CN116307075B - Intelligent algorithm-based electricity-measuring coal-burning cost optimization method and system - Google Patents
Intelligent algorithm-based electricity-measuring coal-burning cost optimization method and system Download PDFInfo
- Publication number
- CN116307075B CN116307075B CN202310050679.4A CN202310050679A CN116307075B CN 116307075 B CN116307075 B CN 116307075B CN 202310050679 A CN202310050679 A CN 202310050679A CN 116307075 B CN116307075 B CN 116307075B
- Authority
- CN
- China
- Prior art keywords
- coal
- electricity
- cost
- algorithm
- data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000004422 calculation algorithm Methods 0.000 title claims abstract description 71
- 238000000034 method Methods 0.000 title claims abstract description 49
- 238000005457 optimization Methods 0.000 title claims abstract description 43
- 239000003245 coal Substances 0.000 claims abstract description 160
- 239000002245 particle Substances 0.000 claims abstract description 33
- 238000002156 mixing Methods 0.000 claims abstract description 27
- 238000012216 screening Methods 0.000 claims abstract description 25
- 230000005611 electricity Effects 0.000 claims abstract description 18
- 241000282461 Canis lupus Species 0.000 claims abstract description 12
- 238000010977 unit operation Methods 0.000 claims abstract description 12
- 238000004364 calculation method Methods 0.000 claims abstract description 11
- 238000012706 support-vector machine Methods 0.000 claims abstract description 11
- 230000006870 function Effects 0.000 claims description 12
- 238000010219 correlation analysis Methods 0.000 claims description 7
- 238000003860 storage Methods 0.000 claims description 5
- 238000012106 screening analysis Methods 0.000 claims description 3
- 238000004781 supercooling Methods 0.000 claims description 3
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 3
- 230000009286 beneficial effect Effects 0.000 description 3
- 230000009194 climbing Effects 0.000 description 3
- 230000002860 competitive effect Effects 0.000 description 3
- 238000004519 manufacturing process Methods 0.000 description 3
- 238000010248 power generation Methods 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 230000000694 effects Effects 0.000 description 2
- 230000009467 reduction Effects 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 241000254158 Lampyridae Species 0.000 description 1
- 230000002159 abnormal effect Effects 0.000 description 1
- 238000002485 combustion reaction Methods 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 239000000446 fuel Substances 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000000611 regression analysis Methods 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 238000012549 training Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
- G06N20/10—Machine learning using kernel methods, e.g. support vector machines [SVM]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Economics (AREA)
- General Physics & Mathematics (AREA)
- Human Resources & Organizations (AREA)
- Software Systems (AREA)
- Health & Medical Sciences (AREA)
- Strategic Management (AREA)
- Computing Systems (AREA)
- Tourism & Hospitality (AREA)
- Artificial Intelligence (AREA)
- General Business, Economics & Management (AREA)
- Mathematical Physics (AREA)
- General Engineering & Computer Science (AREA)
- Marketing (AREA)
- Evolutionary Computation (AREA)
- Data Mining & Analysis (AREA)
- General Health & Medical Sciences (AREA)
- Operations Research (AREA)
- Life Sciences & Earth Sciences (AREA)
- Primary Health Care (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Water Supply & Treatment (AREA)
- Public Health (AREA)
- Molecular Biology (AREA)
- Quality & Reliability (AREA)
- Entrepreneurship & Innovation (AREA)
- Game Theory and Decision Science (AREA)
- Development Economics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Medical Informatics (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a method and a system for optimizing the cost of electricity-fired coal based on an intelligent algorithm, wherein the method comprises the following steps: collecting historical operation data of the coal-fired power plant affecting power supply coal consumption and different load data of a unit; double screening is carried out on the historical operation data and different load data of the unit to obtain input variables; predicting the power supply coal consumption based on a PSO-SVM support vector machine algorithm to obtain a prediction model; calculating the electricity-fired coal cost according to the prediction model and the information of the coal types of the fed coal of the SIS system; and (3) establishing a power electricity coal cost optimization model based on a gray wolf algorithm, and optimizing operation data and coal mixing proportion to obtain a unit operation and coal blending scheme with the lowest cost. The prediction result has higher precision, the final electric coal cost calculation precision is high, the real-time performance is good, meanwhile, the prediction model optimizes the penalty factors and the kernel function parameters in the SVM by utilizing the particle swarm algorithm, the iteration time can be effectively reduced, the algorithm efficiency is ensured, and the prediction precision is more accurate.
