CN117010728B - Comprehensive power generation cost optimization method for thermal power enterprises - Google Patents
Comprehensive power generation cost optimization method for thermal power enterprises Download PDFInfo
- Publication number
- CN117010728B CN117010728B CN202311277172.9A CN202311277172A CN117010728B CN 117010728 B CN117010728 B CN 117010728B CN 202311277172 A CN202311277172 A CN 202311277172A CN 117010728 B CN117010728 B CN 117010728B
- Authority
- CN
- China
- Prior art keywords
- cost
- electricity
- coal
- unit
- heat
- 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
- 238000010248 power generation Methods 0.000 title claims abstract description 56
- 238000005457 optimization Methods 0.000 title claims abstract description 44
- 238000000034 method Methods 0.000 title claims abstract description 39
- 239000003245 coal Substances 0.000 claims abstract description 122
- 230000005611 electricity Effects 0.000 claims abstract description 79
- 238000004519 manufacturing process Methods 0.000 claims abstract description 29
- 238000002485 combustion reaction Methods 0.000 claims abstract description 22
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 81
- 238000004364 calculation method Methods 0.000 claims description 38
- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 claims description 31
- 239000003546 flue gas Substances 0.000 claims description 31
- 239000000243 solution Substances 0.000 claims description 21
- 239000000779 smoke Substances 0.000 claims description 20
- 239000002245 particle Substances 0.000 claims description 16
- 235000019738 Limestone Nutrition 0.000 claims description 14
- NINIDFKCEFEMDL-UHFFFAOYSA-N Sulfur Chemical compound [S] NINIDFKCEFEMDL-UHFFFAOYSA-N 0.000 claims description 14
- 239000006028 limestone Substances 0.000 claims description 14
- 238000012423 maintenance Methods 0.000 claims description 14
- 229910052717 sulfur Inorganic materials 0.000 claims description 14
- 239000011593 sulfur Substances 0.000 claims description 14
- 239000000843 powder Substances 0.000 claims description 13
- 239000000446 fuel Substances 0.000 claims description 12
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims description 10
- 238000006243 chemical reaction Methods 0.000 claims description 10
- 239000001301 oxygen Substances 0.000 claims description 10
- 229910052760 oxygen Inorganic materials 0.000 claims description 10
- XSQUKJJJFZCRTK-UHFFFAOYSA-N Urea Chemical compound NC(N)=O XSQUKJJJFZCRTK-UHFFFAOYSA-N 0.000 claims description 8
- 239000004202 carbamide Substances 0.000 claims description 7
- 239000000428 dust Substances 0.000 claims description 7
- XZPVPNZTYPUODG-UHFFFAOYSA-M sodium;chloride;dihydrate Chemical compound O.O.[Na+].[Cl-] XZPVPNZTYPUODG-UHFFFAOYSA-M 0.000 claims description 7
- 230000007613 environmental effect Effects 0.000 claims description 5
- 238000012417 linear regression Methods 0.000 claims description 5
- 239000000203 mixture Substances 0.000 claims description 5
- 238000006477 desulfuration reaction Methods 0.000 claims description 4
- 230000023556 desulfurization Effects 0.000 claims description 4
- 239000000295 fuel oil Substances 0.000 claims description 4
- JGIATAMCQXIDNZ-UHFFFAOYSA-N calcium sulfide Chemical compound [Ca]=S JGIATAMCQXIDNZ-UHFFFAOYSA-N 0.000 claims description 3
- 238000002156 mixing Methods 0.000 description 12
- RAHZWNYVWXNFOC-UHFFFAOYSA-N Sulphur dioxide Chemical compound O=S=O RAHZWNYVWXNFOC-UHFFFAOYSA-N 0.000 description 6
- MWUXSHHQAYIFBG-UHFFFAOYSA-N nitrogen oxide Inorganic materials O=[N] MWUXSHHQAYIFBG-UHFFFAOYSA-N 0.000 description 6
- QGZKDVFQNNGYKY-UHFFFAOYSA-N Ammonia Chemical compound N QGZKDVFQNNGYKY-UHFFFAOYSA-N 0.000 description 4
- 230000008901 benefit Effects 0.000 description 4
- 239000003344 environmental pollutant Substances 0.000 description 4
- 231100000719 pollutant Toxicity 0.000 description 4
- 229910021529 ammonia Inorganic materials 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000010276 construction Methods 0.000 description 2
- 230000000737 periodic effect Effects 0.000 description 2
- 238000002360 preparation method Methods 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 239000000126 substance Substances 0.000 description 2
- OYPRJOBELJOOCE-UHFFFAOYSA-N Calcium Chemical compound [Ca] OYPRJOBELJOOCE-UHFFFAOYSA-N 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 239000011575 calcium Substances 0.000 description 1
- 229910052791 calcium Inorganic materials 0.000 description 1
- 238000010438 heat treatment Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000013433 optimization analysis Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000001502 supplementing effect Effects 0.000 description 1
- 238000010977 unit operation 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
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0637—Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
-
- 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
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
- G06Q30/0206—Price or cost determination based on market factors
-
- 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
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Human Resources & Organizations (AREA)
- Entrepreneurship & Innovation (AREA)
- Development Economics (AREA)
- General Business, Economics & Management (AREA)
- Accounting & Taxation (AREA)
- Marketing (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Finance (AREA)
- Physics & Mathematics (AREA)
- Tourism & Hospitality (AREA)
- Game Theory and Decision Science (AREA)
- Educational Administration (AREA)
- Health & Medical Sciences (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Data Mining & Analysis (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention relates to a comprehensive cost optimization method for power generation of a thermal power enterprise, and belongs to the technical field of thermal power generation. Comprising the following steps: calculating historical data of a plurality of parameters of a combustion value chain of a thermal power enterprise to obtain a historical electricity operation cost data set; obtaining a power operation cost equation based on the historical power operation cost data set; constructing a cost optimization objective function based on the electricity-to-electricity running cost equation and the electricity-to-electricity fire coal cost equation; solving an optimal solution for the objective function based on constraint conditions; and optimizing the comprehensive power generation cost of the thermal power enterprise based on the optimal solution. The comprehensive power generation cost optimization method for the thermal power enterprises can calculate the optimal configuration value of the collaborative operation parameters of each system related to the thermal power enterprises, and is used for adjusting the operation scheme and guiding the power generation production of the thermal power enterprises.
