Universal carbon flow analysis method for energy flow network

Abstract

The invention provides a universal carbon flow analysis method for an energy flow network, and relates to the technical field of data processing, and the method comprises the steps: obtaining real-time operation data and historical operation data of a power network; extracting the real-time operation data and the historical operation data to obtain key features related to power flow distribution; calculating a dynamic influence factor according to the key features and real-time operation data of the power network; according to the dynamic influence factor, analyzing a power flow distribution condition in the power network to obtain an analysis result; and establishing a relation between the unit and the load to trace the source and the destination of the electric energy in the electric power system so as to obtain a tracking result. According to the method, the dynamic change of the power network can be more accurately captured, so that the accuracy of carbon flow analysis is improved.

Description

Technical Field

[0001] The invention relates to the technical field of data processing, and in particular to a universal carbon flow analysis method for an energy flow network. Background Art

[0002] In a large power company, the traditional carbon flow analysis method mainly estimates the carbon emissions of the power grid based on static annual or quarterly average data. This method assumes that the operation state of the power grid is constant and does not take into account the dynamic changes of power grid load, power output and power grid structure in actual operation.

[0003] However, in actual operation, the load and power output of the power grid often change due to factors such as weather, holidays, economic activities, etc. For example, in the hot summer, the extensive use of refrigeration equipment such as air conditioners will cause a significant increase in the load on the power grid; and during periods of abundant wind or solar resources, the power generation of renewable energy will also increase significantly.

[0004] For example, when using traditional methods for carbon flow analysis, the analysis results deviate greatly from the actual carbon emissions because these dynamic changes are not taken into account. Specifically, during the peak summer period, the power generation of thermal power plants increases due to the surge in air conditioning load, resulting in a significant increase in carbon emissions. However, the traditional method is based on static data and fails to accurately capture this change, thus underestimating the actual carbon emissions. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a universal carbon flow analysis method for energy flow networks, which can more accurately capture the dynamic changes of power networks, thereby improving the accuracy of carbon flow analysis.

[0006] In order to solve the above technical problems, the technical solution of the present invention is as follows: In a first aspect, a general carbon flow analysis method for an energy flow network is provided, the method comprising: Obtain real-time and historical operation data of the power network; Extract real-time and historical operation data to obtain key features related to power flow distribution; Calculate dynamic impact factors based on key features and real-time operation data of the power network; According to the dynamic influencing factors, the power flow distribution in the power network is analyzed to obtain the analysis results; Establish connections between units and loads to trace the source and destination of electric energy in the power system to obtain tracking results; Based on the analysis and tracking results, determine a carbon emission flow allocation matrix from units to loads; When there is network loss in the power network, the original lossy network is converted into an equivalent lossless network through equivalent transformation; According to the equivalent lossless network, the carbon emission flow allocation matrix is ​​modified to obtain a modified allocation matrix; According to the modified distribution matrix, the carbon flow rate obtained by the final distribution of load under the lossy network is calculated.

[0007] Furthermore, the real-time operation data and historical operation data are extracted to obtain key features related to the power flow distribution, including: Determine a fixed-size window according to the characteristics of the real-time operation data and historical operation data of the power network; Move the window from the starting point of the data sequence to the end point, calculate the average value of the data points in the window at each window position, and use the average value as the data value at the center of the window after filtering. By moving point by point and calculating the average value, a filtered data sequence is obtained; Performing a fast Fourier transform on the filtered data sequence, converting the filtered data sequence from the time domain to the frequency domain to obtain a Fourier transform result; storing the Fourier transform result as a complex number array, wherein each complex number represents the amplitude and phase of a frequency component; According to the characteristics of the power grid, identify the key characteristic frequencies related to the power flow distribution, including the fundamental frequency and its harmonic frequencies, and determine the complex numbers corresponding to the key characteristic frequencies in the Fourier transform results; For each key characteristic frequency, the corresponding complex number is extracted from the Fourier transform result. The amplitude of the key characteristic frequency is obtained by calculating the modulus of each complex number. The amplitude reflects the intensity of the key characteristic frequency component in the signal. The phase of the key characteristic frequency is obtained by calculating the argument of each complex number. The phase represents the time offset of the key characteristic frequency component.

[0008] Furthermore, based on the key features and real-time operation data of the power network, dynamic impact factors are calculated, including: Calculate the value of each sub-factor based on key characteristics, real-time operating data, and the physical characteristics of the power grid; According to the importance and influence of each sub-factor, the values ​​of each sub-factor are combined into a dynamic impact factor through weighting.

[0009] Furthermore, the sub-factors include a voltage stability sub-factor and a harmonic impact sub-factor.

[0010] Furthermore, the values ​​of each sub-factor are calculated based on key features, real-time operating data and the physical characteristics of the power grid, including: Obtain the voltage data of the power grid over a period of time, as well as the voltage value of each sampling point; Determine the reference voltage, square the difference between the voltage value of each sampling point and the reference voltage, calculate the average of the squares of the differences of all sampling points, take the square root of the average, and obtain the value of the voltage stability sub-factor; Analyze the current or voltage waveform of the power grid through fast Fourier transform to obtain the amplitude of each harmonic; Determine the fundamental wave amplitude, calculate the sum of the squares of all higher harmonic amplitudes, and divide the sum of the squares of the higher harmonic amplitudes by the square of the fundamental wave amplitude to obtain a calculation result; Take the square root of the calculated result and multiply it by 100 to get the percentage value of the harmonic impact sub-factor.

[0011] Furthermore, according to the importance and influence of each sub-factor, the values ​​of each sub-factor are combined into a dynamic impact factor through weighting, including: According to the key factors in power grid operation, the actual impact of voltage stability sub-factor and harmonic impact sub-factor on power grid performance and carbon emission flow is analyzed to obtain analysis results; Based on the analysis results, calculate the voltage stability index and harmonic impact index; Analyze the correlation between voltage stability index and harmonic impact index and benchmark index to obtain correlation analysis results; Based on the results of the correlation analysis, a 2×2 matrix is ​​constructed, in which each element represents the degree of influence of the corresponding factor on the benchmark indicator; Normalize each column of the matrix so that the sum of each column is 1, calculate the average value of each row, and use the average value as the weight value of the corresponding factor; The voltage stability sub-factor and the harmonic impact sub-factor are standardized so that the values ​​of each sub-factor are on the same scale and standardized data are obtained; Using the weight values ​​of the corresponding factors, the standardized data are weighted and summed to obtain the dynamic impact factor.

