A method and system for optimizing economic operation of coal-fired power units based on micro-operating conditions

Abstract

The present invention belongs to the technical field of coal-fired power unit operation optimization, and discloses a method and system for optimizing the economic operation of coal-fired power units based on micro-conditions, including collecting unit operation data, preprocessing, clustering analysis of the operation data, and dividing the operation conditions into different micro-conditions; establishing an energy efficiency index model to obtain the energy efficiency index of each micro-condition, and the energy efficiency index includes a macro energy efficiency index and an energy efficiency micro-index; for each micro-condition, analyzing the correlation between the energy efficiency index and the existing characteristics of the main and auxiliary equipment, and determining the key factors affecting the unit's economy; based on the key factors, establishing an index optimization model for micro-conditions, and finding the optimal combination of operating parameters through an optimization algorithm; and formulating operation adjustment strategies for different micro-conditions according to the optimization analysis results. The present invention improves the economic operation level of coal-fired power units under complex operating conditions and meets the needs of flexible regulation of power grids through in-depth analysis and optimization of micro-conditions / micro-intervals.

Description

Technical Field

[0001] The present invention relates to the technical field of coal-fired power unit operation optimization, and in particular to a method and system for optimizing the economic operation of a coal-fired power unit based on micro-operating conditions. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] With the advancement of energy transformation, the role of coal-fired power units in the power system has changed significantly. Coal-fired power generation units need to perform deep peak regulation to adapt to the grid regulation requirements brought about by the access of new energy sources, which causes the units to operate at low load for a long time, increasing their energy consumption and significantly reducing their economic efficiency. At the same time, the function of coal-fired power has changed from basic guarantee power supply to system regulation power supply and emergency backup power supply, the utilization hours have been continuously reduced, and the load fluctuations of the units have become more frequent, which has put forward higher requirements for the operation optimization of the units.

[0004] However, the existing operation and management technology of coal-fired power units has many shortcomings. The peak-shaving depth of most thermal power units is limited, which makes it difficult to meet the needs of flexible regulation of the power grid. Most of the coal-fired power units currently in operation are designed based on base load. When deep peak-shaving and rapid load increase and decrease occur, the operating conditions seriously deviate from the design values, which not only affects the safety and life of the unit, but also causes the traditional energy efficiency evaluation indicators to be unable to accurately reflect the actual operating status of the unit. In addition, the existing technology lacks detailed analysis and effective use of micro-conditions / micro-intervals in terms of unit operation optimization, making it difficult to achieve accurate economic operation optimization and unable to adapt to the increasingly complex operating conditions of coal-fired power units. Summary of the invention

[0005] In order to solve the above problems, the present invention proposes a method and system for optimizing the economic operation of coal-fired power units based on micro-operating conditions. Through in-depth analysis and optimization of micro-operating conditions / micro-intervals, the economic operation level of coal-fired power units under complex operating conditions is improved to meet the needs of flexible regulation of the power grid.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for optimizing economic operation of a coal-fired power unit based on micro-operating conditions, comprising the following steps: Collect the operating data of coal-fired power units, pre-process the collected data, perform cluster analysis on the pre-processed data, and divide the operating conditions into different micro-conditions; Establishing an energy efficiency index model to obtain energy efficiency indexes for each micro-operating condition, wherein the energy efficiency indexes include macro energy efficiency indexes and energy efficiency micro-indicators; For each micro-operating condition, analyze the correlation between its energy efficiency index and the existing characteristics of the main and auxiliary equipment, and determine the key factors affecting the economic efficiency of the unit; Based on the key factors affecting the unit's economic efficiency, a micro-condition index optimization model is established, and the optimal operating parameter combination for each micro-condition is found through the optimization algorithm; According to the optimization analysis results, formulate optimization operation adjustment strategies for different micro-operating conditions.

[0007] As an optional implementation method, an energy efficiency index model is established, specifically: Based on the analysis of thermal cycle mechanism and classical thermal system, a macro-energy efficiency index analysis and calculation model for the unit is constructed. Big data analysis is used to explore the patterns in the operating data, and a micro-condition big data index model is established. The macro-energy efficiency index analysis and calculation model is verified and improved, and a micro-condition energy efficiency index model is established.

[0008] As an optional implementation, the macro energy efficiency index analysis and calculation model distinguishes between thermal parameter transfer levels and transfer relationships, and considers changes in operating conditions and operating intervals as well as thermal information in different intervals.

[0009] As an optional implementation method, it also includes real-time monitoring of the energy efficiency index of each micro-operating condition of the unit, and issuing a warning signal when the energy efficiency index deviates from the optimal value within a certain range.

[0010] As an optional implementation manner, the key factors affecting the unit economy include boiler efficiency, turbine heat rate, condenser vacuum, unit heat rate, unit coal consumption and unit plant power consumption rate.

[0011] As an optional implementation, the energy efficiency micro-indicators include load micro-variation heat consumption rate, load micro-variation coal consumption rate, load micro-variation vacuum change rate and load micro-variation main steam parameter fluctuation amplitude.

