CN113887827A - Coal blending combustion optimization decision method based on real-time carbon emission monitoring of thermal power generating unit - Google Patents
Coal blending combustion optimization decision method based on real-time carbon emission monitoring of thermal power generating unit Download PDFInfo
- Publication number
- CN113887827A CN113887827A CN202111240635.5A CN202111240635A CN113887827A CN 113887827 A CN113887827 A CN 113887827A CN 202111240635 A CN202111240635 A CN 202111240635A CN 113887827 A CN113887827 A CN 113887827A
- Authority
- CN
- China
- Prior art keywords
- coal
- carbon emission
- thermal power
- power generating
- generating unit
- 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.)
- Pending
Links
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 title claims abstract description 179
- 229910052799 carbon Inorganic materials 0.000 title claims abstract description 179
- 239000003245 coal Substances 0.000 title claims abstract description 137
- 238000002156 mixing Methods 0.000 title claims abstract description 86
- 238000000034 method Methods 0.000 title claims abstract description 52
- 238000005457 optimization Methods 0.000 title claims abstract description 47
- 238000012544 monitoring process Methods 0.000 title claims abstract description 28
- 238000002485 combustion reaction Methods 0.000 title claims description 38
- 238000013528 artificial neural network Methods 0.000 claims abstract description 22
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 21
- 230000006870 function Effects 0.000 claims description 34
- 229910002092 carbon dioxide Inorganic materials 0.000 claims description 21
- CURLTUGMZLYLDI-UHFFFAOYSA-N Carbon dioxide Chemical compound O=C=O CURLTUGMZLYLDI-UHFFFAOYSA-N 0.000 claims description 18
- 238000010248 power generation Methods 0.000 claims description 14
- 230000005284 excitation Effects 0.000 claims description 12
- 239000001569 carbon dioxide Substances 0.000 claims description 9
- 210000002569 neuron Anatomy 0.000 claims description 9
- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 claims description 7
- NINIDFKCEFEMDL-UHFFFAOYSA-N Sulfur Chemical compound [S] NINIDFKCEFEMDL-UHFFFAOYSA-N 0.000 claims description 7
- 239000003546 flue gas Substances 0.000 claims description 7
- 229910052717 sulfur Inorganic materials 0.000 claims description 7
- 239000011593 sulfur Substances 0.000 claims description 7
- 238000006243 chemical reaction Methods 0.000 claims description 6
- 239000007789 gas Substances 0.000 claims description 6
- 238000010438 heat treatment Methods 0.000 claims description 6
- 239000000779 smoke Substances 0.000 claims description 6
- 238000012549 training Methods 0.000 claims description 6
- 238000004364 calculation method Methods 0.000 claims description 5
- 239000000126 substance Substances 0.000 claims description 5
- 238000004458 analytical method Methods 0.000 claims description 3
- 238000013480 data collection Methods 0.000 claims description 3
- 238000013461 design Methods 0.000 claims description 3
- 238000002474 experimental method Methods 0.000 claims description 3
- -1 heat values Substances 0.000 claims description 3
- 238000005259 measurement Methods 0.000 claims description 3
- 238000012821 model calculation Methods 0.000 claims description 3
- 238000003062 neural network model Methods 0.000 claims description 3
- 238000010606 normalization Methods 0.000 claims description 3
- 238000012545 processing Methods 0.000 claims description 3
- 238000012546 transfer Methods 0.000 claims description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000007613 environmental effect Effects 0.000 description 2
- 238000010344 co-firing Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 239000003344 environmental pollutant Substances 0.000 description 1
- 238000010304 firing Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 231100000719 pollutant Toxicity 0.000 description 1
Images
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
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- 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
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
- Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
-
- 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
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/80—Management or planning
- Y02P90/84—Greenhouse gas [GHG] management systems
-
- 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)
- Health & Medical Sciences (AREA)
- Economics (AREA)
- General Physics & Mathematics (AREA)
- General Health & Medical Sciences (AREA)
- Strategic Management (AREA)
- Human Resources & Organizations (AREA)
- Software Systems (AREA)
- Tourism & Hospitality (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Evolutionary Computation (AREA)
- Mathematical Physics (AREA)
- Data Mining & Analysis (AREA)
- Computational Linguistics (AREA)
- Biophysics (AREA)
- Biomedical Technology (AREA)
- General Business, Economics & Management (AREA)
- Molecular Biology (AREA)
- Artificial Intelligence (AREA)
- Marketing (AREA)
- Life Sciences & Earth Sciences (AREA)
- Primary Health Care (AREA)
- Water Supply & Treatment (AREA)
- Public Health (AREA)
- Development Economics (AREA)
- Game Theory and Decision Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention provides a coal blending optimization decision method based on real-time carbon emission monitoring of a thermal power generating unit, which comprises the steps of collecting real-time carbon emission data of the thermal power generating unit, predicting the carbon emission of the thermal power generating unit on the same day, constructing a neural network algorithm, collecting real-time data of carbon price of a carbon emission right trading market, storing, classifying and updating the collected real-time carbon emission data of the thermal power generating unit, predicting the carbon emission data of the thermal power generating unit on the same day and the real-time data of the carbon price of the carbon emission right trading market, optimizing the classified data through the neural network algorithm to obtain an optimization result, and performing a coal blending scheme of the thermal power generating unit on the coal by the optimization result. The invention combines the implementation emission data of the thermal power plant and the real-time carbon trading price, can easily obtain the optimal coal blending and blending combination method, enables enterprises to obtain the maximum comprehensive benefit, and can save a large amount of energy and cost.