Description
Technical Field
The invention 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 optimizing the electricity-free coal burning cost based on an intelligent algorithm.
Background
At present, two methods are mainly used for calculating the coal cost of power generation enterprises. According to the method, a monthly/annual cost statistics method is adopted, and the monthly/annual coal cost is counted according to the system of the power plant, so that the timeliness is poor, the accuracy is low, and the real-time coal cost cannot be accurately reflected. And secondly, carrying out regression analysis on the power generation cost by using a statistical method, generally using historical data of a power plant for a certain period of time to establish a multiple regression equation, or starting from actual coal burning cost and income of the power plant, obtaining a unitary statistical regression model of the power generation amount and the coal burning cost by using a least square method according to the historical data of the cost, and establishing a primary, secondary, tertiary function and a logarithmic regression equation, thereby selecting the most suitable regression equation. The calculation mode generally ignores the influence of mixed coal blending combustion on the running environment of the unit, so that the calculation is inaccurate and the model accuracy is low.
And the competitive price internet surfing operation is changed under the condition of pushing the electric power market at present. Therefore, the power plant is required to have clear knowledge of the fuel cost, the coal burning electricity cost of the thermal power plant is timely and accurately calculated, and the coal burning electricity cost is reduced by optimizing a unit and coal blending, so that the competitive power of the power plant in competitive price surfing can be greatly improved, and more profits are obtained.
Disclosure of Invention
This section is intended to outline some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section as well as in the description summary and in the title of the application, to avoid obscuring the purpose of this section, the description summary and the title of the invention, which should not be used to limit the scope of the invention.
The present invention has been made in view of the above-described problems occurring in the prior art. Therefore, the invention provides a method for optimizing the cost of the electric fire coal based on an intelligent algorithm, which solves the problems that in the actual process, noise exists in the barometer, static deviation is easily accumulated due to the fact that the setting of a relative altitude difference updating threshold value is too small, and the climbing value is abnormally large under the static condition, and meanwhile, the climbing value under the condition that the actual mountain climbing and other movement processes have up-and-down fluctuation is completely ignored due to the fact that the setting of the relative altitude difference updating threshold value is too large.
In order to solve the technical problems, the invention provides the following technical scheme:
in a first aspect, the invention provides a method for optimizing the cost of electricity-fired coal based on an intelligent algorithm, which comprises the following steps:
collecting historical operation data of the coal-fired power plant affecting power supply coal consumption and different load data of a unit;
double screening is carried out on the historical operation data and different load data of the unit to obtain input variables;
predicting the power supply coal consumption based on a PSO-SVM support vector machine algorithm to obtain a prediction model;
calculating the electricity-fired coal cost according to the prediction model and the information of the coal types fed into the furnace of the SIS system;
and (3) establishing a power electricity coal cost optimization model based on a gray wolf algorithm, and optimizing operation data and coal mixing proportion to obtain a unit operation and coal blending scheme with the lowest cost.
As a preferable scheme of the intelligent algorithm-based electricity-fire coal cost optimization method, the invention comprises the following steps: the historical operation data comprise main steam pressure, main steam temperature, reheat steam temperature, reheater temperature reduction water flow, condenser pressure, exhaust gas temperature, condenser end difference, condenser supercooling degree, primary air pressure, secondary air pressure, primary air total amount, secondary air total amount and station service electricity.
As a preferable scheme of the intelligent algorithm-based electricity-fire coal cost optimization method, the invention comprises the following steps: the screening comprises steady state data screening and correlation analysis screening;
the steady state data filtering uses a Laida criterion to reject both gross errors and random errors.
As a preferable scheme of the intelligent algorithm-based electricity-fire coal cost optimization method, the invention comprises the following steps: the correlation analysis screening comprises the steps of calculating the correlation between the acquired data and the power supply coal consumption, and reserving the data type with the absolute value r of the correlation being larger than 0.6.
