Unit selection method and device for carbon capture transformation of coal-fired power plant

Abstract

The invention provides a coal-fired power plant carbon capture transformation unit selection method and device, which can be applied to the technical field of data processing and the technical field of carbon emission. The unit selection method for coal-fired power plant carbon capture reconstruction comprises the steps that carbon capture reconstruction data of a coal-fired power plant is classified based on a clustering algorithm, multiple to-be-evaluated data sets of a first-level category of a unit are obtained, and the to-be-evaluated data sets comprise multiple to-be-evaluated data subsets of a second-level category; the to-be-evaluated data subset comprises a plurality of to-be-evaluated data with different time; calculating the difference degree between the to-be-evaluated data before updating and the to-be-evaluated data after updating in the multiple to-be-evaluated data subsets based on the updating information; according to the difference degree, determining respective target evaluation weights of the plurality of second-level categories; determining an evaluation result of the unit according to the respective to-be-evaluated data of the multiple second-level categories and the target evaluation weight; and determining a target unit from the plurality of units according to the respective evaluation results and the respective trappable quantities of the plurality of units.

Description

Technical Field

[0001] The present disclosure relates to the fields of data processing technology and carbon emission technology, and more specifically to a method and device for selecting units for carbon capture modification in coal-fired power plants. Background Art

[0002] Related technologies use single-dimensional indicators to retrofit coal-fired power plants with carbon capture, such as installed capacity or unit type. However, carbon capture retrofits at coal-fired power plants are influenced not only by the unit itself but also by numerous external factors. This makes it difficult to accurately identify suitable units for carbon capture retrofits among the vast number of units in a coal-fired power plant, making it difficult to achieve precise emissions reductions. Summary of the Invention

[0003] In view of the above problems, the present disclosure provides a method and apparatus for selecting units for carbon capture modification in coal-fired power plants.

[0004] According to a first aspect of the present disclosure, a method for selecting units for carbon capture retrofitting of coal-fired power plants is provided, comprising: classifying carbon capture retrofitting data of the coal-fired power plant based on a clustering algorithm to obtain a plurality of first-level category data sets to be evaluated of the units, wherein the coal-fired power plant comprises a plurality of units, and the units are used to generate electricity by utilizing heat energy generated by coal combustion, and the data sets to be evaluated include a plurality of second-level category data subsets to be evaluated, and the data subsets to be evaluated include a plurality of data to be evaluated at different times; calculating the difference between the data to be evaluated before the update and the data to be evaluated after the update in the plurality of data subsets to be evaluated based on update information, wherein the update information is obtained from a data source based on a trigger condition and is used to update the data subsets to be evaluated; determining the target evaluation weights of the plurality of second-level categories according to the difference; determining the evaluation results of the units according to the data to be evaluated and the target evaluation weights of the plurality of second-level categories; and determining the target units from the plurality of units according to the evaluation results and the respective capturable amounts of the plurality of units.

[0005] According to an embodiment of the present disclosure, the carbon capture modification data of a coal-fired power plant is classified based on a clustering algorithm to obtain a plurality of first-level category data sets to be evaluated of the unit, including: classifying the carbon capture modification data based on a clustering algorithm to obtain a plurality of category data sets; and determining a category data subset in the category data set that meets a preset correlation condition as a data subset to be evaluated in the data set to be evaluated.

[0006] According to an embodiment of the present disclosure, carbon capture modification data is classified based on a clustering algorithm to obtain multiple category data sets, including: classifying carbon capture modification data based on a clustering algorithm to obtain multiple data sets to be determined; and determining a category data set from the multiple data sets to be determined based on the correlation between the data to be determined in the multiple data sets to be determined and the capture amount.

[0007] According to an embodiment of the present disclosure, the preset correlation condition is that the correlation between the category data in the category data subset and the captureable quantity is greater than a preset correlation threshold.

[0008] According to an embodiment of the present disclosure, the triggering condition includes at least one of the following: receiving an event update request related to carbon capture modification, and the update duration of carbon capture modification data reaching a preset duration threshold.

[0009] According to an embodiment of the present disclosure, the target evaluation weights of the various second-level categories are determined based on the degree of difference, including: determining the historical weight attenuation factor of the second-level category based on the degree of difference and the adjustment factor; obtaining the target evaluation weight based on the historical weight attenuation factor, the first evaluation weight of the data to be evaluated before the update, and the second evaluation weight of the data to be evaluated after the update.

[0010] According to an embodiment of the present disclosure, the evaluation result of the unit is determined according to the data to be evaluated and the target evaluation weights of each of the multiple second-level categories, including: based on the evaluation type of the data to be evaluated, determining the evaluation sub-results of the first-level category according to the data to be evaluated and the target evaluation weights of each of the multiple second-level categories; and determining the evaluation result of the unit according to the multiple first-level evaluation sub-results.

[0011] According to an embodiment of the present disclosure, based on the evaluation type of the data to be evaluated, the evaluation sub-results of the first-level category are determined according to the data to be evaluated and the target evaluation weights of each of the multiple second-level categories, including: when the evaluation type is a quantitative evaluation type, the evaluation sub-results of the first-level category are determined according to the data to be evaluated and the target evaluation weights of each of the multiple second-level categories; when the evaluation type is a classified evaluation type, the evaluation sub-results of the first-level category are determined according to the numerical range to which the data to be evaluated belongs.

[0012] According to an embodiment of the present disclosure, a target unit is determined from a plurality of units based on respective evaluation results and respective capturable quantities of the plurality of units, including: determining a plurality of to-be-determined units that meet preset capturable quantity conditions based on respective capturable quantities of the plurality of units; and determining a target unit that meets the preset evaluation result conditions from the plurality of to-be-determined units.

[0013] A second aspect of the present disclosure provides a unit selection device for carbon capture retrofitting of a coal-fired power plant, comprising: a classification module for classifying carbon capture retrofitting data of the coal-fired power plant based on a clustering algorithm to obtain a plurality of first-level category data sets to be evaluated for the units, wherein the coal-fired power plant includes a plurality of units, each of which is configured to generate electricity using heat energy generated by coal combustion, and the data sets to be evaluated include a plurality of second-level category data subsets to be evaluated, each of which includes a plurality of data to be evaluated at different times; a calculation module for calculating a difference between the pre-update data to be evaluated and the post-update data to be evaluated in the plurality of data subsets to be evaluated based on update information, the update information being obtained from a data source based on a trigger condition and used to update the data subsets to be evaluated; a first determination module for determining target evaluation weights for each of the plurality of second-level categories based on the difference; a second determination module for determining evaluation results for the units based on the data to be evaluated and the target evaluation weights for each of the plurality of second-level categories; and a third determination module for determining a target unit from the plurality of units based on the evaluation results and the respective capturable capacities of the plurality of units.

