Carbon Emission Factor Evaluation Method and System Based on Generator Set Analysis

Abstract

The present invention relates to the technical field of power optimization, and particularly to a carbon emission factor evaluation method and system based on generator set analysis, which can monitor the working state in real time, dynamically evaluate carbon emissions, quantitatively analyze fuel consumption efficiency, construct an evaluation index according to the model weight, flexibly make decisions on emission reduction measures, and differentially manage various types of generator sets; the method includes: collecting the working state data of the generator set at a preset acquisition time interval; arranging the collected working state data of the generator set in chronological order to form a carbon emission characteristic matrix; inputting the carbon emission characteristic matrix into a pre-constructed carbon emission factor evaluation model to obtain a unit carbon emission evaluation parameter that can reflect the carbon emission level of the generator set; obtaining the actual power generation and the corresponding total fuel consumption of the generator set within the same time window, and calculating the fuel consumption per unit power generation; setting an evaluation weight according to the model of the generator set.

Description

Technical Field

[0001] The present invention relates to the technical field of power optimization, and particularly to a carbon emission factor evaluation method and system based on generator set analysis. Background Art

[0002] The carbon emission factor (CEF) is a measurement index used to quantify the carbon dioxide (CO 2 ) or other greenhouse gas emissions corresponding to unit energy or unit activity volume during the production and use of specific energy or a certain activity. This concept is commonly used in fields such as environmental science, climate policy formulation, and corporate carbon footprint calculation.

[0003] With the increasing global awareness of environmental protection and the continuous promotion of international actions to mitigate climate change, the greenhouse gas emissions problem in the power industry has attracted wide attention. As the core equipment for energy production and conversion, the carbon emissions generated during the operation of generator sets are one of the main sources of global greenhouse gas emissions. Traditional carbon emission assessment methods usually rely on static emission factor estimation, while ignoring many variable factors during the actual operation of generator sets, such as unit load changes, combustion efficiency, fuel quality differences, equipment aging, etc. These factors will have a significant impact on actual carbon emissions.

[0004] Currently, there are generally problems of inaccurate assessment and lagging response in the carbon emission management of generator sets at home and abroad, and it is impossible to achieve real-time monitoring and accurate prediction of the carbon emission status of generator sets. Due to the lack of refined dynamic assessment means, when it is found that the carbon emissions exceed the standard, the generator sets have been in a high-emission state for a long time, missing the best opportunity to optimize the operation strategy and reduce carbon emissions. Summary of the Invention

[0005] To solve the above technical problems, the present invention provides a carbon emission factor evaluation method and system based on generator set analysis, which can monitor the working state in real time, dynamically evaluate carbon emissions, quantitatively analyze fuel consumption efficiency, construct an evaluation index according to the model weight, flexibly decide on emission reduction measures, and differentially manage various types of units.

[0006] In the first aspect, the present invention provides a carbon emission factor evaluation method based on generator set analysis, and the method includes:

[0007] Collect the working state data of the generator set at a preset acquisition time interval;

[0008] Arrange the collected working state data of the generator set in chronological order to form a carbon emission characteristic matrix;

[0009] Input the carbon emission characteristic matrix into a pre - constructed carbon emission factor evaluation model to obtain a unit carbon emission evaluation parameter that can reflect the carbon emission level of the generator set;

[0010] Obtain the actual power generation and the total corresponding fuel consumption of the generator set within the same time window, and calculate the fuel consumption per unit power generation;

[0011] According to the model of the generator set, set evaluation weights for the unit carbon emission evaluation parameter and the fuel consumption per unit power generation respectively;

[0012] According to the set evaluation weights, perform weighted calculation on the unit carbon emission evaluation parameter and the fuel consumption per unit power generation to generate a carbon emission factor evaluation index for the generator set;

[0013] Compare the obtained carbon emission factor evaluation index of the generator set with a preset standard value: If the evaluation index exceeds the preset standard value, it indicates that the carbon emission of the generator set is abnormal and energy - saving and emission - reduction measures need to be taken to optimize the operation; If the evaluation index does not exceed the preset standard value, it means that the carbon emission of the generator set is within the normal range and the current operating state can be maintained.

[0014] Furthermore, the working state data includes: the output power of the generator set, the load rate, the generator speed, the power supply frequency, the combustion chamber temperature and pressure, the intake air volume and air - fuel ratio, the fuel calorific value, the generator efficiency, the cooling system efficiency, the degree of wear and aging of mechanical components, the external temperature and humidity, and the altitude.

[0015] Furthermore, the method of arranging the collected working state data of the generator set in chronological order to form a carbon emission characteristic matrix includes:

[0016] Pre - process the collected working state data of the generator set, and organize the pre - processed working state data in chronological order. Each row represents a time point, and each column represents a working state data item.