As a preferable scheme of the intelligent algorithm-based electricity-fire coal cost optimization method, the invention comprises the following steps: the PSO-SVM-based support vector machine algorithm predicts the power supply coal consumption, comprising,
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 speed Vi;
calculating the fitness value of each particle after each iteration according to the fitness function of the particle, comparing the fitness value of each particle with the fitness value of each individual extremum, if the fitness value is more optimal, updating the individual extremum, otherwise, keeping the original value; comparing the updated individual extremum of each particle with the global extremum, if the individual extremum is better, updating the global extremum, otherwise, keeping the original value;
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 number is reached.
As a preferable scheme of the intelligent algorithm-based electricity-fire coal cost optimization method, the invention comprises the following steps: the coal type information of the entering furnace comprises real-time price and proportion information;
the electricity-to-fire coal cost is expressed as:
C cost of ＝B g *P Heald
Wherein B is g To supply coal consumption, P Heald Is the unit price of comprehensive standard coal;
P heald ＝P1*x1+P2*x2+P3*x3+...Pn*xn(x1+x2+x3+...xn＝1)
Wherein, P1, P2, P3..Pn is the price of single coal, X1, X2, X3...Xn is the proportion of single coal.
As a preferable scheme of the intelligent algorithm-based electricity-fire coal cost optimization method, the invention comprises the following steps: based on the gray wolf algorithm, a power electricity coal cost optimization model is established to optimize the operation data and the coal mixing proportion, comprising,
the optimized target is the electricity-to-fire coal cost;
the input parameters are set parameters and the proportion of mixed coal;
the constraint condition of the model is the safe operation range of the unit;
and finally outputting a unit operation and coal blending scheme with lowest electric coal cost after optimization.
In a second aspect, the invention provides a smart algorithm-based electricity-fire coal cost optimization device, comprising,
the acquisition module is used for acquiring historical operation data of the coal-fired power plant, which influences the power supply coal consumption, and different load data of the unit;
the screening module is used for screening the historical operation data and the different load data of the unit to obtain input variables;
the prediction module is used for predicting the power supply coal consumption based on a PSO-SVM support vector machine algorithm to obtain a prediction model;
the cost calculation module is used for calculating the electricity-fired coal cost according to the prediction model and the real-time price and proportion information of the coal types fed into the furnace of the SIS system;
and the optimizing module is used for establishing a power electricity coal cost optimizing model based on a gray wolf algorithm and optimizing the operation data and the coal mixing proportion to obtain a unit operation and coal blending scheme with the lowest cost.
In a third aspect, the present invention provides a computing device comprising:
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, and the computer executable instructions realize the steps of the intelligent algorithm-based electricity-fire coal cost optimization method when being executed by the processor.
In a fourth aspect, the present invention provides a computer readable storage medium storing computer executable instructions which when executed by a processor implement the steps of the intelligent algorithm based electricity-to-fire coal cost optimization method.
Compared with the prior art, the invention has the beneficial effects that: the electricity-measuring coal-burning cost calculation is close to the actual operation and coal quality characteristics of the unit; fitting the relation between the power supply coal consumption, the load and the unit parameters according to the main factors influencing the unit coal consumption, and continuously iterating and updating the training through an improved PSO-SVM prediction model, wherein the accuracy of the prediction result is higher, the calculation accuracy of the final electric coal cost is high, the instantaneity is good, and the data support is provided for the production and operation of a power plant; meanwhile, a PSO-SVM coal power electricity cost prediction model is established based on coal quality characteristics and unit performance, and the model optimizes a punishment factor c and a kernel function parameter g in the SVM by using a particle swarm algorithm, so that iteration time can be effectively reduced, the high efficiency of the algorithm is ensured, and the prediction accuracy is more accurate.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, 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 invention, 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 a method for optimizing the cost of electricity-sensitive coal based on an intelligent algorithm according to an embodiment of the invention;
fig. 2 is a block diagram of power supply coal consumption prediction in the intelligent algorithm-based electricity-fire coal cost optimization method according to an embodiment of the invention.
Detailed Description
So that the manner in which the above recited objects, features and advantages of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present invention 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 invention. 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.
While the embodiments of the present invention have been illustrated and described in detail in the drawings, the cross-sectional view of the device structure is not to scale in the general sense for ease of illustration, and the drawings are merely exemplary and should not be construed as limiting the scope of the invention. In addition, the three-dimensional dimensions of length, width and depth should be included in actual fabrication.