[0014] A third aspect of the present disclosure provides an electronic device, comprising: one or more processors; and a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the above method.

[0015] The fourth aspect of the present disclosure further provides a computer-readable storage medium having a computer program or instructions stored thereon, which implements the steps of the above method when the computer program or instructions are executed by a processor.

[0016] The fifth aspect of the present disclosure further provides a computer program product, comprising a computer program or instructions, which implement the steps of the above method when executed by a processor.

[0017] According to an embodiment of the present disclosure, carbon capture retrofit data for coal-fired power plants is classified using a clustering algorithm to obtain a plurality of first-level category datasets for units to be evaluated, wherein the carbon capture retrofit of coal-fired power plants takes into account the influence of multiple factors. The difference between the pre-update and post-update data in the plurality of subsets of the data to be evaluated is calculated based on update information. Target evaluation weights for each of the plurality of second-level categories are obtained based on the difference, thereby dynamically adjusting the target evaluation weights for the second-level categories based on the update information. The target evaluation weights incorporate the dynamic impact of the various factors on the carbon capture retrofit of coal-fired power plants over time. Since the evaluation results for the units are determined based on the respective data to be evaluated and the target evaluation weights for the plurality of second-level categories, the evaluation results accurately capture the impact of the update information on the carbon capture retrofit. Thus, based on the evaluation results and the respective carbon capture capacities of the plurality of units, target units suitable for carbon capture retrofit are accurately determined from the plurality of units, thereby achieving precise emission reduction. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The above contents and other objects, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0019] Figure 1 A diagram schematically illustrates an application scenario of a method for selecting units for carbon capture retrofitting in a coal-fired power plant according to an embodiment of the present disclosure;

[0020] Figure 2 A flow chart schematically illustrates a method for selecting units for carbon capture retrofitting in a coal-fired power plant according to an embodiment of the present disclosure;

[0021] Figure 3 A flow chart schematically illustrates a method for selecting units for carbon capture retrofitting in a coal-fired power plant according to another embodiment of the present disclosure;

[0022] Figure 4 A block diagram schematically illustrates a structure of a unit selection device for carbon capture retrofitting in a coal-fired power plant according to an embodiment of the present disclosure; and

[0023] Figure 5 A block diagram of an electronic device for implementing a unit selection method for carbon capture modification of a coal-fired power plant according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0024] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present disclosure. In the detailed description below, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0025] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise," "include," etc. used herein indicate the presence of the features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0026] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0027] When expressions such as "at least one of A, B, and C, etc." are used, they should generally be interpreted in accordance with the meaning commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0028] The carbon capture transformation of coal-fired power plants must not only consider the factors of the unit itself, but also the influence of external factors, such as the improvement plan of the power grid structure and the planned carbon emission targets. Therefore, it is difficult to identify the units suitable for carbon capture transformation from the many units, and it is difficult to achieve precise emission reduction.

[0029] An embodiment of the present disclosure provides a method for selecting units for carbon capture retrofitting of coal-fired power plants, comprising: classifying carbon capture retrofitting data of the coal-fired power plant based on a clustering algorithm to obtain a plurality of first-level category data sets to be evaluated for the units, wherein the coal-fired power plant includes a plurality of units, and the units are used to generate electricity by utilizing heat energy generated by coal combustion, and the data sets to be evaluated include a plurality of second-level category data subsets to be evaluated, and the data subsets to be evaluated include a plurality of data to be evaluated at different times; calculating the difference between the data to be evaluated before the update and the data to be evaluated after the update in the plurality of data subsets to be evaluated based on update information, wherein the update information is obtained from a data source based on a trigger condition and is used to update the data subsets to be evaluated; determining the target evaluation weights of the plurality of second-level categories according to the difference; determining the evaluation results of the units according to the data to be evaluated and the target evaluation weights of the plurality of second-level categories; and determining the target unit from the plurality of units according to the evaluation results and the respective capturable amounts of the plurality of units.

[0030] Figure 1 The application scenario diagram of the unit selection method for carbon capture modification of a coal-fired power plant according to an embodiment of the present disclosure is schematically shown.

[0031] like Figure 1 As shown, the application scenario 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 is used as a medium for providing a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 may include various connection types, such as wired or wireless communication links or optical fiber cables.

[0032] A user may use a first terminal device 101, a second terminal device 102, or a third terminal device 103 to interact with a server 105 via a network 104 to receive or send messages, etc. Various communication client applications may be installed on the first terminal device 101, the second terminal device 102, or the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (for example only).

[0033] The first terminal device 101 , the second terminal device 102 , and the third terminal device 103 may be various electronic devices having display screens and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, and the like.

[0034] The server 105 may be a server that provides various services, such as a background management server (for example only) that supports websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103. The background management server may analyze and process received data such as user requests, and feed back processing results (e.g., web pages, information, or data obtained or generated based on user requests) to the terminal devices.

[0035] It should be noted that the unit selection method for carbon capture retrofitting of a coal-fired power plant provided in the embodiment of the present disclosure can generally be executed by the server 105. Accordingly, the unit selection device for carbon capture retrofitting of a coal-fired power plant provided in the embodiment of the present disclosure can generally be set in the server 105. The unit selection method for carbon capture retrofitting of a coal-fired power plant provided in the embodiment of the present disclosure can also be executed by a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105. Accordingly, the unit selection device for carbon capture retrofitting of a coal-fired power plant provided in the embodiment of the present disclosure can also be set in a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105.

[0036] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is merely illustrative. Any number of terminal devices, networks and servers may be provided as required.

[0037] Figure 2 A flow chart schematically illustrates a method for selecting units for carbon capture retrofitting in a coal-fired power plant according to an embodiment of the present disclosure.

[0038] like Figure 2 As shown, the unit selection method for carbon capture retrofitting of a coal-fired power plant in this embodiment includes operations S210 to S250.

[0039] In operation S210 , the carbon capture transformation data of the coal-fired power plant is classified based on a clustering algorithm to obtain a plurality of first-level category data sets to be evaluated for the units.

