CN116777537A - Method and system for calculating and optimizing electricity-measuring coal cost and blending coal based on coal quality characteristics - Google Patents
Method and system for calculating and optimizing electricity-measuring coal cost and blending coal based on coal quality characteristics Download PDFInfo
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Abstract
The application discloses a method for calculating the cost of electric fire coal and optimizing coal blending based on coal quality characteristics, which comprises the following steps: collecting historical mixed coal blending coal quality data of a coal-fired power plant and power supply coal consumption data in an SIS system, and carrying out normalization treatment; establishing and training a soft measurement model of coal quality characteristics and power supply coal consumption based on a PSO-SVM; calculating the electricity-coal-fired cost according to the power supply coal consumption soft measurement model and the information of the coal types fed into the furnace of the SIS system; based on NSGA-II algorithm combination degree electricity coal cost and an objective function determined by coal quality of mixed coal, a coal blending scheme optimization model is established, the current coal blending scheme is optimized, and the optimal coal blending proportion and coal quality characteristics are output. The electricity-less coal-fired cost calculation is close to the dynamic characteristic of the change of the coal blending quality of the mixed coal of the power plant, and the economical goal that the electricity-less coal-fired cost is used for replacing the lowest coal price of the mixed coal is more practical with respect to the optimal adoption of the coal blending scheme, so that the competitive power of the power plant for competitive price surfing is increased.
Description
Technical Field
The application relates to the technical field of coal blending and blending coal burning cost optimization of coal-fired power plants, in particular to a method and a system for calculating and optimizing the electricity-free coal burning cost based on coal quality characteristics.
Background
With the development of thermal power generation technology, the double-rail cancellation of the price of electric coal and the structural reform of the supply side, the market fluctuation of the electric coal is enhanced, and the price competitive surfing strategy of power generation enterprises is implemented in China in these years. Under such a large environment, the power plant is required to clearly know the performance and the fuel cost of the power plant, the current electricity-measuring and coal-burning cost is timely and accurately mastered, the power generation cost is reduced by adopting a coal blending and burning mode, and the competitive power of the power plant in competitive price surfing can be greatly improved.
The coal cost occupies a large proportion in the power generation cost, is also an important component of the power generation fluctuation cost, and has important significance for bidding decision of bidding surfing of power generation enterprises in time and accurately grasping the electricity-coal cost. However, because the hysteresis of the calculation of the power supply coal consumption is limited, the power supply coal consumption for combusting the mixed coal cannot be obtained in real time, so that the mixed coal price can be generally only minimized as an objective function of coal blending optimization, but the cost reduction and synergy of the power plant cannot be ensured. Therefore, the application collects the historical data of a certain 600MW power plant, establishes a soft measurement model of coal quality and power supply coal consumption by using PSO-SVM, then reads the mixed coal proportion and price of SIS system in real time to calculate the electricity-based coal cost, and establishes a coal blending optimization model by taking the coal quality index as a constraint condition and taking the electricity-based coal cost and environmental protection requirement as an objective function. The model can help the power plant to quickly acquire electricity-less coal burning cost for burning or blending certain coal, and provides guidance for competitive price surfing and coal blending optimization of the power plant.
Disclosure of Invention
This section is intended to outline some aspects of embodiments of the application and to briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section as well as in the description of the application and in the title of the application, which may not be used to limit the scope of the application.
The present application has been made in view of the above-described problems occurring in the prior art.
Therefore, the application aims to provide a method for calculating the cost of the electric fire coal and optimizing the coal blending based on the coal quality characteristics.