[0012] Furthermore, according to the dynamic influencing factors, the power flow distribution in the power network is analyzed to obtain analysis results, including: According to the value of dynamic impact factor, power flow calculation is performed, and the voltage distribution and power distribution of each node in the power network are determined through iterative calculation; Analyze the voltage and power distribution of each node in the power network, identify the key nodes and lines in the power grid, and obtain the key points of power flow congestion or voltage fluctuation; According to the key points of power flow congestion or voltage fluctuation, the power flow distribution at different time points is analyzed to obtain the analysis results.

[0013] In a second aspect, a universal carbon flow analysis system for an energy flow network includes: An acquisition module is used to acquire real-time operation data and historical operation data of the power network; extract the real-time operation data and historical operation data to obtain key features related to power flow distribution; calculate dynamic impact factors based on the key features and the real-time operation data of the power network; analyze the power flow distribution in the power network based on the dynamic impact factors to obtain analysis results; The processing module is used to establish a connection between the unit and the load to trace the source and destination of the electric energy in the power system to obtain the tracking result; according to the analysis results and the tracking results, a carbon emission flow distribution matrix from the unit to the load is determined; when there is a network loss in the power network, the original lossy network is converted into an equivalent lossless network by equivalent conversion; according to the equivalent lossless network, the carbon emission flow distribution matrix is ​​corrected to obtain a corrected distribution matrix; according to the corrected distribution matrix, the carbon flow rate obtained by the final distribution of the load under the lossy network is calculated.

[0014] According to a third aspect, a computing device includes: one or more processors; The storage device is used to store one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the method described.

[0015] In a fourth aspect, a computer-readable storage medium stores a program, and when the program is executed by a processor, the method described is implemented.

[0016] The above scheme of the present invention includes at least the following beneficial effects.

[0017] By acquiring the real-time and historical operation data of the power network and extracting key features related to the power flow distribution, this method can more accurately capture the dynamic changes of the power network, thereby improving the accuracy of carbon flow analysis. By calculating the dynamic impact factor, this method can reflect the changes in the power flow distribution in the power network in real time, further enhancing the real-time and dynamic adaptability of the analysis.

[0018] By establishing a connection between the unit and the load, this method can trace the source and destination of electric energy in the power system. The carbon emission flow allocation matrix determined according to the analysis results and tracking results can clearly show the carbon emission flow allocation from the unit to the load. When there is network loss in the power network, this method corrects the carbon emission flow allocation matrix through an equivalent lossless network, ensuring the accuracy of carbon flow rate calculation under lossy network conditions, and further improving the accuracy of carbon emission measurement. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a flow chart of a general carbon flow analysis method for an energy flow network provided by an embodiment of the present invention.

[0020] Figure 2 It is a schematic diagram of a universal carbon flow analysis system for an energy flow network provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0021] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0022] like Figure 1 As shown, an embodiment of the present invention provides a general carbon flow analysis method for an energy flow network, the method comprising the following steps: Step 11, obtaining real-time operation data and historical operation data of the power network; Step 12, extracting real-time operation data and historical operation data to obtain key features related to power flow distribution; Step 13, calculating the dynamic impact factor based on the key characteristics and the real-time operation data of the power network; Step 14, analyzing the power flow distribution in the power network according to the dynamic influencing factors to obtain analysis results; Step 15, establishing a connection between the unit and the load to trace the source and destination of the electric energy in the power system to obtain a tracking result; Step 16, determining a carbon emission flow allocation matrix from the unit to the load based on the analysis results and the tracking results; Step 17, when there is network loss in the power network, the original lossy network is converted into an equivalent lossless network by equivalent means; Step 18, modifying the carbon emission flow allocation matrix according to the equivalent lossless network to obtain a modified allocation matrix; Step 19, based on the modified allocation matrix, calculate the carbon flow rate obtained by the final load allocation under the lossy network.

[0023] In the embodiment of the present invention, step 11, the collection of real-time and historical data provides a rich data source for subsequent analysis, which helps to more accurately understand the operating status and changing trends of the power network. Step 12, through data extraction, the key characteristics of the power flow distribution in the power network can be accurately captured. Step 13, the calculation of the dynamic impact factor takes into account the real-time operating status of the power network, so that the analysis results are closer to the actual situation and the accuracy of the analysis is improved. Step 14, using the dynamic impact factor to analyze the power flow distribution can provide a deeper understanding of the operating status of the power network, providing strong support for optimizing network operation and reducing carbon emissions. Step 15, by tracing the source and destination of electric energy, the relationship between the components in the power system can be clarified. Step 16, the construction of the carbon emission flow allocation matrix helps to quantify the carbon emission responsibilities of each component in the power system. Step 17, by converting the lossy network into a lossless network, the analysis process can be simplified while maintaining the accuracy of the analysis. Step 18, the revised carbon emission flow allocation matrix is ​​closer to the actual operation of the power network, improving the accuracy and reliability of the analysis. Step 19, by calculating the carbon flow rate of the load under the lossy network, the carbon emission of the power network can be more accurately evaluated.

[0024] In another preferred embodiment of the present invention, the above step 11 may include: Determine the source of data, determine which devices and systems to collect data from, such as SCADA systems (supervisory control and data acquisition systems), EMS systems (energy management systems), smart meters, sensor networks, etc., and establish communication channels with administrators or suppliers of related systems and equipment to ensure smooth data acquisition; through real-time monitoring systems such as SCADA, regularly or continuously collect real-time data of the power network, which includes key operating parameters such as voltage, current, power factor, active and reactive power, frequency, etc.

[0025] Retrieve historical operating data from the power company's database or archive system. This data can cover a long period of time and is used to analyze long-term trends and seasonal changes in the power network, verify the integrity and accuracy of historical data, remove or correct outliers and missing data, and perform preliminary processing on the collected data, such as data cleaning, format conversion and standardization, identify and process outliers, noise and missing values ​​in the data to improve data quality.