Description
Technical Field
The invention belongs to the technical field of thermal power generation, and particularly relates to a comprehensive cost optimization method for thermal power enterprise power generation.
Background
On the basis of ensuring safe production of stable and full-automatic generation, the comprehensive benefit and cost of the whole process are required to be considered for the power generation of the thermal power enterprises so as to improve the internet bidding competitiveness of the enterprises. The existing thermal power enterprise cost calculation method generally realizes that the post calculation and calculation time granularity is coarse (month or day), and cannot achieve cost accounting with finer time granularity or real-time cost accounting; and the calculation method usually considers factors which are not comprehensive enough, so that the cost calculation accuracy is low.
Because different blending coal blending schemes can influence each link of the production process and influence the comprehensive power generation cost of thermal power enterprises, a more real-time and accurate cost accounting method is needed, and an optimal blending coal blending scheme is further found based on an accurate cost accounting result so as to achieve the aim of optimizing the cost and promote the competitiveness of enterprises.
Disclosure of Invention
In view of the above analysis, the invention aims to provide a comprehensive cost optimization method for power generation of a thermal power enterprise, which specifically comprises the following steps:
calculating historical data of a plurality of parameters of a combustion value chain of a thermal power enterprise to obtain a historical electricity operation cost data set;
obtaining a power operation cost equation based on the historical power operation cost data set;
constructing a cost optimization objective function based on the electricity-to-electricity running cost equation and the electricity-to-electricity fire coal cost equation;
solving an optimal solution for the objective function based on constraint conditions;
and optimizing the comprehensive power generation cost of the thermal power enterprise based on the optimal solution.
Further, the plurality of parameters include a fire coal parameter, a fuel oil parameter and a water consumption parameter of the boiler combustion system, an equipment electricity consumption parameter of a thermal power enterprise, an equipment maintenance parameter and a thermal power enterprise emission parameter; the electricity-measuring operation cost comprises electricity-measuring electricity generation fuel cost, electricity-measuring water cost, electricity-measuring station electricity cost, electricity-measuring maintenance cost and electricity-measuring discharge cost; and calculating corresponding electricity-measuring operation cost data based on parameter values corresponding to the parameters acquired at a plurality of time points, wherein the electricity-measuring operation cost data and the coal-burning parameter values at corresponding time points form a historical electricity-measuring operation cost data set.
Further, the electricity water cost comprises brine water production cost, circulating water production cost and reclaimed water cost, and the brine water production cost, the circulating water production cost and the reclaimed water cost are respectively calculated based on the flow of the demineralized water outlet pipe per minute, the flow of the circulating water replenishing water per minute and the flow of the reclaimed water main pipe per minute.
Further, the electricity discharge cost comprises limestone powder cost, environment-friendly discharge cost and electricity urea cost; the calculation method of the limestone powder cost comprises the following steps:
；
wherein,representing the SO of the raw flue gas 2 Converting concentration in kg/Nm; />The standard dry flow of the original flue gas is expressed, and the unit is m wave/min; />The desulfurization efficiency is expressed in units of; />Represents CaCO 3 Molar mass (100 g/mol); />Representing SO 2 Molar amount (64 g/mol); />Represents the calcium-sulfur ratio, the unit is; />The purity of limestone powder is expressed in units of;representing limestone powderUnit price in yuan per ton; t represents the power generation time, and the unit is h.
Further, the emission environmental protection cost comprises electricity NO x Pollution discharge tax and electricity SO 2 The calculation methods of the blowdown tax and the electric smoke dust blowdown tax are as follows:
；
；
；
wherein,indicating the clean smoke NO x Concentration conversion, wherein the unit is kg/Nm; />The standard dry flow of the clean flue gas is expressed in m wave/min; />Represents NO x Pollution equivalent value; the unit is m cm/min; />Represents NO x Unit price in yuan per kilogram; />The unit is MW for generating load;
indicating the SO of the clean flue gas 2 Concentration conversion, wherein the unit is kg/Nm; />Representing SO 2 Pollution equivalent value, in m bits/min; />Representing SO 2 Unit price in yuan per kilogram;
the concentration conversion of the smoke and dust of the clean smoke is expressed in kg/Nm; />The smoke pollution equivalent value is expressed in m-zone/min; />The unit of smoke unit is yuan per kilogram.
Further, linear fitting is carried out on the historical power operation cost data set to a specified variable by adopting multiple linear regression, so as to obtain a power operation cost equation; the specified variables are variables corresponding to the coal burning parameters, and comprise final water supply temperature, hearth oxygen amount, total secondary air amount, total coal amount, high-heat high-swing coal ratio and high-heat low-swing coal ratio.
Further, the electrical operation cost equation is expressed as:
；
wherein,representing the electricity running cost; LR represents variable +.>Is a linear equation of (2); equation variable->The method comprises the following steps of: />For the final feed water temperature->Is the oxygen content of the hearth>Is total secondary air volume and->Is the total coal quantity->Is high-heat and high-coal-volatilizing ratio->The ratio of the high-heat low-swing coal is calculated.
Further, the electricity-to-fire coal cost equation is:
；
wherein P is 1 The price of high-heat and high-coal-volatilizing is high; p (P) 2 The price of the coal is high heat and low; p (P) 3 Is the price of economic coal;is the total coal quantity;the ratio of the high-heat high-swing coal is high; />The ratio of the high-heat low-swing coal is the ratio; />Is a power generation load.
