Coal blending method, system, equipment and storage medium for coal-fired power unit

Abstract

The present application belongs to the technical field of coal-fired unit fuel blending optimization, and particularly relates to a coal-fired unit coal blending method, system, device and storage medium, data is collected, and historical power generation cost is calculated; according to the historical power generation cost, the relationship between real-time power generation cost and power generation load is analyzed; the cumulative power generation cost of different coal types under the power generation load is analyzed to obtain a coal supply strategy; a coal mill coal supply ratio optimization model is constructed, the relationship between the power generation cost and the coal supply is analyzed, and the coal supply blending ratio of the coal mill is adjusted; the power generation cost calculated by the historical data provides a clear reference benchmark for subsequent analysis and optimization, the power generation cost of different coal types under different load sections is quantified, the cumulative power generation cost module of the coal type is analyzed to reduce the fuel cost; the coal mill coal supply ratio optimization model is constructed to realize fine tuning and further improve the combustion efficiency.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of coal-fired unit fuel blending optimization, and particularly relates to a coal-fired unit coal blending method, system, device and storage medium. BACKGROUND

[0002] With the rapid development of industrialization and urbanization, the demand for energy in China continues to grow. As an important power supply method, the coal combustion cost of coal-fired power plants accounts for a large proportion of the total cost. However, the combustion of a single coal has many shortcomings, such as unstable combustion, large heat value fluctuation, and high pollutant emission. These problems not only affect the economic benefits of the power plant, but also cause serious pollution to the environment.

[0003] In order to solve these problems, the blending and burning technology of complex coal has gradually become a research hotspot. The blending and burning technology of coal refers to mixing different coal according to a certain proportion to improve the combustion characteristics of coal and reduce pollutant emissions. However, the existing technology of blending and burning of coal depends on experience and lacks scientific and systematic theoretical guidance and practical verification. This leads to unstable blending and burning effect, and it is difficult to achieve the best economic benefit and environmental benefit.

[0004] In addition, due to the complexity and diversity of coal quality, how to accurately and quickly determine the blending ratio and blending strategy of different coals is also a big problem faced by current technology. Therefore, it is of great significance to develop an efficient and energy-saving complex coal blending and burning system and method for improving the economic benefit and environmental benefit of coal-fired power plants. SUMMARY

[0005] In view of the problems existing in the prior art, the present application is proposed.

[0006] Therefore, the technical problem solved by the present application is to provide an efficient and flexible coal blending and burning guidance method. The method differentiates the electricity cost of different coals at different load stages, thereby providing customized coal feeding strategy guidance and coal blending ratio strategy guidance for the stage load plan, realizing the optimized utilization of coal resources, improving the combustion efficiency, reducing the power generation cost, and providing technical support for the sustainable development of coal-fired power plants.

[0007] To solve the above technical problems, the present application provides the following technical solutions: a coal-fired unit blending method, comprising: collecting data and calculating historical electricity cost; analyzing the relationship between real-time electricity cost and power generation load according to the historical electricity cost; analyzing the cumulative electricity cost of different coals under power generation load to obtain a coal feeding strategy; constructing a coal mill coal feeding ratio optimization model to analyze the relationship between electricity cost and coal feeding amount, and adjusting the coal feeding amount blending ratio of the coal mill.

[0008] As a preferred scheme of the coal blending method for coal-fired power generating units, the collected data includes online data and offline data, the online data includes power generation load, coal supply amount, and coal mill power consumption, and the offline data includes coal type price.

[0009] As a preferred scheme of the coal blending method for coal-fired power generating units, the calculation of the historical unit power cost includes time synchronization of the power generation load, the coal supply amount, and the coal type price, and the historical unit power cost is expressed as:

[0010] ,

[0011] wherein, is the historical unit power cost, is the coal supply amount of the coal type, is the price of the coal type, is the power generation load.

[0012] As a preferred scheme of the coal blending method for coal-fired power generating units, the analysis of the relationship between the real-time unit power cost and the power generation load includes fitting analysis of the unit power cost under different load sections according to the historical unit power cost, and the relationship between the real-time unit power cost and the power generation load is expressed as:

[0013] ,

[0014] wherein, is the real-time unit power cost, is the power generation load, is a fitting function of the real-time unit power cost.