[0017] Furthermore, the method for obtaining the carbon emission factor evaluation model includes:

[0018] Collect the historical operation data of the generator set to form a historical data set;

[0019] Based on the historical data set, construct a carbon emission characteristic matrix and annotate the carbon emission characteristic matrix in combination with the carbon emission evaluation parameter of the generator set;

[0020] Select a machine learning model as the basic framework of the carbon emission factor evaluation model;

[0021] Taking the carbon emission characteristic matrix as the input and the unit carbon emission evaluation parameter as the output, training the carbon emission factor evaluation model, and validating and optimizing the carbon emission factor evaluation model.

[0022] Furthermore, the calculation method of the fuel consumption per unit of electricity generation includes:

[0023] Monitoring the actual electricity generation within a set time window;

[0024] Statistical total fuel consumption within a set time window;

[0025] Dividing the actual electricity generation within the same set time window by the total fuel consumption to obtain the fuel consumption per unit of electricity generation.

[0026] Furthermore, the calculation formula for the carbon emission factor evaluation index of the generator set is:

[0027]

[0028] E is the generated carbon emission factor evaluation index of the generator set, P is the unit carbon emission evaluation parameter, w P is the weight of the unit carbon emission evaluation parameter P, FC is the fuel consumption per unit of electricity generation, w FC is the weight of the fuel consumption per unit of electricity generation FC.

[0029] Furthermore, the energy conservation and emission reduction measures include:

[0030] Adjusting the unit operation load curve to optimize the combustion efficiency;

[0031] Replacing or improving the combustion equipment;

[0032] Improving the fuel quality and using clean energy to replace part of the fossil fuel;

[0033] Real-time adjustment of the unit operation strategy;

[0034] Waste heat recovery and utilization;

[0035] Application of carbon capture and storage technology;

[0036] Coupling of energy storage and new energy.

[0037] On the other hand, the present application also provides a carbon emission factor evaluation system based on the analysis of the generator set, and the system includes:

[0038] A data acquisition module for obtaining various working state data of the generator set;

[0039] A data sorting and feature construction module for arranging the collected working state data in an orderly manner according to the time series to form a carbon emission characteristic matrix and sending it;

[0040] A carbon emission factor evaluation module, which is used to receive a carbon emission characteristic matrix, input the carbon emission characteristic matrix into a pre-stored carbon emission factor evaluation model, and output unit carbon emission evaluation parameters;

[0041] An energy efficiency statistics module, which is used to statistically calculate the actual power generation and the total corresponding fuel consumption of a generator set within a set time window;

[0042] A weight configuration module, which is used to respectively set evaluation weights for unit carbon emission evaluation parameters and fuel consumption per unit power generation;

[0043] A carbon emission evaluation index generation module, which is used to perform weighted calculation on unit carbon emission evaluation parameters and fuel consumption per unit power generation to generate a carbon emission factor evaluation index for the generator set;

[0044] An intelligent decision-making module, which is used to perform real-time comparison of the generated carbon emission factor evaluation index of the generator set with a preset standard value. If the evaluation index exceeds the preset standard value, it indicates that the carbon emission of the generator set is abnormal and energy-saving and emission-reduction measures need to be taken to optimize the operation; if the evaluation index does not exceed the preset standard value, it means that the carbon emission of the generator set is within the normal range and the current operation status can be maintained.

[0045] In a third aspect, the present application provides an electronic device, including a bus, a transceiver, a memory, a processor, and a computer program stored on the memory and executable on the processor. The transceiver, the memory, and the processor are connected through the bus, and when the computer program is executed by the processor, the steps in any one of the above methods are implemented.

[0046] In a fourth aspect, the present application further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps in any one of the above methods are implemented.

[0047] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0048] Real-time dynamic monitoring: By continuously collecting the working state data of the generator set at a preset acquisition time interval, real-time monitoring of the carbon emission status of the generator set is achieved, making up for the deficiency that the traditional static emission factor estimation method cannot effectively reflect the real-time operation status of the unit;

[0049] Accurate evaluation: By deeply analyzing the collected data, a carbon emission characteristic matrix is formed, and a core evaluation parameter that can accurately reflect the carbon emission level is extracted by using a carbon emission factor evaluation model, thereby improving the accuracy of carbon emission evaluation and considering the influence of factors such as unit load change, combustion efficiency, fuel quality, and equipment aging;

[0050] Quantitative management: By calculating the fuel consumption per unit of electricity generation and combining different evaluation weights set for different generator set models, a scientific carbon emission factor evaluation index is constructed, realizing the quantitative management and comparative analysis of carbon emissions;

[0051] Flexible decision-making: According to the comparison result between the carbon emission factor evaluation index of the generator set and the preset standard value, it can quickly determine whether the generator set needs to adjust its operation strategy to reduce carbon emissions, which helps the power plant manager to take effective energy-saving and emission-reduction measures in a timely manner, avoiding the problem of continuous high emissions caused by lagging response.

