Operation Of Thermal Power Units Research Articles

Deep Thermal Power Peak Shaving Compensation Considering Operating Costs and Electricity Revenue

Unlike traditional methods of electricity generation such as thermal power, renewable energy sources like wind power exhibit characteristics of randomness and volatility, leading to unstable power generation. With the continuous increase in grid-connected capacity of renewable energy sources like wind power, the demand for grid peak shaving is on the rise, highlighting the issue of inadequate peak shaving capacity in China’s power system. As a primary source for peak shaving in the country, thermal power units incur significant revenue losses when participating in deep peak shaving, and under the current mechanism, they cannot obtain sufficient compensation for deep peak shaving. In response to these issues, the paper proposes a method for compensating thermal power units for deep peak shaving. Firstly, considering the reduced efficiency of thermal power units during low-load operation, a cost model for the operation of thermal power units is established. Secondly, building upon the existing compensated peak shaving baseline, a method for compensating thermal power units for deep peak shaving is introduced. Finally, using a simulation of a local power grid in Liaoning Province as an example, an analysis of the compensation for thermal power peak shaving and overall profits under the existing mechanism and the proposed mechanism is conducted. The results obtained validate the rationality and effectiveness of the method proposed in the paper, offering insights for the development of a compensation mechanism for thermal power deep peak shaving.

Performance enhancement and optimization of primary frequency regulation of coal‐fired units under boundary conditions

AbstractUnder the dual‐carbon goal, due to the long‐term operation of thermal power units under wide load and frequent fluctuating load after heat supply transformation, the dynamic process of subcritical units is modeled, and a frequency modulation optimization technology based on frequency signal accuracy is proposed to ensure the accuracy of the speed signal and improve the qualification rate of primary frequency modulation action. At the same time, the frequency regulation technology of steam engine side control optimization under boundary working conditions is proposed, so as to realize the sensitivity and rapidity of the digital electric hydraulic control system. The coordination optimization technology under boundary working conditions is proposed, and the primary frequency regulation performance of the unit is improved through a series of optimization adjustments. Finally, the engineering application is carried out and the optimization effect is analyzed.

Research on Carbon Emission Accounting and Emission Reduction Route of Thermal Power Plants

The power industry is a major contributor to carbon dioxide (CO2) emission, which accounting for 40% of total domestic carbon emission. Based on the carbon emission accounting method, this paper conducts carbon emission accounting for a number of power generation enterprises, analyses and concludes that the carbon quota allocation is being tightened year by year, proposed countermeasures and emission reduction routes in terms of carbon asset management and carbon reduction technology research. According to the actual situation of a coal-fired power plant in Anhui province, different carbon reduction routes are developed, among which photovoltaic power generation project has the most impressive economic benefits, but carbon capture project has the largest carbon reduction benefits. Under the premise of similar initial investment between the two projects, the carbon reduction brought by the latter is 5 times that of the former. It is suggested to choose the “new energy power generation+carbon capture and utilization” coupling technical route, which can improve the flexible operation of thermal power units and the absorption rate of new energy, and achieve efficient decarbonization. It provides a sustainable development technical route with both economic and environmental benefits for the low-carbon process.

A variable-time day-ahead scheduling strategy considering spectrum analysis

To build a new type of power system, the proportion of renewable energy sources with wind and solar energy as the main body has increased into the power grid. Due to the strong randomness and volatility of renewable energy as a power source, thermal power units need to fluctuate frequently to respond to system power requirements. To ensure the smooth operation of the thermal power units, a variable period day scheduling strategy considering spectrum analysis is proposed. Firstly, different dispatching periods are divided according to the characteristics of system net load fluctuation, secondly, to minimize the total cost, the joint dispatching model of wind power, solar energy, thermal power, and energy storage is established to form a day-ahead dispatching plan with variable time period. Finally, an example shows that this strategy can increase the smooth operation of thermal power units and improve the utilization rate of pumped storage units.