[0026] In a preferred embodiment of the present invention, the above step 12 may include: Step 121, determining a fixed-size window according to the characteristics of the real-time operation data and the historical operation data of the power network; Step 122, moving the window from the starting point to the end point of the data sequence, calculating the average value of the data points in the window at each window position, and taking the average value as the data value at the center position of the window after filtering, and obtaining a filtered data sequence by moving point by point and calculating the average value; Step 123, performing fast Fourier transform on the filtered data sequence, converting the filtered data sequence from the time domain to the frequency domain to obtain a Fourier transform result; storing the Fourier transform result as a complex number array, wherein each complex number represents the amplitude and phase of a frequency component; Step 124, identifying key characteristic frequencies related to power flow distribution according to the power grid characteristics, including the fundamental frequency and its harmonic frequencies, and determining the complex numbers corresponding to the key characteristic frequencies in the Fourier transform results; Step 125, for each key characteristic frequency, extract the corresponding complex number from the Fourier transform result, and obtain the amplitude of the key characteristic frequency by calculating the modulus of each complex number, and the amplitude reflects the intensity of the key characteristic frequency component in the signal; obtain the phase of the key characteristic frequency by calculating the argument of each complex number, and the phase represents the time offset of the key characteristic frequency component.

[0027] In the embodiment of the present invention, this step realizes accurate extraction of key features related to power flow distribution and improves the accuracy of data analysis by finely processing the real-time operation data and historical operation data of the power network. Specifically, the noise and fluctuation in the data are reduced by window filtering, and the fast Fourier transform helps to more clearly identify and analyze the characteristics of the power grid signal in the frequency domain, so as to more accurately capture the dynamic changes and carbon emissions of the power grid.

[0028] The specific implementation process of each step includes: Step 121, select a suitable window size according to the sampling frequency and characteristics of the power network data. For example, if the data sampling frequency is 100 times per second, a window containing 1000 data points (i.e., 10 seconds of data) can be selected. Step 122, move the determined window from the starting point to the end point of the data sequence, calculate the average value of the data points in each window, and assign this average value to the data point at the center of the window, thereby obtaining a filtered smoothed data sequence. Step 123, perform Fourier transform processing on the filtered data sequence to convert the data from the time domain to the frequency domain. The Fourier transform result is an array of complex numbers, in which each complex number represents the amplitude and phase of a specific frequency. Step 124, according to the characteristics and operating experience of the power grid, determine the key characteristic frequencies that are closely related to the power flow distribution, such as the fundamental frequency (usually 50Hz or 60Hz) and its harmonic frequencies. Step 125, find the complex numbers corresponding to the key characteristic frequencies from the Fourier transform results, and calculate the modulus (amplitude) and argument (phase) of these complex numbers. The amplitude reflects the strength of the frequency component in the signal, while the phase represents the time offset of the frequency component.

[0029] For example, a regional power network has been collecting real-time operation data and historical data for a long time. These data include key parameters such as voltage, current, active power, reactive power, etc., with a sampling frequency of 100 times per second. Due to the huge amount of data, direct analysis will bring huge computing pressure, so it is necessary to simplify the analysis process by extracting key features.

[0030] First, based on the characteristics of power network data, a window size of 1,000 data points was determined. This window size can cover 10 seconds of data, which can smooth out instantaneous fluctuations while retaining the main characteristics of the dynamic changes of the power network.

[0031] Next, the data is preprocessed using the window filtering method. A window of 1000 data points is moved from the starting point to the end point of the data sequence, the average value of the data points in the window is calculated point by point, and this average value is assigned to the data point at the center of the window. In this way, a filtered data sequence is obtained, which is obviously smoother than the original data and reduces the influence of noise and instantaneous fluctuations.

[0032] In order to analyze the frequency characteristics of the data more deeply, the fast Fourier transform (FFT) was performed on the filtered data sequence. FFT can convert time domain data into frequency domain data, thereby revealing the periodic components in the data. The results of FFT are stored as a complex array, where each complex number represents the amplitude and phase of a specific frequency. After obtaining the FFT results, the key characteristic frequencies closely related to the power flow distribution are identified based on the characteristics of the power grid and operating experience. These key characteristic frequencies mainly include the fundamental frequency (50Hz) and its harmonic frequencies (such as 100Hz, 150Hz, etc.). These frequency components play an important role in the operation of the power grid and can reflect the load conditions, power factor, and potential harmonic pollution of the power grid. Finally, the complex numbers corresponding to the key characteristic frequencies are extracted from the FFT results, and their amplitudes and phases are calculated. The amplitude reflects the strength of the frequency component in the signal, while the phase represents the offset of the frequency component in time.

[0033] In a preferred embodiment of the present invention, the above step 13 may include: Step 131, calculating the value of each sub-factor according to the key features, real-time operation data and physical characteristics of the power grid, the sub-factors including the voltage stability sub-factor and the harmonic impact sub-factor; Step 132: According to the importance and influence of each sub-factor, the values ​​of each sub-factor are integrated into a dynamic influence factor by weighting.

[0034] In an embodiment of the present invention, by specifically calculating the values ​​of key sub-factors such as the voltage stability sub-factor and the harmonic impact sub-factor, the real-time operating status of the power grid can be more accurately evaluated. By weighting the values ​​of each sub-factor into a dynamic impact factor, the dynamic changes in the operating status of the power grid can be reflected in real time. This dynamic evaluation method can better adapt to various complex situations in the operation of the power grid and improve the flexibility and response speed of power grid management. The dynamic impact factor calculated based on real-time data and physical characteristics can be used as an important indicator for power grid risk warning. When the value of a sub-factor or a dynamic impact factor exceeds a preset safety threshold, an early warning mechanism can be triggered, and timely measures can be taken to prevent potential safety risks.