[0040] According to an embodiment of the present disclosure, a coal-fired power plant includes multiple units that utilize the thermal energy generated by coal combustion to generate electricity. For example, a coal-fired power plant includes units B and C. Units B and C are coal-fired power plants, that is, they utilize the thermal energy generated by coal to generate electricity.

[0041] According to an embodiment of the present disclosure, the data set to be evaluated includes multiple second-level category data subsets to be evaluated, and the data subsets to be evaluated include multiple data to be evaluated at different times.

[0042] According to embodiments of the present disclosure, first-level categories represent broad attributes of carbon capture retrofit data. For example, first-level categories may include unit attributes, carbon emission requirements, and grid attributes. It should be noted that these first-level categories are broad attributes and may vary depending on the carbon capture retrofit data within the carbon capture retrofit dataset.

[0043] According to an embodiment of the present disclosure, the clustering algorithm may be a K-means clustering algorithm, a mean shift clustering algorithm, a clustering algorithm based on a neural network model, or the like.

[0044] For example, a K-means clustering algorithm is used to classify carbon capture retrofit data from coal-fired power plants. K attributes of the carbon capture retrofit data are randomly selected from the retrofit dataset as initial cluster centers. For each remaining attribute, the nearest cluster is assigned based on the distance between the attribute and the cluster center. The mean value for each cluster is then recalculated. This process is repeated until the criterion function converges, resulting in a dataset for the various first-level categories of the unit to be evaluated. The function can be a squared error criterion.

[0045] In operation S220 , the difference between the data to be evaluated before the update and the data to be evaluated after the update in the plurality of subsets of data to be evaluated is calculated based on the update information.

[0046] According to embodiments of the present disclosure, update information is obtained from a data source based on trigger conditions and is used to update the subset of data to be evaluated. For example, the update information may include regulatory information related to carbon capture retrofits, regional power grid organization information, and so on. The data source may be a database on carbon capture retrofits, a webpage related to carbon capture retrofits, and so on.

[0047] For example, when the trigger condition indicates that the rule information related to carbon capture modification or the regional power grid organization information is updated, the updated rule information related to carbon capture modification or the regional power grid organization information is obtained from the data source.

[0048] For example, based on the update information, the data to be evaluated before the update and the data to be evaluated after the update can be determined from the subset of data to be evaluated, and the difference between the data to be evaluated before the update and the data to be evaluated after the update can be calculated.

[0049] In operation S230 , target evaluation weights of respective second-level categories are determined according to the degree of difference.

[0050] For example, the target evaluation weight of the second-level category may be determined by combining the difference between the data to be evaluated before the update and the data to be evaluated after the update in the subset of data to be evaluated and the target evaluation weight of the data to be evaluated before the update.

[0051] For example, the target evaluation weight of the second-level category can be determined by combining the difference between the data to be evaluated before and after the update in the subset of data to be evaluated and the weight of the updated data to be evaluated. The weight of the updated data to be evaluated can be calculated based on the entropy value weighting method.

[0052] In operation S240 , an evaluation result of the fleet is determined based on the to-be-evaluated data and target evaluation weights of the respective second-level categories.

[0053] For example, the data to be evaluated of various second-level categories may be multiplied by the target evaluation weight to obtain the score value of the data to be evaluated. The score values ​​of the data to be evaluated of each second-level category may be multiplied or added to obtain the score result of the unit.

[0054] In operation S250 , a target unit is determined from the plurality of units based on the evaluation results and the respective capturable capacities of the plurality of units.

[0055] According to an embodiment of the present disclosure, the formula for the amount that can be captured is as follows:

[0056] (1);

[0057] CE i is the CO2 emission of coal-fired power plant i; CO i is the initial raw coal consumption of coal-fired power plant i; NCV c is the lower calorific value of raw coal; C c is the carbon content of raw coal; O c is the oxidation rate of raw coal; 44 / 12 is the mass fraction ratio of CO2 to carbon; IC i is the installed capacity of coal-fired power plant i; RT i, represents the utilization hours of coal-fired power plant i; PGCC is the standard coal consumption per kilowatt-hour of electricity generation in each province; T c The conversion factor from raw coal to standard coal; TCE i is the CO2 capture capacity of coal-fired power plant i; Q is the capture rate of coal-fired unit.

[0058] According to an embodiment of the present disclosure, carbon capture retrofit data for coal-fired power plants is classified using a clustering algorithm to obtain a plurality of first-level category datasets for units to be evaluated, wherein the carbon capture retrofit of coal-fired power plants takes into account the influence of multiple factors. The difference between the pre-update and post-update data in the plurality of subsets of the data to be evaluated is calculated based on update information. Target evaluation weights for each of the plurality of second-level categories are obtained based on the difference, thereby dynamically adjusting the target evaluation weights for the second-level categories based on the update information. The target evaluation weights incorporate the dynamic impact of the various factors on the carbon capture retrofit of coal-fired power plants over time. Since the evaluation results for the units are determined based on the respective data to be evaluated and the target evaluation weights for the plurality of second-level categories, the evaluation results can accurately capture the impact of the update information on the carbon capture retrofit. Thus, based on the evaluation results and the respective carbon capture capacities of the plurality of units, target units suitable for carbon capture retrofit are determined from the plurality of units, thereby achieving precise emission reduction.

[0059] According to an embodiment of the present disclosure, the carbon capture modification data of a coal-fired power plant is classified based on a clustering algorithm to obtain a plurality of first-level category data sets to be evaluated of the unit, including: classifying the carbon capture modification data based on a clustering algorithm to obtain a plurality of category data sets; and determining a category data subset in the category data set that meets a preset correlation condition as a data subset to be evaluated in the data set to be evaluated.

[0060] According to an embodiment of the present disclosure, the preset correlation condition is that the correlation between the category data in the category data subset and the captureable quantity is greater than a preset correlation threshold.

[0061] According to an embodiment of the present disclosure, the carbon capture modification data is standardized to eliminate dimensional differences.

[0062] According to an embodiment of the present disclosure, similar or repeated indicators are identified by calculating a correlation matrix between a plurality of carbon capture modification data. The correlation between the plurality of carbon capture modification data is measured by the Pearson correlation coefficient r.

[0063] (2);

[0064] in, and are the observed values ​​of the two carbon capture retrofit data, and The mean values ​​of the two carbon capture modification data are shown respectively. >0.9, remove one of them; when 0.7< When the value is less than or equal to 0.9, further review is conducted. The business significance and interpretability of each indicator are analyzed. The highly correlated indicators are reduced in dimension by principal component analysis, and the main components are extracted to represent these highly correlated indicators. When the correlation is less than 0.7, the indicator is retained, thus achieving a preliminary screening of the carbon capture modification dataset.