[0052] Differentiated treatment: According to the characteristics of different models of generator sets, different evaluation weights are set, making the evaluation more in line with the actual performance and emission characteristics of various units, which is conducive to formulating more refined and targeted environmental protection optimization plans. Description of the Drawings

[0053] Figure 1 is the flowchart of the carbon emission factor evaluation method based on generator set analysis of the present invention;

[0054] Figure 2 is the flowchart of step S3 in the embodiment;

[0055] Figure 3 is the structure diagram of the carbon emission factor evaluation system based on generator set analysis;

[0056] Figure 4 is the schematic diagram of the hardware of the carbon emission factor evaluation electronic device based on generator set analysis of the present invention;

[0057] Reference numerals: 30, electronic device; 301, display device; 302, memory; 303, processor; 304, bus. Detailed Embodiments

[0058] In the description of the present application, those skilled in the art should know that the present application can be implemented as a method, a device, an electronic device, and a computer-readable storage medium. Therefore, the present application can be specifically implemented in the following forms: complete hardware, complete software (including firmware, resident software, microcode, etc.), and a combination of hardware and software. In addition, in some embodiments, the present application can also be implemented in the form of a computer program product in one or more computer-readable storage media, which contains computer program code.

[0059] The above computer-readable storage medium may adopt any combination of one or more computer-readable storage media. Computer-readable storage media include: electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or components, or any combination thereof. More specific examples of computer-readable storage media include: portable computer disks, hard disks, random access memories, read-only memories, erasable programmable read-only memories, flash memories, optical fibers, compact disc read-only memories, optical storage devices, magnetic storage devices, or any combination thereof. In the present application, the computer-readable storage medium may be any tangible medium that contains or stores a program, and this program can be used by or in conjunction with an instruction execution system, device, or component.

[0060] In the technical solution of the present application, the acquisition, storage, use, processing, etc. of data all comply with the relevant provisions of national laws.

[0061] The present application describes the provided method, device, and electronic device through flowcharts and / or block diagrams.

[0062] It should be understood that each block of the flowchart and / or block diagram, as well as the combinations of blocks in the flowchart and / or block diagram, can be implemented by computer-readable program instructions. These computer-readable program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, or other programmable data processing devices, thereby producing a machine. These computer-readable program instructions are executed by a computer or other programmable data processing devices, resulting in a device that realizes the functions / operations specified in the blocks of the flowchart and / or block diagram.

[0063] These computer-readable program instructions can also be stored in a computer-readable storage medium that enables a computer or other programmable data processing device to work in a specific manner. In this way, the instructions stored in the computer-readable storage medium produce an instruction device product that includes the functions / operations specified in the blocks of the flowchart and / or block diagram.

[0064] The computer-readable program instructions can also be loaded onto a computer, other programmable data processing devices, or other devices, so that a series of operation steps are executed on the computer, other programmable data processing devices, or other devices, resulting in a computer-implemented process. Thus, the instructions executed on the computer or other programmable data processing devices can provide a process that realizes the functions / operations specified in the blocks of the flowchart and / or block diagram.

[0065] The present application will be described below with reference to the accompanying drawings in the present application.

[0066] Embodiment 1: As Figure 1 shown, the carbon emission factor assessment method based on generator set analysis of the present invention, the method includes:

[0067] S1. Collect the operating status data of the generator set at a preset acquisition time interval;

[0068] Specifically, step S1 includes the following content:

[0069] Through the sensor network system installed in the generator set, capture the operating status data of the unit in real time. The following categories and examples are included in the operating status data:

[0070] Generator output power: Reflects the actual power generation level of the unit at a certain moment;

[0071] Load rate: That is, the ratio of the current output power of the unit to its rated power, reflecting the operating load status of the unit;

[0072] Generator speed and power supply frequency: Corresponding to the synchronous speed of the alternator and the frequency of the power supply system, which is related to power quality;

[0073] Combustion chamber temperature and pressure: Reflects combustion efficiency. High temperature and high pressure usually mean better combustion effect, but may also lead to higher NOx emissions;

[0074] Intake air volume and air-fuel ratio (AFR): Affects combustion efficiency and emissions generation. Optimizing AFR helps reduce emissions of unburned hydrocarbons and CO;

[0075] Fuel calorific value: Considering the differences in fuel quality, the level of calorific value directly affects combustion efficiency and carbon emission intensity;

[0076] Generator efficiency: That is, the ratio of the effective power converted into electrical energy to the input mechanical power;

[0077] Cooling system efficiency: Includes cooling water temperature, oil temperature and radiator efficiency, which indirectly affects combustion efficiency and equipment life.