In order to solve the technical problems, the application provides the following technical scheme:
in a first aspect, an embodiment of the present application provides a method for calculating a cost of a pilot coal and optimizing blending coal based on characteristics of coal quality, including:
collecting historical mixed coal blending coal quality data of a coal-fired power plant and power supply coal consumption data in an SIS system, and carrying out normalization treatment;
establishing a soft measurement model of coal quality characteristics and power supply coal consumption based on a PSO-SVM, and training the soft measurement model through normalized data so as to reduce iteration time, ensure the high efficiency of an algorithm and improve prediction accuracy;
calculating the electricity-coal-fired cost according to the power supply coal consumption soft measurement model and the information of the coal types fed into the furnace of the SIS system;
based on NSGA-II algorithm, taking the electricity-fire coal cost as an economic target and taking an environmental protection objective function determined by coal quality of mixed coal as an environmental protection optimization target, establishing a coal blending scheme optimization model, optimizing the current coal blending scheme and outputting the optimal coal blending proportion and coal quality characteristics.
As the method for calculating the cost of the electric fire coal and optimizing the coal blending based on the coal quality characteristics, the application comprises the following steps: the historical mixed coal blending and burning coal quality data of the coal-fired power plant comprises heating value, sulfur content, ash melting point, volatile matters, moisture and corresponding power supply coal consumption data.
As the method for calculating the cost of the electric fire coal and optimizing the coal blending based on the coal quality characteristics, the application comprises the following steps: the normalization process may include the steps of,
let M be the original data set of all coal quality data and power supply coal consumption data, the normalization method is as follows:
where x=1, 2,..n is the total number of samples, y=1, 2,..10, m xy As the original value of the y-th parameter of data x, M xy ' is M xy Normalized value, M y-min And M y-max The minimum and maximum values of the y-th parameter in the original dataset, respectively.
As the method for calculating the cost of the electric fire coal and optimizing the coal blending based on the coal quality characteristics, the application comprises the following steps: establishing a soft measurement model of coal quality characteristics and power supply coal consumption based on the PSO-SVM comprises,
the PSO-SVM flow is as follows:
optimizing a penalty factor c and a kernel function parameter g of the SVM, initializing a particle swarm scale, setting a weight factor of an algorithm, and ending conditions and initial particle codes;
setting an individual extremum of each particle as a current position, calculating an adaptability value of each particle by using an adaptability function, and taking the individual extremum corresponding to the good adaptability as an initial global extremum;
iterative calculation is carried out according to a position and speed updating formula of the particles, and the position X of the particles is updated i And velocity V i ；
Calculating the fitness value of each particle after each iteration according to the fitness function of the particle, and comparing the fitness value of each particle with the fitness value of the individual extremum thereof:
if the fitness value of each particle is better than the fitness value of the individual extremum, updating the individual extremum;
if the fitness value of each particle is not better than the fitness value of the individual extremum, the original value is reserved;
comparing the updated individual extremum of each particle with a global extremum:
if the updated individual extremum of each particle is better than the global extremum, updating the global extremum;
if the updated individual extremum of each particle is not better than the global extremum, the original value is reserved;
and updating and iterating until the termination condition is met, and obtaining the parameter combination which enables the prediction model to be optimal when the maximum iteration times are reached.
As the method for calculating the cost of the electric fire coal and optimizing the coal blending based on the coal quality characteristics, the application comprises the following steps: the calculation degree electricity coal cost comprises the following steps of according to the soft measurement model of the power supply coal consumption and the information of the coal types of the SIS system,
the coal type information of the entering furnace comprises real-time price and proportion information;
the electricity-measuring coal cost is calculated as follows:
setting N kinds of coal to participate in blending, and setting the price of the ith (i=1, 2, …, N) kind of coal as C i The mixing proportion is X i (%) the raw coal consumption of power supply is B i The electricity supply coal consumption cost C of burning the ith coal bi The method comprises the following steps:
electric fire coal cost C of mixed coal b The method comprises the following steps:
as the method for calculating the cost of the electric fire coal and optimizing the coal blending based on the coal quality characteristics, the application comprises the following steps: economic objectives for electricity-to-fire coal costs include,
the minimum electricity fire coal cost is as follows:
wherein C is bi For the power supply coal consumption of burning the ith coal, X i At the i-th coal ratio, C b Is the cost of electricity fire coal.