[0065] For example, a clustering algorithm based on a neural network model is used to classify carbon capture retrofit data for coal-fired power plants, generating multiple category data sets. Correlations between category data in category data subsets of the category data sets and the amount of carbon capture available are calculated. Category data in category data subsets whose correlation with the amount of carbon capture available exceeds a preset correlation threshold are identified as the data subsets to be evaluated in the dataset to be evaluated.

[0066] According to an embodiment of the present disclosure, carbon capture modification data is classified based on a clustering algorithm to obtain multiple category data sets, including: classifying carbon capture modification data based on a clustering algorithm to obtain multiple data sets to be determined; and determining a category data set from the multiple data sets to be determined based on the correlation between the data to be determined in the multiple data sets to be determined and the capture amount.

[0067] According to the embodiments of the present disclosure, due to the diverse nature of carbon capture retrofit data, to improve data quality, the data can be first classified using a clustering algorithm to generate multiple datasets to be determined. Then, based on the carbon capture retrofit indicator (capture capacity), high-quality category datasets can be screened from the multiple datasets to improve the accuracy of unit selection for carbon capture retrofits.

[0068] For example, the first-level categories include unit condition, regional production-side, and regional grid-side. Second-level categories within the unit condition category include remaining service life, installed capacity, unit technology type, and utilization hours. Second-level categories within the regional production-side category include regional coal-fired power generation carbon emissions and regional power generation carbon emission factors. Second-level categories within the regional grid-side category include power sector sensitivity coefficient, power sector industry sensitivity coefficient, and net outbound carbon emissions.

[0069] The remaining service life of the unit is calculated and quantified based on the actual year of commissioning of the unit. The longer the remaining service life of the unit, the more conducive it is to transformation.

[0070] (3);

[0071] R represents the remaining service life; L represents the design life on the unit nameplate; U represents the years of use; F represents the cumulative number of failures; and T represents the expected number of failures within the design life.

[0072] The utilization hours are based on the actual utilization hours of coal-fired power units.

[0073] (4);

[0074] H represents utilization hours; E represents actual power generation (unit: megawatt-hour, MWh); N represents the rated capacity of the equipment (unit: megawatt, MW).

[0075] The carbon emission factor of each region's power generation directly reflects the carbon emission intensity per unit of power generation. The larger the value, the higher the emission reduction potential. The calculation formula for the regional power generation carbon emission factor is:

[0076] (5);

[0077] represents the carbon emission factor of power generation in region k; n represents the total number of types of power generation energy. represents the carbon emission coefficient of the i-th power generation energy in region k; It represents the power generation of the i-th power generation energy in region k.

[0078] The calculation formula for carbon emissions from coal-fired power generation in each region is:

[0079] (6);

[0080] represents the carbon emissions from coal-fired power generation in region k; c represents the carbon content of raw coal; represents the oxidation rate of raw coal (dimensionless, usually taken as 0.98); represents the molecular weight conversion coefficient of carbon (C) to carbon dioxide (CO2) (fixed value); H represents the lower calorific value of raw coal (unit: megajoule / kilogram, MJ / kg); G k Represents the raw coal consumption in area k.

[0081] The sensitivity coefficient of each region's power sector is a key indicator of the power industry's sensitivity to changes in demand from other sectors in the economic system. It measures the degree to which the power sector responds to an increase in final consumption by one unit across all economic sectors, specifically the amount of output it needs to provide for production in other sectors. A high sensitivity coefficient indicates that the power sector is more sensitive to changes in demand from other sectors and can effectively stimulate production and development in those sectors. The formula for calculating the sensitivity coefficient of each region's power sector is:

[0082] (7);

[0083] S P represents the sensitivity coefficient of the power sector in each region; b gf is through The calculated elements in the complete consumption coefficient matrix B (I is the identity matrix, A is the direct consumption coefficient matrix); P represents the power sector; n is the total number of sectors.

[0084] The industrial sensitivity coefficient of the power sector in each region comprehensively considers the proportion of the power sector's output value and the contribution of unit output value to the economy, identifies the position of the power sector in the economic system, and evaluates the impact of cross-regional power transmission on regional economic stability.

[0085] (8);

[0086] represents the industrial sensitivity coefficient of the power sector in each region; is the sensitivity coefficient of the power sector (P), is the proportion of the initial investment in the power sector to the total initial investment in the economy.

[0087] The net carbon emissions D3 of each region can quantify the transfer effect of inter-regional power interaction on carbon emissions, avoiding the underestimation of local emission reduction responsibilities due to power outflow. The calculation process is as follows:

[0088] (9);

[0089] Each region net transfers out carbon emissions; The amount of electricity transmitted from this area to other areas; The amount of electricity imported into the province from other areas; Carbon emission factors for regional power grids; is the amount of electricity transferred from the qth area, is the regional emission factor.

[0090] According to the embodiments of the present disclosure, by netting out carbon emissions, tracking the carbon emission transfer caused by power transmission, clarifying the actual carbon emission responsibility of each region due to power interaction, and avoiding the deviation in the allocation of emission reduction responsibilities caused by relying solely on local unit carbon emission data. The relevant technology does not quantify the transfer effect of inter-provincial power transmission on carbon emissions, resulting in a disconnect between planning results and actual emission reduction needs. After identifying the carbon input area, it can be promoted to achieve coordinated optimization of regional carbon neutrality goals by increasing the proportion of local renewable energy or cooperating with the output area to reduce emissions.

[0091] According to an embodiment of the present disclosure, the triggering condition includes at least one of the following: receiving an event update request related to carbon capture modification, and the update duration of carbon capture modification data reaching a preset duration threshold.

[0092] According to embodiments of the present disclosure, when carbon capture retrofit data is updated due to time or due to adjustments to carbon capture retrofit regulations, the target evaluation weights for various second-level categories need to be updated. For example, if the second-level category is carbon emissions, when the carbon emissions data is updated, the data to be evaluated before the update is historical carbon emissions, and after the update, the data to be evaluated is current carbon emissions.

[0093] For example, a time update means that the update time of the carbon capture modification data reaches a preset time threshold, which can be set according to actual needs. When a rule adjustment for carbon capture modification is detected, an event update request related to the carbon capture modification can be sent to the server.