Description
Technical Field
The invention relates to the technical field of environmental monitoring of coal-fired power plants, in particular to a coal blending combustion optimization decision method based on real-time carbon emission monitoring of a thermal power generating unit.
Background
At present, the electric power production mainly takes coal as a main part, and in recent years, coal quality of most coal-fired thermal power plants is unstable due to 'coal scarcity' caused by contradiction between supply and demand of electricity and coal, so that coal consumption of a generating set is increased, generating efficiency is reduced, pollutant discharge exceeds standard and the like are caused, and safe, civilized, economic and environment-friendly operation of the generating set is influenced.
In addition, excessive carbon emissions from thermal power plants contribute to costs after they participate in carbon trading. The thermal power plant uses coal with different coal qualities, so that different economic effects and environmental effects are produced. Although the cost of the coal with poor coal quality is low, the efficiency of the boiler is reduced, and the consumption rate of the power generation coal is increased; although the coal with good coal quality has higher cost, the boiler efficiency can be improved, and the coal consumption rate of power generation can be reduced.
The blending combustion of the blended coal is an important measure for reducing cost and improving efficiency and improving core competitiveness of power generation enterprises, and is an effective method for solving the problems of short coal consumption, variable coal types and poor running performance of units.
The method provided by the invention is used for establishing a thermal power generating unit coal blending combustion optimization decision method by considering the quality and carbon emission of various fire coals and combining real-time carbon emission monitoring data and real-time carbon trading price.
Disclosure of Invention
The invention provides a coal blending combustion optimization decision method based on real-time carbon emission monitoring of a thermal power generating unit.
In order to solve the problems, the technical scheme adopted by the invention is as follows:
the coal blending combustion optimization decision method based on real-time carbon emission monitoring of the thermal power generating unit comprises the following steps:
the method comprises the following steps: collecting real-time carbon emission data of the coal-fired power generation unit;
step two: predicting the carbon emission of the coal-fired thermal power generating unit on the same day;
step three: constructing a neural network algorithm;
step four: acquiring real-time data of carbon prices of a carbon emission right trading market;
step five: storing, classifying and updating the acquired real-time carbon emission data of the coal-fired thermal power generating unit, the carbon emission data of the coal-fired thermal power generating unit on the same day and the real-time data of the carbon price of the carbon emission right trading market in real time;
step six: optimizing the classified data through a neural network algorithm to obtain an optimization result;
step seven: and carrying out coal blending and burning scheme of the coal-fired thermal power generating unit through the optimized result.
Further, in the step one, the method for acquiring the real-time carbon emission data of the coal-fired power generation unit comprises the following steps:
data are collected from a flue gas emission chimney of a coal-fired thermal power generating unit, and the data collection method comprises the following steps: CO analyzed by carbon dioxide analysis equipment2The volume concentration of the gas and the carbon emission concentration are calculated by the following formula:
in the formula: x is CO2Concentration conversion value in mg/Nm3(ii) a C is CO2Concentration measurements, in ppm; m is CO2The molecular weight of (a); t is the temperature of the clean smoke gas in unit; p is net smoke pressure in Pa;
the carbon emission is calculated by the formula:
in the formula: mc is CO in time T2Accumulating the discharge amount in t; x is CO2Concentration conversion value in mg/Nm3(ii) a F is net flue gas flow in Nm3/h；
Calculating the carbon emission in one day according to the method;
obtaining carbon emission amount of N days based on the carbon emission monitoring data of N days: eiAnd i is the observed day i, and the value of N is not less than 50.
Further, in the method for acquiring the real-time carbon emission data of the coal-fired power generation unit, all coal types of the thermal power plant need to be acquired randomly, samples are acquired randomly, and a physical and chemical experiment is performed to acquire coal data, including the heat value u of each coal typeiThe unit is GJ/t, carbon dioxide emission factor CCiIn the unit tCO2GJ, sulfur content SiMoisture content HiIndex, and daily dosage XiUnit is t and unit price index QiThe unit is element/t.
Further, in step three, the neural network algorithm is constructed as follows:
inputting parameters of different coal types and the daily consumption, and predicting the daily carbon emission of the coal-fired thermal power generating unit:
establishing a model:
the neural network model consists of an input layer, a hidden layer and an output layer, and adopts an S-shaped transfer function:
back propagation error function:
and (3) network structure design:
the input layer adds 5 layers of input layers by using different varieties of coal, heat values, carbon dioxide emission factors, carbon content per unit heat value and sulfur content and adding coal for blending and burning, and the number of hidden layers is set as follows:
m is the number of input layers, n is the number of output layers, a is a constant, and a is more than or equal to 1 and less than or equal to 10;
the output layer is carbon emission;
and (3) realizing a model:
adopting an S-type tangent function tansig as an excitation function of a hidden layer neuron, and normalizing the output of the network to the range of [ -1,1], so that the S-type logarithmic function tansig is selected by the prediction model as the excitation function of the output layer neuron;
training sample data is input into a network after being normalized, a hidden layer excitation function and an output layer excitation function of the network are respectively tan sig and logsig functions, the network training function is trailing dx, a network performance function is mse, hidden layer neuron number, network parameters, network iteration number expected errors and learning rate are set, then the network is trained, before the input is started, normalization processing is carried out on input parameters and input amount of various coals, and finally the value is between-1 and 1.