[0015] As a preferred scheme of the coal blending method for coal-fired power generating units, the analysis of the cumulative unit power cost of different coal types under the power generation load includes fitting of the power generation load and time curve, and is expressed as:

[0016] ,

[0017] wherein, is the power generation load, is time, is a fitting function of the power generation load,

[0018] the relationship between the real-time unit power cost and time is expressed as:

[0019] ,

[0020] wherein, is the real-time unit power cost, is time,​​ The cumulative electricity cost is expressed as:

[0021] In , the cumulative electricity cost is expressed as:

[0022] ,

[0023] Wherein, The cumulative electricity cost is expressed as:

[0024] ,

[0025] Wherein, The cumulative electricity cost in is expressed as: The cumulative electricity cost of the coal type is sorted according to the coal consumption time of the first coal, and the cumulative electricity cost ranking is obtained According to the coal warehouse inventory and in combination with the cumulative electricity cost ranking, the coal bunker coal feeding strategy is dynamically adjusted.

[0026] As a preferred scheme of the coal blending method of the thermal power generating unit, wherein: the coal mill coal feeding amount ratio optimization model is constructed, including constructing a comprehensive electricity cost model expressed as:

[0027] ,

[0028] Wherein, The comprehensive electricity cost is expressed as: The current coal feeding amount is expressed as: The cumulative electricity cost of the coal type is expressed as: The coal feeding amount of the coal mill is expressed as:

[0029] ,

[0030] ,

[0031] Wherein, The coal feeding amount of the coal feeder is expressed as: The furnace heat is expressed as: The minimum coal feeding amount of the coal mill is expressed as: The maximum coal feeding amount of the coal mill is expressed as: The constant corresponding to the coal mill is expressed as:

[0032] As a preferred scheme of the coal blending method for a coal-fired thermal power unit, the method comprises: adjusting the blending ratio of the coal supply amount of the coal mill, including solving a comprehensive electricity cost model to obtain an optimal solution of the comprehensive electricity cost satisfying a boundary condition , obtaining a coal mill coal supply suggestion value , calculating the cost change as:

[0033] ,

[0034] wherein, is a comprehensive electricity cost change value, is an initial comprehensive electricity cost value, is a minimum comprehensive electricity cost value, is represented as the coal supply suggestion value of the coal mill .

[0035] As a preferred scheme of the coal blending system for a coal-fired thermal power unit, the system comprises a historical cost calculation module, a real-time electricity cost analysis module, a coal supply strategy judgment module, and an adjustment coal supply blending ratio module; the historical cost calculation module comprises a data acquisition module and a historical electricity cost calculation module, the data acquisition module is used to acquire power generation load, coal supply amount, coal mill power consumption, and coal price, the historical electricity cost calculation module is used to time-synchronize the power generation load, the coal supply amount, and the coal price, and calculate the historical electricity cost; the real-time electricity cost analysis module comprises a fuel electricity cost analysis model and an analysis of the relationship between the electricity cost and the power generation load; the coal supply strategy judgment module comprises an accumulated electricity cost analysis module and a coal supply strategy module, the accumulated electricity cost analysis module is used to fit the power generation load and a time curve to obtain the relationship between the real-time electricity cost and time, and calculate the accumulated electricity cost, the coal supply strategy module is used to sort the accumulated electricity cost of the coal, and adjust the coal bunker coal supply strategy according to the accumulated electricity cost ranking; the adjustment coal supply blending ratio module comprises a comprehensive electricity cost calculation module, a comprehensive electricity cost optimal solution calculation module, and a coal supply calculation module, the comprehensive electricity cost calculation module is used to calculate the comprehensive electricity cost according to the real-time electricity cost and the current coal supply amount, construct a coal supply boundary condition, the comprehensive electricity cost optimal solution calculation module is used to calculate the minimum value of the comprehensive electricity cost according to the coal supply condition, and the coal supply calculation module is used to calculate the coal supply amount corresponding to the minimum value of the comprehensive electricity cost, and calculate the cost change.

[0036] A computer device comprises a memory and a processor, the memory stores a computer program, and the processor implements the steps of the method in any one of the coal blending methods for a coal-fired thermal power unit when executing the computer program.

[0037] A computer readable storage medium, having stored thereon a computer program, wherein the computer program is executed by a processor to implement the steps of the method in any one of the coal blending methods for thermal power generating units.