[0078] Degree of wear and aging of mechanical components: Such as the decline in the efficiency of steam turbine blades and the performance degradation of turbochargers. These factors will affect the overall power generation efficiency and potential additional energy consumption.

[0079] Ambient temperature and humidity: May affect generator efficiency and heat dissipation requirements;

[0080] Altitude: In some cases, altitude will affect atmospheric pressure and combustion characteristics.

[0081] S2. Arrange the collected operating status data of the generator set in chronological order to form a carbon emission characteristic matrix;

[0082] Specifically, step S2 includes the following content:

[0083] S21. After the collected working state data of the original generator set are pre - processed through cleaning, missing value filling, outlier detection and correction, etc., the effectiveness and accuracy of the data are ensured;

[0084] S22. Construct a feature matrix:

[0085] Organize the pre - processed working state data in chronological order. Each row represents a time point, and each column represents a working state data;

[0086] The structure of the carbon emission feature matrix is similar to a time - series data table, where the row index corresponds to the time stamp, and the column headers are the names of the characteristic variables that affect carbon emissions;

[0087] Each cell stores the measured or calculated value of the characteristic variable at the corresponding time point.

[0088] The carbon emission feature matrix constructed in the above way not only retains the time - series characteristics of the generator set operation state but also includes all state parameters related to carbon emissions. This provides a rich input data basis for establishing a carbon emission factor evaluation model using machine learning algorithms or other advanced analysis techniques in the follow - up. Such a dynamic evaluation method can more accurately reflect the real carbon emission situation of the generator set under different operation states, thus helping the power industry achieve real - time monitoring and effective control of carbon emissions.

[0089] As Figure 2 shown, S3. Input the carbon emission feature matrix into a pre - constructed carbon emission factor evaluation model to obtain a unit carbon emission evaluation parameter that can reflect the carbon emission level of the generator set;

[0090] Specifically, step S3 includes the following content:

[0091] The method for obtaining the carbon emission factor evaluation model includes:

[0092] S31. Data preparation and feature selection: Collect a large amount of historical operation data of generator sets, which should include various state parameters related to carbon emissions such as the change of unit load, combustion efficiency data, fuel types and their calorific values used in different time periods, and equipment aging status, etc.; Based on these data, construct the carbon emission feature matrix in step S2, and label the carbon emission feature matrix in combination with the carbon emission evaluation parameters of the generator set;

[0093] S32. Model construction: Select a machine learning model as the basic framework of the carbon emission factor evaluation model, such as a multiple linear regression model, a non - linear regression model, a support vector machine, a random forest, a deep learning network, etc.;

[0094] S33. Model Training and Validation: Using the historical dataset, taking the carbon emission feature matrix as the input and the unit carbon emission evaluation parameters as the output, train using the machine learning model selected in step S32. By adjusting the model parameters, optimize the model performance to achieve the goal of minimizing the prediction error.

[0095] S34. Verify the effectiveness and accuracy of the model. You can use methods such as cross-validation and independent test sets to test the generalization ability of the model, ensuring that the model not only performs well on the training set but also can make reliable predictions on unseen new data.

[0096] By constructing a carbon emission factor evaluation model, it is possible to comprehensively consider various influencing factors (such as unit load changes, combustion efficiency, fuel quality, equipment aging, etc.), achieve dynamic and refined evaluation of the carbon emissions of power generation units, make up for the deficiencies of traditional static emission factor estimation methods. The carbon emission factor evaluation model is based on real-time or historical working state data and can update the unit carbon emission evaluation parameters in real time, which helps to detect carbon emission anomalies in a timely manner, thus avoiding missing the best opportunity to optimize the operation strategy.

[0097] S4. Obtain the actual power generation and the corresponding total fuel consumption of the power generation unit within the same time window, and calculate the fuel consumption per unit of power generation.

[0098] S41. Monitoring of actual power generation within the set time window: In this stage, mainly through the power metering device installed on the power generation unit, record the electric energy produced by the power generation unit in the specified time window in real time or periodically, that is, the actual power generation (usually in kilowatt-hours kWh or megawatt-hours MWh).

[0099] S42. Statistics of the total fuel consumption within the set time window: At the same time, accurately measure the fuel consumption of the power generation unit within the same time window, including the total consumption of coal, natural gas, oil or other combustible substances. The unit may be tons, cubic meters or liters, etc., depending on the type of fuel used.