As the method for calculating the cost of the electric fire coal and optimizing the coal blending based on the coal quality characteristics, the application comprises the following steps: the environment-friendly objective function determined by the coal quality of the mixed coal is taken as an environment-friendly optimization objective as follows:
wherein F is h Is an environmental protection objective function, eta and lambda are environmental protection coefficients, S p S is sulfur content of mixed coal min And S is max Minimum and maximum sulfur content of mixed coal, V p Is the volatile component of the mixed coal, V ad To design the volatile component of the coal, A p Is ash of mixed coal, A max And A min Ash maximum and minimum, respectively.
In a second aspect, embodiments of the present application provide a system for electricity-less coal cost calculation and coal blending optimization based on coal quality characteristics, comprising,
the preprocessing module is used for collecting historical mixed coal blending combustion coal quality data of the coal-fired power plant and power supply coal consumption data in the SIS system and carrying out normalization processing;
the model building and training module is used for building a soft measurement model of coal quality characteristics and power supply coal consumption based on the PSO-SVM, and training the soft measurement model through normalized data so as to reduce iteration time, ensure the high efficiency of an algorithm and improve prediction accuracy;
calculating the electricity-coal-fired cost according to the power supply coal consumption soft measurement model and the information of the coal types fed into the furnace of the SIS system;
based on an NSGA-II algorithm, taking the electricity-to-coal cost as an economic target and taking an environmental protection objective function determined by coal quality of mixed coal as an environmental protection optimization target, establishing a coal blending scheme optimization model;
and the output module optimizes the current coal blending scheme and outputs the optimal coal blending proportion and coal quality characteristics according to the coal blending scheme optimization model.
In a third aspect, embodiments of the present application provide a computing device comprising:
a memory and a processor;
the memory is configured to store computer-executable instructions that, when executed by the one or more processors, cause the one or more processors to implement the electricity-to-coal cost calculation and coal blending optimization method based on coal quality characteristics according to any of the embodiments of the present application.
In a fourth aspect, embodiments of the present application provide a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the electricity-based coal cost calculation and coal blending optimization method based on coal quality characteristics.
The application has the beneficial effects that: and establishing a soft measurement model of coal quality characteristics and power supply coal consumption based on the PSO-SVM, and optimizing a penalty factor C and a kernel function parameter g in the SVM by using a particle swarm algorithm. The iteration time is effectively reduced, the high efficiency of the algorithm is guaranteed, and the prediction accuracy is more accurate. The electricity-less coal-fired cost calculation is close to the dynamic characteristic of the change of the coal blending quality of the mixed coal of the power plant, and the economical goal that the electricity-less coal-fired cost is used for replacing the lowest coal price of the mixed coal is more practical with respect to the optimal adoption of the coal blending scheme, so that the competitive power of the power plant for competitive price surfing is increased.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. Wherein:
FIG. 1 is a schematic overall flow chart of the electricity-based coal cost calculation and coal blending optimization method based on coal quality characteristics.
FIG. 2 is a PSO-SVM flow chart of the electricity-metric coal cost calculation and coal blending optimization method based on coal quality characteristics.
FIG. 3 is a graph showing individual distribution of a solution set of a coal blending optimization model of the method for calculating the cost of the electric fire coal and optimizing the coal blending based on the characteristics of coal quality.
Detailed Description
In order that the above-recited objects, features and advantages of the present application will become more readily apparent, a more particular description of the application will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application, but the present application may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present application is not limited to the specific embodiments disclosed below.
Further, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic can be included in at least one implementation of the application. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
Further, in describing the embodiments of the present application in detail, the cross-sectional view of the device structure is not partially enlarged to a general scale for convenience of description, and the schematic is only an example, which should not limit the scope of protection of the present application. In addition, the three-dimensional dimensions of length, width and depth should be included in actual fabrication.