[0012] In a second aspect, the present invention provides a system for optimizing economic operation of a coal-fired power unit based on micro-operating conditions, comprising: The data acquisition and processing module is configured to: collect the operating data of the coal-fired power unit, pre-process the collected data, perform cluster analysis on the pre-processed data, and divide the operating conditions into different micro-conditions; The energy efficiency index acquisition module is configured to: establish an energy efficiency index model to obtain the energy efficiency index of each micro-operating condition, wherein the energy efficiency index includes a macro energy efficiency index and an energy efficiency micro-indicator; The key factor determination module is configured to: for each micro-operating condition, analyze the correlation between its energy efficiency index and the existing characteristics of the main and auxiliary equipment, and determine the key factors affecting the unit's economy; The optimization analysis module is configured to: establish an index optimization model for micro-operating conditions based on the key factors affecting the unit's economic performance, and find the optimal operating parameter combination for each micro-operating condition through an optimization algorithm; The improvement strategy formulation module is configured to formulate optimized operation adjustment strategies for different micro-operating conditions based on the optimization analysis results.

[0013] In a third aspect, the present invention provides an electronic device comprising a memory and a processor, and computer instructions stored in the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the method described in the first aspect is performed.

[0014] In a fourth aspect, the present invention provides a computer-readable storage medium for storing computer instructions, wherein when the computer instructions are executed by a processor, the method described in the first aspect is performed.

[0015] In a fifth aspect, the present invention provides a computer program product, comprising a computer program, which, when executed by a processor, implements the method described in the first aspect.

[0016] Compared with the prior art, the present invention has the following beneficial effects: This paper proposes a method and system for optimizing the economic operation of coal-fired power units based on micro-operating conditions. Through the precise division and in-depth analysis of micro-operating conditions / micro-intervals, the performance characteristics and energy efficiency change laws of each micro-operating condition of the coal-fired power unit under different operating conditions can be more accurately grasped. The optimization strategy formulated based on this is more targeted and effective, which can significantly improve the economic efficiency of the unit, reduce the cost of power generation, and enhance the competitiveness of coal-fired power units in the energy market.

[0017] By optimizing micro-conditions, we can ensure that each micro-condition is in the most efficient operating state and the economic indicators are optimal, so that the entire operating condition of the unit can be guaranteed to operate under the optimal condition.

[0018] This paper proposes a method and system for optimizing the economic operation of coal-fired power units based on micro-operating conditions, and constructs a multi-scale benchmarking system, which enables the units to be comprehensively compared and optimized with reference to national standards, group standards and their own historical optimal levels. This will not only help to promote the improvement of the operating level of the entire industry, but also motivate the units themselves to continuously pursue excellence and achieve continuous improvement, and also provide strong support for enterprises in energy conservation, emission reduction and fulfillment of social responsibilities.

[0019] The present disclosure proposes a method and system for optimizing the economic operation of coal-fired power units based on micro-operating conditions, and the establishment of an energy efficiency index early warning system provides real-time safety monitoring and risk early warning functions for the operation of the unit. It can timely discover potential problems in the operation of the unit, issue early warning signals at the beginning of energy efficiency decline or before equipment failure occurs, so that the operating personnel have enough time to take adjustment measures to avoid a significant decline in energy efficiency and equipment damage, ensure the safe, stable and economic operation of the unit, and reduce economic losses caused by unplanned shutdowns and inefficient operations.

[0020] This paper proposes a method and system for optimizing the economic operation of coal-fired power units based on micro-operating conditions, which comprehensively uses data collection, processing, model construction, optimization analysis, operation guidance and early warning and other technologies to achieve full process management from data to decision-making. Through the collaborative work between various modules, it provides a comprehensive and systematic technical solution for coal-fired power units to achieve efficient and economic operation under complex and changeable operating conditions, which helps to improve the overall operation management level and intelligence level of coal-fired power units.

[0021] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0023] Figure 1 A flow chart of a method for optimizing economic operation of a coal-fired power unit based on micro-operating conditions provided in Example 1 of the present invention. DETAILED DESCRIPTION

[0024] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0025] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0026] It should be noted that the terms used herein are only for describing specific embodiments, and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that the terms "include" and "have" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0027] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0028] Example 1 like Figure 1 As shown, this embodiment provides a method for optimizing economic operation of a coal-fired power unit based on micro-operating conditions, comprising the following steps: S1. Collect the operating data of the coal-fired power unit, pre-process the collected data, perform cluster analysis on the pre-processed data, and divide the operating conditions into different micro-conditions.

[0029] S2. Establish an energy efficiency index model to obtain energy efficiency indexes for each micro-operating condition, wherein the energy efficiency indexes include macro energy efficiency indexes and energy efficiency micro-indicators.

[0030] S3. For each micro-operating condition, analyze the correlation between its energy efficiency index and the existing characteristics of the main and auxiliary equipment, and determine the key factors affecting the economic efficiency of the unit.