[0100] S43. Calculation of fuel consumption per unit of power generation: Divide the actual power generation within the same time window by the total fuel consumption to obtain the fuel consumption required per unit of power generation.

[0101] The fuel consumption per unit power generation obtained in step S4 can reflect the carbon emission intensity of the generator set under different operating conditions from the side. Since the amount of carbon dioxide released by fuel combustion is directly related to the carbon content and combustion efficiency of the fuel, a low fuel consumption per unit power generation means higher combustion efficiency and lower carbon emission potential. Combining this data with the unit carbon emission evaluation parameters obtained in step S3, through subsequent weight setting and weighted calculation, a comprehensive and dynamic evaluation index of the carbon emission factor of the generator set is finally generated to assist power plant managers in adjusting the operation strategy in a timely manner and effectively implementing energy conservation and emission reduction measures.

[0102] S5. According to the model of the generator set, set evaluation weights for the unit carbon emission evaluation parameters and the fuel consumption per unit power generation of the unit respectively;

[0103] Step S5 reflects the personalized consideration of the importance of different evaluation indicators in the evaluation method. Its main task is to assign corresponding weights to the two key indicators of the unit carbon emission evaluation parameters and the fuel consumption per unit power generation of the unit, so as to more accurately reflect the carbon emission status of a specific model of generator set under actual operating conditions. The following is the specific operation of this step:

[0104] Specifically, step S5 includes the following content:

[0105] S51. Consider the differences in unit models: Different models of generator sets have different degrees of impact on the environment due to differences in design, structure and working principle. For example, there are obvious differences in the impact of factors such as combustion efficiency, pollutant control technology and equipment aging on carbon emissions between coal-fired units and gas-fired units, and between new high-efficiency units and old units. Therefore, when setting evaluation weights, it is necessary to fully consider the model characteristics of the generator set to ensure the pertinence and fairness of the evaluation system;

[0106] S52. Weight assignment for unit carbon emission evaluation parameters: The unit carbon emission evaluation parameters of the unit usually cover multiple dimensions of data such as the unit load change rate, combustion efficiency change, and fuel quality fluctuation. For certain specific models of generator sets, one or several of these parameters may contribute more to the overall carbon emissions of the unit;

[0107] S53. Weight assignment for fuel consumption per unit power generation: The fuel consumption per unit power generation reflects the energy utilization efficiency of the generator set, and it is a key indicator directly affecting the carbon emission intensity. Different types and different ages of units have different energy efficiency characteristics, so appropriate weights also need to be given in the evaluation process. High-efficiency units may be given higher weights to reflect the importance of their energy-saving advantages for emission reduction;

[0108] S54. Comprehensive weight setting principle: The process of setting weights needs to be based on scientific methodology and measured data, combined with industry standards, expert experience, and historical operation data analysis to ensure that the weight distribution not only conforms to the actual situation but also can effectively guide practice; By reasonably weighting various indicators, an evaluation system for carbon emission factors of different types of generator sets can be constructed, thereby accurately guiding power plant operators to formulate optimized operation strategies and reduce the carbon emission level.

[0109] In this step, fully considering the differences between generator set models, setting evaluation weights according to the characteristics of different models helps to establish a highly targeted carbon emission evaluation system, avoiding the "one-size-fits-all" evaluation method and making the evaluation results more accurate and fair; It covers multiple core evaluation parameters affecting carbon emissions, including but not limited to the unit load change rate of the unit, combustion efficiency change, fuel quality fluctuation, etc., ensuring that the evaluation system can comprehensively reflect the actual carbon emission situation of the unit; Flexibly setting weights according to the characteristics and key performance indicators of various types of units can adapt to generator sets of various types, different ages, and different technical levels, which is conducive to the effective management of diverse units in the entire power industry; The weight setting follows scientific methodology, combined with industry standards, expert experience, and actual operation data, ensuring the reliability and effectiveness of the evaluation system; By weighting the key indicator of fuel consumption per unit of electricity generation, which directly affects the carbon emission intensity, it can encourage and guide power plant operators to improve energy utilization efficiency and adopt more advanced technologies and management measures to reduce carbon emissions; The constructed carbon emission factor evaluation system can not only evaluate the current carbon emission status of the unit but also provide specific guidance on optimized operation strategies for power plants, helping to promote the implementation of energy conservation and emission reduction work and having important significance for achieving low-carbon or even zero-carbon goals.