[0035] In a preferred embodiment of the present invention, the above step 131 may include: Step 1311, obtaining voltage data of the power grid within a period of time, and the voltage value of each sampling point; Step 1312, determine the reference voltage, square the difference between the voltage value of each sampling point and the reference voltage, calculate the average of the squares of the differences of all sampling points, take the square root of the average, and obtain the value of the voltage stability sub-factor; Step 1313, analyzing the current or voltage waveform of the power grid by fast Fourier transform to obtain the amplitude of each harmonic; Step 1314, determine the fundamental wave amplitude, calculate the square sum of all higher harmonic amplitudes, and divide the square sum of the higher harmonic amplitudes by the square of the fundamental wave amplitude to obtain a calculation result; Step 1315: Take the square root of the calculation result and multiply it by 100 to obtain the percentage value of the harmonic impact sub-factor.

[0036] In an embodiment of the present invention, step 1311 is a data collection stage, which is crucial for the voltage stability analysis and harmonic impact analysis of the power grid. First, it is necessary to access the monitoring system or data acquisition system of the power grid, which usually samples the voltage of the power grid at a high frequency (such as dozens of times per second or more). Within a specified time period (such as the past hour, day or week), the system will record the voltage value at each sampling time point to form a time series data set. This process is usually completed through an automated data acquisition system to ensure the accuracy and consistency of the data, and the collected data will be stored in a database.

[0037] Step 1312, first determine a reference voltage value, which is usually the rated voltage of the power grid, that is, the standard voltage value specified when the power grid is designed. For example, in residential electricity, this value may be 220V or 110V, depending on the standard of the region. Next, traverse the voltage value of each sampling point, calculate the difference between it and the reference voltage, and square the difference. The purpose of squaring is to amplify the impact of voltage fluctuations, making subsequent statistical analysis more sensitive and accurate. Then, calculate the average value of the square of the difference of all sampling points. This average value reflects the average deviation between the power grid voltage and the rated voltage during the entire sampling period. The larger the average value, the more violent the voltage fluctuation and the worse the stability of the power grid. Finally, take the square root of this average value, and the result is the value of the voltage stability sub-factor. This value is a scalar used to quantify the stability of the power grid voltage. The smaller the value, the closer the power grid voltage is to the rated voltage and the better the stability; the larger the value, the greater the power grid voltage fluctuation and the worse the stability.

[0038] Step 1313, this step is to use fast Fourier transform (FFT) to perform spectrum analysis on the current or voltage waveform of the power grid. FFT is an efficient algorithm that can convert time domain signals into frequency domain signals, thereby revealing the various frequency components in the signal. First, obtain the sampling data of the current or voltage waveform of the power grid. These data can be the voltage data collected in step 1311 or the corresponding current data. Then, the FFT algorithm is applied to process these data. The FFT algorithm decomposes the time domain waveform into a superposition of a series of sine waves (or cosine waves), each of which corresponds to a specific frequency and amplitude. In this process, the amplitude information of each frequency component in the power grid waveform can be obtained. These frequency components include the fundamental wave (the operating frequency of the power grid) and various higher harmonics. Step 1314, in this step, first determine the amplitude of the fundamental wave. The fundamental wave is the main operating frequency component in the power grid, and its amplitude is usually much larger than other harmonic components. The amplitude of the fundamental wave can be easily identified in the results obtained by FFT analysis. Next, calculate the square sum of all higher harmonic amplitudes. Higher harmonics refer to other sinusoidal components with a frequency higher than the fundamental wave. These harmonics may be generated by nonlinear loads, switching power supplies and other devices in the power grid. The presence of harmonics will have an adverse effect on the stability of the power grid and the life of the equipment. In order to quantify the impact of harmonics, the squares of all higher harmonic amplitudes are added together to get a sum value. This sum value reflects the overall strength of the harmonic components in the power grid. Finally, this sum value is divided by the square of the fundamental amplitude to get a ratio value. This ratio value is used to measure the strength of the harmonic component relative to the fundamental component. The higher the ratio value, the more significant the impact of the harmonics; the lower the ratio value, the smaller the impact of the harmonics.

[0039] Step 1315, perform a square root operation on the proportional value obtained in step 1314. The purpose of taking the square root is to convert the proportional value back to a dimension directly related to the harmonic amplitude, which is convenient for subsequent analysis and comparison. Then, multiply the result after taking the square root by 100 to get a percentage value. This percentage value is the harmonic impact sub-factor, which intuitively represents the degree of influence of the harmonic on the power grid. The higher the percentage value, the greater the influence of the harmonic; the lower the percentage value, the smaller the influence of the harmonic. Through this harmonic impact sub-factor, the harmonic pollution of the power grid can be quantitatively evaluated.

[0040] In a preferred embodiment of the present invention, the above step 132 may include: Step 1321, analyzing the actual impact of the voltage stability sub-factor and the harmonic impact sub-factor on the grid performance and carbon emission flow according to the key factors in the grid operation, so as to obtain the analysis results; Step 1322, calculating the voltage stability index and the harmonic influence index according to the analysis results; Step 1323, analyzing the correlation between the voltage stability index and the harmonic impact index and the benchmark index to obtain a correlation analysis result; Step 1324, constructing a 2×2 matrix based on the correlation analysis results, wherein each element represents the degree of influence of the corresponding factor on the benchmark indicator; Step 1325, normalize each column of the matrix so that the sum of each column is 1, calculate the average value of each row, and use the average value as the weight value of the corresponding factor; Step 1326, standardize the voltage stability sub-factor and the harmonic impact sub-factor so that the value of each sub-factor is on the same scale to obtain standardized data; Step 1327, using the weight values ​​of the corresponding factors, perform weighted summation on the standardized data to obtain a dynamic impact factor.

[0041] In an embodiment of the present invention, step 1321 first determines the key factors in the operation of the power grid, including the voltage stability sub-factor and the harmonic impact sub-factor. The voltage stability sub-factor is related to the stable operation of the power grid. Voltage instability may cause equipment damage, power outages and other problems, which in turn affect the overall performance and carbon emissions of the power grid. For example, a voltage sag may cause a decrease in the efficiency of power generation equipment, thereby increasing carbon emissions. The harmonic impact sub-factor is related to harmonic pollution in the power grid. Harmonics will increase the heat loss of electrical equipment, reduce equipment efficiency, and may also cause damage to the power grid, indirectly affecting carbon emissions. By analyzing historical data and operation records, the specific impact of these two sub-factors on performance and carbon emissions in actual power grid operation can be evaluated, including analyzing changes in key indicators such as power grid losses, equipment efficiency, and power outages under different voltage fluctuations and harmonic levels.