[0031] S4. Based on the key factors affecting the unit's economy, a micro-condition index optimization model is established, and the optimal operating parameter combination for each micro-condition is found through the optimization algorithm.

[0032] S5. Based on the optimization analysis results, formulate optimization operation adjustment strategies for different micro-operating conditions.

[0033] First, S1, collect the operating data of the coal-fired power unit, pre-process the collected data, perform cluster analysis on the pre-processed data, and divide the operating conditions into different micro-conditions.

[0034] During the operation of coal-fired power units, high-precision sensors installed at key locations of the units are used to collect the operation data of coal-fired power units in real time. The sensor types include temperature sensors, pressure sensors, flow sensors, component analyzers, etc. The operation data covers unit load, environmental conditions (such as temperature, humidity, air pressure, etc.), coal types (including coal calorific value, volatile matter, sulfur content, ash content and other component information), steam extraction pressure and temperature of each level of turbine, heater outlet water temperature, drain temperature, condenser vacuum, circulating water temperature, circulating water flow, heat transfer coefficient of each heating surface of the boiler, temperature of cold and hot working fluids, flow, combustion parameters, exhaust parameters, carbon content of fly ash, NOx emission concentration and other parameters. These data fully reflect the operating status of the unit, external environmental impact and energy input characteristics.

[0035] Pre-process the collected operating data and use data cleaning technology to identify and remove outliers and erroneous data in the collected data to ensure the accuracy and reliability of the data and provide a high-quality data foundation for subsequent analysis.

[0036] The pre-processed data is clustered and analyzed using a density-based clustering algorithm (such as the DBSCAN algorithm). Based on key factors such as unit load (classified in intervals of 10%), coal type (classified according to different combinations of calorific value, volatile matter and ash content), and environmental conditions (temperature in intervals of 5°C, humidity in intervals of 10%, and air pressure in intervals of 1kPa), the unit's operating conditions are carefully divided into different micro-conditions / micro-intervals. For example, when the unit load is between 50% and 60%, the coal type with a calorific value of 20MJ / kg-22MJ / kg, volatile matter of 15%-20%, and ash content of 25%-30%, the ambient temperature is between 15°C and 20°C, the humidity is between 40% and 50%, and the air pressure is between 100kPa and 101kPa, it is divided into a specific micro-condition interval. This allows for accurate analysis of different categories.

[0037] S2. Establish an energy efficiency index model to obtain energy efficiency indexes for each micro-operating condition, wherein the energy efficiency indexes include macro energy efficiency indexes and energy efficiency micro-indicators.

[0038] Based on the analysis principle of thermodynamic cycle mechanism and the analysis method of classical thermodynamic system, an index analysis and calculation model (i.e., unit macro-energy efficiency index analysis and calculation model) that can accurately reflect the unit's macro-energy efficiency is constructed. This model takes into account the expansion work process of steam in the turbine, the combustion heat transfer process in the boiler, the heat exchange process of the circulating water system, etc. Through the first and second laws of thermodynamics, the mathematical relationship between the unit's power generation efficiency, power supply coal consumption and other energy efficiency indicators and the unit's load, steam parameters, heat transfer coefficient and other operating parameters is established. For example, the work done by the unit is calculated based on the steam flow rate and the inlet and outlet steam enthalpy values, and the power generation power is calculated in combination with the generator efficiency; the power supply coal consumption is calculated based on the calorific value, coal consumption and power generation power of the coal type. The model deeply distinguishes the transmission level and transmission relationship of thermal parameters, fully considers the changes in operating conditions (such as load changes, ambient temperature fluctuations, etc.) and operating ranges (different load ranges, different coal type combustion ranges, etc.), as well as the differences in thermal information in different ranges, and provides a theoretical framework for comprehensive evaluation of unit energy efficiency. These models are general unit models and economic macro-control models.

[0039] The big data model is organically combined with the unit macro-energy efficiency index analysis and calculation model. The big data model uses big data processing technology to mine and analyze a large amount of historical operation data. For example, the association rule mining algorithm is used to discover the potential correlation between different operating parameters, and the mathematical model between energy efficiency indicators and various operating parameters is established by regression analysis, and the energy efficiency index big data model of micro-conditions / micro-intervals is constructed. Then, the micro-condition energy efficiency index model is constructed by coupling the big data model with the unit macro-energy efficiency index analysis and calculation model, specifically: Big data analysis technology is used to mine the potential rules in massive operating data, and to verify and improve the unit's macro energy efficiency index analysis and calculation model. For example, through big data analysis, it is found that there is a certain deviation between the heat transfer efficiency of the boiler in actual operation and the theoretical value in the mechanism model. According to the results of the big data model, the heat transfer coefficient in the mechanism model is adjusted to make the energy efficiency index model more in line with the actual operating characteristics of the unit. Through real-time feedback and correction of the big data model, the energy efficiency index model is more in line with the actual operation of the unit, improving the accuracy and adaptability of the model, thereby establishing a high-precision energy efficiency index model suitable for micro-conditions / micro-intervals (i.e., micro-condition energy efficiency index model).