[0110] S6. According to the set evaluation weights, perform weighted calculations on the unit carbon emission evaluation parameters and fuel consumption per unit of electricity generation of the unit to generate an evaluation index for the carbon emission factor of the generator set;

[0111] The calculation formula for the evaluation index of the carbon emission factor of the generator set is:

[0112] E = w P ·P + w FC ·FC;

[0113] E is the generated evaluation index for the carbon emission factor of the generator set;

[0114] P is the unit carbon emission evaluation parameter, which reflects the carbon emission level of the generator set under a specific influencing factor (such as combustion efficiency, equipment aging degree, etc.);

[0115] w Pis the weight of the unit carbon emission evaluation parameter P of the unit, reflecting the importance of this parameter in the overall evaluation;

[0116] FC is the fuel consumption per unit of electricity generation, reflecting the energy utilization efficiency of the generator set;

[0117] w FC is the weight of the fuel consumption per unit of electricity generation FC, also reflecting its relative importance in the overall carbon emission evaluation.

[0118] S7. Compare the obtained evaluation index of the carbon emission factor of the generator set with the preset standard value: If the evaluation index exceeds the preset standard value, it indicates that the carbon emission of the generator set is abnormal and energy-saving and emission-reduction measures need to be taken to optimize the operation; If the evaluation index does not exceed the preset standard value, it means that the carbon emission of the generator set is within the normal range and the current operating state can be maintained;

[0119] Based on the operations in steps S1 to S6, the collection and collation of the real-time working state data of the generator set have been completed. The unit carbon emission evaluation parameter has been calculated through the carbon emission factor evaluation model, and the corresponding evaluation weights have been set in combination with the characteristics of the unit model. Finally, the evaluation index of the carbon emission factor of the generator set is obtained. This evaluation index is a quantified value, which comprehensively reflects the carbon emission level of the generator set under the current working conditions;

[0120] When step S7 is executed, the system will compare this calculated evaluation index of the carbon emission factor of the generator set with the preset standard value:

[0121] If the evaluation index exceeds the preset standard value: This means that the actual carbon emission of the generator set is higher than the specified environmental protection or efficiency standard, and there may be great potential for emission reduction; At this time, the power operation department can take a series of energy-saving and emission-reduction measures according to the evaluation results; For example, adjust the unit operation load curve to optimize the combustion efficiency, replace or improve the combustion equipment to reduce the additional carbon emission caused by incomplete combustion, or improve the fuel quality, or even consider using clean energy to replace part of the fossil fuel, and can also adopt methods such as waste heat recovery and utilization, carbon capture and storage technology application, and energy storage and new energy coupling; In addition, advanced optimization algorithms can be used to adjust the unit operation strategy in real time to minimize carbon emissions while meeting the power supply demand;

[0122] If the evaluation index does not exceed the preset standard value: it indicates that the current carbon emission level of the generator set is within a reasonable and controllable range, meeting the requirements of relevant environmental protection policies and industry specifications; in this case, the power plant can continue to maintain the current operating state while continuing to monitor and record relevant data for further verification and optimization of existing strategies; even so, potential optimization space should still be continuously concerned, and efforts should be made to gradually improve energy utilization efficiency and reduce unnecessary carbon emissions on the premise of not affecting the safe and stable power supply.

[0123] In this step, the dynamic and accurate management of the carbon emissions of the generator set is ensured, realizing the transformation from passive acceptance to active regulation, which helps the power industry better respond to global environmental protection requirements, actively fulfill emission reduction obligations, and promote sustainable development.

[0124] Embodiment 2: As Figure 3 shown, the carbon emission factor evaluation system based on generator set analysis of the present invention specifically includes the following modules;

[0125] The data acquisition module automatically obtains various key working state data of the generator set from the generator set at preset time intervals, including but not limited to real-time data such as generator load, combustion efficiency, fuel type and consumption, and equipment performance indicators;

[0126] The data sorting and feature construction module is responsible for arranging the collected working state data in an orderly manner according to the time series to form a carbon emission feature matrix and sending it. This matrix contains all key features reflecting the operating conditions of the generator set and possible factors affecting carbon emissions;

[0127] The carbon emission factor evaluation module is pre-trained and deployed to receive the carbon emission feature matrix as input and input the carbon emission feature matrix into a pre-stored carbon emission factor evaluation model to output the carbon emission evaluation parameters of the unit;

[0128] The energy efficiency statistics module is used to statistically calculate the actual power generation and the total fuel consumption corresponding thereto within a set time window of the generator set, and then calculate an important indicator of fuel consumption per unit power generation;

[0129] The weight configuration module sets scientific and reasonable evaluation weights for the carbon emission evaluation parameters of the unit and the fuel consumption per unit power generation respectively according to the technical specifications, design characteristics and historical operation data of different models of generator sets;

[0130] The carbon emission evaluation index generation module fuses and calculates the carbon emission evaluation parameters of the unit and the fuel consumption per unit power generation according to the above-set evaluation weights to generate a carbon emission factor evaluation index of the generator set, which comprehensively reflects the overall performance of the unit's carbon emissions in the current state;

[0131] The intelligent decision-making module compares the generated carbon emission factor evaluation index of the generator set with the preset standard value in real time. If the evaluation index exceeds the preset standard value, it indicates that the carbon emission of the generator set is abnormal and energy-saving and emission-reduction measures need to be taken to optimize the operation. If the evaluation index does not exceed the preset standard value, it means that the carbon emission of the generator set is within the normal range, and the current operation status can be maintained. In this way, the system realizes the accurate monitoring and timely intervention of the carbon emission of the generator set, effectively promoting the realization of the green and low-carbon operation goal.