Example 1
Referring to fig. 1 to 2, for one embodiment of the present application, there is provided a method for calculating a cost of a pilot coal and optimizing blending coal based on characteristics of coal quality, including:
as shown in fig. 1, the specific steps of the present application are as follows:
s1: and collecting historical mixed coal blending combustion coal quality data of the coal-fired power plant and power supply coal consumption data in an SIS system, and carrying out normalization treatment. It should be noted that:
the historical mixed coal blending and burning coal quality data of the coal-fired power plant comprises heating value, sulfur content, ash fusion point, volatile matters, moisture and corresponding power supply coal consumption data.
The normalization process includes the steps of,
let M be the original data set of all coal quality data and power supply coal consumption data, the normalization method is as follows:
where x=1, 2,..n is the total number of samples, y=1, 2,..10, m xy As the original value of the y-th parameter of data x, M xy ' is M xy Normalized value, M y-min And M y-max The minimum and maximum values of the y-th parameter in the original dataset, respectively.
S2: and establishing a soft measurement model of coal quality characteristics and power supply coal consumption based on the PSO-SVM, and training the soft measurement model through the normalized data so as to reduce iteration time, ensure the high efficiency of an algorithm and improve prediction accuracy. It should be noted that:
as shown in fig. 2, the PSO-SVM flow of the present application is as follows:
optimizing a penalty factor c and a kernel function parameter g of the SVM, initializing a particle swarm scale, setting a weight factor of an algorithm, and ending conditions and initial particle codes;
setting an individual extremum of each particle as a current position, calculating an adaptability value of each particle by using an adaptability function, and taking the individual extremum corresponding to the good adaptability as an initial global extremum;
iterative calculation is carried out according to a position and speed updating formula of the particles, and the position X of the particles is updated i And velocity V i ；
Calculating the fitness value of each particle after each iteration according to the fitness function of the particle, and comparing the fitness value of each particle with the fitness value of the individual extremum thereof:
if the fitness value of each particle is better than the fitness value of the individual extremum, updating the individual extremum;
if the fitness value of each particle is not better than the fitness value of the individual extremum, the original value is reserved;
comparing the updated individual extremum of each particle with a global extremum:
if the updated individual extremum of each particle is better than the global extremum, updating the global extremum;
if the updated individual extremum of each particle is not better than the global extremum, the original value is reserved;
and updating and iterating until the termination condition is met, and obtaining the parameter combination which enables the prediction model to be optimal when the maximum iteration times are reached.
S3: and calculating the electricity-fired coal cost according to the power supply coal consumption soft measurement model and the information of the coal types fed into the furnace of the SIS system. It should be noted that:
the information of the coal types entering the furnace comprises real-time price and proportion information;
the electricity-to-fire coal cost is calculated as follows:
setting N kinds of coal to participate in blending, and setting the price of the ith (i=1, 2, …, N) kind of coal as C i The mixing proportion is X i (%) the raw coal consumption of power supply is B i The electricity supply coal consumption cost C of burning the ith coal bi The method comprises the following steps:
electric fire coal cost C of mixed coal b The method comprises the following steps:
s4: based on an NSGA-II algorithm, taking the electricity-fire coal cost as an economic target and taking an environmental protection objective function determined by the coal quality of the mixed coal as an environmental protection optimization target, establishing a coal blending scheme optimization model, optimizing the current coal blending scheme and outputting the optimal coal blending proportion and coal quality characteristics. It should be noted that:
economic objectives for electricity-to-fire coal costs include,
the minimum electricity fire coal cost is as follows:
wherein C is bi For the power supply coal consumption of burning the ith coal, X i At the i-th coal ratio, C b Is the cost of electricity fire coal.
The environment-friendly objective function determined by the coal quality of the mixed coal is taken as an environment-friendly optimization objective as follows:
wherein F is h Is an environmental protection objective function, eta and lambda are environmental protection coefficients, S p S is sulfur content of mixed coal min And S is max Minimum and maximum sulfur content of mixed coal, V p Is the volatile component of the mixed coal, V ad To design the volatile component of the coal, A p Is ash of mixed coal, A max And A min Ash maximum and minimum, respectively.