Also in the description of the present invention, it should be noted that the orientation or positional relationship indicated by the terms "upper, lower, inner and outer", etc. are based on the orientation or positional relationship shown in the drawings, are merely for convenience of describing the present invention and simplifying the description, and do not indicate or imply that the apparatus or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention. Furthermore, the terms "first, second, or third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The terms "mounted, connected, and coupled" should be construed broadly in this disclosure unless otherwise specifically indicated and defined, such as: can be fixed connection, detachable connection or integral connection; it may also be a mechanical connection, an electrical connection, or a direct connection, or may be indirectly connected through an intermediate medium, or may be a communication between two elements. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
Example 1
1-2, for one embodiment of the present invention, there is provided a method for optimizing electricity-to-fire coal costs based on an intelligent algorithm, including:
s1: collecting historical operation data of the coal-fired power plant affecting power supply coal consumption and different load data of a unit;
further, the historical operation data comprises main steam pressure, main steam temperature, reheat steam temperature, reheater attemperation water flow, condenser pressure, exhaust gas temperature, condenser end difference, condenser supercooling degree, primary air pressure, secondary air pressure, primary air total amount, secondary air total amount and station service electricity rate.
S2: double screening is carried out on the historical operation data and different load data of the unit to obtain input variables;
still further, screening includes steady state data screening and correlation analysis screening;
steady state data screening uses the lagrangian criterion to reject both gross and random errors,
it should be noted that, specifically, the sample data is represented as X 1 ，X 2 ，X 3 ，X 4 .....X n Mean value isDeviation ofThe standard deviation S is:
if a certain sample data X i The deviation Vi (1. Ltoreq.i.ltoreq.n) of (1. Ltoreq.i) satisfies:
|Vi|≥3δ
then consider X i Is abnormal data, should be rejected.
Further, correlation analysis screening comprises calculating correlation between collected data and power supply coal consumption, and reserving data types with absolute values of correlation r being larger than 0.6.
It should be noted that the pearson correlation coefficient method includes the following steps:
the pearson correlation coefficient calculation formula:
wherein X is i 、Y i Is a variable, n is the number of samples, rxy ranges from [ -1,1]The correlation strength of the variables is typically determined by the range of values of | rxy | or below: 0.8-1.0 extremely strong correlation, 0.6-0.8 strong correlation, 0.4-0.6 moderate correlation, 0.2-0.4 weak correlation, 0.0-0.2 extremely weak correlation or no correlation.
It should be noted that, this step adopts two data screening modes, can more accurately propose unstable and influence less parameter, thus make the predictive model more accurate, reject the unstable data in the boiler operation process at first, then through the correlation analysis, have carried on the dimension reduction to a certain extent to the data, has reduced the repeated data type, has reduced the operation time of the model, compared with the method that adopts the collected data to predict directly, this method can provide more stable and accurate method, can also reduce the input parameter of the predictive model effectively at the same time, raise the operation speed of the model.
S3: as shown in fig. 2, the power supply coal consumption is predicted based on a PSO-SVM support vector machine algorithm to obtain a prediction model;
further, the power supply coal consumption is predicted based on a PSO-SVM support vector machine algorithm, which comprises,
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;
performing iterative computation according to a position and speed updating formula of the particles, and updating the position and speed of the particles;
calculating the fitness value of each particle after each iteration according to the fitness function of the particle, comparing the fitness value of each particle with the fitness value of each individual extremum, if the fitness value is more optimal, updating the individual extremum, otherwise, keeping the original value; comparing the updated individual extremum of each particle with the global extremum, if the individual extremum is better, updating the global extremum, otherwise, keeping the original value;
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 number is reached.
It should be noted that, the output optimal particles of PSO represent penalty factor c and kernel function parameter g of SVM, respectively, and the PSO-SVM support vector machine is selected in this step to effectively reduce iteration time, ensure high efficiency of algorithm, and ensure more accurate prediction accuracy
S4: calculating the electricity-fired coal cost according to the prediction model and the information of the coal types of the fed coal of the SIS system;
further, the information of the coal types entering the furnace comprises real-time price and proportion information;
the electricity-to-fire coal cost is expressed as:
C cost of ＝B g *P Heald
Wherein B is g To supply coal consumption, P Heald Is the unit price of comprehensive standard coal;
P heald ＝P1*x1+P2*x2+P3*x3+...Pn*xn(x1+x2+x3+...xn＝1)
Wherein, P1, P2, P3..Pn is the price of single coal, X1, X2, X3...Xn is the proportion of single coal.