[0132] Through the automated and refined data collection and processing process, the system realizes the real-time monitoring and accurate evaluation of the carbon emission of the generator set. Based on model calculation and customized weight configuration, it generates the carbon emission factor evaluation index of the generator set, ensuring the comprehensive reflection and efficient management of the unit operation status. The system can intelligently identify abnormalities and guide the optimization of operation strategies, reduce carbon emissions, improve energy use efficiency, and strongly support the implementation and maintenance of the green and low-carbon operation goal of the power plant.

[0133] All the various change methods and specific embodiments of the carbon emission factor evaluation method based on generator set analysis in the foregoing Embodiment 1 are equally applicable to the carbon emission factor evaluation system based on generator set analysis in this embodiment. Through the foregoing detailed description of the carbon emission factor evaluation method based on generator set analysis, those skilled in the art can clearly know the implementation method of the carbon emission factor evaluation system based on generator set analysis in this embodiment. Therefore, for the sake of simplicity of the specification, it will not be elaborated herein.

[0134] In addition, the present application also provides an electronic device, including a bus, a transceiver, a memory, a processor, and a computer program stored on the memory and executable on the processor. The transceiver, the memory, and the processor are respectively connected through the bus. When the computer program is executed by the processor, it realizes each process of the method embodiment for controlling the output data and can achieve the same technical effect.

[0135] Figure 4 It is a schematic diagram of the hardware structure of the electronic device provided by the embodiment of the present invention. As Figure 4 shown, the electronic device 30 provided in this embodiment may include: the display device 301, the memory 302, and the processor 303 described in the foregoing embodiment; optionally, a bus 304 may also be included. Among them, the bus 304 is used to realize the connection between each component.

[0136] The memory 302 stores computer-executable instructions; the processor 303 executes the computer-executable instructions stored in the memory 302. Among them, the memory 302 and the processor 303 are electrically connected directly or indirectly to achieve data transmission or interaction. For example, these components can be electrically connected to each other through one or more communication buses or signal lines, such as being connected through the bus 304. The memory 302 stores computer-executable instructions for implementing the data access control method, including at least one software function module that can be stored in the memory 302 in the form of software or firmware. The processor 303 executes various functional applications and data processing by running the software programs and modules stored in the memory 302. The memory 302 can be, but is not limited to, a random access memory, a read-only memory, a programmable read-only memory, an erasable read-only memory, an electrically erasable read-only memory, etc. Among them, the memory 302 is used to store programs, and the processor 303 executes the programs after receiving the execution instructions. Further, the software programs and modules in the memory 302 may further include an operating system, which may include various software components and / or drivers for managing system tasks (such as memory management, storage device control, power management, etc.), and may communicate with various hardware or software components to provide a running environment for other software components. The processor 303 can be an integrated circuit chip with signal processing capabilities. The above-mentioned processor 303 can be a general-purpose processor, including a central processing unit, a network processor, etc. It can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. It can be understood that Figure 4 The structure of is only schematic, and it may further include more or fewer components than Figure 4 shown in, or have a different configuration from Figure 4 shown. Figure 4 Each component shown in can be implemented by hardware and / or software.

[0137] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the technical principle of the present invention, several improvements and modifications can be made, and these improvements and modifications should also be regarded as the protection scope of the present invention.

Claims

1. A carbon emission factor assessment method based on power generation unit analysis, characterized in that: The method comprises: Collect the working status data of the generator set according to the preset collection time interval; Arrange the collected working status data of the generator sets in chronological order to form a carbon emission characteristic matrix; Inputting the carbon emission characteristic matrix into a pre-built carbon emission factor assessment model to obtain a unit carbon emission evaluation parameter that can reflect the carbon emission level of the power generation unit; Obtain the actual power generation of the generator set in the same time window and the corresponding total fuel consumption, and calculate the fuel consumption per unit power generation; According to the model of the generating unit, the evaluation weights are set for the carbon emission evaluation parameters and the fuel consumption per unit of power generation; According to the set evaluation weights, the carbon emission evaluation parameters of the unit and the fuel consumption per unit of power generation are weighted and calculated to generate the carbon emission factor evaluation index of the generator unit; Compare the obtained carbon emission factor assessment index of the generator set with the preset standard value: if the assessment index exceeds the preset standard value, it means that the carbon emissions of the generator set are abnormal, and energy-saving and emission reduction measures need to be taken to optimize operation; if the assessment index does not exceed the preset standard value, it means that the carbon emissions of the generator set are within the normal range and the current operating status can be maintained.