[0040] For each micro-operating condition / micro-interval, micro-indicators for energy efficiency analysis (i.e., energy efficiency micro-indicators) are established based on big data methods and mechanism methods. The energy efficiency micro-indicators include load micro-variable heat consumption rate, load micro-variable coal consumption rate, load micro-variable vacuum change rate, load micro-variable main steam parameter fluctuation amplitude, etc.

[0041] S3. For each micro-operating condition, analyze the correlation between its energy efficiency index and the existing characteristics of the main and auxiliary equipment, and determine the key factors affecting the economic efficiency of the unit.

[0042] For each well-divided micro-operating condition / micro-interval, the complex correlation between its energy efficiency indicators (macro energy efficiency indicators and energy efficiency micro-indicators) and the existing characteristics of the main and auxiliary equipment is deeply analyzed. For example, in a specific micro-operating condition interval (unit load is 60%-70%, burning high-volatile coal, and high ambient temperature), it is found that the turbine cylinder efficiency is closely related to the steam flow and temperature. When the steam flow increases and the temperature rises within a certain range, the cylinder efficiency increases; the heat transfer end difference of the heating surface is related to the flue gas temperature and the cleanliness of the heating surface. Scaling of the heating surface will cause the heat transfer end difference to increase, affecting the heat transfer efficiency; the efficiency of the feedwater pump is affected by factors such as the pump speed and the inlet and outlet pressure difference. Determine the key factors that affect the unit economy, such as boiler efficiency, turbine heat consumption, condenser vacuum, unit cycle thermal efficiency, unit heat consumption rate, unit coal consumption rate, unit plant power consumption rate, etc., and clarify the degree of influence and mechanism of these factors on energy efficiency in different micro-operating conditions / micro-intervals.

[0043] S4. Based on the key factors affecting the unit's economy, a micro-condition index optimization model is established, and the optimal operating parameter combination for each micro-condition is found through the optimization algorithm.

[0044] Establish an index optimization model for micro-conditions / micro-intervals, and use advanced optimization algorithms (such as genetic algorithms, particle swarm optimization algorithms, vector machine algorithms, fuzzy optimization algorithms, neural network algorithms, etc.) to find the optimal combination of operating parameters in each micro-condition / micro-interval.

[0045] For example, the optimization goal is set to minimize the power supply coal consumption, and the decision variables include the opening of the turbine regulating valve (ranging from 30% to 100%), the speed of the feedwater pump (ranging from 1000rpm to 5000rpm), the air volume of the forced draft fan (ranging from 50000m³ / h to 150000m³ / h), the air volume of the induced draft fan (ranging from 60000m³ / h to 160000m³ / h), etc. Through the iterative calculation of the optimization algorithm, the energy efficiency index gradually approaches the optimal value, thereby realizing the economic operation optimization of the unit within the micro-operating condition / micro-interval.

[0046] The parameters of the genetic algorithm are set as follows: population size is 50, crossover probability is 0.7, mutation probability is 0.05, and number of iterations is 100. In the optimization process, the initial population is first randomly generated, and each individual represents a set of operating parameter combinations. Then the fitness value of each individual is calculated, that is, the corresponding power supply coal consumption. The smaller the fitness value, the better the individual. Through selection, crossover and mutation operations, new populations are continuously generated, gradually approaching the optimal solution.

[0047] For example, in a certain micro-operating range, after multiple iterations, the optimization results show that when the opening of the steam turbine regulating valve is 70%, the speed of the feedwater pump is 3000rpm, the air volume of the forced draft fan is 100000m³ / h, and the air volume of the induced draft fan is 120000m³ / h, the power supply coal consumption reaches the minimum value. Compared with the initial operating parameters, the power supply coal consumption is reduced by 5g / kWh.

[0048] Multi-scale benchmarking is carried out to compare the energy efficiency indicators of each micro-operating condition / micro-interval of the unit with national standards (such as standard values ​​of power supply coal consumption), group standards (average energy efficiency levels of similar units within the group) and the historical optimal indicators of the unit. In a micro-operating condition interval with a load of 40%-50%, it is found that the current power supply coal consumption of the unit is 250g / kWh, the national standard is 260g / kWh, the group standard is 255g / kWh, and the historical optimal value of the unit is 245g / kWh. Through comparative analysis, it is found that the main difference lies in the fact that the efficiency of the turbine cylinder is slightly lower than the historical optimal level during current operation, and the heat transfer end difference of the heating surface is slightly greater than the group standard. Through comparative analysis, the gap between the current operating status and the standards and optimal levels at all levels is accurately found, the direction of improvement is clarified, and a basis is provided for formulating targeted improvement strategies.

[0049] S5. Based on the optimization analysis results, formulate optimization operation adjustment strategies for different micro-operating conditions.

[0050] According to the results of optimization analysis, formulate operation adjustment strategies that are compatible with different micro-conditions / micro-intervals. For example, in terms of unit operation parameter optimization, load scheduling, and peak load regulation strategy, optimize the load distribution plan according to the load characteristics and energy efficiency optimization requirements under different micro-conditions; in circulating water pump scheduling, adjust the water pump operation parameters according to the relationship between circulating water temperature, flow rate and energy efficiency; optimize air distribution according to changes in fuel properties to ensure combustion efficiency.