[0038] The present application has the following advantages: the degree electricity cost calculated by historical data provides a clear reference benchmark for subsequent analysis and optimization, the relationship between real-time degree electricity cost and power generation load is analyzed, the degree electricity cost of different coal types at different load segments can be quantified, that is, the cost difference caused by burning different coal types at different loads can be considered, thereby performing dynamic cost analysis under variable load; by constructing an economic coal type analysis model, combining economic analysis of different coal types at different loads, a basis can be provided for coal quantity optimization, the next stage coal feeding strategy can be guided according to the load plan, optimal fuel blending at different load segments is achieved, and fuel cost is reduced; by constructing a coal feeder quantity ratio optimization model, the relationship between degree electricity cost and coal quantity is analyzed, the blending ratio of coal quantity is adjusted, the optimal coal quantity of the coal feeder is recommended, fine-tuning is realized, and the combustion efficiency is further improved. BRIEF DESCRIPTION OF DRAWINGS

[0039] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0040] Figure 1 The flowchart of the coal blending method for thermal power generating units provided by the first embodiment of the present application.

[0041] Figure 2 The degree electricity cost and load relationship diagram of the coal blending method for thermal power generating units provided by the first embodiment of the present application.

[0042] Figure 3 The load and time relationship diagram of the coal blending method for thermal power generating units provided by the first embodiment of the present application.

[0043] Figure 4 The degree electricity cost and load relationship diagram of the coal blending method for thermal power generating units provided by the second embodiment of the present application.

[0044] Figure 5 The load distribution diagram of the coal blending method for thermal power generating units provided by the second embodiment of the present application.

[0045] Figure 6 The change diagram of the 10-day average degree electricity cost of the coal blending method for thermal power generating units provided by the second embodiment of the present application. DETAILED DESCRIPTION

[0046] In order to make the above objectives, features and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should fall within the scope of protection of the present application.

[0047] Embodiment 1, Reference Figures 1-3 For the first embodiment of the present application, the embodiment provides a coal blending method for thermal power generating units, comprising:

[0048] S1: Collecting data and calculating historical power generation cost.

[0049] It should be noted that the collected data includes online data and offline data. The online data includes power generation load, coal supply amount, and coal mill power consumption. The offline data includes coal price.

[0050] It should also be noted that the implementation of coal blending economic analysis requires data analysis and calculation on a large amount of historical data. The data to be collected includes online data and offline data. The online data includes but is not limited to: unit load, coal supply amount of each coal feeder, coal mill, coal belt power consumption, slurry circulating pump power consumption, and limestone consumption. The offline data includes but is not limited to: coal quality into the furnace test data, coal purchasing price, fuel transportation cost, etc. Through industrial Internet of Things (IoT) and intelligent sensors, power generation load, coal price, and coal mill power consumption data can be collected in real time. These systems can automatically upload data to cloud platforms or local data centers for calculation and analysis, ensuring real-time and accuracy of data.

[0051] It should also be noted that the calculation of historical power generation cost includes time synchronization of power generation load, coal supply amount, and coal price. The calculation of historical power generation cost is represented as:

[0052] ,

[0053] wherein, is the historical power generation cost, is the coal supply amount of coal , is the price of coal , is the power generation load.

[0054] It should be noted that the power generation load, coal supply and coal price are time-synchronized, and the historical unit power cost is calculated based on the synchronized data. Through the time-synchronized data, it can be analyzed which time period has a higher or lower cost, thereby providing a scientific basis for power plant scheduling, coal procurement and coal supply ratio optimization decision-making. For example, when the coal price fluctuates greatly, the power plant can adjust the coal procurement plan in advance to avoid additional costs caused by price peaks.

[0055] S2: According to the historical unit power cost, analyze the relationship between the real-time unit power cost and the power generation load.

[0056] It should be noted that analyzing the relationship between the real-time unit power cost and the power generation load includes fitting and analyzing the unit power cost under different load segments based on the historical unit power cost, and obtaining the relationship between the real-time unit power cost and the power generation load, which is expressed as:

[0057]

[0058] wherein, is the real-time unit power cost, is the power generation load, is the real-time unit power cost fitting function.

[0059] Through fitting analysis of historical unit power cost, the variation law of unit power cost under different load segments can be accurately revealed. In the low load segment, the efficiency of the generator set may be low, resulting in higher unit power cost; while in the high load segment, the generator set may be more efficient, resulting in lower unit power cost. By analyzing each load segment, power plant managers can better understand the specific impact of load changes on cost, thereby optimizing load scheduling and improving overall economic efficiency; by establishing a relationship model between historical unit power cost and load segment, accurate reference can be provided for the calculation of real-time unit power cost. When the real-time load changes, the corresponding unit power cost can be predicted according to the model, thereby realizing more accurate cost control. For example, when the load suddenly rises, the power plant can predict the cost fluctuation trend according to the fitting result of historical data, and take timely measures such as adjusting coal type, optimizing coal supply, etc., thereby reducing excessive fluctuations in cost.