The embodiment also provides a system for calculating the cost of the electric fire coal and optimizing the coal blending based on the coal quality characteristics, which comprises the following components:
the preprocessing module is used for collecting historical mixed coal blending combustion coal quality data of the coal-fired power plant and power supply coal consumption data in the SIS system and carrying out normalization processing;
the model building and training module is used for building a soft measurement model of coal quality characteristics and power supply coal consumption based on the PSO-SVM, and training the soft measurement model through the normalized data so as to reduce iteration time, ensure the high efficiency of an algorithm and improve prediction accuracy;
calculating the electricity-coal-fired cost according to the power supply coal consumption soft measurement model and the information of the coal types fed into the furnace of the SIS system;
based on an NSGA-II algorithm, taking the electricity-fire coal cost as an economic target and taking an environmental protection objective function determined by the coal quality of the mixed coal as an environmental protection optimization target, establishing a coal blending scheme optimization model;
and the output module optimizes the current coal blending scheme and outputs the optimal coal blending proportion and coal quality characteristics according to the coal blending scheme optimization model.
The embodiment also provides a computing device, which is suitable for the situations of the electricity-measuring coal cost computing and coal blending optimizing method based on the coal quality characteristics, and comprises the following steps:
a memory and a processor; the memory is used for storing computer executable instructions, and the processor is used for executing the computer executable instructions to realize the electricity-based coal cost calculation and coal blending optimization method based on the coal quality characteristics.
The computer device may be a terminal comprising a processor, a memory, a communication interface, a display screen and input means connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless mode can be realized through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
The present embodiment also provides a storage medium having stored thereon a computer program which, when executed by a processor, implements the method for achieving electricity-based coal cost calculation and coal blending optimization based on the coal quality characteristics as proposed in the above embodiment.
The storage medium according to the present embodiment belongs to the same inventive concept as the data storage method according to the above embodiment, and technical details not described in detail in the present embodiment can be seen in the above embodiment, and the present embodiment has the same advantageous effects as the above embodiment.
Example 2
Referring to fig. 3, for another embodiment of the present application, a verification test of a method for calculating the cost of electricity-rich coal and optimizing coal blending based on coal quality characteristics is provided, and the technical effects adopted in the method are verified and explained.
According to the embodiment, a soft measurement model of power supply coal consumption and coal quality characteristics and a multi-target coal blending scheme optimization model are built through an MATLAB simulation platform, and the power supply coal consumption of mixed coal and the coal blending scheme of electricity-fired coal cost are calculated and optimized respectively.
According to the historical data of a power plant, a soft measurement model based on PSO-SVM coal quality data and power supply coal consumption is built, and the application adopts Mean Absolute Error (MAE), mean Absolute Relative Error (MARE), root Mean Square Error (RMSE) and decision coefficient (R) 2 ) The effect of the PSO-SVM model was evaluated, and the evaluation of the calculated effect of the PSO-SVM model is shown in Table 1.
Table 1: evaluation table of model effect
| Model | MAE | MARE | RMSE | R 2 |
| PSO-SVM | 0.1567 | 4.748×10 -4 | 0.2456 | 0.9984 |
Results showed that the MAE of the model was less than 0.16gkw -1 h -1 MARE is less than 4.8X10 -4 RMSE less than 0.25gkw -1 h -1 、R 2 The accuracy of the PSO-SVM model is higher and the reliability is better, and the method is more than 0.99 and suitable for actual calculation of power supply coal consumption of a coal-fired power plant.
According to the relation model of the coal quality and the power supply coal consumption, the electricity-fire coal cost is further calculated, then the electricity-fire coal cost with the lowest electricity-fire coal cost is used as an economic target to establish a coal blending optimization model based on an NSGA-II algorithm, the distribution of solution sets of the coal blending optimization model is shown in the figure 3, and partial solution sets of the individual optimization solutions are shown in the following table 3.
First, 10 kinds of single coal data commonly used in power plants are collected, as shown in table 2.