[0051] In the above-mentioned micro-operating range of 40%-50% load, since the turbine cylinder efficiency needs to be improved, the adjustment strategy is to optimize the turbine steam inlet parameters to ensure that the steam pressure and temperature are stable within a reasonable range, and at the same time strengthen the maintenance and cleaning of the turbine blades to reduce the impact of blade scaling on efficiency; in view of the problem of large heat transfer end difference of the heating surface, optimize the operating logic of the soot blowing system, increase the soot blowing frequency, keep the heating surface clean, and improve the heat transfer efficiency.

[0052] Build an energy efficiency indicator early warning system and set a reasonable energy efficiency indicator threshold range. When the actual energy efficiency indicator of the unit deviates from the optimal value to a certain extent, the early warning system will promptly send a signal to prompt the operator to make corresponding adjustments to prevent further deterioration of energy efficiency and ensure that the unit is always in an economical operation state. For example, during the operation of the unit, when the power supply coal consumption rises to 255g / kWh, the early warning system will immediately issue an early warning to prompt the operator to pay attention to the operating status of the unit.

[0053] Continuously track and evaluate the implementation effect of the adjustment strategy, analyze the actual impact of the adjustment strategy on the unit operation by real-time monitoring of unit operation data and energy efficiency index changes. According to the evaluation results, timely optimize and improve the adjustment strategy to form a closed-loop optimization management to ensure that the unit can achieve long-term and stable economic operation optimization.

[0054] Within a week after the adjustment strategy was implemented, the unit operation data and energy efficiency index changes were closely monitored. It was found that after optimizing the turbine steam inlet parameters and strengthening the soot blowing, the turbine cylinder efficiency increased by 2%, the heat transfer end difference of the heating surface decreased by 5°C, and the power supply coal consumption dropped to 251g / kWh, close to the historical optimal level of the unit. Based on the evaluation results, the adjustment strategy was further optimized, such as fine-tuning the soot blowing time interval, to achieve better energy-saving effects.

[0055] The technical solution of the present invention can also achieve micro-condition optimization under different coal types, operation optimization under ambient temperature changes, dynamic optimization when loads fluctuate frequently, and long-term operation data verification and optimization adjustment, as follows: Optimization of micro-operating conditions under different coal types.

[0056] 1. When the unit burns different types of coal, such as switching from low-volatile coal to high-volatile coal, repeat the above data collection and preprocessing steps. The collected data shows that the flame temperature is higher and the flue gas volume is larger when the high-volatile coal is burned.

[0057] 2. In the stage of establishing the energy efficiency index model, the combustion reaction heat calculation part in the mechanism model is revised according to the characteristics of high-volatile coal to more accurately reflect the law of fuel calorific value release. At the same time, the big data model relearns and adjusts parameters based on the operating data of the new coal to establish an energy efficiency index model suitable for the micro-operating conditions of high-volatile coal.

[0058] 3. In the micro-condition / micro-interval optimization analysis, it was found that for high-volatile coal, under the same load, appropriately increasing the air volume of the blower and the induced draft fan can improve the combustion efficiency, but at the same time, it is necessary to pay attention to controlling the air-coal ratio to avoid excessive excess air coefficients that lead to increased heat losses. Through optimization calculations, it was determined that under the micro-condition of this coal type, the combustion efficiency is highest when the air volume of the blower is adjusted to 120,000 m³ / h and the air volume of the induced draft fan is adjusted to 140,000 m³ / h. At this time, the excess air coefficient is maintained between 1.1-1.2, which can effectively reduce incomplete combustion losses and exhaust heat losses, and reduce the unit's power supply coal consumption by 8g / kWh.

[0059] 4. In terms of formulating comprehensive energy efficiency improvement strategies, in order to address the coking problem that may occur when burning high-volatile coal, the inspection frequency of boiler heating surfaces is increased, and the air distribution method of the burner is optimized to make the flame shape more reasonable, reduce local high-temperature areas, and reduce the risk of coking. At the same time, according to the new optimization results, the operating frequency of the blower and the induced draft fan is adjusted to ensure that the air volume is stable near the optimal value.

[0060] After a period of operation, the energy efficiency index early warning system shows that under the new operation strategy, the unit's energy efficiency index has stabilized at a high level and has not exceeded the early warning threshold. By optimizing the micro-operating conditions of different types of coal, the unit's adaptability to fuel changes has been improved, further improving the overall economy.

[0061] Optimizing operation under changing ambient temperature 1. When the ambient temperature changes significantly due to seasonal changes (such as the ambient temperature drops to -10℃-0℃ in winter and rises to 30℃-40℃ in summer), the unit operation data is re-collected and it is found that the ambient temperature has a significant impact on the condenser vacuum. In a low temperature environment, the condenser vacuum increases, the turbine exhaust pressure decreases, and the unit power generation efficiency is improved; while in a high temperature environment, the condenser vacuum decreases, affecting the unit's economic efficiency.