[0060] It should be noted that in the case of burning only three coal types, fitting analysis of real-time unit power cost under different load segments reveals the following characteristics: the power generation unit power cost and the power generation load have a quadratic function relationship, and the real-time unit power cost is the lowest when the load is close to about 80% of the rated load, as shown in Figure 2 .

[0061] S3: Analyze the cumulative unit power cost of different coal types under the power generation load to obtain the coal supply strategy.

[0062] ​It should be noted that the cumulative cost of electricity of different coal types under the power generation load includes fitting the power generation load and time curve, which is expressed as:

[0063] ,

[0064] wherein, is the power generation load, is the time, is the power generation load fitting function,

[0065] The relationship between real-time cost of electricity and time is expressed as:

[0066] ,

[0067] wherein, is the real-time cost of electricity, is the time, is the real-time cost of electricity and time relationship function,

[0068] In time, the cumulative cost of electricity is expressed as:

[0069] ,

[0070] wherein, is the cumulative cost of electricity, and the calculation of the cumulative cost of electricity is expressed as:

[0071] ,

[0072] wherein, is the cumulative cost of electricity in time, is the time of coal consumption when the coal is first added, and the cumulative cost of electricity of the coal is sorted to obtain the cumulative cost of electricity ranking , according to the coal warehouse inventory and in combination with the cumulative cost of electricity ranking, the coal warehouse coal adding strategy is dynamically adjusted.

[0073] It should also be noted that the load plan given by the dispatch is used as load distribution prediction data for calculation, the load plan and time curve are linearly fitted, the fitting of the load curve is used to dynamically predict the power generation demand, and then a decision basis is provided for subsequent coal selection and cost optimization, and the relationship between the power generation load and the time is as shown in Figure 3 .

[0074] Further, through the relationship between the real-time cost of electricity and the power generation load, the relationship between the real-time cost of electricity and the time is obtained, through the real-time cost of electricity and the time relationship model, and the cumulative cost of electricity in time is calculated, in this embodiment, The value is 8, according to the industry experience, the last coal in the coal bunker of the power plant can guarantee 8 hours of consumption, calculate the cumulative degree electric cost of 8 hours, the power plant can dynamically adjust the cost prediction according to the real-time load, coal consumption and time, so that the power plant can better manage and control the cost.

[0075] It should also be noted that according to the calculation of 8-hour cumulative cost, the cumulative cost ranking of different coal types under 8-hour load distribution is obtained, and the coal bunker charging strategy is dynamically adjusted according to the inventory situation. The cumulative cost ranking of coal types is dynamically calculated based on real-time degree electric cost and load condition, to ensure that the power plant selects the coal type with the lowest cumulative cost under different load demands. The operation and maintenance personnel can arrange the coal bunker with lower storage position to charge the coal type with higher cumulative cost according to the coal field coal distribution and inventory situation, to ensure that the coal type can fully participate in combustion within the next 8-hour load segment, reduce the comprehensive fuel cost, and improve the economic efficiency of unit operation.

[0076] S4: Construct a coal mill coal feed ratio optimization model to analyze the relationship between degree electric cost and coal feed, and adjust the coal feed blending ratio of the coal mill.

[0077] It should be noted that the construction of the coal mill coal feed ratio optimization model includes the construction of the comprehensive degree electric cost model, which is represented as:

[0078] ,

[0079] Among them, is the comprehensive degree electric cost, is the current coal feed, is the cumulative degree electric cost of the coal type, is the coal feed of the coal mill, is the coal feed of the coal mill, The boundary condition of the coal feed is represented as:

[0080] ,

[0081] ,

[0082] Among them, is the coal feed of the coal mill, is the heat input, is the minimum coal feed of the coal mill, is the maximum coal feed of the coal mill, is the constant corresponding to the coal mill.

[0083] ​​​​It should be noted that by constructing the comprehensive degree electricity cost model, the real-time degree electricity cost can be combined with the changes of coal supply and coal type, and the influence of coal selection and ratio on cost can be systematically analyzed. This helps the power plant to more accurately adjust the coal supply and coal type during operation, and avoid the cost increase caused by the deviation of a single factor.