S5: and (3) establishing a power electricity coal cost optimization model based on a gray wolf algorithm, and optimizing operation data and coal mixing proportion to obtain a unit operation and coal blending scheme with the lowest cost.
Furthermore, the electric coal cost optimization model is established based on the gray wolf algorithm, and the operation data and the coal mixing proportion are optimized, including,
the optimized target is the electricity-to-fire coal cost;
the input parameters are set parameters and the proportion of mixed coal;
the constraint condition of the model is the safe operation range of the unit;
and finally outputting a unit operation and coal blending scheme with lowest electric coal cost after optimization.
It should be noted that, in this step, the gray wolf algorithm is selected to build the electricity-fire coal cost optimization model, compared with the common optimization algorithms such as PSO and firefly, the algorithm flow and steps are greatly simplified, and the mathematical model is also very beautiful. The gray wolf algorithm has stronger global searching capability due to no greedy algorithm, meanwhile, the parameter A also controls the local searching range of the algorithm, the global searching capability and the local searching capability of the algorithm are relatively balanced, and the optimization result is relatively mature.
The above is a schematic scheme of the electricity-fire coal cost optimization method based on the intelligent algorithm in this embodiment. It should be noted that, the technical solution of the intelligent algorithm-based electricity-fire coal cost optimization method device and the technical solution of the intelligent algorithm-based electricity-fire coal cost optimization method belong to the same concept, and details of the technical solution of the intelligent algorithm-based electricity-fire coal cost optimization method device in this embodiment, which are not described in detail, can be referred to the description of the intelligent algorithm-based electricity-fire coal cost optimization method technical solution.
The method and the device for optimizing the electricity-to-fire coal cost based on the intelligent algorithm in the embodiment comprise the following steps:
the acquisition module is used for acquiring historical operation data of the coal-fired power plant, which influences the power supply coal consumption, and different load data of the unit;
the screening module is used for screening the historical operation data and the different load data of the unit to obtain input variables;
the prediction module is used for predicting the power supply coal consumption based on a PSO-SVM support vector machine algorithm to obtain a prediction model;
the cost calculation module is used for calculating the electricity-fired coal cost according to the prediction model and the real-time price and proportion information of the coal type fed into the furnace of the SIS system;
and the optimizing module is used for establishing a power electricity coal cost optimizing model based on a gray wolf algorithm and optimizing the operation data and the coal mixing proportion to obtain a unit operation and coal blending scheme with the lowest cost.
The embodiment also provides an electronic device, which is suitable for the condition of the intelligent algorithm-based electricity-fire coal cost optimization method, 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 burning cost optimization method based on the intelligent algorithm according to the embodiment.
The present embodiment also provides a storage medium having stored thereon a computer program which, when executed by a processor, implements a method for implementing a smart algorithm-based electricity-fire coal cost optimization as set forth in the above embodiments.
The storage medium proposed in this embodiment belongs to the same inventive concept as the implementation method for optimizing the electricity-less coal costs based on the intelligent algorithm proposed in the above embodiment, and technical details not described in detail in this embodiment can be seen in the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.
From the above description of embodiments, it will be clear to a person skilled in the art that the present invention may be implemented by means of software and necessary general purpose hardware, but of course also by means of hardware, although in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as a floppy disk, a Read Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a FLASH Memory (FLASH), a hard disk or an optical disk of a computer, etc., including several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to execute the method of the embodiments of the present invention.
Example 2
Referring to tables 1-3, for one embodiment of the present invention, a method for optimizing the cost of electricity-rich coal based on an intelligent algorithm is provided, and in order to verify the beneficial effects, a comparison result of the two schemes is provided.
And building a prediction model of power supply coal consumption and an optimization model of electricity-less coal cost through an MATLAB simulation platform, and respectively predicting the power supply coal consumption of the mixed coal and optimizing a unit operation scheme and a coal blending scheme of the electricity-less coal cost.