Online Coal Identification Based on One-Dimensional Convolution and Its Industrial Applications

In order to improve the utilization rate of coal generation and reduce carbon emissions from coal-fired boilers, the operation parameters of power plant boilers should be matched with the actual burning coal. Due to the complex and high-risk blending process of multiple coal types, the actual application of coal types inconsistent with expectations may lead to low combustion efficiency of boilers, cause disturbances to the normal operation of thermal power units, increased energy waste and carbon emissions, and even lead to serious explosion accidents. Therefore, the online identification of coal types for thermal power units is of great significance. To obtain the precise type of coal online, in the present work, a data-driven coal identification method is proposed based on convolutional networks that do not necessitate additional hardware detection equipment and are easy to implement. Experimental results indicate that the proposed method exhibits superior performance in comparison to traditional methods, thus ultimately improving the performance of thermal power plant.

Flow and thermal mixing characteristics of a multi-jet bypass flue with main flue baffles

The decrease of inlet flue temperature of denitrification reaction unit is a major challenge that restricts the low load operation of thermal power units. In this study, a new bypass flue heating technology based on multi-pipe and multipoint was proposed. The effect of different baffle openings (RA), directions and position distance (Db) on the mixing characteristics of multiple jets was quantitatively investigated by the experimental system. Combined with the simulation, the changes of the baffle on the multi-jet flow and mixing are revealed. The results show that the mixing temperature increases with increasing RA, but high RA leads to a significant increase in the thermal mixing temperature inhomogeneity (ζT). As RA increases from 0.4 to 0.7, ζT rises from the minimum 0.368 to the maximum 0.559. The mixing temperature and ζT of the baffle in the +x direction are higher than those in the −x direction. When Db increases from 70 mm to 130 mm, the maximum heating effect decreases from 25.97 °C to 18.89 °C, and ζT decreases gradually at y/Dh = 1.25. The findings provide guidance for bypass flue modification and low load denitrification in engineering.

Storage Optimization (r, Q) Strategy under Condition-Based Maintenance of Key Equipment of Coal-Fired Power Units in Carbon Neutrality Era

For the safe, stable, and economic operation of thermal power units in new power systems, the condition-based maintenance mode and storage strategy of key equipment and materials for power generation enterprises were selected. According to the storage linkage demand of condition-based maintenance, a Weibull probability density function was used to calculate spare parts demand, and an intelligent storage optimization model with an availability constraint was established. The application cases of spare parts cost and availability of high-value key equipment and low-value key equipment of coal-fired thermal power units were analyzed, respectively, and the influence of different life spans and the number of covered units on the model were expounded. The results show that the cost of spare parts borne by a single unit is greatly reduced via the optimization of an intelligent inventory (r, Q) strategy on the premise that the availability of units is not less than 99.5%.

Design and Optimization of Coal to Hydrogen System Coupled with Non-Nominal Operation of Thermal Power Unit

In an actual thermal power plant, deep peak shaving will cause thermal power units to run under non-nominal conditions for an extended period, resulting in serious problems such as increased equipment wearing, low equipment utilization efficiency and decreased benefits. To this end, in this work, both the design and optimization method for a coal to hydrogen system which is coupled with the expected non-nominal operation of thermal power units are proposed. Aiming towards maximum profit in the context of thermal power plants, a mathematical optimization model for a coal to hydrogen system based on the multi-period operating conditions of thermal power plants is established. The corresponding optimal design scheme of the coal to hydrogen system is determined using variable operating conditions. The superiority of the integrated system compared with an independent system is explored and the feasibility of the proposed method is verified by using the case study of an actual thermal power plant. The results show that compared with the independent system, the economic benefits of the integrated system can increase by 13.56%, where the sale of hydrogen in the coal to hydrogen system accounts for 60.3% of the total benefit. The main expenditure associated with the system is the purchase cost of feedstock coal, accounting for 91.8%. Since the required power and medium-pressure steam in the coal to hydrogen process are provided by thermal power units, the minimum operating load of the thermal power plant in the integrated system increases from 40% to 60.1%, which significantly improves the utilization efficiency and service life of the generator units. In addition, the proposed integration scheme of the system is simple and controllable, which can contribute to the maintenance of the safe and stable operation of power generation and hydrogen production processes. These results are expected to provide the necessary methodological guidance for the integration and optimization of coal-fired power plants and coal to hydrogen systems.