[0062] 2. When establishing an energy efficiency index model suitable for different ambient temperature micro-conditions, the ambient temperature correction coefficient is introduced into the mechanism model to calculate the condenser heat transfer process more accurately. The big data model analyzes the relationship between condenser pressure, circulating water temperature, unit load and other parameters based on historical operating data under different ambient temperatures to further optimize the model.

[0063] 3. Micro-condition / micro-interval optimization analysis is carried out for ambient temperature changes. Under low-temperature conditions in winter, the optimization results show that appropriately reducing the circulating water flow rate (from 15,000 m³ / h to 12,000 m³ / h) can maintain the condenser vacuum within a reasonable range, while reducing the energy consumption of the circulating water pump, reducing the unit's power supply coal consumption by 3g / kWh; under high-temperature conditions in summer, increasing the cooling capacity of the cooling tower (such as increasing the cooling tower fan speed) to reduce the circulating water temperature, thereby increasing the condenser vacuum, and optimizing the operating parameters of the last-stage blades of the low-pressure cylinder of the turbine, improving the efficiency of the turbine in high-temperature environments, and reducing the power supply coal consumption by 6g / kWh.

[0064] 4. Formulate a comprehensive energy efficiency improvement strategy based on the optimization results under ambient temperature changes. In winter, adjust the operating mode of the circulating water system and adjust the circulating water flow in real time according to the ambient temperature; in summer, strengthen the maintenance and operation management of the cooling tower to ensure the cooling effect. At the same time, dynamically adjust the load distribution strategy of the unit under different ambient temperatures, give priority to operating in the high-efficiency operating range, and improve overall economy.

[0065] Through the energy efficiency indicator early warning system, we continuously monitor the impact of ambient temperature changes on the unit's energy efficiency, promptly discover and deal with energy efficiency fluctuations caused by environmental factors, ensure that the unit always operates in an economical state, effectively respond to the challenges brought by ambient temperature changes, and improve the unit's operating economy throughout the year.

[0066] Dynamic optimization with frequent load fluctuations 1. During the period of frequent fluctuations in power grid load demand (such as large daily peak-to-valley difference in electricity consumption, and rapid changes in unit load between 30%-100%), closely track the unit operation data collection. The data shows that when the load changes rapidly, the response speed of the turbine speed control system, the boiler combustion stability, and the matching of various auxiliary equipment have a significant impact on the unit's economic efficiency.

[0067] 2. Establish a dynamic energy efficiency index model, and consider the impact of load change rate on the dynamic characteristics of each component of the unit in the mechanism model, such as the thermal stress change of the turbine rotor, the dynamic response of the boiler steam-water system, etc. The big data model focuses on analyzing historical data during load fluctuations and exploring the optimal operating parameter combination mode under different load change rates.

[0068] 3. Micro-condition / micro-interval optimization analysis is carried out in real time for frequent load fluctuations. When the load rises rapidly, the opening rate and sequence of the turbine regulating valve are optimized to ensure that the turbine increases the load smoothly and quickly. At the same time, the load increase rate of the blower, induced draft fan and feed water pump is coordinated to keep the air-coal ratio and water-coal ratio within a reasonable range to avoid efficiency loss due to parameter imbalance; when the load drops rapidly, the load reduction rate of each equipment is reasonably controlled to prevent safety problems such as turbine water hammer caused by excessive fluctuations in steam pressure and temperature. At the same time, the fuel supply and air distribution method of the burner are adjusted to maintain stable boiler combustion. After optimization, during the period of frequent load fluctuations, the load response speed of the unit increased by 30%, and the fluctuation range of power supply coal consumption decreased, and the average power supply coal consumption decreased by 4g / kWh.

[0069] 4. Formulate a comprehensive energy efficiency improvement strategy to cope with load fluctuations. Establish a load forecasting model, combine it with grid dispatch information, and predict the load change trend in advance, so that the unit can prepare for the adjustment of operating parameters in advance. During the load trough period, optimize the unit operation mode, such as using a single circulating water pump to operate, reducing the operating frequency of some auxiliary equipment, and other energy-saving measures; during the load peak period, ensure that all main and auxiliary equipment operate efficiently and collaboratively to give full play to the power generation capacity of the unit.

[0070] The energy efficiency indicator early warning system is used to monitor the changes in energy efficiency indicators during load fluctuations in real time. When an abnormal downward trend in energy efficiency indicators is found, an early warning signal is issued in a timely manner to remind operators to check the operating status of the equipment or adjust the operating parameters. Through continuous dynamic optimization and monitoring, the unit can maintain high operating economy and safety under complex working conditions with frequent load fluctuations, effectively adapt to the dispatching needs of the power grid, and provide strong support for the stable operation of the power grid.