Further, the cost optimization objective function is expressed as:
；
the constraint conditions include:
the values are all in the corresponding preset range;
；
；
wherein,is the calorific value of high-heat high-volatile coal>Is high heat and low coal volatilizing heat value +.>Is the calorific value of economic coal>The heat value of the standard coal is kcal/kg; />Standard coal consumption for power supply, unit: g/kWh;
；
wherein,sulfur content of high-heat high-volatile coal>Is sulfur content of high-heat low-volatile coal>The unit is the economic coal sulfur; />Sulfur index of coal mixture fed into the boiler for boiler combustion;
；
wherein,is high in heat and moisture content of volatile coal>Is high heat and low volatile water content>The water content of the economic coal is in units of; />The water index of the coal mixture fed into the boiler for boiler combustion;
；
wherein,the heat value index of the mixed coal for boiler combustion.
Further, an optimal solution is obtained for the objective function by using a particle swarm algorithm, wherein the value of each group of variables is taken as one particle, the objective function is taken as an fitness function, and the particle which enables the minimum value of the objective function is obtained, namely the optimal solution.
The invention can realize at least one of the following beneficial effects:
the cost of pollutant treatment is calculated according to the flow rate of the flue gas and the concentration value of the pollutant by using a chemical formula based on the treatment principle of sulfur dioxide and nitrogen oxides, so that the discharge cost accounting of the 'minute' level can be realized; compared with the prior art for realizing the discharge cost calculation of one test period or the discharge cost calculation method taking 'month' as the period, the method has the real-time performance for realizing the discharge cost accounting of 'minute' level.
The electricity consumption cost of the 'minute' level is calculated and obtained by collecting the water consumption of the circulating water, the desalted water and the reclaimed water pipeline flow value every minute of a thermal power enterprise, and the problems that the electricity consumption can only be pushed based on the water taking coefficient of the power plant and the electricity consumption cost can not be calculated in real time in the prior art are solved.
The electricity consumption cost calculation and the electricity discharge cost calculation of the minute level are realized, the electricity generation fuel cost, the electricity plant electricity cost and the electricity maintenance cost of the corresponding time points are calculated, the electricity operation cost of the minute level can be calculated, and an accurate data basis is laid for establishing a cost optimization objective function and solving an optimal solution.
By adopting a multiple linear regression method, the historical electric operation cost based on the 'minute' level and the corresponding coal burning parameters are fitted to form an electric operation cost equation, and the construction of a cost optimization objective function is simplified.
Constructing a cost optimization objective function based on a power-on running cost equation and a power-on coal cost equation, and providing a mathematical basis for optimizing the comprehensive cost of power generation of thermal power enterprises; the cost optimization objective function is solved by adopting a particle swarm algorithm with strong optimizing calculation capability and strong robustness, so that the optimal solution of the objective function can be efficiently and accurately calculated, the comprehensive power generation cost of the thermal power enterprise after optimization is obtained, and the thermal power enterprise is guided to reduce the cost in actual power generation production.
The method has the advantages that the electricity-fire coal cost equation is provided, the electricity-fire coal cost can be calculated based on the instantaneous coal quantity data of the coal machine, the accurate calculation of the cost in the unit of 'minutes' is realized, the real-time operation cost of the thermal power unit can be reflected, and compared with the method in the prior art that macroscopic calculation is carried out based on the change of the stock quantity of the coal yard and periodic calculation is carried out mostly in the unit of 'days', the calculation result is more real-time and accurate, and the unit operation is more conveniently guided.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
The drawings are only for purposes of illustrating particular embodiments and are not to be construed as limiting the invention, like reference numerals being used to designate like parts throughout the drawings;
FIG. 1 is a flow chart of a comprehensive cost optimization method for power generation of a thermal power enterprise;
FIG. 2 is a particle swarm optimization result of the total cost of two-degree electricity according to an embodiment;
FIG. 3 is a graph showing the results of optimizing the cost of the secondary electric fire coal of the example;
FIG. 4 is a graph showing the result of optimizing the second-degree electric operation cost of the embodiment.
Detailed Description
Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form a part hereof, and together with the description serve to explain the principles of the invention, and are not intended to limit the scope of the invention.
Example 1
The invention discloses a method for optimizing the comprehensive power generation cost of a thermal power enterprise, which comprises the following steps:
step S01, calculating historical data based on a plurality of parameters of a combustion value chain of a thermal power enterprise to obtain a historical electric operation cost data set;
the combustion value chain refers to a value system formed by production link operation costs which are mutually influenced and interdependent in the coal-fired power generation production of thermal power enterprises; in the coal-fired power generation process, the preparation and execution of a blending scheme of blending coal can influence the production process, and the coal quality and the coal quantity of the blending coal under different blending schemes have different influences on the running cost of each link, so that the whole combustion value chain is influenced;
step S02, obtaining a power operation cost equation based on the historical power operation cost data set;
specifically, the electric operation cost equation reflects the linear relation between the electric operation cost and a plurality of variables of the coal blending combustion scheme;
s03, constructing a cost optimization objective function based on the electricity-to-electricity running cost equation and the electricity-to-electricity fire coal cost equation;
step S04, solving an optimal solution for the objective function;
and step S05, obtaining the optimized thermal power enterprise power generation comprehensive cost based on the cost optimization objective function and the optimal solution.
According to the method, based on a historical electricity operation cost set of a combustion value chain of a thermal power enterprise, a linear relation between electricity operation cost and a variable of a coal blending combustion scheme is determined, and a cost optimization objective function is constructed based on an electricity operation cost equation and an electricity coal cost equation, so that a mathematical basis is provided for comprehensive cost optimization of power generation of the thermal power enterprise; by solving the cost optimization objective function, the optimal solution of the objective function can be efficiently and accurately calculated, so that the comprehensive power generation cost of the thermal power enterprise after optimization is obtained, and the thermal power enterprise is guided to reduce the cost in actual power generation production.