[0084] It should be noted that by constructing the coal supply boundary condition, the definition of the coal supply range under different conditions (such as maximum coal supply and minimum coal supply) is involved. The setting of this boundary condition ensures the rationality of the model optimization and avoids the adverse effects caused by excessive or insufficient coal supply. According to the historical data of the power plant, the upper and lower boundaries of the coal supply of each coal mill are obtained, and finally the coal supply boundary condition is obtained by further verification of the power plant experts.

[0085] Further, adjusting the coal supply blending ratio of the coal mill includes solving the comprehensive degree electricity cost model to obtain the optimal solution of the comprehensive degree electricity cost that meets the boundary condition , obtaining the coal supply recommended value of the coal mill , and calculating the cost change as follows:

[0086] ,

[0087] wherein, is the comprehensive degree electricity cost change value, is the initial value of the comprehensive degree electricity cost, is the minimum value of the comprehensive degree electricity cost, represents the coal supply recommended value of the coal mill.

[0088] It should be noted that by solving the comprehensive degree electricity cost model, the optimal solution that meets the boundary condition is obtained, i.e. the minimum value of the comprehensive degree electricity cost, and the optimal coal supply recommended value is calculated. A cost calculation model is established for all coal mill operation combinations. According to the current coal mill combination, the corresponding model is calculated, and the comprehensive degree electricity cost needs to be corrected. In addition to the calculation based on coal price, heat value and load, the desulfurization cost caused by sulfur content and ash content of coal type and the byproduct income need to be analyzed. Through the initial value of the comprehensive degree electricity cost and the minimum value of the comprehensive degree electricity cost, the change value of the comprehensive degree electricity cost is calculated. The power plant can evaluate the effect of the adjusted strategy and determine whether the expected cost saving target is achieved.

[0089] Embodiment 2, referring to Figures 4-6 is an embodiment of the present application, which provides a coal blending method for thermal power units. In order to verify the beneficial effects of the present application, economic benefit calculation and simulation experiment are used for scientific demonstration.

[0090] ​The basic conditions of the test machine set are as follows: the maximum continuous evaporation capacity of the boiler is 2070 t / h. The design coal calorific value is 5122 Kcal / Kg, and the checking coal calorific value is 5026 Kcal / Kg. The boiler adopts a low-nitrogen burner + SCR configuration with a four-corner tangential arrangement. The desulfurization adopts a double-tower gypsum limestone wet desulfurization process, which can meet the emission standards when the maximum sulfur content of the coal fed into the furnace under the MCR condition is 2.2%. The boiler stable combustion and ignition system is equipped with three layers of oil guns and one layer of plasma. Six medium-speed bowl-type coal mills, with a maximum coal feeding capacity of 59 tons / hour, can meet the full load under the design coal condition.

[0091] In this embodiment, 10 consecutive summer days with sunny weather and a maximum temperature of about 35-39°C are selected, so that the load distribution is as similar as possible.

[0092] The first 5 days are according to the monthly blending scheme and experience for coal feeding and combustion adjustment, and the last 5 days are according to the recommended method of this embodiment for coal feeding and combustion adjustment.

[0093] The daily power generation cost is calculated according to the daily coal consumption, power generation, and coal price, and the comparison and trend are analyzed.

[0094] During the test period, each coal mill can select 3 kinds of coal for coal feeding: coal type 1 is Qinggang coal (calorific value 4700 kJ / kg), coal type 2 is Liulang coal (4400 kJ / kg), and coal type 3 is Xichuan coal (calorific value 4000 kJ / kg). The relationship between the power generation cost of the three kinds of coal and the load is as shown in Figure 4 .

[0095] The first 5 days are according to the monthly blending scheme and experience for coal feeding and combustion adjustment, and the last 5 days are according to the recommended method of this embodiment for coal feeding and combustion adjustment.

[0096] Table 1 Coal blending and burning arrangement table

[0097] Coal bunker A coal bunker B coal bunker C coal bunker D coal bunker E coal bunker F coal bunker Recommended coal type (0-8) Qinggang Liuxiang Liuxiang or Xichuan Liuxiang Xichuan Liuxiang

[0098] The last 5 days are according to the recommended coal feeding scheme of this embodiment according to the load distribution. The load distribution trend of the 5 days is roughly similar, and the load distribution of one day is as shown in Figure 5 .

[0099] The coal feeding scheme recommended by the load in this embodiment is as shown in Table 2.