[0094] According to an embodiment of the present disclosure, the target evaluation weights of the various second-level categories are determined based on the degree of difference, including: determining the historical weight attenuation factor of the second-level category based on the degree of difference and the adjustment factor; obtaining the target evaluation weight based on the historical weight attenuation factor, the first evaluation weight of the data to be evaluated before the update, and the second evaluation weight of the data to be evaluated after the update.

[0095] According to an embodiment of the present disclosure, the first evaluation weight of the data to be evaluated before updating and the second evaluation weight of the data to be evaluated after updating of each second-level category are calculated by an entropy weight method.

[0096] For example, there are n samples and m second-level categories of data to be evaluated before updating , forming the original data matrix X.

[0097] (10);

[0098] In order to eliminate the impact of different indicator dimensions and orders of magnitude, it is necessary to standardize the indicators to be evaluated before updating.

[0099] For positive indicators (indicators with larger values, better), use the formula:

[0100] (11);

[0101] For negative indicators (indicators with smaller values, the better), use the formula:

[0102] (12);

[0103] in It is the data to be evaluated before updating. It is the standardized data to be evaluated before the update and are the maximum and minimum values ​​of the data to be evaluated before the jth update.

[0104] Calculate the proportion of the i-th sample under the data to be evaluated before the j-th update .

[0105] (13);

[0106] represents the proportion of the i-th sample in the data to be evaluated before the j-th update, and

[0107] Calculate the entropy of the data to be evaluated before the jth update .

[0108] ;

[0109] Entropy It reflects the degree of information disorder of the data to be evaluated before the j-th item is updated. The larger the entropy value, the higher the degree of disorder of the indicator, and the smaller the role of the data to be evaluated before the update in the comprehensive evaluation.

[0110] Calculate the first evaluation weight of the data to be evaluated before the jth update .

[0111] (15);

[0112] The entropy weight of the data to be evaluated before each update, and . It reflects the relative importance of the data to be evaluated before the update. The larger the coefficient of difference, the greater the degree of variation of the data to be evaluated, and the greater the impact on the evaluation results.

[0113] It should be noted that the second evaluation weight of the updated data to be evaluated is calculated based on the entropy weight method.

[0114] According to an embodiment of the present disclosure, the target evaluation weights are regularly updated through a dynamic optimization mechanism to ensure adaptation to the latest data and the rule adjustment requirements for carbon capture transformation.

[0115] Based on the entropy weight method, a historical weight attenuation factor is introduced to dynamically balance the impact of historical data and new data.

[0116] (16);

[0117] represents the target evaluation weight of the 𝑗th item of data to be evaluated after the 𝑡th update; represents the first evaluation weight of the data to be evaluated before the 𝑡−1th update; represents the second evaluation weight calculated based on the updated data to be evaluated by the entropy weight method; Represents the historical weight decay factor , representing the retention ratio of historical weights.

[0118] When the update time of carbon capture transformation data reaches the preset time threshold, the updated data to be evaluated will be input into the entropy weight method to recalculate , update the target evaluation weight according to the formula, and retain some historical weights to smooth the mutation.

[0119] Upon receiving an event update request related to carbon capture modification, the target evaluation weight is immediately recalculated and the target evaluation weight is adjusted. Events related to carbon capture retrofits could include adjustments to carbon emission regulations (such as tightening carbon quotas) and major changes to regional power grid structures (such as the commissioning of ultra-high voltage transmission lines).

[0120] pass The value can flexibly adjust the weight ratio of historical data and new data to ensure the stability and adaptability of weight distribution. Monitor the difference between the data to be evaluated before the update and the data to be evaluated after the update (such as the change range of the indicator value). If the change is large, reduce the to adapt to new data faster; otherwise, maintain a higher To maintain stability.

[0121] Adaptively calculate historical weight decay factors based on data volatility .

[0122] ;

[0123] Indicates the difference between the data to be evaluated before the update and the data to be evaluated after the update. The formula for the difference is as follows:

[0124] (18);

[0125] Represents the regulatory factor , used to control the attenuation strength.

[0126] According to an embodiment of the present disclosure, the evaluation result of the unit is determined according to the data to be evaluated and the target evaluation weights of each of the multiple second-level categories, including: based on the evaluation type of the data to be evaluated, determining the evaluation sub-results of the first-level category according to the data to be evaluated and the target evaluation weights of each of the multiple second-level categories; and determining the evaluation result of the unit according to the multiple first-level evaluation sub-results.

[0127] Evaluation results of unit i , which can reflect the suitability of carbon capture modification for unit i, The larger the value, the more suitable the unit i is for carbon capture modification. If the first-level categories include B, C, and D respectively. is the first-level evaluation sub-result of unit i in the first-level category B; is the first-level evaluation sub-result of unit i in the first-level category C; It is the first-level evaluation sub-result of unit i in the first-level category D.

[0128] (19);

[0129] Data to be evaluated under the first level category B The target evaluation weight is There are a total of uB second-level categories of data to be evaluated under the first-level category. .

[0130] (20);

[0131] Data to be evaluated under the first level category C The target evaluation weight is There are a total of uC second-level categories of data to be evaluated under the first-level category. .

[0132] (twenty one);

[0133] Data to be evaluated under the first level category D The target evaluation weight is There are uD second-level categories of data to be evaluated under the first-level category. .

[0134] (twenty two)

[0135] According to an embodiment of the present disclosure, based on the evaluation type of the data to be evaluated, the evaluation sub-results of the first-level category are determined according to the data to be evaluated and the target evaluation weights of each of the multiple second-level categories, including: when the evaluation type is a quantitative evaluation type, the evaluation sub-results of the first-level category are determined according to the data to be evaluated and the target evaluation weights of each of the multiple second-level categories; when the evaluation type is a classified evaluation type, the evaluation sub-results of the first-level category are determined according to the numerical range to which the data to be evaluated belongs.

[0136] For example, the evaluation type of the data to be evaluated for each of the second-level categories is shown in Table 1.

[0137] Table 1

[0138]

[0139] The evaluation type can be quantitative evaluation, that is, calculating and quantifying to determine the value, or it can be classification evaluation, that is, assigning classification values.

[0140] The quantitative assessment includes the remaining service life of the units, utilization hours, carbon emission factors of power generation on the production side, carbon emissions of coal-fired power generation, and the sensitivity coefficient of the power sector on the grid side, industry sensitivity coefficient, and net carbon emissions.

[0141] The classification assessment includes installed capacity and unit type.