Firstly, screening data, adopting two data screening modes, and screening data with more relevant coefficients to obtain input parameters of a prediction model, wherein the input parameters are shown in the following table 1:
table 1 input parameters screened by correlation coefficient method
| Variable name | Correlation coefficient | Variable name | Correlation coefficient |
| Main steam pressure | -0.781 | Total amount of primary air | -0.654 |
| Exhaust gas temperature | -0.742 | Total amount of secondary air | -0.786 |
| Primary wind pressure | -0.655 | Main steam flow | -0.813 |
| Secondary air pressure | -0.727 |
The screened data is used for establishing a prediction model, and the prediction effect is shown in the following table 2:
TABLE 2 index of prediction effect
As can be seen from Table 2, the average relative error of the predictions is small, and the mean square error MSE of all the predictions is less than 0.001, so that the prediction accuracy is accurate.
The operation parameters of the unit and the proportion of the coal blending are optimized through an optimization model, and the optimal solution of the solved model is shown in the following table 3:
TABLE 3 optimal solution for unit operation and coal blending scheme
| Scheme for the production of a semiconductor device | 1 | 2 | 3 |
| Coal 1 | 2 | 5 | 6 |
| Coal 2 | 3 | 3 | 4 |
| 1 proportion of coal (percent) | 42 | 33 | 35 |
| Coal 2 proportion (%) | 58 | 67 | 65 |
| Main steam pressure (MPa) | 13.56 | 13.74 | 15.49 |
| Main steam flow (t/h) | 589.16 | 585.44 | 690.64 |
| Total amount of primary air (kNm 3/h) | 342.56 | 296.24 | 326.30 |
| Total amount of secondary air (kNm 3/h) | 407.32 | 468.48 | 432.16 |
| Primary air pressure (kPa) | 11.09 | 9.97 | 10.26 |
| Secondary air pressure (kPa) | -0.0159 | -0.0282 | 0.0228 |
| Exhaust gas temperature | 132.53 | 126.77 | 133.57 |
| Electric fire coal cost (Yuan/kWh) | 0.1796 | 0.1764 | 0.1719 |
It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made thereto without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered by the scope of the claims of the present invention.
Claims (7)
1. The intelligent algorithm-based electricity-fired coal cost optimization method is characterized by comprising the following steps of:
collecting historical operation data of the coal-fired power plant affecting power supply coal consumption and different load data of a unit;
double screening is carried out on the historical operation data and different load data of the unit to obtain input variables;
the screening comprises steady state data screening and correlation analysis screening;
the steady-state data screening uses a Laida criterion to eliminate error and random error;
predicting the power supply coal consumption based on a PSO-SVM support vector machine algorithm to obtain a prediction model;
the PSO-SVM-based support vector machine algorithm predicts the power supply coal consumption, comprising,
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 speed Vi;
calculating the fitness value of each particle after each iteration according to the fitness function of the particle, comparing the fitness value of each particle with the fitness value of each individual extremum, if the fitness value is more optimal, updating the individual extremum, otherwise, keeping the original value; comparing the updated individual extremum of each particle with the global extremum, if the individual extremum is better, updating the global extremum, otherwise, keeping the original value;
iterating until the termination condition is met, and obtaining the optimal parameter combination of the prediction model by reaching the maximum iteration times;
calculating the electricity-fired coal cost according to the prediction model and the information of the coal types fed into the furnace of the SIS system;
establishing a power electricity coal cost optimization model based on a gray wolf algorithm, and optimizing operation data and coal mixing proportion to obtain a unit operation and coal blending scheme with the lowest cost;
based on the gray wolf algorithm, a power electricity coal cost optimization model is established to optimize the operation data and the coal mixing proportion, comprising,
the optimized target is the electricity-to-fire coal cost;
the input parameters are set parameters and the proportion of mixed coal;
the constraint condition of the model is the safe operation range of the unit;
and finally outputting a unit operation and coal blending scheme with lowest electric coal cost after optimization.
2. The intelligent algorithm-based electricity-fired coal cost optimization method according to claim 1, wherein the historical operation data comprises main steam pressure, main steam temperature, reheat steam temperature, reheater desuperheat water flow, condenser pressure, exhaust gas temperature, condenser end difference, condenser supercooling degree, primary air pressure, secondary air pressure, primary air total amount, secondary air total amount and station service electricity.
