydrogen production and hydrogen fuel cell system (HPHFCS). Considering that the wind power, photovoltaic and HPHFCS are connected to the auxiliary power system of thermal power plant, according to the output characteristics of the multiple power sources and auxiliary power load, an energy optimization model of multi energy interaction at the auxiliary power is proposed and analysed. This paper quantitatively analyses the role of wind power, photovoltaic and HPHFCS connected to the auxiliary power system of thermal power plants in promoting the local consumption of renewable energy sources and reducing the power consumption rate of thermal power plants. Numerical cases and simulation results validate the correctness and effectiveness of the energy optimization model in this paper.

Predictive control of SCR denitrification system in thermal power plants based on GA‐BP and PSO

AbstractAiming at the characteristics of strong non‐linearity and large inertia in the reaction process of a selective catalytic reduction (SCR) denitrification system, a predictive control algorithm based on a back propagation neural network optimized by genetic algorithm (GA‐BP) and particle swarm optimization (PSO) is proposed. First, the prediction model of a SCR denitrification system is established by GA‐BP. Second, output feedback and bias correction are used to reduce the prediction error. Third, the optimal inlet ammonia concentration is obtained by PSO. At the same time, in order to solve the problems of high dimension, large noise, and strong coupling in the original data of the SCR system in the process of establishing the prediction model, the least absolute shrinkage selection operator (LASSO) algorithm and the local outlier factor (LOF) detection algorithm are used to screen important variables and samples in the original data set of the SCR system to remove redundant variables and outliers. Finally, the simulation results show that the prediction model has good prediction accuracy and that the proposed predictive control method can achieve accurate control of ammonia injection concentration. This method improves the denitrification efficiency and reduces the NOx emission concentration, which can provide good guidance for on‐site production.

Research on optimal scheduling of thermal power system in thermal power plant based on deep reinforcement learning

Optimal dispatch is one of the key technologies to realize the efficient and economical operation of the thermal power system in thermal power plants. In order to reduce the energy consumption of thermal power system in thermal power plants, ensure the optimal dispatching effect and improve the efficiency of optimal dispatching, this paper introduces deep reinforcement learning to design a new optimal dispatching method for thermal power system in thermal power plants. The thermal power system structure of thermal power plant is analyzed, and the models of boiler, steam turbine and temperature and pressure reducer are established. The optimal scheduling problem of steam turbine and boiler thermal system is studied. By setting the objective function and determining the constraint function, the relevant optimal scheduling model is constructed. The SAC algorithm in deep reinforcement learning is used to solve the model to achieve the important goal of optimal scheduling. The experimental results show that the total fuel consumption of the proposed method is small, and the proposed method has a better optimal scheduling effect of thermal power system in thermal power plants, and can effectively improve the optimal scheduling efficiency.

Simulation and economic analysis of the high-temperature heat storage system of thermal power plants oriented to the smart grid

With the continuous increase of the grid-connected proportion of intermittent renewable energy, in order to ensure the reliability of smart grid operation, it is urgent to improve the operational flexibility of thermal power plants. Electric heat storage technology has broad prospects in terms of in-depth peak shaving of power grids, improving new energy utilization rates and improving the environment. It is an important means to promote electric energy substitution. In this study, the economics of technical application scenarios are compared and analyzed, the principle of solid heat storage technology is discussed, and its application in heating fields such as industrial steam, district heating, and deep peak regulation of congeneration units is expounded. The results indicate that in the scenario where the peak shaving subsidy and the heat storage duration are the same, as the unit output increases, the investment recovery period increases. Moreover, the results also indicate that in the 0.3 yuan/kW power market peaking subsidy scenario, only when the unit output is 0 and the heat storage time is greater than 8 h, the investment can be recovered in 5 years, while in the 0.7 yuan/kW power market peaking subsidy scenario, except for the scenario where the unit output is 40% and the heat storage time is 7 h, the investment cannot be recovered; in other scenarios, the investment can be recovered within 5 years.

Performance optimisation of the feed water system of thermal power plant using stochastic Petri nets with comparative analysis using PSO

Performance optimisation of the feed water system of thermal power plant using stochastic Petri nets with comparative analysis using PSO

Thermo-economic analysis of the integrated system of thermal power plant and liquid air energy storage

In the context of the rapid development of renewable energy, load regulation of the power grid has become a vital issue, and many researches on load regulation by thermal power plants (TPP) have been conducted. As a promising large-scale energy storage technology, the liquid air energy storage (LAES) system has significant advantages such as clean, low investment cost, and no geographical restrictions. In this paper, a coupled system combined by TPP and LAES system (TPP-LAES) is proposed, and technical and economic analysis are carried out to obtain the best configuration of the integrated system and verify its economic benefits. Technical analysis results demonstrate that the maximum round trip efficiency (RTE) of the LAES subsystem can reach 93.74 % when the extraction pressure is 242.00 bar. The RTE of the LAES subsystem of the optimal configuration is 58.31 %, at which the extraction pressure is 8.55 bar. The comprehensive efficiency of the optimal TPP-LAES integrated system is 40.86 %, which is about 1 % higher than the standalone LAES system. The energy storage density is 95.80 kWh/m3, and the net output power is 79.30 MW, exhibiting excellent system performance. The economic analysis results reveal that the initial investment cost of the TPP-LAES integrated system is 67.17 % of that of the standalone LAES system, and the levelized cost of storage (LCOS) is 154.30 USD/MWh, 24.63 % lower than that of the standalone LAES system, with outstanding economic benefits.

Application of Fieldbus Technology in Electric Control System of Thermal Power Plant

Application of Fieldbus Technology in Electric Control System of Thermal Power Plant

Brief Discussion on Energy Saving of Thermal Dynamic System in Thermal Power Plant

Brief Discussion on Energy Saving of Thermal Dynamic System in Thermal Power Plant