Power system carbon flow tracking method based on carbon emission flow theory

Abstract

The invention discloses an electric power system carbon flow tracking method based on a carbon emission flow theory, and belongs to the technical field of electric power system carbon emission monitoring, and the method comprises the steps: 1, obtaining a carbon emission factor of a region and electric power operation data information of each branch of an electric power system; step 2, performing comprehensive analysis according to the acquired information to obtain a carbon emission value of each branch of the power system, and combining the carbon emission value with power flow analysis of the power system to establish a carbon flow model of the power system; 3, judging whether the carbon emission of each branch in the power system is abnormal or not according to the distribution condition of the carbon emission values in the carbon flow model; and 4, according to the carbon emission condition of each branch, carrying out efficiency evaluation on the carbon emission treatment of the region. According to the method, comprehensive analysis is carried out according to the carbon emission factors in the region and the power operation data information of each branch of the power system, and influence factors such as temperature change and resistance change when each branch works are considered in a calculation standard, so that the obtained carbon emission is more accurate.

Description

Technical Field

[0001] The present invention belongs to the technical field of carbon emission monitoring of power systems, and in particular relates to a carbon flow tracking method for power systems based on carbon emission flow theory. Background Art

[0002] As the world's attention to climate change deepens, the monitoring and management of carbon emissions in the power industry, as one of the main sources of carbon emissions, has become particularly important. Existing carbon flow tracking in power systems mostly uses post-statistical analysis methods to determine whether the carbon emissions in the area exceed the standard, and then energy conservation and emission reduction are carried out in the areas that exceed the standard. Although this method can detect whether carbon emissions exceed the standard, at this time, carbon emissions have already exceeded the standard, which will still cause excessive carbon emission pollution, and it cannot predict potential emission risks; at the same time, the existing method of obtaining carbon emissions is mostly determined by the power consumption displayed on the branch. Due to factors such as line loss, the actual power consumption on the branch is more than the displayed power consumption, so the detection is not accurate enough. Summary of the invention

[0003] The purpose of the present invention is to provide a carbon flow tracing method for a power system based on carbon emission flow theory, so as to solve the problems faced in the above-mentioned background technology.

[0004] The purpose of the present invention can be achieved through the following technical solutions:

[0005] A method for tracking carbon flow in a power system based on carbon emission flow theory, the method comprising:

[0006] Step 1: Determine the carbon emission factor based on the energy consumption of regional power plants and obtain the power operation data information of each branch of the power system;

[0007] Step 2: Perform a comprehensive analysis based on the power operation data information combined with the carbon emission factor to obtain the carbon emission value of each branch of the power system, and combine the carbon emission value with the power system flow analysis to establish a carbon flow model of the power system;

[0008] Step 3: According to the distribution of carbon emission values ​​in the carbon flow model, determine whether the carbon emission of each branch in the power system is abnormal, and optimize the carbon emission of the branch with abnormal carbon emission;

[0009] Step 4: Evaluate the efficiency of regional carbon emission management based on the carbon emission of each branch.

[0010] Step 5: At the same time, relevant parameter information of the branch with normal carbon emissions is obtained, and a risk judgment coefficient is generated based on a comprehensive analysis of the relevant parameter information, so as to judge the potential abnormal emission risk of the branch based on the risk judgment coefficient.

[0011] Furthermore, the method for obtaining the carbon emission value in step 2 is:

[0012] The power operation data information includes resistance information of the branch circuit during operation, line temperature information, and power consumption obtained by the branch circuit;

[0013] Obtain the resistance information, line temperature information and power consumption Q of the branch in real time, so as to construct a mathematical model to obtain the carbon emission value Car;

[0014] The mathematical model expression is:

[0015] Among them, ρ is the distribution loss rate, μ is the carbon emission factor, τ1 and τ2 are proportional coefficients, Y is the service life of the branch, Y0 is the reference value of the service life of the branch, L is the length of the transmission line of the branch, L0 is the reference value of the length of the branch transmission line, Rv is the resistance change value, and Tv is the temperature change value.

[0016] Furthermore, the resistance change value Rv and the temperature change value Tv are determined as follows:

[0017] Obtain the branch resistance value R and the line temperature value T in real time, and compare them with their respective preset standard thresholds R th , T th To compare:

[0018] When R≤R th When R th =0, when R>R th When R th =RR th ;

[0019] When T≤T th When R th =0, when T>T th When Tv=TT th .

[0020] Furthermore, the method for determining whether the carbon emissions of each branch of the power system are abnormal in step 3 is:

[0021] The obtained carbon emission value Car is compared with the preset carbon emission threshold Car of each branch th To compare:

[0022] When Car>Car th When the carbon emission is abnormal.