[0071] Long-term operation data verification and optimization adjustment 1. After a period of time (e.g., one year) of economic operation optimization practice based on micro-conditions / micro-intervals, a large amount of unit operation data has been accumulated. These long-term operation data are analyzed in detail to verify the effectiveness of the optimization method and system. Statistical data show that the overall power supply coal consumption of the unit has been reduced to varying degrees in different load intervals and various operating conditions, and the average power supply coal consumption has been reduced by 5.5g / kWh, with significant energy-saving and emission reduction effects.

[0072] 2. In-depth analysis of the operating data under different micro-conditions / micro-intervals revealed that there is still room for further optimization under certain specific conditions. For example, when burning medium and low loads and high-ash coal, the wear problem of the heating surface at the rear of the boiler has a certain impact on the long-term economic operation of the unit. In response to this situation, the boiler soot blowing strategy is adjusted, and the soot blowing cycle and soot blowing method are optimized according to the ash content of the coal and the load conditions to reduce the wear of the heating surface, while keeping the heating surface clean and improving the heat transfer efficiency.

[0073] 3. Regularly update and optimize the energy efficiency index model. With the aging of unit equipment, changes in the operating environment, and fluctuations in the coal market, the distribution pattern of operating data may change. Use the newly accumulated data to retrain the big data model and revise the key parameters in the mechanism model to ensure that the energy efficiency index model can always accurately reflect the actual operating characteristics of the unit. For example, adjust the reaction kinetic parameters in the combustion model based on the latest coal quality analysis data and actual combustion effects to make the combustion calculation more accurate.

[0074] 4. Based on the analysis results of long-term operation data, continuously improve the comprehensive energy efficiency improvement strategy. In terms of operation management, formulate more refined operation procedures, clarify the operation points and precautions of each position under different micro-conditions / micro-intervals; in terms of equipment maintenance, strengthen the status monitoring and preventive maintenance of key equipment, reasonably arrange maintenance plans according to the wear law and performance change trend of the equipment, extend the service life of the equipment, reduce the equipment failure rate, and further improve the reliability and economy of the unit.

[0075] Through verification and optimization adjustment of long-term operating data, we continuously improve the economic operation optimization method and system of coal-fired power units based on micro-conditions / micro-intervals, so that they can continuously adapt to changes in the operating status of the units in actual applications, providing solid guarantees for the efficient, stable and economical operation of coal-fired power units, and also providing useful reference and reference for the operation optimization of similar units.

[0076] Example 2 This embodiment provides a system for optimizing economic operation of coal-fired power generation units based on micro-operating conditions, including: The data acquisition and processing module is configured to: collect the operating data of the coal-fired power unit, pre-process the collected data, perform cluster analysis on the pre-processed data, and divide the operating conditions into different micro-conditions; The energy efficiency index acquisition module is configured to: establish an energy efficiency index model to obtain the energy efficiency index of each micro-operating condition, wherein the energy efficiency index includes a macro energy efficiency index and an energy efficiency micro-indicator; The key factor determination module is configured to: for each micro-operating condition, analyze the correlation between its energy efficiency index and the existing characteristics of the main and auxiliary equipment, and determine the key factors affecting the unit's economy; The optimization analysis module is configured to: establish an index optimization model for micro-operating conditions based on the key factors affecting the unit's economic performance, and find the optimal operating parameter combination for each micro-operating condition through an optimization algorithm; The improvement strategy formulation module is configured to formulate optimized operation adjustment strategies for different micro-operating conditions based on the optimization analysis results.

[0077] It also includes an early warning module, which is configured to: monitor the unit's energy efficiency indicators in real time, and send out early warning signals when the indicators are abnormal, reminding the operating personnel to deal with them in time.

[0078] It should be noted that the above modules correspond to the steps described in Example 1, and the examples and application scenarios implemented by the above modules and the corresponding steps are the same, but are not limited to the contents disclosed in the above Example 1. It should be noted that the above modules, as part of the system, can be executed in a computer system such as a set of computer executable instructions.

[0079] In further embodiments, there is also provided: An electronic device includes a memory and a processor, and computer instructions stored in the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the method described in Embodiment 1 is performed. For the sake of brevity, it will not be described in detail here.

[0080] It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general-purpose processors, digital signal processors DSP, application-specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0081] The memory may include a read-only memory and a random access memory, and provide instructions and data to the processor. A portion of the memory may also include a non-volatile random access memory. For example, the memory may also store information about the device type.

[0082] A computer-readable storage medium is used to store computer instructions, and when the computer instructions are executed by a processor, the method described in Example 1 is completed.

[0083] The method in Example 1 can be directly embodied as a hardware processor, or a combination of hardware and software modules in the processor. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware. To avoid repetition, it is not described in detail here.

[0084] A computer program product includes a computer program, and when the computer program is executed by a processor, the method described in embodiment 1 is implemented.

[0085] The present invention also provides at least one computer program product tangibly stored on a non-transitory computer-readable storage medium. The computer program product includes computer executable instructions, such as instructions included in a program module, which are executed in a device on a real or virtual processor of the target to perform the process / method as described above. Typically, program modules include routines, programs, libraries, objects, classes, components, data structures, etc. that perform specific tasks or implement specific abstract data types. In various embodiments, the functions of program modules can be combined or divided between program modules as needed. Machine executable instructions for program modules can be executed in local or distributed devices. In distributed devices, program modules can be located in local and remote storage media.