[0142] For example, as shown in Table 2, the classification and assignment of unit capacity are divided into four categories, including <300MW (megawatt), >=300MW (mainly including 300MW, 330MW and 350MW), >=600MW (mainly including 600MW and 660MW) and >=1000MW.

[0143] Table 2

[0144]

[0145] As shown in Table 3, the classification and assignment of unit types are divided into four categories, including ultra-supercritical, supercritical, and subcritical. The efficiency of the four types of units is ranked from high to low, with scores ranging from 3 to 1 point.

[0146] Table 3

[0147]

[0148] It should be noted that when the evaluation type is a classified evaluation type, not only the evaluation sub-results of the first-level category are determined based on the numerical range of the data to be evaluated, but also the evaluation sub-results of the unit type can be determined based on the unit type represented by the data to be evaluated.

[0149] Figure 3 A flow chart schematically illustrates a method for selecting units for carbon capture retrofitting in a coal-fired power plant according to another embodiment of the present disclosure.

[0150] like Figure 3 As shown, determining the target evaluation weights of the respective second-level categories according to the difference includes steps S310 to S370.

[0151] In operation S310 , the carbon capture transformation data of the coal-fired power plant is classified based on a clustering algorithm to obtain a plurality of first-level category data sets to be evaluated for the units.

[0152] In operation S320 , the difference between the data to be evaluated before the update and the data to be evaluated after the update in the plurality of subsets of data to be evaluated is calculated based on the update information.

[0153] In operation S330 , a historical weight decay factor of the second-level category is determined based on the difference and the adjustment factor.

[0154] In operation S340 , a target evaluation weight is obtained according to the historical weight attenuation factor, the first evaluation weight of the data to be evaluated before the update, and the second evaluation weight of the data to be evaluated after the update.

[0155] In operation S350 , based on the evaluation type of the data to be evaluated, evaluation sub-results of the first-level categories are determined according to the data to be evaluated and the target evaluation weights of the respective second-level categories.

[0156] In operation S360 , an evaluation result of the fleet is determined based on the plurality of first-level evaluation sub-results.

[0157] In operation S370 , a target unit is determined from the plurality of units based on the evaluation results and the respective capturable capacities of the plurality of units.

[0158] According to an embodiment of the present disclosure, a target unit is determined from a plurality of units based on respective evaluation results and respective capturable quantities of the plurality of units, including: determining a plurality of to-be-determined units that meet preset capturable quantity conditions based on respective capturable quantities of the plurality of units; and determining a target unit that meets the preset evaluation result conditions from the plurality of to-be-determined units.

[0159] CE i is the CO2 emissions of coal-fired power plant i. The preset capture capacity condition can be that the total CO2 emissions of the n units to be determined are greater than the preset capture capacity M. The preset capture capacity M can be the total emission reduction required.

[0160] (twenty three)

[0161] The evaluation results of the plurality of units to be determined may be sorted, and the preset evaluation result may be that the sum of the evaluation results of the n units to be determined is greater than the evaluation results of other combinations of units to be determined.

[0162] According to the embodiments of the present disclosure, a multi-level evaluation index system is constructed to comprehensively quantify the combined impact of unit and regional characteristics. By combining unit attributes, regional production side, and grid side characteristics, the limitation of related technologies that rely only on single-dimensional indicators is overcome. This solves the problem of the one-sidedness of evaluation dimensions in existing technologies and can fully reflect the correlation and impact of unit transformation on regional power systems and carbon emission dynamics, ensuring that planning results are closely aligned with actual emission reduction needs, and improving the scientificity and accuracy of screening.

[0163] According to the embodiments of the present disclosure, net outbound carbon emissions from the power grid are introduced to quantify the dynamic correlation of regional carbon emissions. A new "net outbound carbon emissions" indicator is added to the regional power grid-side indicators. By calculating the difference in carbon emissions between regional power outflows and inflows, the impact of cross-regional power transmission on regional carbon emission responsibilities is dynamically reflected. This solves the problem of ignoring the indirect impact of power interaction, avoids the distortion of emission reduction responsibility allocation due to power outflow, ensures that carbon capture and transformation plans are accurately matched with regional carbon neutrality goals, and improves the global optimization capability of emission reduction benefits.

[0164] According to the embodiments of the present disclosure, emission reduction targets and available capture capacity are dynamically linked to achieve path optimization. By combining unit evaluation results with available capture capacity calculations and the overall emission reduction target, priority units for transformation are dynamically selected. This overcomes the disconnect between planning results and actual emission reduction targets. Dynamic matching optimizes resource allocation, maximizes emission reduction benefits within a limited cost, and helps efficiently achieve regional carbon neutrality goals.

[0165] According to the embodiments of the present disclosure, by improving the entropy weight method and the historical weight attenuation factor, dynamic adjustment of indicator weights is achieved, thereby improving the adaptability of planning results to data changes and adjustments to carbon capture transformation rules.

[0166] Figure 4 The structural block diagram of the unit selection device for carbon capture modification of a coal-fired power plant according to an embodiment of the present disclosure is schematically shown.

[0167] like Figure 4 As shown, the unit selection device 400 for carbon capture retrofitting in a coal-fired power plant of this embodiment includes a classification module 410 , a calculation module 420 , a first determination module 430 , a second determination module 440 and a third determination module 450 .

[0168] Classification module 410 is configured to classify carbon capture retrofit data for coal-fired power plants using a clustering algorithm to obtain a dataset of units to be evaluated, each of which is classified into multiple first-level categories. A coal-fired power plant comprises multiple units that utilize heat energy generated by coal combustion to generate electricity. The dataset to be evaluated includes multiple subsets of data to be evaluated, each of which includes multiple data sets at different times. In one embodiment, classification module 410 can be configured to perform operation S210 described above, and will not be further described here.

[0169] Calculation module 420 is configured to calculate the difference between the pre-update and post-update data to be evaluated in the various subsets of data to be evaluated based on the update information. The update information is obtained from a data source based on a trigger condition and is used to update the subsets of data to be evaluated. In one embodiment, calculation module 420 may be configured to perform operation S220 described above, which will not be further described herein.

[0170] The first determination module 430 is used to determine the target evaluation weights of the plurality of second-level categories according to the difference. In one embodiment, the first determination module 430 can be used to perform the operation S230 described above, which will not be described in detail here.

[0171] The second determination module 440 is used to determine the evaluation result of the unit according to the to-be-evaluated data and target evaluation weights of the various second-level categories. In one embodiment, the second determination module 440 can be used to perform the operation S240 described above, which will not be repeated here.