[0100] Table 2 Coal feeding scheme recommended by load

[0101] Coal bunker A coal bunker B coal bunker C coal bunker D coal bunker E coal bunker F coal bunker Recommended coal type (8-16) Liuxiang Liuxiang Xichuan Xichuan Liuxiang Xichuan Recommended coal type (16-24) Qinggang Liuxiang Xichuan Xichuan Xichuan Xichuan Figure 6 ​ ​ ​ ​ ​ ​

[0102] In addition to the coal recommendation, the embodiment also carries out the coal quantity recommendation adjustment of each coal mill, further realizes the fine adjustment of the blending mode, analyzes the average degree of electricity cost per day, and compares the difference between the monthly blending plan and the recommended blending mode of the embodiment: the average degree of electricity cost of the first 5 days is 0.21 yuan / degree, and the average degree of electricity cost of the last 5 days is 0.195 yuan / degree, and the degree of electricity cost of the last 5 days decreases by about 7% compared with the first 5 days, as shown in ​

[0103] Because of the blending according to the embodiment, more Xikuan coal, that is, coal with lower calorific value, is added at high load, although the average degree of electricity cost decreases, but because the calorific value is lower, the coal consumption will also increase, compared with Liuxiang coal, the calorific value of which decreases by about 10%, the power consumption of unloading, coal conveying and coal grinding will increase, and the environmental protection cost will also increase. According to the experience value of one degree of electricity, the power consumption cost of unloading, coal conveying and coal grinding is about 0.003 yuan / degree, the environmental protection cost is about 0.027 yuan / degree, and the total is about 0.03 yuan / degree. The total coal quantity increases by 10%, and the power consumption cost of coal is calculated as 0.03*0.1=0.003 yuan / degree, and the cost of the last 5 days is about 0.198 yuan / degree, which is about 5.7% lower than 0.21 yuan / degree of the first 5 days. This shows that the embodiment realizes the optimal fuel blending under different load sections, reduces the fuel cost, realizes the fine adjustment, and further improves the combustion efficiency.

[0104] Embodiment 3 is an embodiment of the present application, which provides a coal blending system for thermal power generating units, comprising a historical cost calculation module, a real-time degree of electricity cost analysis module, a coal quantity strategy judgment module, and an adjustment coal quantity blending ratio module.

[0105] The S5: the historical cost calculation module comprises a data acquisition module and a historical degree of electricity cost calculation module. The data acquisition module is used for acquiring power generation load, coal quantity, coal mill power consumption and coal price, and the historical degree of electricity cost calculation module is used for time synchronization of the power generation load, coal quantity and coal price, and calculation of the historical degree of electricity cost.

[0106] It should be noted that the data acquisition module transmits the real-time acquired data to the historical degree of electricity cost calculation module as data input, and the calculated historical degree of electricity cost is transmitted to the real-time degree of electricity cost analysis module in real time, as data support for subsequent analysis of the real-time degree of electricity cost.

[0107] The S6: the real-time degree of electricity cost analysis module comprises a fuel degree of electricity cost analysis model and analysis of the relationship between the degree of electricity cost and the power generation load.

[0108] ​It should also be noted that the real-time electricity cost analysis module receives the historical electricity cost from the historical electricity cost module, calculates the real-time electricity cost after calculation, and transmits the result to the analysis coal cumulative electricity cost module of the coal supply strategy judgment module as the basic data for deducing the cumulative electricity cost.

[0109] S7: The coal supply strategy judgment module includes an analysis coal cumulative electricity cost module and a coal supply strategy module. The analysis coal cumulative electricity cost module is used to fit the power generation load and the time curve to obtain the relationship between the real-time electricity cost and the time, and to calculate the cumulative electricity cost. The coal supply strategy module is used to sort the cumulative electricity cost of the coal, and to adjust the coal supply strategy of the coal bunker according to the cumulative electricity cost ranking.

[0110] It should also be noted that the coal supply strategy judgment module receives data from the real-time electricity cost analysis module, deduces the cumulative electricity cost after calculation, and transmits the result to the coal supply strategy module as the basic data for calculating the cumulative electricity cost ranking of the coal. The coal supply strategy module obtains the coal supply strategy according to the received data, and transmits the coal supply to the coal quantity adjustment blending ratio module as the basic data for building the comprehensive electricity cost model.