[0023] Furthermore, the method for judging the potential abnormal emission risk of the branch according to the risk judgment coefficient in step 5 is:

[0024] When the carbon emission of the branch is normal, the carbon emission value Car of the branch is obtained over time every detection period ΔT, and the historical carbon emission average Car in the next detection period is obtained. v ;

[0025] By formula The risk judgment coefficient Dr is obtained;

[0026] When Dr>1, it is judged that the branch has potential abnormal emission risk;

[0027] Among them, Car0(t) is the preset standard curve of carbon emission value changing with time, t1 is the start time of the detection cycle, and t2 is the end time of the detection cycle. is the maximum slope value of Car(t) during the detection period, is the minimum slope of Car(t) within the detection period, ΔCar is the preset reference value, and Z th is the preset risk judgment threshold, and γ is the weather influence coefficient.

[0028] Furthermore, the method for obtaining the weather influence coefficient γ is:

[0029] Get the temperature value T of the next detection period from the weather forecast system emp , and compare it with the preset temperature threshold range For comparison:

[0030]

[0031] Furthermore, the method for evaluating the efficiency of regional carbon emission management in step 4 is:

[0032] The efficiency of carbon emission management in the region is divided into four levels, namely excellent, good, poor and very poor;

[0033] Get the number of abnormal carbon emissions in each branch in this area within a period of time w i ;

[0034] when , it is judged as excellent;

[0035] when , it is judged as good;

[0036] when When , it is judged as poor;

[0037] when , it is judged as very poor;

[0038] Among them, m is the total number of branches, and i∈(1,m), b1, b2, and b3 are all set judgment thresholds.

[0039] Beneficial effects of the present invention:

[0040] The present invention conducts a comprehensive analysis based on the carbon emission factors in the region and the power operation data information of each branch of the power system, and takes into account the influencing factors such as temperature changes and resistance changes of each branch during operation into the calculation standard, so that the obtained carbon emissions are more accurate; at the same time, the carbon emission values ​​are combined with the power system flow analysis to establish a carbon flow model of the power system, so that management personnel can intuitively and in real time understand the carbon emissions generated by each branch, which is convenient for tracking and controlling the carbon flow; then, according to the distribution of carbon emission values ​​in the carbon flow model, it is judged whether the carbon emissions of each branch in the power system are abnormal, and the corresponding carbon emission optimization adjustments are made to the branches with abnormal emissions, thereby reducing carbon emission pollution.

[0041] When carbon emissions are normal, the present invention obtains relevant parameter information of the corresponding branch, and generates a risk judgment coefficient based on a comprehensive analysis of the relevant parameter information, thereby judging the potential abnormal carbon emission risk of the branch according to the risk judgment coefficient. In this way, the potential abnormal carbon emission risk of the branch can be predicted, thereby timely reducing the occurrence of excessive carbon emissions and better managing carbon emissions in the region.

[0042] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0044] Figure 1 The present invention is a flow chart of the method. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0046] In one embodiment, a method for tracking carbon flow in a power system based on carbon emission flow theory is disclosed, such as Figure 1 As shown, the carbon flow tracking method includes:

[0047] Step 1: Determine the carbon emission factor based on the energy consumption of regional power plants and obtain the power operation data information of each branch of the power system;

[0048] Step 2: Perform a comprehensive analysis based on the power operation data information combined with the carbon emission factor to obtain the carbon emission value of each branch of the power system, and combine the carbon emission value with the power system flow analysis to establish a carbon flow model of the power system;

[0049] Step 3: According to the distribution of carbon emission values ​​in the carbon flow model, determine whether the carbon emission of each branch in the power system is abnormal, and optimize the carbon emission of the branch with abnormal carbon emission;

[0050] Step 4: Evaluate the efficiency of regional carbon emission management based on the carbon emission of each branch.

[0051] Step 5: At the same time, relevant parameter information of the branch with normal carbon emissions is obtained, and a risk judgment coefficient is generated based on a comprehensive analysis of the relevant parameter information, so as to judge the potential abnormal emission risk of the branch based on the risk judgment coefficient.

[0052] Through the above technical scheme, the present application first determines the carbon emission factor according to the energy consumption of the regional power plant, and obtains the power operation data information of each branch of the power system, including the resistance change information, temperature change information and power consumption of each branch, etc., and then combines the two for comprehensive analysis to obtain the carbon emission value of each branch of the power system in real time. In this way, the influencing factors such as temperature change and resistance change during branch operation are taken into account in the calculation standard, so that the obtained carbon emissions are more accurate; at the same time, the carbon emission value is combined with the power system flow analysis to establish a carbon flow model of the power system, so that managers can intuitively understand in real time. The carbon emissions generated by each branch are then judged based on the distribution of carbon emission values ​​in the carbon flow model to determine whether the carbon emissions of each branch in the power system are abnormal, and corresponding carbon emission optimization adjustments are made to the branches with abnormal emissions, thereby reducing carbon emission pollution; in addition, the present application also obtains the relevant parameter information of the corresponding branch when the carbon emissions are normal, and generates a risk judgment coefficient based on a comprehensive analysis of the relevant parameter information, so as to judge the potential abnormal emission risk of the branch based on the risk judgment coefficient. In this way, the potential abnormal carbon emission risk of the branch can be predicted, thereby timely reducing the occurrence of excessive carbon emissions and better managing carbon emissions in the region.