Specifically, in step S01, the plurality of parameters include a coal-fired parameter, a fuel oil parameter, a water consumption parameter, an equipment electricity consumption parameter, an equipment maintenance parameter, and an emission parameter of the thermal power plant; the electricity-measuring operation cost comprises electricity-measuring electricity generation fuel cost, electricity-measuring water cost, electricity-measuring station electricity cost, electricity-measuring maintenance cost and electricity-measuring discharge cost; and calculating corresponding electricity-measuring operation cost data based on parameter values corresponding to the plurality of parameters acquired at a plurality of time points to form a historical electricity-measuring operation cost data set.
Preferably, the parameter values of the minute level corresponding to the plurality of parameters in the last two months are selected, the electricity-measuring operation cost data of the corresponding minute level is obtained through calculation, and the electricity-measuring operation cost data and the coal-burning parameter values of the corresponding time points form a historical electricity-measuring operation cost data set.
Specifically, the coal-fired parameters of the boiler combustion system comprise the final feed water temperature, the hearth oxygen, the total secondary air, the total coal, the high-heat high-swing coal ratio and the high-heat low-swing coal ratio of the boiler.
Specifically, the calculation formula and the related parameters of the fuel cost of the power generation of each time point in history are as follows:
；
wherein,the unit of the fuel cost of the power generation is meta/(kW.h); />The fuel price is expressed in yuan/t; />The current fuel consumption is represented, and the unit is t (ton); />The unit is MW for generating load; />The unit is h for generating time; it should be noted that, in the present invention, T in all the formulas is the corresponding power generation time when each formula is calculated.
Specifically, the electricity water cost comprises brine water production cost, circulating water production cost and reclaimed water cost, and the brine water production cost, the circulating water production cost and the reclaimed water cost are respectively calculated based on the flow of a demineralized water outlet pipe per minute, the flow of circulating water replenishing water per minute and the flow of a reclaimed water main pipe per minute.
Further, the brine water production cost, the circulating water production cost and the reclaimed water cost are respectively calculated by the following methods and related parameters:
；
wherein,the unit of the representative degree electric demineralized water production cost is Yuan/(kW.h); />The flow of the desalted water outlet mother pipe is expressed as t/h; />The unit price of desalted water production is expressed, and the unit is yuan/t; />The unit is MW for generating load;
；
wherein,the unit of the representative degree electricity circulating water production cost is Yuan/(kW.h); />The water supplementing flow of the circulating water is expressed, and the unit is t/h; />The unit price of the circulating water is represented, and the unit is yuan/t;
；
wherein,representing the water cost in the power supply, wherein the unit is Yuan/(kW.h); />The flow of the water main pipe is expressed, and the unit is t/h; />The unit price of the reclaimed water is expressed, and the unit is yuan/t.
Specifically, the electricity consumption cost of the power plant comprises the equipment electricity consumption cost of all production links of a thermal power enterprise, and the calculation formula and related parameters are as follows:
；
wherein,the unit of the power consumption cost of the power station is meta/(kW.h); />Representing the power consumption of the plant; />The unit of the online electricity price is meta/(kW.h).
Specifically, the calculation formula and the related parameters of the electricity maintenance cost are as follows:
；
wherein,representing the degree of electric maintenance cost, wherein the unit is Yuan/(kW.h); />Representing daily maintenance material fees (replacement parts) in units of elements in the month; />Representing the overhaul cost in the current month, wherein the unit is yuan; />Indicating the total power generation amount in the current month.
Specifically, the electricity discharge cost comprises limestone powder cost, environment-friendly discharge cost and electricity urea cost.
Specifically, the calculation method and related parameters of the limestone powder cost are as follows:
；
wherein,representing the SO of the raw flue gas 2 Converting concentration in kg/Nm; />The standard dry flow of the original flue gas is expressed, and the unit is m wave/min; />The desulfurization efficiency is expressed in units of; />Represents CaCO 3 Molar mass (100 g/mol); />Representing SO 2 Molar amount (64 g/mol); />Represents the calcium-sulfur ratio, the unit is; />The purity of limestone powder is expressed in units of;the unit of limestone powder is yuan/ton.
Further, the environmental protection cost of emission includes electricity NO x Pollution discharge tax and electricity SO 2 The calculation method and related parameters of the blowdown tax and the electric smoke dust blowdown tax are as follows:；
；
；
wherein,indicating the clean smoke NO x Concentration conversion, wherein the unit is kg/Nm; />The standard dry flow of the clean flue gas is expressed in m wave/min; />Represents NO x Pollution equivalent value; the unit is m cm/min; />Represents NO x Unit price in yuan per kilogram; />The unit is MW for generating load;
indicating the SO of the clean flue gas 2 Concentration conversion, wherein the unit is kg/Nm; />Representing SO 2 Pollution equivalent value, in m bits/min; />Representing SO 2 Unit price in yuan per kilogram;
the concentration conversion of the smoke and dust of the clean smoke is expressed in kg/Nm; />The smoke pollution equivalent value is expressed in m-zone/min; />Represents smoke unit price in yuan per kilogram;
the flue gas standard dry flow is the flow obtained by converting the actually measured flue gas flow into the standard state (0 ℃ =273K, 101.325 Kpa), and the calculation methods of the original flue gas standard dry flow and the net flue gas standard dry flow are respectively as follows:
；
；
wherein,represents the standard dry flow of the original flue gas, and the unit is m 3 /min;/>Represents the cross-sectional area of the flue gas inlet, and the unit is m 2 ;/>The flow rate of the original flue gas is expressed, and the unit is m/s; />Represents atmospheric pressure in Pa; ->The static pressure of the raw flue gas at the measuring point is expressed in Pa; />The unit of the moisture content in the original flue gas is the volume percentage; />The temperature of the original flue gas is expressed in the unit of DEG C; />Represents the dry flow of the clean flue gas, and the unit is m 3 /min;/>Represents the cross-sectional area of the smoke outlet in unitsIs m 2 ;/>The flow rate of the clean flue gas is expressed in m/s; />The static pressure of the clean flue gas at the measuring point is expressed in Pa; />The unit of the water content volume percentage in the clean flue gas is shown as follows; />The net flue gas temperature is expressed in degrees celsius.