[0111] S8: The coal quantity adjustment blending ratio module includes a comprehensive electricity cost calculation module, a comprehensive electricity cost optimal solution calculation module, and a coal supply calculation module. The comprehensive electricity cost calculation module is used to calculate the comprehensive electricity cost according to the real-time electricity cost and the current coal supply, and to build the coal supply boundary condition. The comprehensive electricity cost optimal solution calculation module is used to calculate the minimum value of the comprehensive electricity cost according to the coal supply condition. The coal supply calculation module is used to calculate the coal supply corresponding to the minimum value of the comprehensive electricity cost, and to calculate the cost change.

[0112] It should also be noted that the comprehensive electricity cost calculation module depends on the coal supply strategy of the coal supply strategy module, calculates the comprehensive electricity cost, and sets the boundary condition as the data support for the calculation of the comprehensive electricity cost optimal solution module. The comprehensive electricity cost optimal solution calculation module obtains the minimum value of the comprehensive electricity cost according to the input data. The coal supply calculation module calculates the coal supply suggestion value through the minimum value, and realizes the data closed-loop management.

[0113] If the functions are implemented in software, the functions can be stored in or implemented as one or more instructions or code on a computer-readable medium. Computer-readable media include both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage medium can be any available medium that can be accessed by a computer. By way of example, and not limitation, such computer-readable media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. Also, any connection is properly termed a computer-readable medium. For example, if the software is transmitted from a website, server, or other remote source using a coaxial cable, fiber optic cable, or twisted pair, then the coaxial cable, fiber optic cable, or twisted pair are included in the definition of medium. Disk and disc, as used herein, include compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), and Blu-Ray® disc where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media.

[0114] In other words, like a human driver of a vehicle, the autonomous vehicle 100 can be programmed to follow traffic laws and rules of the road, and to make decisions based on its programming and the information it receives from its sensors and other sources. The autonomous vehicle 100 can also be programmed to make decisions based on its programming and the information it receives from its sensors and other sources, even if those decisions are not in accordance with traffic laws and rules of the road. For example, the autonomous vehicle 100 can be programmed to avoid a collision with another vehicle, even if doing so would violate a traffic law or rule of the road.

[0115] In other words, like a human driver of a vehicle, the autonomous vehicle 100 can be programmed to follow traffic laws and rules of the road, and to make decisions based on its programming and the information it receives from its sensors and other sources. The autonomous vehicle 100 can also be programmed to make decisions based on its programming and the information it receives from its sensors and other sources, even if those decisions are not in accordance with traffic laws and rules of the road. For example, the autonomous vehicle 100 can be programmed to avoid a collision with another vehicle, even if doing so would violate a traffic law or rule of the road.

[0116] It should be understood that portions of the present application can be implemented in hardware, software, firmware, or combinations thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, implementation can be with any or a combination of the following technologies, which are all well known in the art: a discrete logic circuit having logic gates for implementing logic functions upon an application of data signals, an application specific integrated circuit having appropriate combinational logic gates, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0117] It should be noted that the above examples are only used to illustrate the technical solutions of the present application but not limit the present application. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced equivalently without departing from the spirit and scope of the present application, and all of them should be covered in the scope of the claims of the present application.

Claims

1. A method for blending coal for a coal-fired power unit, the method comprising: The application relates to a coal mill coal feeding amount proportioning optimization method and device. ​ Collecting data, calculating historical degree electricity cost; According to the historical degree electricity cost, analyzing the relationship between real-time degree electricity cost and power generation load; Analyzing the cumulative degree electricity cost of different coal types under power generation load to obtain a coal feeding amount strategy; Building a coal mill coal feeding amount proportioning optimization model, analyzing the relationship between degree electricity cost and coal feeding amount, and adjusting the coal feeding amount mixing ratio of the coal mill; The cumulative degree electricity cost of different coal types under power generation load is analyzed, including fitting the power generation load and the time curve, and being expressed as: ， wherein, is the power load, is time, is the power load fitting function, The relationship between real-time degree electricity cost and time is expressed as: ， wherein, is the real-time electricity cost, is time, is the real-time electricity cost versus time function, In The cumulative degree-hour cost is expressed as: ， wherein, The cumulative degree-hour cost is the cumulative degree-hour cost, which is calculated as: ， wherein, to accumulate the electricity cost of the coal, for a certain period of time, for the consumption time of the coal type when the coal is loaded once, the accumulated electricity cost of the coal type is sorted to obtain an accumulated electricity cost ranking , according to the coal warehouse inventory and in combination with the accumulated electricity cost ranking, the coal loading strategy of the coal warehouse is dynamically adjusted; The coal mill coal feeding amount proportioning optimization model is built, including building a comprehensive degree electricity cost model expressed as: ， wherein, is the integrated electricity cost, is the current coal feed rate, is the coal type is the cumulative electricity cost, is the coal mill feed rate, The boundary condition of the coal feeding amount is built expressed as: ， ， wherein, the coal feed to the coal mill, the coal feed to the coal mill, the heat input to the furnace, the coal feed to the coal mill, the minimum coal feed to the coal mill, the coal feed to the coal mill, the maximum coal feed to the coal mill, the coal feed to the coal mill, the corresponding constant.