[0086] The computer program code for implementing the method of the present invention can be written in one or more programming languages. These computer program codes can be provided to the processor of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that the program code, when executed by the computer or other programmable data processing device, causes the function / operation specified in the flow chart and / or block diagram to be implemented. The program code can be executed completely on a computer, partially on a computer, as an independent software package, partially on a computer and partially on a remote computer or completely on a remote computer or server.

[0087] In the context of the present invention, computer program codes or related data may be carried by any appropriate carrier to enable a device, apparatus or processor to perform the various processes and operations described above. Examples of carriers include signals, computer readable media, and the like. Examples of signals may include electrical, optical, radio, acoustic or other forms of propagation signals, such as carrier waves, infrared signals, and the like.

[0088] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with this embodiment can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0089] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without creative work are still within the scope of protection of the present invention.

Claims

1. A method for optimizing the economic operation of coal-fired power units based on micro-operating conditions, characterized in that: The following steps are involved: Collect the operating data of coal-fired power units, pre-process the collected data, perform cluster analysis on the pre-processed data, and divide the operating conditions into different micro-conditions; Establishing an energy efficiency index model to obtain energy efficiency indexes for each micro-operating condition, wherein the energy efficiency indexes include macro energy efficiency indexes and energy efficiency micro-indicators; For each micro-operating condition, analyze the correlation between its energy efficiency index and the existing characteristics of the main and auxiliary equipment, and determine the key factors affecting the economic efficiency of the unit; Based on the key factors affecting the unit's economic efficiency, a micro-condition index optimization model is established, and the optimal operating parameter combination for each micro-condition is found through the optimization algorithm; According to the optimization analysis results, formulate optimization operation adjustment strategies for different micro-operating conditions.

2. The method for optimizing economic operation of a coal-fired power unit based on micro-operating conditions as claimed in claim 1, characterized in that: Establish an energy efficiency index model, specifically: Based on the analysis of thermal cycle mechanism and classical thermal system, a macro-energy efficiency index analysis and calculation model for the unit is constructed. Big data analysis is used to explore the patterns in the operating data, and a micro-condition big data index model is established. The macro-energy efficiency index analysis and calculation model is verified and improved, and a micro-condition energy efficiency index model is established.

3. The method for optimizing economic operation of coal-fired power units based on micro-operating conditions as claimed in claim 2, characterized in that: The macro energy efficiency index analysis and calculation model distinguishes the thermal parameter transfer levels and transfer relationships, and considers the changes in operating conditions and operating intervals as well as the thermal information in different intervals.

4. The method for optimizing economic operation of a coal-fired power unit based on micro-operating conditions according to claim 1, characterized in that: It also includes real-time monitoring of the energy efficiency index of each micro-operating condition of the unit, and issuing a warning signal when the energy efficiency index deviates from the optimal value within a certain range.

5. The method for optimizing economic operation of coal-fired power units based on micro-operating conditions according to claim 1, characterized in that: The key factors affecting the unit economy include boiler efficiency, turbine heat rate, condenser vacuum, unit heat rate, unit coal consumption and unit plant power consumption rate.

6. The method for optimizing economic operation of coal-fired power units based on micro-operating conditions as claimed in claim 1, characterized in that: The energy efficiency micro-indicators include load micro-variation heat consumption rate, load micro-variation coal consumption rate, load micro-variation vacuum change rate and load micro-variation main steam parameter fluctuation amplitude.

7. A system for optimizing economic operation of coal-fired power units based on micro-operating conditions, characterized in that: include: The data acquisition and processing module is configured to: collect the operating data of the coal-fired power unit, pre-process the collected data, perform cluster analysis on the pre-processed data, and divide the operating conditions into different micro-conditions; The energy efficiency index acquisition module is configured to: establish an energy efficiency index model to obtain the energy efficiency index of each micro-operating condition, wherein the energy efficiency index includes a macro energy efficiency index and an energy efficiency micro-indicator; The key factor determination module is configured to: for each micro-operating condition, analyze the correlation between its energy efficiency index and the existing characteristics of the main and auxiliary equipment, and determine the key factors affecting the unit's economy; The optimization analysis module is configured to: establish an index optimization model for micro-operating conditions based on the key factors affecting the unit's economic performance, and find the optimal operating parameter combination for each micro-operating condition through an optimization algorithm; The improvement strategy formulation module is configured to formulate optimized operation adjustment strategies for different micro-operating conditions based on the optimization analysis results.

8. An electronic device, characterized in that: The method comprises a memory and a processor and computer instructions stored in the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the method according to any one of claims 1 to 6 is completed.

9. A computer-readable storage medium, characterized in that: Used to store computer instructions, which, when executed by a processor, complete the method described in any one of claims 1 to 6.

10. A computer program product, characterized in that The invention comprises a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 6.

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