3. The intelligent algorithm-based electricity-fire coal cost optimization method according to claim 1 or 2, wherein the correlation analysis screening comprises calculating correlation of collected data and power supply coal consumption, and reserving data types with absolute values of correlation r being larger than 0.6.
4. The intelligent algorithm-based electricity-fire coal cost optimization method according to claim 3, wherein the coal type information in the furnace comprises real-time price and proportion information;
the electricity-to-fire coal cost is expressed as:
C cost of ＝B g *P Heald
Wherein B is g To supply coal consumption, P Heald Is the unit price of comprehensive standard coal;
P heald ＝P1*x1+P2*x2+P3*x3+...Pn*xn(x1+x2+x3+...xn＝1)
Wherein, P1, P2, P3..Pn is the price of single coal, X1, X2, X3...Xn is the proportion of single coal.
5. An apparatus for applying the intelligent algorithm-based electricity-fire coal cost optimization method according to claim 1, comprising,
the acquisition module is used for acquiring historical operation data of the coal-fired power plant, which influences the power supply coal consumption, and different load data of the unit;
the screening module is used for screening the historical operation data and the different load data of the unit to obtain input variables;
the prediction module is used for predicting the power supply coal consumption based on a PSO-SVM support vector machine algorithm to obtain a prediction model;
the cost calculation module is used for calculating the electricity-fired coal cost according to the prediction model and the real-time price and proportion information of the coal types fed into the furnace of the SIS system;
and the optimizing module is used for establishing a power electricity coal cost optimizing model based on a gray wolf algorithm and optimizing the operation data and the coal mixing proportion to obtain a unit operation and coal blending scheme with the lowest cost.
6. An electronic 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 intelligent algorithm-based electricity-fire coal cost optimization method of any one of claims 1 to 4.
7. A computer readable storage medium storing computer executable instructions which when executed by a processor implement the steps of the smart algorithm-based electricity-fire coal cost optimization method of any one of claims 1 to 4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310050679.4A CN116307075B (en) | 2023-02-01 | 2023-02-01 | Intelligent algorithm-based electricity-measuring coal-burning cost optimization method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310050679.4A CN116307075B (en) | 2023-02-01 | 2023-02-01 | Intelligent algorithm-based electricity-measuring coal-burning cost optimization method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116307075A CN116307075A (en) | 2023-06-23 |
| CN116307075B true CN116307075B (en) | 2024-04-05 |
Family
ID=86785872
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310050679.4A Active CN116307075B (en) | 2023-02-01 | 2023-02-01 | Intelligent algorithm-based electricity-measuring coal-burning cost optimization method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116307075B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117010728B (en) * | 2023-10-07 | 2024-01-02 | 华北电力大学 | Comprehensive power generation cost optimization method for thermal power enterprises |
| CN117391734B (en) * | 2023-10-07 | 2024-03-26 | 华北电力大学 | Power generation cost prediction method based on support vector machine regression model |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103426032A (en) * | 2013-07-25 | 2013-12-04 | 广东电网公司电力科学研究院 | Method for economically and optimally dispatching cogeneration units |
| CN103440528A (en) * | 2013-08-12 | 2013-12-11 | 国家电网公司 | Thermal power generating unit operation optimization method and device based on consumption difference analysis |
| CN107274027A (en) * | 2017-06-22 | 2017-10-20 | 湖南华润电力鲤鱼江有限公司 | A kind of many coal coal mixing combustion optimization methods of coal unit |
| CN109003145A (en) * | 2018-08-17 | 2018-12-14 | 华北电力大学 | A kind of power coal price prediction technique |
| CN110826794A (en) * | 2019-10-31 | 2020-02-21 | 上海电力大学 | Power plant coal consumption reference value rolling prediction method and device based on PSO (particle swarm optimization) SVM (support vector machine) |
| CN112381268A (en) * | 2020-10-29 | 2021-02-19 | 武汉华中思能科技有限公司 | Short-term fire coal cost prediction method and system for electric power spot market |
| KR102271070B1 (en) * | 2020-03-31 | 2021-06-29 | 조선대학교산학협력단 | Method and apparatus for determining mixed coal combination |
| CN113703406A (en) * | 2021-08-27 | 2021-11-26 | 西安热工研究院有限公司 | Operation optimization method and system for coal-fired water and electricity cogeneration unit by adopting low-temperature multi-effect technology |