Table 2: coal quality of stored coal
Table 3: partial individual solution set
From the results in table 3, it can be seen that various indexes of the mixed coal in the solution set are moderate and the coal price is lower than the market price, which indicates that the improved multi-target coal blending model can simultaneously consider the requirements of economy and environmental protection. The data in table 3 prove that the mixed coal price of solution set 3 is the lowest, but the electricity-less coal cost is the highest, the low-price coal mixed combustion of the power plant means the reduction of the thermal efficiency of the boiler and the improvement of the power plant power consumption, so that the power supply coal consumption is increased, and further the electricity-less coal cost is increased, which indicates that the power plant aims at the lowest mixed coal price when making the coal blending scheme and cannot completely ensure the increase of the actual economic benefit of the power plant. From the data in the table, individual 2 is selected when the desired electrical fire coal cost is lowest and individual 5 is selected when the desired sulfur content is lowest. The staff of the power plant can also change the specific gravity of the target according to the current power plant requirement to enable the coal blending scheme to be more in line with the current optimal solution, and guidance is provided for the mixed coal blending and burning optimization of the power plant.
It should be noted that the above embodiments are only for illustrating the technical solution of the present application and not for limiting the same, and although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present application may be modified or substituted without departing from the spirit and scope of the technical solution of the present application, which is intended to be covered in the scope of the claims of the present application.
Claims (10)
1. The method for calculating the cost of the electric fire coal and optimizing the coal blending based on the coal quality characteristics is characterized by comprising the following steps of: comprising the steps of (a) a step of,
collecting historical mixed coal blending coal quality data of a coal-fired power plant and power supply coal consumption data in an SIS system, and carrying out normalization treatment;
establishing a soft measurement model of coal quality characteristics and power supply coal consumption based on a PSO-SVM, and training the soft measurement model through normalized data so as to reduce iteration time, ensure the high efficiency of an algorithm and improve prediction accuracy;
calculating the electricity-coal-fired cost according to the power supply coal consumption soft measurement model and the information of the coal types fed into the furnace of the SIS system;
based on NSGA-II algorithm, taking the electricity-fire coal cost as an economic target and taking an environmental protection objective function determined by coal quality of mixed coal as an environmental protection optimization target, establishing a coal blending scheme optimization model, optimizing the current coal blending scheme and outputting the optimal coal blending proportion and coal quality characteristics.
2. The method for calculating the electricity-to-coal cost and optimizing the coal blending based on the coal quality characteristics according to claim 1, wherein the method comprises the following steps: the historical mixed coal blending and burning coal quality data of the coal-fired power plant comprises heating value, sulfur content, ash melting point, volatile matters, moisture and corresponding power supply coal consumption data.
3. The method for calculating the electricity-to-coal cost and optimizing the coal blending based on the coal quality characteristics according to claim 2, wherein the method comprises the following steps: the normalization process may include the steps of,
let M be the original data set of all coal quality data and power supply coal consumption data, the normalization method is as follows:
where x=1, 2,..n is the total number of samples, y=1, 2,..10, m xy As the original value of the y-th parameter of data x, M xy ' is M xy Normalized value, M y-min And M y-max The minimum and maximum values of the y-th parameter in the original dataset, respectively.