[0042] Step 1322, after completing the actual impact analysis on voltage stability and harmonics, these impacts are quantified into specific indicators. Voltage stability indicators include voltage fluctuation range, voltage sag frequency, etc., which can be calculated through monitoring data and historical records, among which: The formula for voltage fluctuation range VFR is: VFR = V max -V min , where V max is the maximum voltage value during the monitoring period, V min is the minimum voltage value during the monitoring period; the formula for voltage deviation VD is: ,in, is the average voltage value during the monitoring period, is the rated voltage value. The formula for voltage sag depth VSD is: ,in, is the voltage value before the voltage sag occurs, It is the lowest voltage value when a voltage sag occurs.

[0043] The harmonic impact indicators include harmonic distortion rate, additional losses caused by harmonics, etc. The calculation of these indicators is based on the real-time monitoring data of the power grid and the long-term impact evaluation of harmonics on the performance of power grid equipment. Among them, the calculation formula of total harmonic distortion THD is: ,in, is the effective value of the fundamental current; , , …, is the effective value of each harmonic current; is the effective value of the nth harmonic current. The calculation formula for the influence of harmonics on the power factor HPFI is: ,in, is the phase difference between the fundamental voltage and the fundamental current, It is the phase difference between the total voltage and the total current.

[0044] Step 1323, the benchmark indicators can be key performance indicators such as the overall efficiency of the power grid, carbon emissions, and equipment failure rate. By analyzing the correlation between these indicators and the voltage stability and harmonic impact indicators, we can understand how these two factors affect the overall performance of the power grid. The correlation analysis may use statistical methods, such as the Pearson correlation coefficient, to quantify the strength and direction of the relationship between the indicators. The results of this step will help understand which factors have a direct impact on the performance of the power grid and the magnitude of these impacts.

[0045] In step 1324, the rows of the matrix may represent voltage stability and harmonic impact, and the columns may represent different benchmark indicators (such as grid efficiency and carbon emissions). Each element in the matrix will be a numerical value, indicating the degree of impact of the corresponding factor on a specific benchmark indicator.

[0046] Step 1325, the normalization process is to ensure that the influence of different columns (i.e., different benchmark indicators) can be compared with each other. After normalization, the sum of the elements in each column is 1, which means that the influence of each factor on a specific benchmark indicator is converted into a value between 0 and 1. The average value of each row is calculated. This average value can be used as the comprehensive influence of the factor on all benchmark indicators, that is, the weight value of the factor.

[0047] Step 1326, the standardization process is to eliminate the influence of the dimensions and numerical ranges between different factors so that they can be compared and calculated on the same scale. The standardization methods include Z-score standardization and Min-Max standardization. Here, a method suitable for the data characteristics can be selected. The standardized data will have the same scale and mean, which is convenient for subsequent weighted summation operations.

[0048] Step 1327, the previously calculated weight values ​​and the standardized data are used to obtain a comprehensive dynamic impact factor through weighted summation. The dynamic impact factor is an indicator that combines multiple factors such as voltage stability and harmonic influence. It can more comprehensively reflect the performance status of the power grid. By comparing the dynamic impact factors at different time points, the changing trend of the power grid performance can be understood, so that corresponding optimization measures can be taken.

[0049] In a preferred embodiment of the present invention, the above step 14 may include: Step 141, performing power flow calculation according to the value of the dynamic impact factor, and determining the voltage distribution and power distribution of each node in the power network through iterative calculation; Step 142, analyzing the voltage distribution and power distribution of each node in the power network, identifying the key nodes and lines in the power grid, and obtaining the key points of power flow congestion or voltage fluctuation; Step 143, analyzing the power flow distribution at different time points according to the key points of power flow congestion or voltage fluctuation to obtain analysis results.

[0050] In the embodiment of the present invention, step 141 specifically includes: Set the initial voltage value of each node in the power network, which can be set to the rated voltage (such as 1.0 pu, i.e. per unit value); set a convergence criterion to determine when the iterative calculation stops, usually based on the voltage or power imbalance; establish the node power equation based on the topological structure and parameters of the power network (line impedance, transformer ratio, etc.), where the node power equation is: ,in, is the complex power of node i, including active power and reactive power , is the voltage phasor at node i, where V i is the voltage amplitude, is the voltage phase angle, is the conjugate of the voltage phasor of other nodes j connected to node i, and are the conductance and susceptance between nodes i and j respectively; calculate the Jacobian matrix, which is the partial derivative matrix of the power flow equation with respect to the real and imaginary parts of the node voltage. It reflects the change of node power when the node voltage changes slightly. Before each iteration, the Jacobian matrix needs to be recalculated according to the current voltage value.

[0051] The node power equation is solved using Newton's method. In each iteration, a voltage correction is calculated based on the current voltage value and the Jacobian matrix. This correction is added to the current voltage value to obtain a new voltage value. The node power equation is solved using Newton's method, specifically including: Set the initial voltage value of each node in the power network , set it to the rated voltage (such as 1.0 pu), and give the initial phase angle; set the convergence criterion ϵ (for example, 10 −6 pu) and the maximum number of iterations k max ; According to the line impedance and transformer parameters of the power network, calculate the conductivity matrix G ij and the susceptance matrix B ij ; For each node i, calculate the node power according to the current voltage value ; Calculate power imbalance ,in, is the specified power of the node, represents the complex power of node i at the kth iteration; represents the active power of node i at the kth iteration; represents the reactive power of node i at the kth iteration; represents the power imbalance of node i at the kth iteration; calculate the Jacobian matrix J, whose elements are composed of the partial derivatives of the power equation with respect to the real and imaginary parts of the voltage at each node. The Jacobian matrix is ​​a 2n×2n matrix, where n is the number of nodes; the iteration counter k=0, when For any node i, and k < k max , perform the following steps: a. Solve the linear equation system J△V=−△S, where △V is the voltage correction vector.