[0172] The third determining module 450 is used to determine a target unit from the plurality of units based on the evaluation results and the respective capacitive quantities of the plurality of units. In one embodiment, the third determining module 450 may be used to perform the operation S250 described above, which will not be described in detail here.

[0173] According to an embodiment of the present disclosure, classification module 410 includes a classification submodule and a first determination submodule. The classification submodule is configured to classify the carbon capture retrofit data based on a clustering algorithm to obtain multiple category data sets. The first determination submodule is configured to determine a category data subset within the category data set that meets a preset relevance condition as a subset of the data to be evaluated within the dataset to be evaluated.

[0174] According to an embodiment of the present disclosure, the classification submodule includes a classification unit and a first determination unit. The classification unit is configured to classify the carbon capture modification data based on a clustering algorithm to obtain multiple data sets to be determined; the first determination unit is configured to determine a category data set from the multiple data sets to be determined based on the correlation between the data sets to be determined and the amount of carbon capture available.

[0175] According to an embodiment of the present disclosure, the preset correlation condition is that the correlation between the category data in the category data subset and the captureable quantity is greater than a preset correlation threshold.

[0176] According to an embodiment of the present disclosure, the triggering condition includes at least one of the following: receiving an event update request related to carbon capture modification, and the update duration of carbon capture modification data reaching a preset duration threshold.

[0177] According to an embodiment of the present disclosure, the first determination module includes a second determination submodule and an acquisition submodule. The second determination submodule is configured to determine a historical weight attenuation factor for the second-level category based on the difference and the adjustment factor; the acquisition submodule is configured to obtain a target evaluation weight based on the historical weight attenuation factor, the first evaluation weight of the data to be evaluated before the update, and the second evaluation weight of the data to be evaluated after the update.

[0178] According to an embodiment of the present disclosure, the second determination module includes a third determination submodule and a fourth determination submodule. The third determination submodule is configured to determine the evaluation subresults for the first-level categories based on the evaluation type of the data to be evaluated, the data to be evaluated, and the target evaluation weights for each of the multiple second-level categories. The fourth determination submodule is configured to determine the evaluation result for the unit based on the multiple first-level evaluation subresults.

[0179] According to an embodiment of the present disclosure, the third determination submodule includes a second determination unit and a third determination unit. The second determination unit is used to determine the evaluation sub-results of the first-level categories based on the data to be evaluated and the target evaluation weights of the respective second-level categories when the evaluation type is a quantitative evaluation type; the third determination unit is used to determine the evaluation sub-results of the first-level categories based on the numerical range to which the data to be evaluated belongs when the evaluation type is a categorical evaluation type.

[0180] According to an embodiment of the present disclosure, the third determination module includes: determining multiple units to be determined that meet preset capture capacity conditions based on the respective capture capacity of multiple units; and determining a target unit that meets preset evaluation result conditions from the multiple units to be determined.

[0181] According to embodiments of the present disclosure, any multiple modules among the classification module 410, calculation module 420, first determination module 430, second determination module 440, and third determination module 450 may be combined into a single module, or any one of these modules may be split into multiple modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of other modules and implemented in a single module. According to embodiments of the present disclosure, at least one of the classification module 410, calculation module 420, first determination module 430, second determination module 440, and third determination module 450 may be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application-specific integrated circuit (ASIC), or may be implemented in hardware or firmware through any other reasonable means of circuit integration or packaging, or may be implemented in any one of the three implementation methods of software, hardware, and firmware, or any appropriate combination of any of these. Alternatively, at least one of the classification module 410 , the calculation module 420 , the first determination module 430 , the second determination module 440 , and the third determination module 450 may be at least partially implemented as a computer program module, which may perform corresponding functions when executed.

[0182] Figure 5 A block diagram of an electronic device for implementing a unit selection method for carbon capture modification of a coal-fired power plant according to an embodiment of the present disclosure is schematically shown.

[0183] like Figure 5As shown, the electronic device 500 according to an embodiment of the present disclosure includes a processor 501, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 502 or a program loaded from a storage unit 508 into a random access memory (RAM) 503. The processor 501 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 501 may also include onboard memory for caching purposes. The processor 501 may include a single processing unit or multiple processing units for performing different actions of the method flow according to the embodiment of the present disclosure.

[0184] Various programs and data required for the operation of the electronic device 500 are stored in the RAM 503. The processor 501, ROM 502, and RAM 503 are connected to each other via a bus 504. The processor 501 executes the various operations of the method flow according to the embodiment of the present disclosure by executing the programs in the ROM 502 and / or RAM 503. It should be noted that the programs may also be stored in one or more memories other than the ROM 502 and RAM 503. The processor 501 may also execute the various operations of the method flow according to the embodiment of the present disclosure by executing the programs stored in the one or more memories.

[0185] According to an embodiment of the present disclosure, electronic device 500 may further include an input / output (I / O) interface 505, which is also connected to bus 504. Electronic device 500 may also include one or more of the following components connected to I / O interface 505: an input section 506 including a keyboard, mouse, etc.; an output section 507 including devices such as a cathode ray tube (CRT), liquid crystal display (LCD), and speakers; a storage section 508 including a hard disk; and a communication section 509 including a network interface card such as a LAN card or modem. Communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to I / O interface 505 as needed. Removable media 511, such as a magnetic disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed in drive 510 as needed, so that computer programs read from the removable media can be installed into storage section 508 as needed.

[0186] The present disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments, or may exist independently and not be incorporated into the device / apparatus / system. The computer-readable storage medium carries one or more programs, and when executed, implements the method according to the embodiments of the present disclosure.

[0187] According to an embodiment of the present disclosure, a computer-readable storage medium may be a non-volatile computer-readable storage medium, and may include, for example, but not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to an embodiment of the present disclosure, a computer-readable storage medium may include the ROM 502 and / or RAM 503 described above, and / or one or more memories other than ROM 502 and RAM 503.

[0188] Embodiments of the present disclosure also include a computer program product comprising a computer program containing program code for executing the method shown in the flowchart. When the computer program product is executed in a computer system, the program code causes the computer system to implement the unit selection method for carbon capture retrofitting in coal-fired power plants provided in the embodiments of the present disclosure.