Fault monitoring and diagnosis of high-pressure heater system based on improved particle swarm optimization and probabilistic neural network

The high-pressure heater system is an important part of the return heat system of thermal power units, which can significantly reduce the boiler fuel consumption and is of great significance to the safe and economic operation of the units. Taking into consideration the issues that the high-pressure heater system data has strong non-linear characteristics and the fault diagnosis accuracy is low, this paper proposes a hybrid model-based fault monitoring and diagnosis method for a high-pressure heater system. Firstly, an improved particle swarm optimization algorithm (IEDPSO) is proposed. A differential evolution operation is introduced to enhance particle diversity, and the inertia weight coefficients and learning factor parameters are improved to optimize the particle position and velocity update process. The problem that PSO tends to fall into local optimum at the late stage of iterative optimization search is solved. Numerical simulation experiments demonstrate that IDEPSO has high convergence speed and accuracy in the optimization process of the test function. Secondly, a fault monitoring and diagnosis method based on a hybrid kernel principal component analysis (KPCA)-IDEPSO-probabilistic neural network (PNN) model is proposed. Non-linear features are extracted using KPCA for fault monitoring. The IDEPSO algorithm is used to iteratively find the best PNN to improve the fault diagnosis accuracy. Simulation experiments prove that compared with the traditional PNN model, the fault diagnosis accuracy of the KPCA-IDEPSO-PNN model is improved by 4.9% and the number of fault misclassifications is reduced by 34, effectively improving the fault diagnosis accuracy of the high-pressure heater system and ensuring the safe and stable operation of thermal power units.

Automated Analysis of Service Life of Air-Lines of the Electricity Transmission of Electric Power Systems

Basic EPS objects, which service life has exceeded normative value, increasingly affect – every year to a greater extent – the efficiency of overall performance. This manifests itself in increase of a number of automatic emergency shutdowns, an amount and complexities of accident-hazardous defects. After the expiration of the standard service life, there is a special need for a quantitative assessment of reliability and safety of an object. It is recommended to organize the operation, maintenance and repair of these objects according to their technical condition, and since it determines the reliability and safety of the object, these properties should be taken into account more fully. The relevant recommendations in electric power systems are implemented at a qualitative level, intuitively, according to the operating experience. There are neither quantitative evaluations nor methodology for their performance. Therefore, a method and algorithm of quantitative assessment of integral indicators of reliability and safety of operation of thermal power units of thermal power plants as concentrated objects of continuous operation were previously analyzed by the authors. The present paper examines distributed objects of continuous operation, viz. overhead power transmission lines with a voltage of 110 kV and higher, whose service life exceeds the standard value. Attention is paid to the issues of quantitative assessment of the degree of aging for a set of overhead power transmission lines, classification of these lines to identify the most significant classes and methodology for assessing the difference in the degree of aging when classifying them according to specified types of signs (for example, the difference in the degree of aging of overhead power transmission lines of grid enterprises of electric power systems). It is shown that it unacceptable to use the estimates of the relative number of overhead power transmission lines, the service life of which exceeds the calculated one, for comparison since it causes a great risk of an erroneous decision. The methodology and algorithm of methodological support of the management of electric power systems and grid enterprises in the organization of operation, maintenance and repair have been developed.

Application of fast adaptive moth-flame optimization in flexible operation modeling for supercritical unit

As main measure to stabilize fluctuation of power grid under renewable energy utilization, the flexible operation of thermal power unit reveals remarkable significant. To serve for peak shaving and fast load varying, a novel T-S fuzzy modeling approach based on fast adaptive moth-flame optimization (FAMFO) algorithm is proposed for dynamics investigation of supercritical unit. This modeling scheme is deemed as hierarchical process with data partition stage and parameter determination stage. Firstly, an automatic clustering strategy relying on FAMFO is fulfilled for more reasonable data partition result, which devotes to accuracy modeling without subjective interference. In this stage, the FAMFO is attained with assistance of four improvements and its merits in balancing search exploration and exploitation guarantee the width of covered operation range of supercritical unit for peak shaving. Secondly, inherent advantages of least square technics in parameter identification witness the successful implementation of a well-performed exponentially-weighted least squares algorithm in later stage of fuzzy modeling. In effectiveness verification of the proposed modeling approach, the superiorities of FAMFO in promoting search precision and speed are demonstrated via benchmark function test and Friedman test. Then, the flexible operation modeling performance is proved via extensive simulation experiments using on-site data of supercritical unit.