[0052] b. Update voltage value ,in, is the voltage correction vector of node i, k represents the iteration counter, which is used to record the number of Newton method iterations. represents the voltage value of node i at the kth iteration; Indicated in The updated voltage value of node i at the iteration.

[0053] c. Recalculate node power and imbalance using the new voltage values .

[0054] d. Check whether the convergence condition is met. If for all nodes i, then ≤ϵ, then stop the iteration. Indicated in The power imbalance of node i after iterations.

[0055] e. If the convergence condition is not met, increase the iteration counter k+1 and return to step a.

[0056] When the iteration converges, the voltage amplitude, phase angle and power distribution of each node in the power network are output.

[0057] Use the new voltage value to recalculate the node power and unbalance, check whether the unbalance meets the convergence criteria, if so, stop the iteration and output the current voltage and power distribution, if not, return and continue the iterative calculation; when the iterative calculation converges, output the voltage amplitude, phase angle and power distribution of each node in the power network.

[0058] Step 142 specifically includes: After completing the flow calculation, the purpose of step 142 is to analyze the voltage distribution and power distribution of each node in the power network, so as to identify the key nodes and lines in the power grid, which are the weak links in the operation of the power grid and may cause flow congestion or voltage fluctuations. Using the voltage and power distribution data obtained in step 141, analyze the voltage level, power factor and power flow of each node on the line to obtain the results of data analysis, including nodes and lines with low voltage, unreasonable power factor or power flow on the line close to or exceeding the limit; according to the results of data analysis, identify nodes with large voltage fluctuations and poor stability as key nodes. These nodes may be areas with insufficient reactive compensation, overload or insufficient power generation output. The identification of key nodes helps to focus on them in power grid planning and operation, and improve the stability and safety of the power grid.

[0059] Analyze the power flow on the lines and identify those lines whose power flow is close to or exceeds the design limit as key lines. These lines undertake important power transmission tasks in the operation of the power grid. Once a failure or overload occurs, it may have a serious impact on the power grid. The identification of key lines helps to formulate reasonable maintenance plans and capacity expansion plans to ensure the reliable operation of the power grid.

[0060] Step 143 specifically includes: Step 143 is to further analyze the power flow distribution of the power grid at different time points after identifying the key nodes and lines. This analysis helps to understand the dynamic characteristics of the power grid operation.

[0061] Collect the operation data of the power grid at different time points (such as peak hours, valley hours, etc.), including node voltage, line power, etc., and pre-process the data, such as outlier detection, missing value filling, etc., to ensure the accuracy and completeness of the data; use the collected data to analyze the power flow distribution of the power grid at different time points, for example, the voltage and power changes of key nodes and lines at different time points; through comparative analysis, find out the bottlenecks and potential risk points in the operation of the power grid, such as line overload in certain periods, low node voltage and other problems. Display the analysis results in the form of charts or reports to facilitate the power grid operators to intuitively understand the operation status of the power grid. The report contains the power flow distribution of the power grid at different time points, the operating status of key nodes and lines, as well as potential risk points and improvement suggestions. In summary, step 14 uses the Newton-Raphson method to perform power flow calculation, identify key nodes and lines in the power grid, and analyze the power flow distribution at different time points, providing strong data support and decision-making basis for the safe and stable operation of the power grid.

[0062] In another preferred embodiment of the present invention, the above step 15 may include: Collect and integrate real-time output data of all generators in the power system (including thermal, hydropower, wind power, solar power, etc.); collect load data of each node in the power grid, including industrial, commercial and residential electricity consumption. Use the power network topology and equipment parameters to build an energy tracing model that can reflect the flow path of electricity from the generator to the load. In the energy tracing model, each generator and load node is assigned a unique identifier to facilitate tracking and recording the flow of electricity. Further, using the power network topology and equipment parameters to build an energy tracing model, it can include: The topological structure data of the power network is obtained from the power grid operation management system or geographic information system (GIS). The topological structure includes key equipment such as power plants, substations, transmission lines, distribution lines, transformers, and their connection methods; the technical parameters of each equipment (such as generators, transformers, transmission lines, etc.) are collected, such as capacity, impedance, voltage level, etc.; in the power traceability model, each important node of the power network (such as power plants, substations, important loads, etc.) is abstracted as a node model, and the transmission lines, transformers and other equipment are abstracted as branch models connecting the nodes; a unique node identifier (NodeID) is assigned to each node model to uniquely identify the node in the model; for generator group nodes, they can be further subdivided into different generator identifiers (GeneratorID), and for load nodes, a unique load identifier (LoadID) is also assigned; based on the collected equipment parameters, the electrical parameters of each branch are defined, such as impedance, capacity, etc.; data associations between nodes and branches are established in the model to ensure that the flow path of electric energy in the power network can be accurately described; for example, a generator group node is connected to a load node through a branch (transmission line or transformer).

[0063] Using the results of the power flow calculation (as obtained in step 14), determine the flow path of electric energy in the power system, which includes identifying which generators supply power to which loads, as well as the transmission lines and transformers through which the electric energy passes; for each load node, record which generators the electric energy it receives comes from, and the proportion of these electric energies.

[0064] Implement real-time monitoring in the power system to track the source and destination of electric energy. When the output or load of the generator set in the power system changes, update the electric energy tracing model in time to ensure the real-time and accuracy of the model, and record the flow of electric energy at each time point, including the output of the generator set, the consumption of the load, and the flow path of electric energy in the power grid.

[0065] In another preferred embodiment of the present invention, the above step 16 may include: Collect or calculate carbon emission intensity data for each power generation unit, where carbon emission intensity is expressed as the amount of carbon emissions per unit of electricity produced (e.g., grams of CO2 / kWh). This data can be obtained from power plant operation records, fuel consumption data, or emission monitoring systems.

[0066] For each generator set, the total carbon emissions in a specific time period are calculated based on its output data and carbon emission intensity. The formula is: Carbon emissions = generator set output (kWh) × carbon emission intensity (gCO2 / kWh); Using the results of the power traceability model, determine the proportion of electricity received by each load node from each generator set, and according to this proportion, distribute the carbon emissions of the generator set to each load node. For example, if a load receives 30% of the electricity from generator set A, then the load should bear 30% of the carbon emissions generated by generator set A.