2. The carbon emission factor evaluation method based on generator set analysis according to claim 1, characterized in that: The working status data include: generator set output power, load rate, generator speed, power supply frequency, combustion chamber temperature and pressure, intake volume and air-fuel ratio, fuel calorific value, generator efficiency, cooling system efficiency, mechanical parts wear and aging degree, external temperature and humidity, and altitude.

3. The carbon emission factor evaluation method based on power generation unit analysis according to claim 1, characterized in that: The method of arranging the collected working status data of the generator set in chronological order to form a carbon emission characteristic matrix includes: The collected working status data of the generator set are preprocessed, and the preprocessed working status data are organized in chronological order, with each row representing a time point and each column representing a working status data.

4. The carbon emission factor evaluation method based on power generation unit analysis according to claim 1, characterized in that: The method for obtaining the carbon emission factor assessment model includes: Collect historical operating data of generator sets to form historical data sets; Based on the historical data set, a carbon emission characteristic matrix is ​​constructed, and the carbon emission characteristic matrix is ​​annotated in combination with the carbon emission evaluation parameters of the power generation unit; Select the machine learning model as the basic framework of the carbon emission factor assessment model; Taking the carbon emission characteristic matrix as input and the unit carbon emission evaluation parameters as output, the carbon emission factor assessment model is trained, verified and optimized.

5. The carbon emission factor evaluation method based on power generation unit analysis according to claim 1, characterized in that: The calculation method of fuel consumption per unit of electricity generation includes: Monitor actual power generation within a set time window; Count the total fuel consumption within the set time window; The actual power generation within the same set time window is divided by the total fuel consumption to obtain the fuel consumption per unit power generation.

6. The carbon emission factor evaluation method based on power generation unit analysis according to claim 1, characterized in that: The calculation formula for the carbon emission factor assessment index of the generator set is: E=w P ·P+w FC ·FC; E is the generated carbon emission factor evaluation index of the generating unit, P is the carbon emission evaluation parameter of the unit, and w P is the weight of the unit carbon emission evaluation parameter P, FC is the fuel consumption per unit of power generation, and w FC is the weight of fuel consumption per unit of electricity generation FC.

7. The carbon emission factor evaluation method based on power generation unit analysis according to claim 1, characterized in that: Energy conservation and emission reduction measures include: Adjust the unit operating load curve to optimize combustion efficiency; Replacement or improvement of combustion equipment; Improve fuel quality and replace some fossil fuels with clean energy; Adjust unit operation strategy in real time; Waste heat recovery and utilization; Carbon capture and storage technology applications; Energy storage is coupled with new energy.

8. A carbon emission factor evaluation system based on power generation unit analysis, characterized in that: The system comprises: Data acquisition module, used to obtain various working status data of the generator set; The data sorting and feature construction module is used to arrange the collected working status data in order according to the time series, form a carbon emission feature matrix, and send it; A carbon emission factor evaluation module is used to receive a carbon emission characteristic matrix, input the carbon emission characteristic matrix into a pre-stored carbon emission factor evaluation model, and output the unit carbon emission evaluation parameters; Energy efficiency statistics module, used to count the actual power generation of the generator set within the set time window and the corresponding total fuel consumption; A weight configuration module is used to set evaluation weights for the unit carbon emission evaluation parameters and unit power generation fuel consumption respectively; The carbon emission assessment index generation module is used to perform weighted calculation on the carbon emission evaluation parameters of the unit and the fuel consumption per unit of power generation to generate the carbon emission factor assessment index of the generator unit; The intelligent decision-making module is used to compare the generated carbon emission factor evaluation index of the generator set with the preset standard value in real time. If the evaluation index exceeds the preset standard value, it means that the carbon emissions of the generator set are abnormal and energy-saving and emission reduction measures need to be taken to optimize operation; if the evaluation index does not exceed the preset standard value, it means that the carbon emissions of the generator set are within the normal range and the current operating status can be maintained.

9. An electronic device for carbon emission factor assessment based on generator set analysis, comprising a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected via the bus, characterized in that: When the computer program is executed by the processor, the steps in the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps in the method according to any one of claims 1 to 7 are implemented.

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