Specifically, the calculation method and related parameters of the cost of the electricity-measuring urea are as follows:
；
wherein,representing the electricity urea cost per unit cell/(kW.h); />The ammonia amount (ammonia supply flow rate) is expressed in kg/h; />The urea unit price is expressed in yuan/kg.
Specifically, the electricity-to-electricity running cost is the sum of the electricity-to-electricity power generation fuel cost, electricity-to-electricity water cost, electricity-to-electricity station electricity cost, electricity-to-electricity maintenance cost and electricity-to-electricity discharge cost, and is expressed as:；
wherein,indicating the cost of electricity water, +.>；
Representing the cost of electrical discharge,/->；
Represents the environmental protection cost of emission, < >>。
Preferably, in step S01, the parameter values of the minute level in the last two months are selected, and the corresponding minute-level power operation cost data is calculatedCorresponding electrical operating cost data ∈>And said coal-fired parameter values at respective points in time constitute a historical electrical operating cost data set.
Specifically, in step S02, linear fitting is performed on the historical power running cost data set to a specified variable by using multiple linear regression, so as to obtain a power running cost equation; the appointed variable is a variable of the blending and burning scheme of the coal, and corresponds to the coal burning parameters, including final water supply temperature, hearth oxygen amount, total secondary air amount, total coal amount, high-heat high-volatile coal ratio and high-heat low-volatile coal ratio.
Specifically, the electrical operating cost equation is expressed as:
；
wherein,representing the electricity running cost; LR represents variable +.>Is a linear equation of (2); equation variable->The method comprises the following steps of: />Is the final water supply temperature (unit:. Degree.C.),>is the oxygen content (unit:%), of the hearth>Is the total secondary air quantity (unit: t/h),>is the total coal quantity (unit: t),>is high-heat high-coal-volatilizing ratio (unit:%),>the ratio of the high-heat low-volatile coal is expressed in percent.
Specifically, in step S03, the construction cost optimization objective function is expressed as:
；
wherein,the calculation equation of the electric coal cost is as follows:
；
wherein P is 1 The price of high-heat and high-coal-volatilizing is high; p (P) 2 Is high in heat and low in coal volatilizing price；P 3 Is the price of economic coal;is the total coal quantity;the ratio of the high-heat high-swing coal is high; />The ratio of the high-heat low-swing coal is the ratio; />Is a power generation load.
Further, constraints of the cost optimization objective function include:
variable(s)The values are all in the corresponding preset range:
；
blending ratio of economic coal:；
power generation demand constraint:
；
wherein,is the calorific value of high-heat high-volatile coal>Is high heat and low coal volatilizing heat value +.>Is the calorific value of economic coal>The heat value of the standard coal is kcal/kg; />Standard coal consumption for power supply, unit: g/kWh;
sulfur constraint:
；
wherein,sulfur content of high-heat high-volatile coal>Is sulfur content of high-heat low-volatile coal>The unit is the economic coal sulfur; />Sulfur index of coal mixture fed into the boiler for boiler combustion; preferably, a +>The preset value is 15;
moisture constraint:
；
wherein,is high in heat and moisture content of volatile coal>Is high heat and low volatile water content>The water content of the economic coal is in units of; />Water content index of mixed coal for boiler combustion, preferably +.>The preset value is 13;
heating value:
；
wherein,for the heat value index of the mixed coal of the boiler combustion, the preferable +.>The preset value is 2800.
Specifically, in step S04, an optimal solution is found for the objective function using a particle swarm algorithm, wherein each set of variables is calculatedTakes the value of (2) as a particle, and the objective function is +.>As a fitness function of the particle swarm algorithm, an optimal solution, which is a particle that minimizes the objective function, is obtained.
Specifically, in step S05, the obtained optimal solutionIs substituted into the value of (a)And calculating to obtain the optimized comprehensive power generation cost of the thermal power enterprise.
The embodiment discloses a comprehensive cost optimization method for power generation of a thermal power enterprise, which is characterized in that the cost of electricity generation fuel oil, electricity consumption of a power plant and electricity maintenance cost of corresponding time points are calculated through the calculation of electricity consumption cost and electricity discharge cost of a power enterprise of 'minutes', so that the electricity operation cost of the power enterprise of 'minutes' can be calculated, and an accurate data basis is laid for establishing a cost optimization objective function and solving an optimal solution.
The embodiment uses a chemical formula to calculate the cost of pollutant treatment according to the flow rate of the flue gas and the concentration value of the pollutant based on the treatment principle of sulfur dioxide and nitrogen oxide, so that the accounting of the emission cost of the 'minute' level can be realized; compared with the emission cost calculation method for realizing one test period or taking a month as a period in the prior art, the embodiment has the real-time performance for realizing the emission cost accounting of the minute level.
According to the embodiment, the electricity water cost calculation and electricity discharge cost calculation of the minute level are realized, the electricity power generation fuel cost, the electricity plant electricity cost and the electricity maintenance cost of corresponding time points are calculated, the minute level electricity operation cost can be calculated, and an accurate data basis is laid for establishing a cost optimization objective function and solving an optimal solution.
According to the embodiment, a cost optimization objective function is constructed based on a power-on running cost equation and a power-on coal cost equation, so that a mathematical basis is provided for power generation comprehensive cost optimization of thermal power enterprises; the cost optimization objective function is solved by adopting a particle swarm algorithm with strong optimizing calculation capability and strong robustness, so that the optimal solution of the objective function can be efficiently and accurately calculated, the comprehensive power generation cost of the thermal power enterprise after optimization is obtained, and the thermal power enterprise is guided to reduce the cost in actual power generation production.