[0189] The computer program executes the above functions defined in the system / device of the embodiment of the present disclosure when the computer program is executed by the processor 501. According to the embodiment of the present disclosure, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0190] In one embodiment, the computer program may be stored on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may be transmitted and distributed in the form of a signal on a network medium, downloaded and installed via the communication portion 509, and / or installed from a removable medium 511. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to wireless, wired, or any suitable combination thereof.

[0191] In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 509, and / or installed from a removable medium 511. When the computer program is executed by the processor 501, the above-described functions defined in the system of the embodiment of the present disclosure are performed. According to the embodiment of the present disclosure, the systems, devices, means, modules, units, etc. described above can be implemented by computer program modules.

[0192] According to an embodiment of the present disclosure, the program code for executing the computer program provided by the embodiment of the present disclosure can be written in any combination of one or more programming languages. Specifically, these computer programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, languages ​​such as Java, C++, Python, "C" or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect via the Internet).

[0193] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the above-mentioned module, program segment, or a part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0194] Those skilled in the art will appreciate that the features described in the various embodiments of the present disclosure may be combined and / or coupled in various ways, even if such combinations or couplings are not explicitly described in the present disclosure. In particular, the features described in the various embodiments of the present disclosure may be combined and / or coupled in various ways without departing from the spirit and teachings of the present disclosure. All such combinations and / or couplings fall within the scope of the present disclosure.

[0195] The above describes the embodiments of the present disclosure. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although each embodiment has been described separately above, this does not mean that the measures in each embodiment cannot be advantageously used in combination. Without departing from the scope of the present disclosure, those skilled in the art may make various substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims

1. A method for selecting units for carbon capture retrofitting in coal-fired power plants, characterized in that: The method comprises: Classifying the carbon capture transformation data of the coal-fired power plant based on a clustering algorithm to obtain a plurality of first-level category data sets to be evaluated for units, wherein the coal-fired power plant includes a plurality of units, each of which is configured to generate electricity using heat energy generated by coal combustion, the data sets to be evaluated including a plurality of second-level category data subsets to be evaluated, each of which includes a plurality of data to be evaluated at different times; Calculating the difference between the data to be evaluated before the update and the data to be evaluated after the update in the plurality of subsets of the data to be evaluated based on the update information, wherein the update information is obtained from the data source based on the trigger condition and is used to update the subsets of the data to be evaluated; determining target evaluation weights for each of the plurality of second-level categories based on the degree of difference; determining an evaluation result of the unit according to the to-be-evaluated data and the target evaluation weights of the respective plurality of second-level categories; A target unit is determined from among the plurality of units based on the evaluation results and the respective collectible amounts of the plurality of units.

2. The method according to claim 1, characterized in that The carbon capture transformation data of the coal-fired power plant is classified based on the clustering algorithm to obtain multiple first-level category data sets to be evaluated for the unit, including: Classifying the carbon capture transformation data based on the clustering algorithm to obtain multiple category data sets; A category data subset in the category data set that meets a preset correlation condition is determined as the data subset to be evaluated in the data set to be evaluated.

3. The method according to claim 2, characterized in that The carbon capture transformation data is classified based on the clustering algorithm to obtain multiple category data sets, including: Classifying the carbon capture transformation data based on the clustering algorithm to obtain multiple data sets to be determined; The category data set is determined from the plurality of data sets to be determined based on the correlation between the data to be determined in the plurality of data sets to be determined and the captureable amount.

4. The method according to claim 2, characterized in that The preset correlation condition is that the correlation between the category data in the category data subset and the captureable quantity is greater than a preset correlation threshold.

5. The method according to claim 1, wherein The triggering condition includes at least one of the following: receiving an event update request related to the carbon capture modification, and the update duration of the carbon capture modification data reaching a preset duration threshold.

6. The method according to claim 1, characterized in that Determining target evaluation weights of the plurality of second-level categories according to the difference includes: Determining a historical weight attenuation factor for the second-level category based on the difference and the adjustment factor; The target evaluation weight is obtained according to the historical weight attenuation factor, the first evaluation weight of the data to be evaluated before the update, and the second evaluation weight of the data to be evaluated after the update.

7. The method according to claim 1, characterized in that Determining the evaluation result of the unit according to the to-be-evaluated data and the target evaluation weights of the respective plurality of second-level categories includes: Determining, based on the evaluation type of the data to be evaluated and the target evaluation weights of the respective data to be evaluated and the target evaluation weights of the plurality of second-level categories, an evaluation sub-result of the first-level category; An evaluation result of the unit is determined according to a plurality of the first-level evaluation sub-results.

8. The method according to claim 7, characterized in that The determining of the evaluation sub-results of the first-level category based on the evaluation type of the data to be evaluated and the target evaluation weights of the respective data to be evaluated of the plurality of second-level categories includes: In the case where the evaluation type is a quantitative evaluation type, determining the evaluation sub-results of the first-level categories according to the to-be-evaluated data and the target evaluation weights of the respective plurality of second-level categories; In the case where the evaluation type is a classification evaluation type, the evaluation sub-result of the first-level category is determined according to the numerical range to which the data to be evaluated belongs.

9. The method according to claim 1, characterized in that The step of determining a target unit from the plurality of units according to the respective evaluation results and the respective capturable quantities of the plurality of units comprises: Determining a plurality of to-be-determined units that meet preset conditions for the amount of capture available for collection based on the respective amounts of capture available for collection of the plurality of units; The target unit that meets the preset evaluation result conditions is determined from the multiple units to be determined.

10. A unit selection device for carbon capture modification in coal-fired power plants, characterized in that: The device comprises: a classification module, configured to classify the carbon capture retrofit data of the coal-fired power plant based on a clustering algorithm to obtain a plurality of first-level category data sets to be evaluated for the units, wherein the coal-fired power plant includes a plurality of the units, each of which is configured to generate electricity using heat energy generated by coal combustion, the data sets to be evaluated including a plurality of second-level category data subsets to be evaluated, each of which includes a plurality of data to be evaluated at different times; a calculation module, configured to calculate, based on update information, differences between the data to be evaluated before and after the update in the plurality of subsets of the data to be evaluated, wherein the update information is obtained from a data source based on a trigger condition and is used to update the subsets of the data to be evaluated; A first determination module is configured to determine target evaluation weights of respective plurality of second-level categories according to the difference; a second determining module, configured to determine an evaluation result of the unit according to the to-be-evaluated data and the target evaluation weights of the respective plurality of second-level categories; The third determining module is configured to determine a target unit from the plurality of units according to the respective evaluation results and respective capturable quantities of the plurality of units.

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