Further, in the fourth step, the method for collecting the carbon price of the carbon emission right trading market in real time comprises the following steps:
the device is used for calculating the daily carbon emission cost of the coal-fired thermal power generating unit and acquiring the carbon price of the national carbon emission right trading market in real time, and the carbon price of the past 1 day is acquired on a carbon trading platform;
the first condition is as follows: the carbon emission quota of the thermal power generating unit is K, the unit is t, the thermal power generating unit is used up, namely the carbon emission quota K is less than or equal to 0, and the carbon emission cost is obtained by multiplying the emission by the carbon price;
case two: if the carbon emission quota K of the thermal power generating unit is larger than 0 before use, the carbon emission cost is obtained by multiplying the emission by the average unit carbon emission cost in the previous performance period;
carbon emission cost calculation formula:
the carbon emission cost is P, the unit is element, if the thermal power generating unit is in case one, the carbon emission cost is substituted into the formula (1), and P isc1The carbon number is the current carbon number, the unit is element/t, if the thermal power generating unit is the second case, the formula (2) is substituted, and P isc2Is the average unit carbon emission cost of the previous fulfillment period in units of yuan/t.
Further, in the sixth step, the specific method for optimizing the classified data by using the neural network algorithm and obtaining the optimization result is as follows:
giving an initial coal blending combustion state, obtaining carbon emission generated by different blending combustion modes through a neural network algorithm, exhausting all possibilities of carbon emission and carbon emission cost generated by power generation of a thermal power generating unit, changing the coal blending combustion state and the proportion of different coal types, and then taking a maximum benefit value and optimizing a target:
maxw-benefit of power generation-cost of carbon emission-cost of coal purchase
Constraint conditions are as follows:
Si＜Smax
Hi＜Hmax；
i is the generation benefit in Yuan, P is the carbon emission cost, XiThe unit of the usage amount of the coal for participating in coal blending and burning is t and QiCorresponding to the purchase price of coal, the unit is Yuan/t, CALminThe unit is GJ which is the lowest daily combustion heating value of the thermal generator set;
coal blending quantity XiAnd (3) obtaining the optimal quantity of coal blending of each coal variety through model calculation for decision variables, combining the coal varieties, changing the coal blending state, repeatedly executing the optimization model to obtain the maximum comprehensive benefit of all the coal blending states, and giving the coal blending result according to the corresponding decision variables.
Further, in the seventh step, the coal-fired thermal power generating unit coal blending co-combustion scheme is performed through the optimization result, which specifically comprises the following steps:
and under the goal that the optimization result shows the maximum comprehensive benefit, the optimization result is used for a coal blending and burning scheme of the coal-fired thermal power generating unit on the next day, the optimization result on the second day is conducted to a coal blending control device to realize the coal blending and burning function, the optimization result on the second day is used as the initial state of optimization on the third day, the coal blending proportion and the carbon emission monitoring result on the second day are incorporated into the neural network algorithm, and data are updated.
Compared with the prior art, the invention has the following beneficial effects:
the method for optimizing the coal blending combustion of the thermal power generating unit is established based on the method for optimizing the coal blending combustion of the thermal power generating unit based on the real-time carbon emission monitoring, the method for combining the emission data of the thermal power generating unit and the real-time carbon trading price is combined, the optimal coal blending combustion coal combination can be easily obtained, and by the method, enterprises can obtain the maximum comprehensive benefit, a large amount of energy can be saved, and a large amount of cost can be saved.
Drawings
FIG. 1 is a flow chart of a coal blending combustion optimization decision method based on real-time carbon emission monitoring of a thermal power generating unit.
Detailed Description
The present invention will now be described in connection with particular embodiments, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar components or components having the same or similar functionality throughout.
Referring to fig. 1, fig. 1 is a flowchart of a coal blending optimization decision method based on real-time carbon emission monitoring of a thermal power generating unit according to the present invention.
The coal blending combustion optimization decision method based on real-time carbon emission monitoring of the thermal power generating unit comprises the following steps:
the method comprises the following steps: collecting real-time carbon emission data of the coal-fired power generation unit;
the method for acquiring the real-time carbon emission data of the coal-fired power generation unit comprises the following steps:
data are collected from a flue gas emission chimney of a coal-fired thermal power generating unit, and the data collection method comprises the following steps: CO analyzed by carbon dioxide analysis equipment2The volume concentration of the gas and the carbon emission concentration are calculated by the following formula:
in the formula:x is CO2Concentration conversion value in mg/Nm3(ii) a C is CO2Concentration measurements, in ppm; m is CO2The molecular weight of (a); t is the temperature of the clean smoke gas in unit; p is net smoke pressure in Pa;
the carbon emission is calculated by the formula:
in the formula: mc is CO in time T2Accumulating the discharge amount in t; x is CO2Concentration conversion value in mg/Nm3(ii) a F is net flue gas flow in Nm3/h；
Calculating the carbon emission in one day according to the method;
obtaining carbon emission amount of N days based on the carbon emission monitoring data of N days: eiAnd i is the observed day i, and the value of N is not less than 50.
Step two: predicting the carbon emission of the coal-fired thermal power generating unit on the same day;
the method for acquiring the real-time carbon emission data of the coal-fired power generation unit needs to randomly acquire samples aiming at all coal types of a thermal power plant and perform physical and chemical experiment acquisition to obtain coal data, including the heat value u of each coal typeiThe unit is GJ/t, carbon dioxide emission factor CCiIn the unit tCO2GJ, sulfur content SiMoisture content HiIndex, and daily dosage XiUnit is t and unit price index QiThe unit is element/t.
Step three: constructing a neural network algorithm;
the method comprises the following specific steps:
inputting parameters of different coal types and the daily consumption, and predicting the daily carbon emission of the coal-fired thermal power generating unit:
establishing a model:
the neural network model consists of an input layer, a hidden layer and an output layer, and adopts an S-shaped transfer function:
back propagation error function:
and (3) network structure design:
the input layer adds 5 layers of input layers by using different varieties of coal, heat values, carbon dioxide emission factors, carbon content per unit heat value and sulfur content and adding coal for blending and burning, and the number of hidden layers is set as follows:
m is the number of input layers, n is the number of output layers, a is a constant, and a is more than or equal to 1 and less than or equal to 10;
the output layer is carbon emission;
and (3) realizing a model:
adopting an S-type tangent function tansig as an excitation function of a hidden layer neuron, and normalizing the output of the network to the range of [ -1,1], so that the S-type logarithmic function tansig is selected by the prediction model as the excitation function of the output layer neuron;
training sample data is input into a network after being normalized, a hidden layer excitation function and an output layer excitation function of the network are respectively tan sig and logsig functions, the network training function is trailing dx, a network performance function is mse, hidden layer neuron number, network parameters, network iteration number expected errors and learning rate are set, then the network is trained, before the input is started, normalization processing is carried out on input parameters and input amount of various coals, and finally the value is between-1 and 1.
Step four: acquiring real-time data of carbon prices of a carbon emission right trading market;
the method for acquiring the real-time data of the carbon price of the carbon emission right trading market comprises the following steps:
the device is used for calculating the daily carbon emission cost of the coal-fired thermal power generating unit and acquiring the carbon price of the national carbon emission right trading market in real time, and the carbon price of the past 1 day is acquired on a carbon trading platform;
the first condition is as follows: the carbon emission quota of the thermal power generating unit is K, the unit is t, the thermal power generating unit is used up, namely the carbon emission quota K is less than or equal to 0, and the carbon emission cost is obtained by multiplying the emission by the carbon price;
case two: if the carbon emission quota K of the thermal power generating unit is larger than 0 before use, the carbon emission cost is obtained by multiplying the emission by the average unit carbon emission cost in the previous performance period;
carbon emission cost calculation formula:
the carbon emission cost is P, the unit is element, if the thermal power generating unit is in case one, the carbon emission cost is substituted into the formula (1), and P isc1The carbon number is the current carbon number, the unit is element/t, if the thermal power generating unit is the second case, the formula (2) is substituted, and P isc2Is the average unit carbon emission cost of the previous fulfillment period in units of yuan/t.
Step five: and storing, classifying and updating the acquired real-time carbon emission data of the coal-fired thermal power generating unit, the carbon emission data of the predicted coal-fired thermal power generating unit on the same day and the real-time data of the carbon price of the carbon emission right trading market in real time.
Step six: optimizing the classified data through a neural network algorithm to obtain an optimization result;
the specific method for optimizing the classified data through the neural network algorithm and obtaining the optimization result is as follows:
giving an initial coal blending combustion state, obtaining carbon emission generated by different blending combustion modes through a neural network algorithm, exhausting all possibilities of carbon emission and carbon emission cost generated by power generation of a thermal power generating unit, changing the coal blending combustion state and the proportion of different coal types, and then taking a maximum benefit value and optimizing a target:
maxw-benefit of power generation-cost of carbon emission-cost of coal purchase
Constraint conditions are as follows:
Si＜Smax
Hi＜Hmax；
i is the generation benefit in Yuan, P is the carbon emission cost, XiThe unit of the usage amount of the coal for participating in coal blending and burning is t and QiCorresponding to the purchase price of coal, the unit is Yuan/t, CALminThe unit is GJ which is the lowest daily combustion heating value of the thermal generator set;
coal blending quantity XiAnd (3) obtaining the optimal quantity of coal blending of each coal variety through model calculation for decision variables, combining the coal varieties, changing the coal blending state, repeatedly executing the optimization model to obtain the maximum comprehensive benefit of all the coal blending states, and giving the coal blending result according to the corresponding decision variables.
Step seven: carrying out a coal blending and blending scheme of the coal-fired thermal power generating unit according to the optimization result;
the method comprises the following specific steps:
and under the goal that the optimization result shows the maximum comprehensive benefit, the optimization result is used for a coal blending and burning scheme of the coal-fired thermal power generating unit on the next day, the optimization result on the second day is conducted to a coal blending control device to realize the coal blending and burning function, the optimization result on the second day is used as the initial state of optimization on the third day, the coal blending proportion and the carbon emission monitoring result on the second day are incorporated into the neural network algorithm, and data are updated.
Example (b):
according to the device for acquiring the real-time carbon emission data of the coal-fired thermal power generating unit in a certain thermal power plant, relevant data are acquired from a flue gas emission chimney of the coal-fired thermal power generating unit, and a coal blending combustion optimization decision-making method based on the real-time carbon emission monitoring of the thermal power generating unit is used for monitoring and obtaining the carbon emission of the thermal power generating plant in the first 14 days, the type and the amount of coal used in the thermal power generating plant and corresponding relevant chemical and physical data aiming at all coal types of the thermal power generating plant. As shown in table 1.
TABLE 1 indexes of carbon emission and coal consumption in the first 14 days of a thermal power plant
And the type, the amount and the corresponding relevant chemical and physical data of the coal used by the thermal power plant on the fifteenth day. As shown in table 2.
TABLE 2 fifteenth day coal usage data for thermal power plants
| Coal varieties | Calorific value (GJ/t) | Carbon content per calorific value (tC/GJ) | Sulfur content (%) | Univalent (Yuan/t) |
| 1 | 31.44 | 0.00290 | 0.79 | 1650 |
| 2 | 21.03 | 0.00263 | 1.67 | 1050 |
| 3 | 13.22 | 0.00236 | 2.17 | 750 |
On the premise of not exceeding the cost threshold of the thermal power plant and ensuring the power generation amount, a plurality of blending combustion ratios are determined, and the blending combustion ratio to be selected is shown in a table 3.
TABLE 3 blending ratio of coal to be blended
| Ratio 1 | Ratio 2 | Ratio 3 | Ratio 4 | Ratio 5 | Ratio 6 | Ratio 7 | Ratio 8 | |
| Coal 1 | 2600 | 2400 | 2800 | 2400 | 2200 | 2400 | 3200 | 2600 |
| Coal 2 | 2200 | 2000 | 2000 | 2200 | 2400 | 2400 | 1800 | 2000 |
| Coal 3 | 1200 | 1600 | 1200 | 1400 | 1200 | 1200 | 1000 | 1400 |
At this point the thermal power unit has run out of carbon emission quota. And acquiring a real-time data acquisition device according to the carbon price of the national carbon emission right trading market, wherein the carbon price of the past 1 day is 50 yuan/t on a carbon trading platform. The income of the thermal power plant in the previous day is 800 ten thousand yuan. The minimum daily combustion heating value of the thermal generator set is 131380 GJ.
And (3) outputting the data in the tables 1, 2 and 3 through a neural network algorithm to obtain carbon emission data of different coal blending co-combustion and thermal power plants. Because the weight value initialized during calculation of the neural network algorithm and the threshold value are random, the result of each time is different, and for the phenomenon, 5 times of neural network calculation are carried out according to each pre-selected match burning ratio, and the result is shown in table 4.
Table 4: specific carbon emission per compositional ratio
The overall yield obtained for each co-firing ratio was determined from the average carbon emissions and the results are shown in table 5.
TABLE 5 Total yield from respective firing ratios
| Ratio 1 | Ratio 2 | Ratio 3 | Ratio 4 | Ratio 5 | Ratio 6 | Ratio 7 | Ratio 8 | |
| Integrated income (Yuan) | 430008 | 663357 | 307851 | 604288 | 870462 | 538366 | 3294 | 478312 |
According to the table 5, the maximum profit is 5 of the blending coal blending ratio, namely, the used amounts of the blending coal 1, the coal 2 and the coal 3 on the fifteenth day are 2400t, 2400t and 1200t, and the expected comprehensive profit is 870462 yuan. The complete combustion heating value is 141792GJ, which is larger than the lowest required heating value of the thermal power generating unit and meets the requirement. And outputting the blending ratio 5 to a blending control device to realize the blending function.
Compared with the prior art, the invention has the following beneficial effects:
the method for optimizing the coal blending combustion of the thermal power generating unit is established based on the method for optimizing the coal blending combustion of the thermal power generating unit based on the real-time carbon emission monitoring, the method for combining the emission data of the thermal power generating unit and the real-time carbon trading price is combined, the optimal coal blending combustion coal combination can be easily obtained, and by the method, enterprises can obtain the maximum comprehensive benefit, a large amount of energy can be saved, and a large amount of cost can be saved.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (7)
1. A coal blending combustion optimization decision method based on real-time carbon emission monitoring of a thermal power generating unit is characterized by comprising the following steps:
the method comprises the following steps: collecting real-time carbon emission data of the coal-fired power generation unit;
step two: predicting the carbon emission of the coal-fired thermal power generating unit on the same day;
step three: constructing a neural network algorithm;
step four: acquiring real-time data of carbon prices of a carbon emission right trading market;
step five: storing, classifying and updating the acquired real-time carbon emission data of the coal-fired thermal power generating unit, the carbon emission data of the coal-fired thermal power generating unit on the same day and the real-time data of the carbon price of the carbon emission right trading market in real time;
step six: optimizing the classified data through a neural network algorithm to obtain an optimization result;
step seven: and carrying out coal blending and burning scheme of the coal-fired thermal power generating unit through the optimized result.
2. The coal blending combustion optimization decision method based on the real-time carbon emission monitoring of the thermal power generating unit as claimed in claim 1, wherein in the step one, the method for collecting the real-time carbon emission data of the thermal power generating unit comprises the following steps:
data are collected from a flue gas emission chimney of a coal-fired thermal power generating unit, and the data collection method comprises the following steps: CO analyzed by carbon dioxide analysis equipment2The volume concentration of the gas and the carbon emission concentration are calculated by the following formula:
in the formula: x is CO2Concentration conversion value in mg/Nm3(ii) a C is CO2Concentration measurements, in ppm; m is CO2The molecular weight of (a); t is the temperature of the clean smoke gas in unit; p is net smoke pressure in Pa;
the carbon emission is calculated by the formula:
in the formula: mc is CO in time T2Accumulating the discharge amount in t; x is CO2Concentration conversion value in mg/Nm3(ii) a F is net flue gas flow in Nm3/h；
Calculating the carbon emission in one day according to the method;
obtaining carbon emission amount of N days based on the carbon emission monitoring data of N days: eiAnd i is the observed day i, and the value of N is not less than 50.
3. The coal blending combustion optimization decision method based on real-time carbon emission monitoring of the thermal power generating unit as claimed in claim 2, wherein in the method for acquiring the real-time carbon emission data of the coal-fired power generating unit, all coal types of the thermal power plant need to be subjected to physical and chemical experiment acquisition by randomly acquiring samples to obtain coal data, including the heat value u of each coal typeiThe unit is GJ/t, carbon dioxide emission factor CCiIn the unit tCO2GJ, sulfur content SiMoisture content HiIndex, and daily dosage XiUnit is t and unit price index QiThe unit is element/t.
4. The coal blending optimization decision method based on real-time carbon emission monitoring of the thermal power generating unit according to claim 3, wherein in the third step, a neural network algorithm is constructed, specifically as follows:
inputting parameters of different coal types and the daily consumption, and predicting the daily carbon emission of the coal-fired thermal power generating unit:
establishing a model:
the neural network model consists of an input layer, a hidden layer and an output layer, and adopts an S-shaped transfer function:
back propagation error function:
and (3) network structure design:
the input layer adds 5 layers of input layers by using different varieties of coal, heat values, carbon dioxide emission factors, carbon content per unit heat value and sulfur content and adding coal for blending and burning, and the number of hidden layers is set as follows:
m is the number of input layers, n is the number of output layers, a is a constant, and a is more than or equal to 1 and less than or equal to 10;
the output layer is carbon emission;
and (3) realizing a model:
adopting an S-type tangent function tansig as an excitation function of a hidden layer neuron, and normalizing the output of the network to the range of [ -1,1], so that the S-type logarithmic function tansig is selected by the prediction model as the excitation function of the output layer neuron;
training sample data is input into a network after being normalized, a hidden layer excitation function and an output layer excitation function of the network are respectively tan sig and logsig functions, the network training function is trailing dx, a network performance function is mse, hidden layer neuron number, network parameters, network iteration number expected errors and learning rate are set, then the network is trained, before the input is started, normalization processing is carried out on input parameters and input amount of various coals, and finally the value is between-1 and 1.
5. The coal blending optimization decision method based on real-time carbon emission monitoring of the thermal power generating unit as claimed in claim 4, wherein in the fourth step, the method for acquiring the carbon value of the carbon emission right trading market in real time comprises the following steps:
the device is used for calculating the daily carbon emission cost of the coal-fired thermal power generating unit and acquiring the carbon price of the national carbon emission right trading market in real time, and the carbon price of the past 1 day is acquired on a carbon trading platform;
the first condition is as follows: the carbon emission quota of the thermal power generating unit is K, the unit is t, the thermal power generating unit is used up, namely the carbon emission quota K is less than or equal to 0, and the carbon emission cost is obtained by multiplying the emission by the carbon price;
case two: if the carbon emission quota K of the thermal power generating unit is larger than 0 before use, the carbon emission cost is obtained by multiplying the emission by the average unit carbon emission cost in the previous performance period;
carbon emission cost calculation formula:
the carbon emission cost is P, the unit is element, if the thermal power generating unit is in case one, the carbon emission cost is substituted into the formula (1), and P isc1The carbon number is the current carbon number, the unit is element/t, if the thermal power generating unit is the second case, the formula (2) is substituted, and P isc2Is the average unit carbon emission cost of the previous fulfillment period in units of yuan/t.
6. The coal blending optimization decision method based on real-time carbon emission monitoring of the thermal power generating unit as claimed in claim 5, wherein in step six, the specific method for optimizing the classified data through a neural network algorithm and obtaining the optimization result is as follows:
giving an initial coal blending combustion state, obtaining carbon emission generated by different blending combustion modes through a neural network algorithm, exhausting all possibilities of carbon emission and carbon emission cost generated by power generation of a thermal power generating unit, changing the coal blending combustion state and the proportion of different coal types, and then taking a maximum benefit value and optimizing a target:
maxw-benefit of power generation-cost of carbon emission-cost of coal purchase
Constraint conditions are as follows:
Si＜Smax
Hi＜Hmax；
i is the generation benefit in Yuan, P is the carbon emission cost, XiThe unit of the usage amount of the coal for participating in coal blending and burning is t and QiCorresponding to the purchase price of coal, the unit is Yuan/t, CALminThe unit is GJ which is the lowest daily combustion heating value of the thermal generator set;
coal blending quantity XiAnd (3) obtaining the optimal quantity of coal blending of each coal variety through model calculation for decision variables, combining the coal varieties, changing the coal blending state, repeatedly executing the optimization model to obtain the maximum comprehensive benefit of all the coal blending states, and giving the coal blending result according to the corresponding decision variables.
7. The thermal power generating unit real-time carbon emission monitoring-based coal blending optimization decision method and device as claimed in claim 6, wherein in step seven, the coal blending and combustion scheme of the coal-fired thermal power generating unit is performed according to the optimization result, specifically as follows:
and under the goal that the optimization result shows the maximum comprehensive benefit, the optimization result is used for a coal blending and burning scheme of the coal-fired thermal power generating unit on the next day, the optimization result on the second day is conducted to a coal blending control device to realize the coal blending and burning function, the optimization result on the second day is used as the initial state of optimization on the third day, the coal blending proportion and the carbon emission monitoring result on the second day are incorporated into the neural network algorithm, and data are updated.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111240635.5A CN113887827A (en) | 2021-10-25 | 2021-10-25 | Coal blending combustion optimization decision method based on real-time carbon emission monitoring of thermal power generating unit |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111240635.5A CN113887827A (en) | 2021-10-25 | 2021-10-25 | Coal blending combustion optimization decision method based on real-time carbon emission monitoring of thermal power generating unit |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113887827A true CN113887827A (en) | 2022-01-04 |
Family
ID=79013575
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111240635.5A Pending CN113887827A (en) | 2021-10-25 | 2021-10-25 | Coal blending combustion optimization decision method based on real-time carbon emission monitoring of thermal power generating unit |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113887827A (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115115473A (en) * | 2022-07-19 | 2022-09-27 | 东南大学溧阳研究院 | Diesel generating set carbon emission quantitative calculation method based on BP neural network |
| CN115222153A (en) * | 2022-08-30 | 2022-10-21 | 华能靖远热电有限公司 | Low-carbon scheduling optimization method and system for thermal power enterprise |
| CN115509126A (en) * | 2022-08-06 | 2022-12-23 | 福建华电福瑞能源发展有限公司福建分公司 | Mixed coal co-combustion method based on big data and deep mining technology |
| CN116305769A (en) * | 2022-12-30 | 2023-06-23 | 数字双碳科技(合肥)有限公司 | Carbon emission data-based blending combustion proportion optimization method |
| CN117010728A (en) * | 2023-10-07 | 2023-11-07 | 华北电力大学 | Comprehensive power generation cost optimization method for thermal power enterprises |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109325682A (en) * | 2018-09-18 | 2019-02-12 | 中国电力科学研究院有限公司 | A kind of fired power generating unit generation capacity allocation method considering carbon transaction cost |
| CN109886471A (en) * | 2019-01-22 | 2019-06-14 | 中国大唐集团科学技术研究院有限公司火力发电技术研究院 | Fired power generating unit load distribution method based on neural network and intelligent optimization algorithm |
| CN111091230A (en) * | 2019-11-20 | 2020-05-01 | 国电浙能宁东发电有限公司 | Scientific coal blending co-combustion system based on coal full-value chain management of coal-fired power plant |
| US20200372588A1 (en) * | 2019-05-20 | 2020-11-26 | Singularity Energy, Inc. | Methods and systems for machine-learning for prediction of grid carbon emissions |
| CN112682810A (en) * | 2020-12-28 | 2021-04-20 | 国电电力邯郸东郊热电有限责任公司 | Coal yard fire coal control system and method |
| KR102271070B1 (en) * | 2020-03-31 | 2021-06-29 | 조선대학교산학협력단 | Method and apparatus for determining mixed coal combination |
-
2021
- 2021-10-25 CN CN202111240635.5A patent/CN113887827A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109325682A (en) * | 2018-09-18 | 2019-02-12 | 中国电力科学研究院有限公司 | A kind of fired power generating unit generation capacity allocation method considering carbon transaction cost |
| CN109886471A (en) * | 2019-01-22 | 2019-06-14 | 中国大唐集团科学技术研究院有限公司火力发电技术研究院 | Fired power generating unit load distribution method based on neural network and intelligent optimization algorithm |
| US20200372588A1 (en) * | 2019-05-20 | 2020-11-26 | Singularity Energy, Inc. | Methods and systems for machine-learning for prediction of grid carbon emissions |
| CN111091230A (en) * | 2019-11-20 | 2020-05-01 | 国电浙能宁东发电有限公司 | Scientific coal blending co-combustion system based on coal full-value chain management of coal-fired power plant |
| KR102271070B1 (en) * | 2020-03-31 | 2021-06-29 | 조선대학교산학협력단 | Method and apparatus for determining mixed coal combination |
| CN112682810A (en) * | 2020-12-28 | 2021-04-20 | 国电电力邯郸东郊热电有限责任公司 | Coal yard fire coal control system and method |
Non-Patent Citations (1)
| Title |
|---|
| 黄忠明;印琪民;付轩熠;韩万里;: "基于数字化平台的配煤掺烧优化方法", 上海电机学院学报, no. 02, 25 April 2019 (2019-04-25) * |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115115473A (en) * | 2022-07-19 | 2022-09-27 | 东南大学溧阳研究院 | Diesel generating set carbon emission quantitative calculation method based on BP neural network |
| CN115115473B (en) * | 2022-07-19 | 2023-05-12 | 东南大学溧阳研究院 | BP neural network-based carbon emission quantitative calculation method for diesel generator set |
| CN115509126A (en) * | 2022-08-06 | 2022-12-23 | 福建华电福瑞能源发展有限公司福建分公司 | Mixed coal co-combustion method based on big data and deep mining technology |
| CN115509126B (en) * | 2022-08-06 | 2023-10-20 | 福建华电福瑞能源发展有限公司福建分公司 | Mixed coal blending combustion method based on big data and deep mining technology |
| CN115222153A (en) * | 2022-08-30 | 2022-10-21 | 华能靖远热电有限公司 | Low-carbon scheduling optimization method and system for thermal power enterprise |
| CN115222153B (en) * | 2022-08-30 | 2023-11-07 | 华能靖远热电有限公司 | Low-carbon scheduling optimizing method and system for thermal power enterprises |
| CN116305769A (en) * | 2022-12-30 | 2023-06-23 | 数字双碳科技(合肥)有限公司 | Carbon emission data-based blending combustion proportion optimization method |
| CN116305769B (en) * | 2022-12-30 | 2024-03-29 | 数字双碳科技(合肥)有限公司 | Carbon emission data-based blending combustion proportion optimization method |
| CN117010728A (en) * | 2023-10-07 | 2023-11-07 | 华北电力大学 | Comprehensive power generation cost optimization method for thermal power enterprises |
| CN117010728B (en) * | 2023-10-07 | 2024-01-02 | 华北电力大学 | Comprehensive power generation cost optimization method for thermal power enterprises |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113887827A (en) | Coal blending combustion optimization decision method based on real-time carbon emission monitoring of thermal power generating unit | |
| CN104613468B (en) | Circulating fluidized bedboiler combustion optimizing control method based on fuzzy adaptive inference | |
| CN113554296A (en) | Multi-index evaluation method for planning of park comprehensive energy system | |
| CN111985702A (en) | Park level comprehensive energy system optimization method considering electric energy substitution effect | |
| CN103729691B (en) | Multi-target coal distribution method based on uniform design | |
| CN112330179B (en) | Fuzzy comprehensive evaluation method for coal blending combustion based on improved entropy weight method | |
| CN115238597A (en) | Construction method of source network carbon-loaded emission model of park level comprehensive energy system | |
| CN112446630A (en) | Method and system for evaluating technical economy of school comprehensive energy system | |
| CN116542370A (en) | Park low-carbon economic operation method considering carbon capture and carbon transaction | |
| CN114971230A (en) | Coal blending combustion effect evaluation method based on combined weighted-improved TOPSIS method | |
| CN111949936A (en) | Method and system for evaluating comprehensive energy system of suburb | |
| CN107634547A (en) | Contributed based on new energy and predict that the electric association system of error goes out electric control method | |
| CN105160499B (en) | A kind of fuel analysis System and method in thermal power plant | |
| CN115719180A (en) | Method for analyzing carbon reduction key influence factors of industrial park | |
| CN111553608A (en) | Benefit evaluation method for urban distribution network comprehensive energy system | |
| Liu et al. | Planning mechanism design and benefit analysis of electric energy substitution: A case study of tobacco industry in Yunnan province, China | |
| CN114118662A (en) | Policy evaluation method for joint defense joint control system of atmospheric pollution area | |
| CN110991870B (en) | Data processing and calculating method for integrated energy system with intensive characteristics | |
| CN112200469A (en) | Comprehensive energy system operation service evaluation method and device based on entropy weight and analytic hierarchy process | |
| CN115496336A (en) | Thermal power generating unit benefit evaluation method under peak regulation auxiliary service market mechanism | |
| CN113297725B (en) | Regional comprehensive energy system energy efficiency evaluation method based on improved EWM method | |
| CN117391734B (en) | Power generation cost prediction method based on support vector machine regression model | |
| Jiang et al. | An Integrated Energy System Evaluation Method Based on FAHP-EWM-TOPSIS | |
| CN114372609A (en) | Multi-source load complementary planning method considering new energy consumption cost optimization | |
| Gruenwald et al. | Optimization model of energy mix taking into account the environmental impact |
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 |