Analysis on Optimal Mode of Operation of Small and Medium Pumped Storage Power Station

With the rapid development of new energy and peak-shaving of power grid, pumped storage power station has been paid more and more attention as an economical and reliable means of peak-shaving. Pumped storage power station can play the role of peak-shaving frequency modulation, accident reserve, black start power supply and so on. It is an effective means to ensure the safe operation of power grid, optimize the economic operation of thermal power units and promote the absorption of new energy. This paper first briefly introduces the operation mode of pumped storage power station, and analyzes the operation benefit of pumped storage power station from the actual operation demand of power grid. Further, the main operation mode of pumped storage power station is analyzed, and the operation mode suitable for small and medium pumped storage power station is put forward.

A Comprehensive Thermal and Structural Transient Analysis of a Boiler’s Steam Outlet Header by Means of a Dedicated Algorithm and FEM Simulation

Increasing the share of renewables in energy markets influences the daily operation of thermal power units. High capacity power units are more frequently operated to balance power grids and, thus, steam boilers are exposed to unfavorable transient states. The aim of this work was to perform thermal and structural analyses of a boiler’s outlet steam header, with a capacity of 650∙103 kg/h (180 kg/s) of live steam. Based on the measured steam pressure and temperatures on the outer surface of the component, transient temperature fields were determined by means of an algorithm that allows determination of transient stress distributions on the internal and external surfaces, as well as at stress concentration regions. In parallel, a finite element method simulation was performed. A comparison of the obtained results to a finite element analysis showed satisfactory agreement. The analyses showed that the start-up time could be reduced because the total stress did not exceed the allowed values during the regular start-up of the analyzed power unit. The algorithm was efficient and easy to implement in the real control systems of the power units. The numerical approach employed in the presented algorithm also allowed for determination of the time- and place-dependent heating rate value, which can be used as input data for the control system of the power unit.

Application of Load Optimization Control of Thermal Power Unit Based on the Characteristics of Regulating Valve

In the long-term operation of thermal power units, the nonlinear and more complex viscous faults often occur in the regulator valve, which have obvious adverse effects on the dynamic performance of the load control. By using two point line models and Presiach models that are switched to each other to describe the switch asymmetry of the regulating valve and the different viscosity characteristics, the viscous fault of the regulating valve is effectively diagnosed, and the statistical consistency of the estimated values of model parameters are proved. Several key control technologies of thermal unit are given, and the control method of compensating the viscous fault of the regulating valve is presented, which weakens the adverse effect of the non-linear characteristics of the control valve on the dynamic control performance, and makes the main variables such as main steam pressure of unit achieve good dynamic performance.

Modelling of thermal power unit target value based on hierarchical regression

The operation optimization of thermal power units is of great significance for energy saving and consumption reduction. Under certain boundary constraints, determining the target value of adjustable parameters is the key to optimal operation of the unit. The basic method is to analyse the historical operation data, from where select the data with the best performance indicators in all range of working condition as the target sample, to establish the model between the boundary parameters and the adjustable parameters. The most difficult step is to solve the conditional probability distribution of the performance parameters with respect to the boundary parameters. In this paper, a hierarchical regression method is proposed to slice the raw data about the performance index, and the historical data is conditionally segmented. Based on this, a modelling framework is designed for the target value of the adjustable parameters of the thermal power unit. Finally, the operation data of an ultra-supercritical 1000MW thermal power unit is selected for verification. The results show that the method has the advantages of stable, and can provide the adjustment direction for the optimal operation of thermal power units.

Wind Power Consumption Research Based on Green Economic Indicators

As a representative form of new energy generation, wind power has effectively alleviated environmental pollution and energy shortages. This paper constructs a green economic indicator to measure the degree of coordinated development of environmental and social benefits. To increase the amount of wind power consumption, an economic dispatch model based on the coordinated operation of cogeneration units and electric boilers was established; we also introduced the green certificate transaction cost, which effectively meets the strategic needs of China’s energy low-carbon transformation top-level system design. Wind power output has instability and volatility, so it puts higher requirements on the stable operation of thermal power units. To solve the stability problem, this paper introduces the output index of the thermal power unit and rationally plans the unit combination strategy, as well as introducing the concept of chance-constrained programming due to the uncertainty of load and wind power in the model. Uncertainty factors are transformed into load forecasting errors and wind power prediction errors for processing. Based on the normal distribution theory, the uncertainty model is transformed into a certain equivalence class model, and the improved disturbance mutated particle swarm optimization algorithm is used to solve the problem. Finally, the validity and feasibility of the proposed model are verified based on the IEEE30 node system.