[0067] Create a matrix with rows representing each generator and columns representing each load node. In each cell of the matrix, fill in the carbon emission allocation from the corresponding generator to the load node. This amount is calculated by multiplying the carbon emission of the generator by the proportion of electricity received by the load node from the generator. Ultimately, this matrix will show the distribution of carbon emission flows from each generator to each load node.

[0068] In another preferred embodiment of the present invention, the above step 17 may include: Through the operation data of the power system, the network loss data of each transmission line and transformer is collected and recorded. These data may include the resistance loss of the line, the copper loss and iron loss of the transformer, etc. For each lossy component (line or transformer), according to its design parameters (such as resistance, reactance, voltage level, etc.) and the loss data in actual operation, the equivalent lossless parameters are calculated through circuit analysis. For example, for the transmission line, its main loss comes from the resistance R. In order to obtain a lossless line, the resistance R can be set to 0, while the reactance X and the susceptance B are kept unchanged. This is because in high-voltage transmission systems, the reactance is usually much larger than the resistance, so the impact of resistance on power transmission is relatively small; for the transformer, its losses include copper loss and iron loss. These losses can be considered together and indirectly compensated by adjusting the transformer ratio. Specifically, the transformer reactance parameters can be kept unchanged, and the voltage on both sides can be adjusted by an equivalent transformation ratio, so that the power transmitted in the lossless model is the same as that in the lossy model; these lossless parameters make the replaced components produce no energy loss under the same working conditions; in the power system model, the models of the identified lossy lines and transformers are replaced with component models with equivalent lossless parameters. By comparing the actual operation data, it is verified whether the equivalent lossless network model can accurately reflect the power transmission characteristics of the original lossy network.

[0069] In another preferred embodiment of the present invention, the above step 18 may include: Analyze the specific impact of network losses on the flow of electric energy in the original lossy network, especially the impact on the actual electric energy transmission from each generator set to the load node; use power flow calculation to recalculate the electric energy distribution ratio from each generator set to the load node in the equivalent lossless network; according to the electric energy distribution ratio in the equivalent lossless network, correct the original carbon emission flow distribution matrix by multiplying each element in the original matrix (that is, the carbon emission distribution amount from each generator set to each load node) by the corresponding electric energy distribution ratio in the equivalent lossless network; verify the accuracy and rationality of the corrected carbon emission flow distribution matrix by comparing with other reliable data sources or simulation results.

[0070] In another preferred embodiment of the present invention, the above step 19 may include: Using the revised carbon emission flow allocation matrix, for each load node, the carbon emissions received from each generator set are added together to obtain the total carbon emissions of the load node. The total carbon emissions of the load node are divided by the total electrical energy it receives to obtain the carbon flow rate of the load node (carbon emissions corresponding to unit electrical energy).

[0071] Figure 2 As shown, an embodiment of the present invention further provides an energy flow network universal carbon flow analysis system 20, comprising: The acquisition module 21 is used to acquire the real-time operation data and the historical operation data of the power network; extract the real-time operation data and the historical operation data to obtain the key features related to the power flow distribution; calculate the dynamic impact factor according to the key features and the real-time operation data of the power network; analyze the power flow distribution in the power network according to the dynamic impact factor to obtain the analysis result; The processing module 22 is used to establish a connection between the unit and the load to trace the source and destination of the electric energy in the power system to obtain a tracking result; determine a carbon emission flow distribution matrix from the unit to the load based on the analysis results and the tracking results; when there is a network loss in the power network, the original lossy network is converted into an equivalent lossless network by equivalent conversion; based on the equivalent lossless network, the carbon emission flow distribution matrix is ​​corrected to obtain a corrected distribution matrix; based on the corrected distribution matrix, the carbon flow rate obtained by the final distribution of the load under the lossy network is calculated.

[0072] It should be noted that the system is a system corresponding to the above method, and all implementation methods in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0073] The embodiment of the present invention further provides a computing device, comprising: a processor, a memory storing a computer program, wherein when the computer program is executed by the processor, the method described above is executed. All implementations in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0074] The embodiment of the present invention also provides a computer-readable storage medium storing instructions, which, when executed on a computer, enable the computer to execute the method described above. All implementations in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0075] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0076] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0077] In the embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0078] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0079] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0080] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, ROM, RAM, disk or optical disk, etc., various media that can store program codes.

[0081] In addition, it should be pointed out that in the apparatus and method of the present invention, it is obvious that each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent schemes of the present invention. Moreover, the steps of performing the above series of processing can naturally be performed in chronological order according to the order of description, but it is not necessary to perform them in chronological order, and some steps can be performed in parallel or independently of each other. For those of ordinary skill in the art, it can be understood that all or any steps or components of the method and apparatus of the present invention can be implemented in hardware, firmware, software or a combination thereof in any computing device (including processors, storage media, etc.) or a network of computing devices, which can be achieved by those of ordinary skill in the art using basic programming skills after reading the description of the present invention.

[0082] Therefore, the purpose of the present invention can also be achieved by running a program or a group of programs on any computing device. The computing device can be a well-known general device. Therefore, the purpose of the present invention can also be achieved by simply providing a program product containing a program code that implements the method or device. That is to say, such a program product also constitutes the present invention, and the storage medium storing such a program product also constitutes the present invention. Obviously, the storage medium can be any well-known storage medium or any storage medium developed in the future. It should also be pointed out that in the device and method of the present invention, it is obvious that each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent schemes of the present invention. In addition, the steps of performing the above-mentioned series of processing can naturally be performed in chronological order according to the order of description, but it is not necessary to perform them in chronological order. Some steps can be performed in parallel or independently of each other.