The embodiment also provides a degree electricity coal cost equation, which can calculate the degree electricity coal cost based on instantaneous coal quantity data of the coal machine, realizes accurate calculation of cost in minutes, can reflect real-time operation cost of the thermal power unit, and is more real-time and accurate compared with a method in the prior art that macroscopic calculation is carried out based on change of stock quantity of a coal yard and periodic calculation is carried out in daily units, and the calculation result is more beneficial to guiding the operation of the unit.
Example two
The invention discloses a method for optimizing the comprehensive power generation cost of a thermal power enterprise, which comprises the following steps:
and S11, calculating historical degree electric operation cost data sets based on historical data of a plurality of parameters of the combustion value chain of the thermal power enterprise.
For example, table 1 is point location data corresponding to the plurality of parameters at a certain moment:
the #1 machine_final water supply temperature, the #1 machine_hearth oxygen amount and the total secondary air amount data corresponding to the corresponding time points are respectively as follows: 242.8901 ℃, 3.8141% and 657.3721t/h.
Illustratively, the calculation process of the electrical operation cost of the minute level at the corresponding time point is as follows:
fuel cost of the power generation by electricity:
；
wherein 7.87 is the fuel price.
Demineralized water preparation cost:
；
wherein 0.26 is the unit price of desalted water for preparing water;
the cost of water production by circulating water is as follows:
；
wherein 0.15 is the unit price of the circulating water for preparing water;
cost of reclaimed water:
；
wherein 0.15 is the price per unit of reclaimed water.
Electricity consumption cost of power plant:
；
wherein, 0.808 is the internet electricity price.
And (3) the electricity maintenance cost:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein->。
And (3) the electricity discharge cost:
raw flue gas standard flow:
；
wherein,。
clean flue gas standard dry flow:
；
wherein,。
limestone powder cost:
；
wherein,the percentage of desulfurization efficiency is converted; the term "1000" is used to refer to milligrams as grams;1.01 is the ratio of calcium to sulfur, which is the data obtained manually according to the actual situation; 0.94 is limestone powder purity, which is data obtained manually according to actual conditions; 518 is a monovalent element per t,518/1000000 converted to elements per gram. />
Degree of electric NO x Pollution discharge tax:
；
wherein,； />the method comprises the steps of carrying out a first treatment on the surface of the Per 1000000 units mg are converted to kg.
Electricity SO 2 Pollution discharge tax:
；
wherein,； />the method comprises the steps of carrying out a first treatment on the surface of the Per 1000000 units mg are converted to kg.
Electricity smoke dust pollution discharge tax:
；
wherein,； />the method comprises the steps of carrying out a first treatment on the surface of the Per 1000000 units mg are converted to kg.
Cost of electricity urea:
；
wherein,the method comprises the steps of carrying out a first treatment on the surface of the And/1000/60 is used for unit conversion, kg/h is converted to t/min, and the corresponding urea price is matched.
The electric operation cost of the degree is calculated according to the result:
；
step S12, obtaining a power operation cost equation based on the historical power operation cost data set;
specifically, linear fitting is carried out on a historical power operation cost data set to a specified variable by adopting multiple linear regression, so as to obtain a power operation cost equation; the specified variables include #1 machine_final feedwater temperature, #1 machine_furnace oxygen, total secondary air, total coal, high heat high swing coal duty, high heat low swing coal duty.
The obtained degree electric operation cost equation is as follows:
；
the variable names are shown in table 2:
/>
s13, constructing a cost optimization objective function based on the electricity-to-electricity running cost equation and the electricity-to-electricity fire coal cost equation;
the cost optimization objective function is expressed as:；
wherein,the representative electricity-to-fire coal costs are expressed as:
。
the constraint is the same as that in the first embodiment.
S14, solving an optimal solution for the objective function;
by way of example, the present embodiment selects a power generation load of 223MW as an example for power generation comprehensive cost optimization analysis. The optimization result of the particle swarm optimization of the total cost of the degree electricity is shown in figure 2, the abscissa is the optimizing iteration number of the particle swarm optimization, the ordinate is the total cost of the degree electricity, the optimal value can be obtained by iteration 20 times, and the optimal value of the total cost is 0.38009. Corresponding variables #1 machine-final water supply temperature, #1 machine-hearth oxygen amount, total secondary air amount, total coal amount, high heat and high swing ratio and high heat and low swing ratio are 242.65 ℃,5.02%, 780t/h,127.45t,65% and 15% respectively.
And step S15, obtaining the optimized thermal power enterprise power generation comprehensive cost based on the cost optimization objective function and the optimal solution.
The optimized result of the comprehensive power generation cost, namely the total power generation cost of the thermal power enterprise after optimization is shown in fig. 2, wherein the 20 th time is the optimal cost. The optimization results of the electricity-to-fire coal cost and the electricity-to-electricity operation cost are shown in fig. 3 and 4, respectively. In combination with the optimizing process of the electricity-to-electricity total cost, the electricity-to-coal cost and the electricity-to-electricity running cost, it can be seen that when the iteration is performed for 20 times, the electricity-to-coal cost value and the electricity-to-electricity running cost value are not the minimum values in the whole iteration process, and the electricity-to-electricity coal cost and the electricity-to-electricity running cost are comprehensively considered by the optimal value of the electricity-to-electricity total cost.
In this embodiment, the disclosed comprehensive cost optimization method for generating electricity in a thermal power enterprise exemplarily provides a method for calculating corresponding electricity-measuring operation cost based on point data corresponding to a plurality of parameters in table 1 at a certain moment, obtains an electricity-measuring operation cost equation through fitting a plurality of historical data, constructs a cost optimization objective function based on the electricity-measuring operation cost equation and the electricity-measuring coal cost equation obtained through fitting, and obtains an optimal solution and the most effective electricity-measuring total cost of cost by using a particle swarm algorithm. The embodiment also provides a method for calculating the electricity fire coal cost, which can calculate the minute-scale electricity fire coal cost in real time according to the point location data. The example calculation result provided by the embodiment is real-time and accurate, and can be used for optimizing the comprehensive cost of power generation of the thermal power plant in actual production and guiding the operation of the unit.