-
2023
- 2023-02-01 CN CN202310050679.4A patent/CN116307075B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103426032A (en) * | 2013-07-25 | 2013-12-04 | 广东电网公司电力科学研究院 | Method for economically and optimally dispatching cogeneration units |
| CN103440528A (en) * | 2013-08-12 | 2013-12-11 | 国家电网公司 | Thermal power generating unit operation optimization method and device based on consumption difference analysis |
| CN107274027A (en) * | 2017-06-22 | 2017-10-20 | 湖南华润电力鲤鱼江有限公司 | A kind of many coal coal mixing combustion optimization methods of coal unit |
| CN109003145A (en) * | 2018-08-17 | 2018-12-14 | 华北电力大学 | A kind of power coal price prediction technique |
| CN110826794A (en) * | 2019-10-31 | 2020-02-21 | 上海电力大学 | Power plant coal consumption reference value rolling prediction method and device based on PSO (particle swarm optimization) SVM (support vector machine) |
| KR102271070B1 (en) * | 2020-03-31 | 2021-06-29 | 조선대학교산학협력단 | Method and apparatus for determining mixed coal combination |
| CN112381268A (en) * | 2020-10-29 | 2021-02-19 | 武汉华中思能科技有限公司 | Short-term fire coal cost prediction method and system for electric power spot market |
| CN113703406A (en) * | 2021-08-27 | 2021-11-26 | 西安热工研究院有限公司 | Operation optimization method and system for coal-fired water and electricity cogeneration unit by adopting low-temperature multi-effect technology |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116307075A (en) | 2023-06-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN116307075B (en) | Intelligent algorithm-based electricity-measuring coal-burning cost optimization method and system | |
| CN108416691B (en) | Energy substitution environment-friendly potential calculation method | |
| CN113723870B (en) | Distributed power generation CO2 emission reduction accounting method, device, equipment and medium | |
| CN113991742B (en) | Distributed photovoltaic double-layer collaborative optimization investment decision-making method for power distribution network | |
| CN112950098A (en) | Energy planning method and device based on comprehensive energy system and terminal equipment | |
| CN115456406A (en) | Evaluation method, device, equipment and storage medium of comprehensive energy system | |
| CN111612244A (en) | QRA-LSTM-based method for predicting nonparametric probability of photovoltaic power before day | |
| CN114154744A (en) | Capacity expansion planning method and device of comprehensive energy system and electronic equipment | |
| CN107122599B (en) | Method for evaluating capacity of thermal storage electric boiler for consuming abandoned wind and abandoned light in real time | |
| CN111625770A (en) | Energy efficiency evaluation method and system for power distribution network with distributed power supply | |
| CN113078677B (en) | Energy consumption risk eliminating method considering uncertainty of renewable energy | |
| Chen et al. | Improved progressive optimality algorithm and its application to determination of optimal release trajectory of long-term power generation operation of cascade reservoirs | |
| CN112653180A (en) | Wind-fire-storage combined system environment economic dispatching method and system | |
| CN116128154A (en) | Energy optimal configuration method and device for agricultural park comprehensive energy system | |
| CN110689269A (en) | Carbon emission evaluation method based on autoregressive distribution hysteresis model and Kaya formula | |
| CN115908047A (en) | Multi-time scale operation method of comprehensive energy system and application thereof | |
| CN115758763A (en) | Multi-energy flow system optimal configuration method and system considering source load uncertainty | |
| CN115495862A (en) | Power transmission network extension planning method and system considering extreme scenes of renewable energy sources | |
| CN115713252A (en) | Water, wind, light and energy storage multi-energy complementary system comprehensive benefit evaluation scheme optimization method | |
| CN112668751B (en) | Method and device for establishing unit optimization scheduling model | |
| CN114037272A (en) | Energy efficiency assessment method for regional comprehensive energy system | |
| CN112668764A (en) | Offshore wind farm energy storage system optimization configuration method based on cloud model and FCM algorithm | |
| CN116911424A (en) | Method for constructing user side distributed green electricity model | |
| Juárez-Luna et al. | Electricity generation portfolios in Mexico: environmental, economic, and policy implications | |
| CN118037328A (en) | New energy hydrogen adding station optimal scheduling method and terminal |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |