# Multi-unit multi-mode heat supply power plant operation comprehensive evaluation method based on fuzzy analysis

## A comprehensive evaluation and technology for heating power plants, applied in multi-objective optimization, complex mathematical operations, computer-aided design, etc., can solve the problem of inability to accurately know the intelligent quantitative operation scheduling decision at the plant level of peak-shaving thermal power plants, and the optimization indicators and constraints are complex, It is difficult to comprehensively evaluate and other problems to achieve the effect of ensuring economic and environmental protection operation, large peak shaving benefits, and reducing deviations.

Pending Publication Date: 2022-01-28

浙江英集动力科技有限公司

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## AI-Extracted Technical Summary

### Problems solved by technology

[0004] However, when performing deep peak regulation of heating units for a combination of multiple heating modes, there are many peak regulation conditions, complex optimization indicators and constraints, and it is difficult to quantitatively evaluate comprehensively, and it is impossible to accurately know the plant-level intelligence of the peak regulation thermal power plant. Quantitative operation scheduling decision-making, ...

### Method used

In actual application, the most mature ones are hot water tank heat storage heat supply and electric boiler heat supply technology. These two technologies have better thermal economy, but the transformation cost is relatively high, and the occupied area is also large ; The second is the steam turbine bypass technology, which is low in transformation cost, but the thermal economy is poor; the high and low pressure bypass combined heat supply uses the high pressure and low pressure bypass valves to extract more high-quality steam for heating, and the cylinder cutting is through the middle The pressure cylinder exhaust steam is directly used for heating in the back pressure heating mode to improve the heating capacity. Both heating modes have their pros and cons. High and low pressure bypass combined heating uses high-grade steam for low-grade heating, and the overall thermal economy is low; cylinder cutting technology realizes cascaded utilization of heat, which has high energy efficiency, but there are high safety hazards.

In the reverse identification process of digital twin model characteristic parameter, select the multiple groups of measured data of power plant under various steady-state working conditions; Calculate the characteristic coefficient theoretical value of each equipment; Utilize particle swarm algorithm to combine multiple groups of measured data subsequently Identify and calculate the correction amount of the characteristic coefficient of each device, and obtain the corrected parameters. By comparing the corrections of different characteristic coefficients in the simulation model with the corresponding measured data under multiple working conditions, the optimal characteristic parameters are identified. The revised model can better describe the actual physical system and improve the calculation accuracy of the power plant multi-condition calculation simulation.

The present invention adopts fuzzy analytic hierarchy process to set up evaluation hierarchical structure, builds the judgment matrix between each evaluation index, determines each evaluation index to the weight vector of unit peak regulation and establishes each multi-heating mode combined scheme and each evaluation index The fuzzy decision matrix among them, and the fuzzy calculation of the weight vector and the fuzzy decision matrix using the fuzzy decision algorithm, obtain the comprehensive evaluation value of each multi-heating mode combination scheme, and determine the multi-heating mode combination scheme with the highest evaluation value as the optimal combination The scheme can set reasonable weights for each eval...

## Abstract

The invention discloses a multi-unit multi-mode heat supply power plant operation comprehensive evaluation method based on fuzzy analysis. The method comprises the steps of S1, setting a multi-heat supply mode combination scheme when heat supply units participate in deep peak regulation cooperative operation; S2, constructing a digital twin model for multi-mode deep peak regulation of the units; S3, establishing a heat supply unit multi-mode deep peak regulation evaluation model at least comprising an economic evaluation index, an environmental protection evaluation index and a multi-dimensional constraint condition; S4, establishing a judgment matrix among the evaluation indexes, determining a weight vector of each evaluation index for peak regulation of the units, and establishing a fuzzy decision matrix between each multi-heat-supply-mode combination scheme and each evaluation index; and S5, performing fuzzy calculation on the weight vector and the fuzzy decision matrix by adopting a fuzzy decision algorithm. According to the method, the units of different heat supply mode combination schemes are evaluated, the optimization of the unit transformation technology combination scheme is realized, and the plant-level intelligent quantitative operation scheduling decision of a peak regulation thermal power plant is realized.

Application Domain

Data processing applicationsDesign optimisation/simulation +3

Technology Topic

Economic evaluationFuzzy decision +7

## Image

## Examples

- Experimental program(1)
- Effect test(1)

### Example Embodiment

[0097] Example 1

[0098] figure 1 It is a schematic diagram of the principle of operation of a multi-machine group multi-mode heating power plant based on fuzzy analysis according to the present invention.

[0099] figure 2 It is a flow chart of a multi-mode multi-mode heating power plant based on fuzzy analysis according to the present invention.

[0100] like figure 1 , figure 2 As shown, this Example 1 provides a comprehensive evaluation method based on a multi-mode multi-mode heating power plant based on fuzzy analysis, and comprehensive evaluation methods include:

[0101] Step S1, setting the multi-heat mode combination scheme for the heating unit to participate in depth peaks;

[0102] Step S2, using mechanism modeling and data identification method to build a digital twin model of the multi-mode depth peak of the unit;

[0103] Step S3, based on the dimension indicator of the heating unit to participate in the depth peak, establish a multi-mode depth peak evaluation model including the heating unit including economic evaluation indicators, environmental evaluation indicators, and multi-dimensional constraints, for different heating The unit of the mode combination scheme is evaluated;

[0104] Step S4, using a fuzzy hierarchical analysis method to establish an evaluation hierarchical structure, construct the judgment matrix between each evaluation index, determine the weight vectors of each evaluation index on the peak peak of the unit and establish each multi-heating mode combination plan and each evaluation index Fuzzy decision matrix;

[0105] Step S5, the fuzzy decision algorithm is used to fuzzy the weight vector and the fuzzy decision matrix, obtain a comprehensive evaluation value of each multi-heating mode combination scheme, determine the multi-heat mode combination scheme with the highest value value as a preferred combination scheme;

[0106] Step S6, through the digital twin model to perform unit performance verification of the preferred combination scheme, and the verification scheme is used as the best combination of multi-heating mode, and the factory-level intelligent quantitative operation scheduling decision for the peak thermoelectric plant is realized according to the best combination scheme.

[0107] image 3 It is a combination of multi-heat mode according to the present invention.

[0108] like image 3 As shown in the present embodiment, step S1 is set to participate in a multi-heating mode combination scheme when the depth peak synergistic operation is included, including:

[0109] Based on the cost, complexity, thermal economy, power plant demand and coal species, according to high back pressure, pose, cut cylinder, high and low pressure bypass, heat storage jug, electric boiler different heating mode Adaptability Select a multi-mode heating combination scheme, multi-mode heating combination scheme includes two two modes binding or more mode combination.

[0110] In actual applications, the most mature is hot water tank heatspring heat, electric boiler heating technology, these two technologies are better, but the transformation cost is relatively high, and the area is also large; secondly Steam turbine bypass technology, low transformation cost, but the thermal economy is poor; high, low-pressure bypass combined heating is taken with high-quality steam heating by means of high pressure and low pressure bypass valves, cut cylinders Automobiles are directly used in supplying back pressure heating methods to improve heat transfer capacity. Both heating modes have excellent. High and low pressure bypass combined heating will be used in high-grade steam for low-grade heating. The overall thermal economy is low; the cutting technology is highly thermocycladed, and the energy efficiency is high, but the presence is high.

[0111] Due to the cost and complexity of the unit transformation, and the unstable set of units in the unit, currently use is a combination of two heating modes. Typical combination include: high back pressure + strip, high back pressure + cut cylinder, high and low + drawn, high and low + cut cylinder, stripping steam + heat storage jug, cut cylinder + heat storage jug, pumping steam + Electric boiler, cut cylinder + electric boiler, high back pressure + electric boiler, high and low + electric boiler, etc. There are many combinations of different modes, and the demand and coal species of different places of the power plant are needed, and the corresponding multi-mode combination is required according to the specific techniques. In the adaptability of technology, different ways are selected according to different units. The technical adaptability and peak ability of the heat storage electric boiler is stronger than other transformation techniques, but the static investment is high, the thermal efficiency of the system is also low; back pressure heating (including optical axes and cutting cylinder), low static investment, system The total efficiency is also higher, but to consider the safety of low pressure cylinders, it is necessary to further modify the back pressure supply and heat transfer; and the high and low pressure bypass heating method must also meet the blade strength and the lateral thrust of the unit. Meet the safety operation standard. The actual renovation technique is selected, and it is necessary to consider integrated economic and technology adaptability.

[0112] Figure 4 It is a schematic diagram of a single set simulation model according to the present invention.

[0113] like Figure 4 As shown, in the present embodiment, the digital twin model of the multi-mode depth peak of the unit multi-mode depth peak is used in step S2, and specific includes:

[0114]Based on the basic principle of engineering thermodynamics, fluid mechanics, heat transfer, the use of construction simulation technology constructs consistent structure mechanism model consistent with the system structure of the heating unit, and achieves different load conditions by inputting structural parameters, attribute information, setting boundary conditions. The theoretical calculation of the operation performance of the unit and the full plant thermal system; the structural mechanism model includes at least the steam turbine module, the boiler module, the water supply modifier module, the condenser module, the steam flow module, the water pump module, the pipeline and the integrated cross module. Each module needs to meet the respective quality conservation equations, energy conservation equations and constraints;

[0115] The real-time running data access simulation model of the heating unit is admitted to the model's simulation results of the model for adaptive recognition correction;

[0116] Among them, the reverse identification method includes: After the operation data of the heating unit multi-working condition, the abnormal value processing, and data smooth pre-processing operations, the operation data containing the measurement error in real-time operation data is preliminary by the history run data. Fixed to meet the basic mechanism rule data; the input variable input mechanism simulation model in the basic mechanism rule data will be met the predicted value of the variable to be corrected in the variable; construct the error membership function, the basic mechanism rule data to meet the correction variables is detected and Identification Get error data; detect and identify errors by predictive values, and to determine the cause of the error in the error of the error to be corrected by the expert system.

[0117] In practical applications, data twin model generation is to make a logical model, a logical model, a simulation model, and a data-driven model, and finally establish a digital twin model of the physical entity of the thermal power plant in virtual space. Summary of each model is as follows: (1) Physical model reflects the individual physical entities in the thermal power plant physics system, defines the geometric properties and functional properties of the physical model depending on the geometric shape and mechanical mechanism of the physical entity; the physical entity mainly includes boiler, steam turbine, pump / Fan, valve, heat exchanger, condenser and cooling tower, according to the physical entity of the thermoelectric substitute, construct a thinking of "overall-module-overall" physical model, establish a separate physical model for each device, and finally connect each module Become a whole. (2) The logical model is based on the supply and demand relationship between the various physical entities of the thermal power plant, and the convection to establish controlled closed-loop logic models, and map the physical model to the logical model. (3) The simulation model is based on the operation data, state data, physical attribute data of the acquired thermal plant, and can be tuned according to the error size of the simulation model output prediction value and the actual value of the actual value. (4) Data drive model based on the normal operation of the heat power plant, the data fusion and depth learning algorithm can be used, and the physical entities of the thermal power plant according to the working principle, and the normal runtime input data is extracted, as the data drive model Input, the predicted value of the model output can be used for parameter tuning.

[0118] During the reverse recognition process of the digital twin model feature parameter, the multi-group measured data of the power plant under various steady state conditions is selected; the theoretical value of the characteristic factor of each device; subsequently utilized the particle group algorithm to combine multiple sets of measured data identification calculations The characterization of the device is corrected, and the corrected parameters are obtained. The optimal feature parameters are identified by comparing different characteristic factor corrections in the simulation model. The corrected model can better describe the actual physical system, improve the calculation accuracy of the calculation of the mode of multi-working conditions.

[0119] In the present embodiment, the economy evaluation index and environmental assessment index in step S3 are one-level evaluation index; economic objectives also include coal-fired procurement costs CCOST_I and carbon emissions transaction cost C cq , Power supply income C 1 Heating income C 2 And peak income C 3 Secondary evaluation index; the total revenue c of the thermal power plant is expressed as:

[0120] C = C 1 + C 2 + C 3 -C cost;

[0121] Coal-fired procurement cost CCOST_I According to the design parameters and actual data of the power plant, consider the number of steam turbine valve opens with the gradual increase of the power generation power of the unit, the current-level valve is completely opened while the post-horizontal valve is just opened, and the steam circulation is hindered. The coal consumption is increased, resulting in a valve point effect, causing the coal consumption of nonlinearity and discontinuities, and fits the supply coal consumption characteristics of each unit by increasing the sinusoidal function.

[0122] C cost_i (P i ) = A i P i 2 + B i P i + C i + | D i Sin {e i (P i min -P i )} |, I = 1, 2, ..., n is the number of each unit; i Distribution load, MW; The lower limit of the load of the i-th unit, MW; A i , B i , C i , D i E i The coal consumption characteristics of the unit; CCOST_I is the cost of electricity coal consumption, element / h;

[0123] Carbon emission transaction cost C cq Carbon emissions e o And carbon quota E q Decided, expressed as: C cq = C (e o -E q ), C is carbon transaction price; carbon emissions E o Carbon emissions E of thermofors o1 And thermoelectric gauge carbon emissions E o2 Two parts, μ i Indicates the strength of the carbon emissions from the unit of thermofer units in the IT. gamma i Indicates the carbon emission intensity of the unit of IT thermal unit unit; carbon quota E q Motor unit carbon quota E q1 And thermoelectric gauge carbon quota E q2 Two parts, P i For the power generation power of the IT thermal engine; λ is the unit power carbon emission distribution coefficient; P ZS,i = P CHP,i + B v,i Hide CHP,i , P ZS,i Indicates power generation power under the pure coagulation conditions of the thermoelectric gauge unit; CHP，i Hidden CHP，i The net power generation power and thermal power of the thermoelectric set I is respectively, respectively; B v，i In order to increase the amount of electric exoction force corresponding to the unit hot out force by increasing the intake air volume of the pumping thermomechanical unit;

[0124] Cost_i and carbon emissions transaction cost C cost C according to coal-fired procurement cost CCOST_I and carbon emissions cq Calculate the total cost of running C cost ,Expressed as:

[0125] Power supply income C 1 The power supply income of the pure condolence unit and the thermofor unit is expressed as: Heating income C 2 The combined heating income of the thermogravimeter and the electric heat storage is shown as: p g For the Internet price; P heat For the unit heating charge; T is the system run time; regard the same model of the thermal power unit as a whole, m is the number of pure cavity units, n is the number of thermograde types; peak income C 3 Participation in depth peak income C 3ck And the thermoelectric gauge involvement in depth peak income C 3rl Composition, expressed as:

[0126]

[0127] p t1 , P t2 For the first, two-speed peak price;

[0128] The peak power supply for the pure condenser set K in the second gear;

[0129] For the heat generator set L in the first and second gear power;

[0130] The relationship between environmental assessment indicators through pollutant emissions and unit loads is: g i (P i ) = Α i P i 2 + β i P i + γ i Α i Β i Γ i , Is a unit of smoke and NO, respectively. x Emission quality concentration characteristic factor; g i Indicates the pollutant emissions of the first unit unit; the total pollution emissions G are expressed as:

[0131] It should be noted that the "valve point effect" is usually displayed when the steam turbine intake valve is suddenly turned on, resulting in such phenomena that the number of turbine valves will be turned on with the power generation power of the unit. As gradually increased, the current-stage steam door is completely opened while the post-stage steam gate has just opened, and the steam circulation is hindered such that the coal consumption is increased, the coal consumption characteristic curve projections, generates a valve point effect. At this time, the use of smooth quadrators cannot explain the actual input and output characteristics of the unit, and if the nonlinearity and discontinuities caused by the valve point effect, 1 corresponding sinusoidal function should be added based on the formula.

[0132] The depth peak transaction is based on the day, and the paid peak costs received by the thermal power plant are related to the peak power and peak electricity prices, and the peak step compensation price list is as follows:

[0133]

[0134] Two quotations and processes of the thermal electromechanical set, when the two-speed load rate of the pure coagulating machine is α = 40%, β 1 = 48%; the two-speed load rate of the thermoelectric network is α = 40%, β 2 = 50%. The power unit exactly the amount of electricity in the load rate [α, β] interval is the first germree, which is lower than the amount of electricity of the load rate α is the second gear property.

[0135] In the present embodiment, the multi-mode depth peak evaluation model of the heating unit in step S3 is established, including: the total pollution emissions G, the total contamination emissions G, and Establishment of multi - mode depth peak evaluation model of heating unit based on multi-dimensional constraints;

[0136] The multi-dimensional constraint condition includes at least: electrical energy balancing constraint, thermal balance constraint, unit itself of operation constraint, operating constraints of the electric boiler, the operating constraints and depth peak power constraints of the heat storage device;

[0137] Electrical energy balance constraint:

[0138] P load (t) = P CHP (t) -p EB (t) -p CY(t);

[0139] Where: P load (t) is the electric load demand of the system in the T period; CHP (t) is the electrical power output value of the thermometric set at T moment; EB (t) is the electric power consumed by the T hour electric boiler; CY (t) is a power load for T period.

[0140] Thermal balance constraint:

[0141] Hide load (t) = h CHP (t) + h EB (t) + h TS (t) -h TS (t-Δt);

[0142] Where: h load (t) is the thermal load demand of the system in the T period; H CHP (t) is the heat generated by the time period T thermograde; h EB (t) is the heat generated by the electric boiler at T, H TS (t) is the heat storage capacity in the time period t in the heat storage device; Δt is the length of the unit;

[0143] The operating constraint of the thermoelectric sets:

[0144]

[0145]

[0146] -P CHP,down ΔT≤p CHP (t) -p CHP (T-ΔT) ≤p CHP,up ΔT;

[0147] In: and The upper and lower limits of the electric assembly force of the thermoelectric gauge; and The upper and lower limits of the thermal output units are respectively; CHP,down And P CHP,up The downward climbing rate and upward climbing rate of the thermoelectric gauge;

[0148] Running constraint of the electric boiler:

[0149] 0≤P EB (t) ≤p EB,max;

[0150] -P EB,down ΔT≤p EB (t) -p EB (T-ΔT) ≤p EB,up ΔT;

[0151] Where: P EB,max Maximum power installed for electric boilers; P EB,down And P EB,up The downward climbing rate and upward climbing rate of the electric boiler are respectively.

[0152] Running constraint of the heat storage device:

[0153] 0≤h TS (t) ≤ h TS,max;

[0154]

[0155]

[0156] Where: h TS,max The maximum capacity installed for the heat storage device; and The minimum and maximum value of the heat storage power of the heat storage device is respectively; and The minimum and maximum value of the heat storage power of the heat storage device, respectively; U c (t) and u d (t) is a storage factor of the energy storage device, which is 0-1 variable, wherein 0 means that the energy storage device does not perform energy or discharge, 1 represents the energy storage device for energy storage or discharge. Since the energy storage device cannot be stored at the same time, the reservoir is satisfied with 0≤u c (t) + u d (t) ≤1;

[0157] Depth peak power constraint:

[0158]

[0159] In P feasible For the thermal power plant to meet the minimum load of power generation on the basis of heating, it is based on the depth peak ability of various units to operate; For T period grid load command; P ability It is the maximum load capacity of the thermal power plant, that is, the top peak ability.

[0160] Figure 5 It is a hierarchical structure of a multi-mode heating power plant according to the present invention.

[0161] like Figure 5 As shown in the present embodiment, the evaluation hierarchy in step S4 includes: the scheme layer, the standard layer and the target layer, the scheme layer includes a multi-heating mode combination scheme, and the standard layer includes various evaluation indicators, the target layer includes unit peak Evaluation results;

[0162] Constructing the judgment matrix between each evaluation index, including: the degree of comparison matrix is determined by the effect of each factor of two two two-two comparison layers, which is determined to be a comparison matrix, which is a judgment matrix; set A ij Represents the comparison result of the Impulsical Item I. If the number of standards of the standard layer evaluation indicator is Q, the judgment matrix is:

[0163]

[0164] In step S4, each evaluation indicator is determined to the weight vectors of the unit peak, including:

[0165] The determination matrix A is solved to obtain a corresponding feature root and feature vector, and perform a consistency test. If the normalized feature vector corresponding to the maximum feature value of the matrix is determined by testing, the normalized feature vectors of the criteria are evaluated. The weight of the target is written as W; if it is not tested, the parameters of the judgment matrix are re-adjusted until it is checked;

[0166] Step S4 establishes a fuzzy decision matrix between the various multi-heating mode combined schemes and each evaluation index, including:

[0167] With a blur mathematics membership function, each multi-heat mode combination scheme of the protocol layer is sorted by the peak degree of each single evaluation index by blurring, and the fuzzy decision matrix B, which is from the plan layer to the standard layer. The normalization process is expressed as:

[0168]

[0169]

[0170]

[0171] Among them, Q is the number of evaluation indicators; B ol For the heating multi-mode combination scheme L on the fuzzy membership degree of evaluation target O; R ol The sequence number is sorted for the peak degree of evaluation index O for the heating multi-mode combination scheme.

[0172] In the present embodiment, in step S5, the fuzzy decision algorithm is made to fuzzy the weight vector and the fuzzy decision matrix, obtain a comprehensive evaluation value of each multi-heating mode combination scheme, and the multi-heating mode combination scheme with the highest value value is determined. For the preferred combination, including:

[0173] Step S51, the fuzzy number of economic evaluation indicators and environmental assessment indicators in the fuzzy decision matrix is standardized to obtain standardized standardized decision matrices;

[0174] Step S52, based on normalized decision matrix and weight vector, the average attribute value of each multi-heating mode combination scheme corresponds to each multi-heating mode combination scheme corresponding to each multi-heating mode combination scheme;

[0175] Step S53, based on the average attribute value, calculate the comprehensive evaluation value of each multi-heating mode combination scheme according to the score function, and sort the calculated comprehensive evaluation value, the multi-heat mode combination scheme with the highest value value is determined as a preferred combination. plan.

[0176] In the present embodiment, the comprehensive evaluation method also includes: after establishing a fuzzy decision matrix, a smart algorithm is used to solve the calculation of the multi-mode depth peak evaluation model of the heating unit, obtain a multi-mode depth peak of the heating unit, and obtain the preferred combination of high-mode depth peaks. .

[0177] In this embodiment, the intelligent algorithm uses artificial hivega algorithm;

[0178] The intelligent algorithm is used to solve the calculation of the multi-mode depth peak evaluation model of the heating unit, obtaining a preferred combination scheme for multi-mode depth peaks of the heating unit, including:

[0179] Initialization algorithm parameters, including the scale of the bee group, picking bees and follows the scale of the burst, population individual vector, the same honey source mining limit, the maximum number of iterations;

[0180] The reconnaissance beias generates the initial solution of the best honey source with chaotic search. Leading the beias search strategy for the introduction index distribution proportional factor is searching near the currently reserved best honey source, follow the bees to use adaptive proportional selection strategies in the search honey source And becomes collar;

[0181] It is determined whether the selected honey source location is further improved within a predetermined mining limit. If it is not, the collar of the bee will give up the honey source, become a reconnaissance bee, and recombine new honey sources;

[0182] Decliminate iterative termination conditions, if the position of the optimal honey source is acceptable or reaches the maximum number of iterations, stop the calculation and output the optimal honey source position, that is, a preferred combination scheme; otherwise, the new honey source is re-searched.

[0183] In actual applications, the evaluation model can be used to obtain a preferred combination scheme by the intelligent algorithm, and the optimum combination scheme obtained by the intelligent algorithm and the fuzzy decision decision algorithm is verified, and the best combination scheme is obtained after comprehensively evaluating.

[0184] In the present embodiment, step S6 performs unit performance verification by digital twin models to the preferred combination scheme, and the verification program is the best combination of multi-heating mode, according to the best combination scheme to realize the factory-class intelligence of the peak thermoelectric factory. Quantitative operation scheduling decision, including: simulation of the preferred combination scheme by digital twin model, calculating the preferred combination of unit, thermal consumption, power consumption, power generation, power supply rate, power supply rate, unit efficiency , Unit heat transfer and maximum power generation capacity, maximum heating capacity, and minimum heat transfer parameter indicator, if the parameter indicator meets the expected value, the preferred scheme is best combined with the best combination of heat mode, and according to the best combination Implementation of Plan - level Intelligent Quantitative Operation Scheduling Decision - making Decision.

[0185] In actual applications, coal consumption characteristics are an important indicator of the energy efficiency of the power plant, based on fixed heat load, and study the change in coal consumption of the mainstation of the main steam flow by fixed thermal load. Whether the power supply coal consumption is within the range of preset indicators; and verifying the peak peak and low-tuning compulsion in the peak of electricity, including maximum power generation capacity, maximum heating capacity, and minimum heat transfer parameter indicators, improve Energy utilization efficiency; by multi-heating mode optimal combination scheme can calculate a working condition of the target adaptivity function on the basis of the heating load, according to the instructions issued by the power grid, the total power load requirements are allocated to each unit. , Implement the optimization of the comprehensive energy efficiency target of the whole plant.

[0186] By using the technical method combined with "structural mechanism modeling + data identification correction", based on the basic principles of engineering thermodynamics, fluid mechanics, heat transfer, the real structure of digital twin modeling technology and demonstration power plant heat system The structural mechanism model of the mapping, while using reverse identification method to adaptive identification correction of the model's simulation results, minimize the deviation between theoretical value and measured value; and by inputting structural parameters, attribute information, setting boundary conditions, The theoretical calculation of the unit and the full factory thermal system operating performance of different load conditions, realizing high-precision simulation and simulation of the operating performance of the thermogravimeter, to further quantify the operation safety, energy consumption and environmental emission indicators, and research and establishment Multi-mode collaborative running schemes provide core technology support under load demand conditions.

[0187] The present invention establishes a peak evaluation model by setting economic and environmental level evaluation indicators, as well as various secondary indicators and corresponding constraints included under the first-level evaluation index, and can meet the peak evaluation model, which can meet the peak under multidimensional constraints. The goal of the maximum income, the minimum pollutant emissions, ensuring the economic environmental operation of the heating unit.

[0188] The present invention employs the evaluation hierarchy, constructs the judgment matrix between each evaluation index, determines the weight vectors of each evaluation index on the peak peak of the unit and the establishment of various multi-heating mode combination schemes and various evaluation indicators. The decision matrix, and the fuzzy decision algorithm for fuzzy calculation of the weight vector and fuzzy decision matrix, obtain a comprehensive evaluation value of each multi-heating mode combination program, determine the multi-heating mode combination plan for the evaluation value as a preferred combination plan, According to the characteristics of the peak, the rational weight is set to each evaluation index, considering the interaction between the evaluation index, and select the best multi-heating mode combination scheme, so that the final solution can be more objective and scientifically.

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