It should be noted that, the above embodiments are based on the same inventive concept, and the description is not repeated, and the description may be referred to each other.
The present invention is not limited to the above-mentioned embodiments, and any changes or substitutions that can be easily understood by those skilled in the art within the technical scope of the present invention are intended to be included in the scope of the present invention.
Claims (5)
1. The comprehensive power generation cost optimization method for the thermal power enterprise is characterized by comprising the following steps of:
calculating historical data of a plurality of parameters of a combustion value chain of a thermal power enterprise to obtain a historical electricity operation cost data set; the parameters comprise a coal burning parameter, a fuel oil parameter and a water consumption parameter of a boiler combustion system, an equipment electricity consumption parameter, an equipment maintenance parameter and a thermal power enterprise emission parameter of a thermal power enterprise; the electricity-measuring operation cost comprises electricity-measuring electricity generation fuel cost, electricity-measuring water cost, electricity-measuring station electricity cost, electricity-measuring maintenance cost and electricity-measuring discharge cost; calculating to obtain the electricity-measuring operation cost data of the corresponding minute level based on parameter values corresponding to the plurality of parameters acquired at the time points of the plurality of minute levels, wherein the electricity-measuring operation cost data and the coal-burning parameter values of the corresponding time points form a historical electricity-measuring operation cost data set; the electricity water cost comprises brine water production cost, circulating water production cost and reclaimed water cost, and the brine water production cost, the circulating water production cost and the reclaimed water cost are respectively calculated based on the flow of a demineralized water outlet pipe per minute, the flow of circulating water replenishing water per minute and the flow of a reclaimed water main pipe per minute;
obtaining a power operation cost equation based on the historical power operation cost data set, wherein the power operation cost equation is expressed as:wherein->Representing the electricity running cost; LR represents variable +.>Is a linear equation of (2); equation variable->The method comprises the following steps of: />For the final feed water temperature->Is the oxygen content of the hearth>Is total secondary air volume and->Is the total coal quantity->Is high-heat and high-coal-volatilizing ratio->The ratio of the high-heat low-swing coal is the ratio;
constructing a cost optimization objective function based on the electricity-to-electricity running cost equation and the electricity-to-electricity fire coal cost equation, wherein the electricity-to-electricity fire coal cost equation is expressed as:
，
wherein P is 1 The price of high-heat and high-coal-volatilizing is high; p (P) 2 The price of the coal is high heat and low; p (P) 3 Is the price of economic coal;is the total coal quantity; />The ratio of the high-heat high-swing coal is high; />The ratio of the high-heat low-swing coal is the ratio; />Is a power generation load; t represents power generation time, and the unit is h; the cost optimization objective function is expressed as: />；
Solving an optimal solution for the objective function based on constraint conditions, wherein the constraint conditions comprise:the values are all in the corresponding preset range; />；
；
Wherein,is the calorific value of high-heat high-volatile coal>Is high heat and low coal volatilizing heat value +.>Is the calorific value of economic coal>The heat value of the standard coal is kcal/kg; />Standard coal consumption for power supply, unit: g/kWh;the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>Sulfur content of high-heat high-volatile coal>Is sulfur content of high-heat low-volatile coal>The unit is the economic coal sulfur; />Sulfur index of coal mixture fed into the boiler for boiler combustion;
；
wherein,is high in heat and moisture content of volatile coal>Is high heat and low volatile water content>The water content of the economic coal is in units of;the water index of the coal mixture fed into the boiler for boiler combustion;
；
wherein,the heat value index of the mixed coal fed into the boiler for boiler combustion;
and optimizing the comprehensive power generation cost of the thermal power enterprise based on the optimal solution.
2. The method for optimizing the comprehensive power generation cost of the thermal power enterprise according to claim 1, wherein the electricity-generating emission cost comprises limestone powder cost, emission environmental protection cost and electricity-generating urea cost; the calculation method of the limestone powder cost comprises the following steps:
；
wherein,representing the SO of the raw flue gas 2 Converting concentration in kg/Nm; />The standard dry flow of the original flue gas is expressed, and the unit is m wave/min; />The desulfurization efficiency is expressed in units of; />Represents CaCO 3 Molar mass (100 g/mol)；/>Representing SO 2 Molar amount (64 g/mol); />Represents the calcium-sulfur ratio, the unit is; />The purity of limestone powder is expressed in units of;the unit of limestone powder is yuan/ton; t represents the power generation time, and the unit is h.
3. The method for optimizing comprehensive power generation cost of thermal power enterprise according to claim 2, wherein the environmental protection cost comprises electricity generation NO x Pollution discharge tax and electricity SO 2 The calculation methods of the blowdown tax and the electric smoke dust blowdown tax are as follows:
；
；
；
wherein,indicating the clean smoke NO x Concentration conversion, wherein the unit is kg/Nm; />The standard dry flow of the clean flue gas is expressed in m wave/min; />Represents NO x Pollution equivalent value; the unit is m cm/min; />Represents NO x Unit price in yuan per kilogram;the unit is MW for generating load;
indicating the SO of the clean flue gas 2 Concentration conversion, wherein the unit is kg/Nm; />Representing SO 2 Pollution equivalent value, in m bits/min; />Representing SO 2 Unit price in yuan per kilogram;
the concentration conversion of the smoke and dust of the clean smoke is expressed in kg/Nm; />The smoke pollution equivalent value is expressed in m-zone/min; />The unit of smoke unit is yuan per kilogram.
4. The method for optimizing the comprehensive power generation cost of the thermal power enterprise according to claim 1, wherein a multi-linear regression is adopted to linearly fit the historical power operation cost data set to a specified variable, so as to obtain a power operation cost equation; the specified variables are variables corresponding to the coal burning parameters, and comprise final water supply temperature, hearth oxygen amount, total secondary air amount, total coal amount, high-heat high-swing coal ratio and high-heat low-swing coal ratio.