4. The electricity-less coal cost calculation and coal blending optimization method based on coal quality characteristics as claimed in claim 3, wherein: establishing a soft measurement model of coal quality characteristics and power supply coal consumption based on the PSO-SVM comprises,
the PSO-SVM flow is as follows:
optimizing a penalty factor c and a kernel function parameter g of the SVM, initializing a particle swarm scale, setting a weight factor of an algorithm, and ending conditions and initial particle codes;
setting an individual extremum of each particle as a current position, calculating an adaptability value of each particle by using an adaptability function, and taking the individual extremum corresponding to the good adaptability as an initial global extremum;
iterative calculation is carried out according to a position and speed updating formula of the particles, and the position X of the particles is updated i And velocity V i ；
Calculating the fitness value of each particle after each iteration according to the fitness function of the particle, and comparing the fitness value of each particle with the fitness value of the individual extremum thereof:
if the fitness value of each particle is better than the fitness value of the individual extremum, updating the individual extremum;
if the fitness value of each particle is not better than the fitness value of the individual extremum, the original value is reserved;
comparing the updated individual extremum of each particle with a global extremum:
if the updated individual extremum of each particle is better than the global extremum, updating the global extremum;
if the updated individual extremum of each particle is not better than the global extremum, the original value is reserved;
and updating and iterating until the termination condition is met, and obtaining the parameter combination which enables the prediction model to be optimal when the maximum iteration times are reached.
5. The method for calculating the electricity-less coal cost and optimizing the coal blending based on the coal quality characteristics according to claim 4, which is characterized in that: the calculation degree electricity coal cost comprises the following steps of according to the soft measurement model of the power supply coal consumption and the information of the coal types of the SIS system,
the coal type information of the entering furnace comprises real-time price and proportion information;
the electricity-measuring coal cost is calculated as follows:
setting N kinds of coal to participate in blending, and setting the price of the ith (i=1, 2, …, N) kind of coal as C i The mixing proportion is X i (%) the raw coal consumption of power supply is B i The electricity supply coal consumption cost C of burning the ith coal bi The method comprises the following steps:
electric fire coal cost C of mixed coal b The method comprises the following steps:
6. the method for calculating the electricity-less coal cost and optimizing the coal blending based on the coal quality characteristics according to claim 5, wherein the method comprises the following steps: economic objectives for electricity-to-fire coal costs include,
the minimum electricity fire coal cost is as follows:
wherein C is bi For the power supply coal consumption of burning the ith coal, X i At the i-th coal ratio, C b Is the cost of electricity fire coal.
7. The method for calculating the electricity-to-coal cost and optimizing the coal blending based on the coal quality characteristics according to claim 6, wherein the method comprises the following steps: the environment-friendly objective function determined by the coal quality of the mixed coal is taken as an environment-friendly optimization objective as follows:
wherein F is h Is an environmental protection objective function, eta and lambda are environmental protection coefficients, S p S is sulfur content of mixed coal min And S is max Minimum and maximum sulfur content of mixed coal, V p Is the volatile component of the mixed coal, V ad To design the volatile component of the coal, A p Is ash of mixed coal, A max And A min Ash maximum and minimum, respectively.
8. The system for calculating the cost of the electric fire coal and optimizing the coal blending based on the characteristics of the coal quality is characterized by comprising,
the preprocessing module is used for collecting historical mixed coal blending combustion coal quality data of the coal-fired power plant and power supply coal consumption data in the SIS system and carrying out normalization processing;
the model building and training module is used for building a soft measurement model of coal quality characteristics and power supply coal consumption based on the PSO-SVM, and training the soft measurement model through normalized data so as to reduce iteration time, ensure the high efficiency of an algorithm and improve prediction accuracy;
calculating the electricity-coal-fired cost according to the power supply coal consumption soft measurement model and the information of the coal types fed into the furnace of the SIS system;
based on an NSGA-II algorithm, taking the electricity-to-coal cost as an economic target and taking an environmental protection objective function determined by coal quality of mixed coal as an environmental protection optimization target, establishing a coal blending scheme optimization model;
and the output module optimizes the current coal blending scheme and outputs the optimal coal blending proportion and coal quality characteristics according to the coal blending scheme optimization model.
9. A computing device, comprising:
a memory and a processor;
the memory is configured to store computer executable instructions that, when executed by the processor, implement the steps of the electricity-less coal cost calculation and coal blending optimization method based on coal quality characteristics of any one of claims 1 to 7.
10. A computer readable storage medium storing computer executable instructions which when executed by a processor perform the steps of the electricity-less coal cost calculation and coal blending optimization method based on coal quality characteristics of any one of claims 1 to 7.
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