Analysis of Influence of Fast-Cut-Back Test on 1000-MW Steam Turbine Safety

The capacity of the power grid in China is increasing rapidly. Because of the reliability of the multi-infeed direct current (DC) system in the grid, power-system damping is relatively weak. Realizing isolated island operation of thermal power units with fast-cut-back (FCB) ability is considered to be the best solution for the quick restoration of the power grid. Taking a 1000-MW ultra-supercritical (USC) unit as an example, based on the field-test data of load rejection, this paper studies the dynamic responses of the turbine side during the FCB process. First, the maximum speed increase of the rotor during the FCB process with different loads is simulated using software. Second, the water levels of the deaerator and condenser during the FCB process are predicted with different loads using the same method. Third, the highest exhaust steam temperature of the high-pressure (HP) cylinder during the FCB process is calculated and predicted with different loads using finit-element modeling (FEM). Fourth, these key parameters are compared with the field FCB test data, and the comparison shows that the predicted results agree with the field-test data very well. Finally, the influence of the FCB test on the safety and life loss of the turbine side is analyzed.

Modelling and control of pulverizing system considering coal moisture

The pulverizing system serves an important function in the safe and efficient operation of thermal power units. The effect of coal moisture and pulverized coal moisture on the pulverizing system was considered in establishing a mass and energy balance-based dynamic mathematical model of a coal mill. The parameters of the model were identified by using the genetic algorithm and later validated with measurements. An extended Kalman filter was designed to estimate and verify the internal states of the coal mill. Based on the dynamic mathematical model of coal mill, pulverized coal moisture was used as a feed-forward signal, and a feed-forward compensation controller was designed to optimize the coal mill outlet temperature. Simulation results showed that the dynamic characteristic of the mill can be forecasted effectively with the established model. Moreover, pulverized coal moisture was reduced with feed-forward compensation, thereby improving the efficiency of the boiler.

Multi-Factors Analysis of Condenser Vacuum under Overall Working Conditions

It is of great significance to determine an optimal condenser vacuum for energy-saving diagnosis, for the vacuum means a lot to the safe and economic operation of thermal power units. The key parameters were calculated by the practical data, such as the cleanliness factor. The condenser heat transfer coefficient is affected by both the dirty of condenser water side and other factors on the basis of the method of adjusting the circulating-water flow unilaterally to get the optimal vacuum of condenser in this paper. The impacts of the exhausting steam resistance, the oxygen content of condensate caused by the change of the circulating-water flow were considered in this paper. The practical operation data was analysed with the results from HEI. The simulations were examined in the comparison of heat transfer coefficient. The impacts of unit energy consumption characteristics under overall working conditions caused by condenser vacuum were obtained in the approach based on the theory of energy specific fuel consumption (ESFC). The variation of auxiliary specific consumption as the temperature of circulating-water changing was obtained. The results indicated that the optimal condenser vacuum determined by the method aiming at maximum output power and many factors under overall working conditions accounted for played an important role in the energy saving diagnosis of thermal power units.

An overview of modelling and simulation of thermal power plant

Thermal power plants constitute the largest proportion of installed capability in global power generation system and consume large quantities of coal. Achieving optimal operation of thermal power units, improving the efficiency and reducing the coal consumption is of great significance for the reduction of greenhouse gas and pollutants emissions. Modelling and simulation is the base of optimal operation and control in thermal power unit. In this paper, research results of modelling and simulation of thermal power units are reviewed. Firstly, several common models which are used for researches of thermal power control systems are analysed, including simplified turbine and furnace models for unit coordinated control system (CCS) as well as local equipment models. Then the system structure, function and application of thermal power stimulated simulator are described. Finally, the structure and function of digital power plant is introduced. The challenges of modelling and simulation of thermal power plant researches in the future are discussed.