5. The method for optimizing the comprehensive power generation cost of a thermal power plant according to any one of claims 1 to 4, wherein a particle swarm algorithm is used to solve the objective function optimally, wherein the value of each set of variables is taken as a particle, the objective function is taken as a fitness function, and the particle which minimizes the objective function is the optimal solution.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311277172.9A CN117010728B (en) | 2023-10-07 | 2023-10-07 | Comprehensive power generation cost optimization method for thermal power enterprises |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311277172.9A CN117010728B (en) | 2023-10-07 | 2023-10-07 | Comprehensive power generation cost optimization method for thermal power enterprises |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117010728A CN117010728A (en) | 2023-11-07 |
| CN117010728B true CN117010728B (en) | 2024-01-02 |
Family
ID=88567566
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311277172.9A Active CN117010728B (en) | 2023-10-07 | 2023-10-07 | Comprehensive power generation cost optimization method for thermal power enterprises |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117010728B (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| AU2020103709A4 (en) * | 2020-11-26 | 2021-02-11 | Daqing Oilfield Design Institute Co., Ltd | A modified particle swarm intelligent optimization method for solving high-dimensional optimization problems of large oil and gas production systems |
| CN113887827A (en) * | 2021-10-25 | 2022-01-04 | 国网安徽省电力有限公司电力科学研究院 | Coal blending combustion optimization decision method based on real-time carbon emission monitoring of thermal power generating unit |
| CN115455719A (en) * | 2022-09-23 | 2022-12-09 | 国网江苏省电力有限公司南京供电分公司 | Comprehensive energy system optimization simulation method based on full life cycle carbon cost |
| CN115965116A (en) * | 2022-11-16 | 2023-04-14 | 国网辽宁省电力有限公司盘锦供电公司 | Multi-target coordination regulation and control method for regional multi-energy system |
| CN116307075A (en) * | 2023-02-01 | 2023-06-23 | 华能国际电力股份有限公司上海石洞口第二电厂 | Intelligent algorithm-based electricity-measuring coal-burning cost optimization method and system |
-
2023
- 2023-10-07 CN CN202311277172.9A patent/CN117010728B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| AU2020103709A4 (en) * | 2020-11-26 | 2021-02-11 | Daqing Oilfield Design Institute Co., Ltd | A modified particle swarm intelligent optimization method for solving high-dimensional optimization problems of large oil and gas production systems |
| CN113887827A (en) * | 2021-10-25 | 2022-01-04 | 国网安徽省电力有限公司电力科学研究院 | Coal blending combustion optimization decision method based on real-time carbon emission monitoring of thermal power generating unit |
| CN115455719A (en) * | 2022-09-23 | 2022-12-09 | 国网江苏省电力有限公司南京供电分公司 | Comprehensive energy system optimization simulation method based on full life cycle carbon cost |
| CN115965116A (en) * | 2022-11-16 | 2023-04-14 | 国网辽宁省电力有限公司盘锦供电公司 | Multi-target coordination regulation and control method for regional multi-energy system |
| CN116307075A (en) * | 2023-02-01 | 2023-06-23 | 华能国际电力股份有限公司上海石洞口第二电厂 | Intelligent algorithm-based electricity-measuring coal-burning cost optimization method and system |
Non-Patent Citations (2)
| Title |
|---|
| 火电厂发电污染成本核算及敏感度分析;闫顺林;谷兵;艾书剑;田东旭;;电力科学与工程(07);正文第0-4节 * |
| 火电厂燃煤掺烧经济性边界分析研究;金生祥;;发电设备(03);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117010728A (en) | 2023-11-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109376945A (en) | A kind of coal mixing combustion optimization system based on more coals | |
| CN110111030A (en) | A kind of coal mixing combustion evaluation method and system | |
| CN104598761B (en) | The analysis method that the change of multi-fuel fired boiler operating parameter influences unit generation coal consumption | |
| CN101063872A (en) | System for optimizing oxygen in a boiler | |
| Huang et al. | Bi-level multi-objective programming approach for carbon emission quota allocation towards co-combustion of coal and sewage sludge | |
| CN104268433B (en) | Method for monitoring unit power generation coal consumption deviation caused by variation of gas boiler operating parameters | |
| CN110319455A (en) | A kind of boiler mixed coal blending | |
| Li et al. | An inexact fuzzy programming approach for power coal blending | |
| CN114925328A (en) | Coal blending and burning calculation method and system for coal-fired unit | |
| CN117010728B (en) | Comprehensive power generation cost optimization method for thermal power enterprises | |
| CN112664975B (en) | Air volume control method suitable for pulverized coal fired boiler | |
| CN116543846A (en) | Mixed coal blending combustion method and system for relieving ash slagging of heated area of boiler | |
| CN112418664B (en) | Particle swarm optimization-based binning combination blending method and system | |
| CN117391240B (en) | Thermal power generation and coal combustion blending scheme optimization method based on similarity calculation | |
| CN112580890B (en) | Method and system for predicting variable cost of power generation by blending and burning mixed coal of boiler for power generation | |
| CN112862632B (en) | Method and system for blending and burning coal in thermal power plant | |
| JP2004038626A (en) | Support method of power generation business and support system of power generation business | |
| Šljivac et al. | Overview of the efficient tehnologies for power generation based on coal | |
| CN117391734B (en) | Power generation cost prediction method based on support vector machine regression model | |
| CN116379464A (en) | Automatic optimizing method for total cost of NOx under full load of coal-fired unit | |
| CN112348703B (en) | Optimal operation oxygen quantity simplified analysis method based on lowest power supply coal consumption | |
| Jiang et al. | Research on the construction of carbon emission model of power generation industry in Jilin province | |
| Lähteenmäki | Operational and financial effects of fluidized bed process electrification | |
| JP2021028557A (en) | Combustion facility and combustion method | |
| CN117575658A (en) | Coal-fired unit depth peak shaving cost prediction method based on life cycle and environmental protection compensation |
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 |