[0083] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A general carbon flow analysis method for energy flow network, characterized in that: The method comprises: Obtain real-time and historical operation data of the power network; Extract real-time and historical operation data to obtain key features related to power flow distribution; Calculate dynamic impact factors based on key features and real-time operation data of the power network; According to the dynamic influencing factors, the power flow distribution in the power network is analyzed to obtain the analysis results; Establish connections between units and loads to trace the source and destination of electric energy in the power system to obtain tracking results; Based on the analysis and tracking results, determine a carbon emission flow allocation matrix from units to loads; When there is network loss in the power network, the original lossy network is converted into an equivalent lossless network through equivalent conversion; According to the equivalent lossless network, the carbon emission flow allocation matrix is ​​modified to obtain a modified allocation matrix; According to the modified distribution matrix, the carbon flow rate obtained by the final distribution of load under the lossy network is calculated.

2. The energy flow network universal carbon flow analysis method according to claim 1 is characterized in that: Real-time and historical operation data are extracted to obtain key features related to power flow distribution, including: Determine a fixed-size window according to the characteristics of the real-time operation data and historical operation data of the power network; Move the window from the starting point of the data sequence to the end point, calculate the average value of the data points in the window at each window position, and use the average value as the data value at the center of the window after filtering. By moving point by point and calculating the average value, a filtered data sequence is obtained; Performing a fast Fourier transform on the filtered data sequence, converting the filtered data sequence from the time domain to the frequency domain to obtain a Fourier transform result; storing the Fourier transform result as a complex number array, wherein each complex number represents the amplitude and phase of a frequency component; According to the characteristics of the power grid, identify the key characteristic frequencies related to the power flow distribution, including the fundamental frequency and its harmonic frequencies, and determine the complex numbers corresponding to the key characteristic frequencies in the Fourier transform results; For each key characteristic frequency, the corresponding complex number is extracted from the Fourier transform result. The amplitude of the key characteristic frequency is obtained by calculating the modulus of each complex number. The amplitude reflects the intensity of the key characteristic frequency component in the signal. The phase of the key characteristic frequency is obtained by calculating the argument of each complex number. The phase represents the time offset of the key characteristic frequency component.

3. The energy flow network universal carbon flow analysis method according to claim 2 is characterized in that: Based on key characteristics and real-time operation data of the power network, dynamic impact factors are calculated, including: Calculate the value of each sub-factor based on key characteristics, real-time operating data, and the physical characteristics of the power grid; According to the importance and influence of each sub-factor, the values ​​of each sub-factor are combined into a dynamic impact factor through weighting.

4. The energy flow network universal carbon flow analysis method according to claim 3 is characterized in that: The sub-factors include a voltage stability sub-factor and a harmonic impact sub-factor.

5. The energy flow network universal carbon flow analysis method according to claim 4, characterized in that: The values ​​of each sub-factor are calculated based on key characteristics, real-time operating data, and the physical characteristics of the power grid, including: Obtain the voltage data of the power grid over a period of time, as well as the voltage value of each sampling point; Determine the reference voltage, square the difference between the voltage value of each sampling point and the reference voltage, calculate the average of the squares of the differences of all sampling points, take the square root of the average, and obtain the value of the voltage stability sub-factor; Analyze the current or voltage waveform of the power grid through fast Fourier transform to obtain the amplitude of each harmonic; Determine the fundamental wave amplitude, calculate the sum of the squares of all higher harmonic amplitudes, and divide the sum of the squares of the higher harmonic amplitudes by the square of the fundamental wave amplitude to obtain a calculation result; Take the square root of the calculated result and multiply it by 100 to get the percentage value of the harmonic impact sub-factor.

6. The energy flow network universal carbon flow analysis method according to claim 5, characterized in that: According to the importance and influence of each sub-factor, the values ​​of each sub-factor are combined into a dynamic impact factor through weighting, including: According to the key factors in power grid operation, the actual impact of voltage stability sub-factor and harmonic impact sub-factor on power grid performance and carbon emission flow is analyzed to obtain analysis results; Based on the analysis results, calculate the voltage stability index and harmonic impact index; Analyze the correlation between voltage stability index and harmonic impact index and benchmark index to obtain correlation analysis results; Based on the results of the correlation analysis, a 2×2 matrix is ​​constructed, in which each element represents the degree of influence of the corresponding factor on the benchmark indicator; Normalize each column of the matrix so that the sum of each column is 1, calculate the average value of each row, and use the average value as the weight value of the corresponding factor; The voltage stability sub-factor and the harmonic impact sub-factor are standardized so that the values ​​of each sub-factor are on the same scale and standardized data are obtained; Using the weight values ​​of the corresponding factors, the standardized data are weighted and summed to obtain the dynamic impact factor.

7. The energy flow network universal carbon flow analysis method according to claim 6, characterized in that: According to the dynamic influencing factors, the power flow distribution in the power network is analyzed to obtain the analysis results, including: According to the value of dynamic impact factor, power flow calculation is performed, and the voltage distribution and power distribution of each node in the power network are determined through iterative calculation; Analyze the voltage and power distribution of each node in the power network, identify the key nodes and lines in the power grid, and obtain the key points of power flow congestion or voltage fluctuation; According to the key points of power flow congestion or voltage fluctuation, the power flow distribution at different time points is analyzed to obtain the analysis results.

8. A universal carbon flow analysis system for energy flow network, characterized in that: include: An acquisition module is used to acquire real-time operation data and historical operation data of the power network; Extract real-time and historical operation data to obtain key features related to power flow distribution; Calculate dynamic impact factors based on key features and real-time operation data of the power network; According to the dynamic influencing factors, the power flow distribution in the power network is analyzed to obtain the analysis results; The processing module is used to establish a connection between the unit and the load to trace the source and destination of the electric energy in the power system to obtain the tracking results; according to the analysis results and the tracking results, a carbon emission flow distribution matrix from the unit to the load is determined; when there is a network loss in the power network, the original lossy network is converted into an equivalent lossless network through equivalent; According to the equivalent lossless network, the carbon emission flow distribution matrix is ​​corrected to obtain a corrected distribution matrix; according to the corrected distribution matrix, the carbon flow rate obtained by the final distribution of the load under the lossy network is calculated.

9. A computing device, characterized in that include: one or more processors; A storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the method as claimed in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program, which, when executed by a processor, implements the method according to any one of claims 1 to 7.

Images