2. The coal blending method for a coal-fired power unit according to claim 1, characterized in that: The collecting data includes collecting online data and offline data, the online data includes power generation load, coal feeding amount and coal mill power consumption, and the offline data includes coal type price.

3. The coal blending method for a coal-fired power unit according to claim 1 or 2, characterized in that: The calculating historical degree electricity cost includes time synchronization of the power generation load, the coal feeding amount and the coal type price, and calculating the historical degree electricity cost expressed as: ， wherein, is the historical electricity cost, is the coal type is the coal supply, is the coal type is the price of the coal type, is the power generation load.

4. The coal blending method for a coal-fired power unit according to claim 3, characterized in that: The analyzing the relationship between real-time degree electricity cost and power generation load includes fitting and analyzing the degree electricity cost under different load sections according to the historical degree electricity cost, and obtaining the relationship between real-time degree electricity cost and power generation load expressed as: ， wherein, is the real-time electricity cost, is the generation load, is the real-time electricity cost fitting function.

5. The coal blending method for a coal-fired power unit according to claim 1, 2 or 4, characterized in that: The adjusting the blending ratio of the coal supply amount of the coal mill comprises solving a comprehensive electricity cost model to obtain an optimal solution of the comprehensive electricity cost satisfying boundary conditions , obtaining a coal mill coal supply amount suggestion value , and calculating the cost change is represented as: ， in, This represents the overall change in cost per kilowatt-hour. This is the initial value for the overall cost per kilowatt-hour. To minimize the overall cost per kilowatt-hour, Represented as a coal mill Recommended coal feed rate.

6. The coal blending system for thermal power generating units, which adopts the coal blending method for thermal power generating units according to any one of claims 1-5, characterized in that: The method includes a historical cost calculation module, a real-time degree electricity cost analysis module, a coal feeding amount strategy judgment module and an adjustment coal amount mixing ratio module. The calculating historical cost calculation module includes a data collection module and a historical degree electricity cost calculation module, the data collection module is used for collecting power generation load, coal feeding amount, coal mill power consumption and coal type price, and the historical degree electricity cost calculation module is used for time synchronization of the power generation load, the coal feeding amount and the coal type price and calculating the historical degree electricity cost; The real-time degree electricity cost analysis module includes building a fuel degree electricity cost analysis model and analyzing the relationship between degree electricity cost and power generation load; The coal feeding amount strategy judgment module includes a coal type cumulative degree electricity cost analysis module and a coal feeding amount strategy module, the coal type cumulative degree electricity cost analysis module is used for fitting the power generation load and the time curve, obtaining the relationship between real-time degree electricity cost and time, and calculating the cumulative degree electricity cost, and the coal feeding amount strategy module is used for sorting the cumulative degree electricity cost of the coal types, adjusting the coal bunker coal feeding strategy according to the cumulative degree electricity cost ranking; The adjustment coal amount mixing ratio module includes a comprehensive degree electricity cost calculation module, a comprehensive degree electricity cost optimal solution calculation module and a coal feeding amount calculation module, the comprehensive degree electricity cost calculation module is used for calculating the comprehensive degree electricity cost according to real-time degree electricity cost and current coal feeding amount, building a coal feeding amount boundary condition, the comprehensive degree electricity cost optimal solution calculation module is used for calculating the minimum value of the comprehensive degree electricity cost according to the coal feeding amount condition, and the coal feeding amount calculation module is used for calculating the coal feeding amount corresponding to the minimum value of the comprehensive degree electricity cost and calculating the cost change. 7.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-8 when the computer program is executed by the processor. The processor executes the computer program to realize the steps of the method in any one of claims 1 to 5.

8. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the steps of the method in any one of claims 1 to 5.

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