[0053] As an implementation of the present invention, the method for obtaining the carbon emission value in step 2 is:

[0054] The power operation data information includes the resistance information of the branch during operation, the line temperature information, and the power consumption obtained by the branch;

[0055] Obtain the resistance information, line temperature information and power consumption Q of the branch in real time, so as to construct a mathematical model to obtain the carbon emission value Car;

[0056] The mathematical model expression is:

[0057] Among them, ρ is the distribution loss rate, μ is the carbon emission factor, τ1 and τ2 are proportional coefficients, Y is the service life of the branch, Y0 is the reference value of the service life of the branch, L is the length of the transmission line of the branch, L0 is the reference value of the length of the branch transmission line, Rv is the resistance change value, and Tv is the temperature change value;

[0058] The resistance change value Rv and the temperature change value Tv are determined by obtaining the resistance value R of the branch and the temperature value T of the line in real time, and comparing them with the respective preset standard thresholds R th , T th To compare:

[0059] When R≤R th When R th =0, when R>R th When R th =RR th ;

[0060] When T≤T th When R th =0, when T>T th When Tv=TT th .

[0061] Through the above technical solution, this embodiment provides a specific method for obtaining carbon emission values. First, the resistance information, line temperature information and power consumption Q of the branch in real time are obtained, and then the mathematical model expression is used to express The carbon emission value Car is obtained; since the branch is affected by the length and service life of the transmission line, generally the longer the length and service life of the transmission line, the more power loss it generates. Similarly, the higher the heating temperature in the line or the greater the resistance of the line, the more power loss it generates. Therefore, considering these influencing factors in the formula, the carbon emission value of the branch can be obtained more accurately; and the determination method of the resistance change value Rv and the temperature change value Tv in the formula is: the resistance value R of the branch and the temperature value T of the line are obtained in real time, and they are compared with their respective preset standard thresholds R th , T th Compare: When R≤R th , indicating that the resistance change of the line will not affect the original power loss, then R th =0, when R>R th, indicating that the resistance change of the line will affect the original power loss, then R th =RR th ; Similarly, when T≤T th When , it means that the line temperature is not significantly heated and will not affect the original power loss. th =0, when T>T th When the circuit loss of the original circuit is affected, Tv = TT th ; From the formula It can be seen that the older the branch is, the longer the transmission line is, or the greater the temperature change and resistance change in the line are, the higher the carbon emissions generated will be. This method can more accurately determine the actual carbon emissions of each branch, making it easier to track and judge the carbon flow of each branch.

[0062] In the above technical solution, the distribution loss rate ρ is determined according to the actual branch loss rate, the proportional coefficients τ1 and τ2, the reference value of the branch service life Y0, the reference value of the branch transmission line length L0, the preset resistance, and the temperature standard threshold R th 、T th All of them can be determined based on the historical data of the branch and empirical data, so I will not elaborate on them here.

[0063] As an implementation mode of the present invention, the method for determining whether the carbon emissions of each branch of the power system are abnormal in step 3 is:

[0064] The obtained carbon emission value Car is compared with the preset carbon emission threshold Car of each branch th To compare:

[0065] When Car>Car th When , carbon emissions are considered abnormal;

[0066] The method for evaluating the efficiency of regional carbon emission management in step 4 is to classify the efficiency of regional carbon emission management into four levels, namely excellent, good, poor, and very poor;

[0067] Get the number of abnormal carbon emissions in each branch in this area within a period of time w i ;

[0068] when , it is judged as excellent;

[0069] when , it is judged as good;

[0070] when When , it is judged as poor;

[0071] when , it is judged as very poor;

[0072] Among them, m is the total number of branches, and i∈(1,m), b1, b2, and b3 are all set judgment thresholds.

[0073] Through the above technical solution, this embodiment provides a method for determining whether the carbon emissions of each branch are abnormal and a method for evaluating the carbon emission management of the entire area according to the abnormal carbon emissions. First, the carbon emission value Car of each branch is obtained from the carbon flow model, and compared with the preset carbon emission threshold Car of each branch. th Compare, when Car>Car th , it means that the carbon emissions exceed the standard at this time, and the carbon emissions of this branch are considered abnormal. In this way, we can judge whether the carbon emissions exceed the standard according to the carbon emission standards of each branch. Once the standard is exceeded, we can promptly find out which branch exceeds the standard and the amount of excess, so as to facilitate the tracking and management of carbon flow. When a branch has abnormal carbon emissions, we can timely optimize the carbon emissions of the branch. For example, when a branch has abnormal carbon emissions, we can increase the proportion of renewable energy use according to the amount of excess, or reduce the electricity use in the corresponding time period according to the amount of excess in a certain period of time, so as to reduce carbon emissions. At the same time, the efficiency of carbon emission management in the entire region is evaluated according to the carbon emissions of each branch in the region. Due to the uncertainty of a single sample, the number of times w when carbon emissions abnormalities occur in each branch in the region over a period of time is obtained. i The length of a period of time can be set manually, and the number of abnormal carbon emissions in each branch during the period of time is obtained. Generally, the more times, the worse the carbon emission control effect, so the formula Get the average number of carbon emission anomalies in the entire area. When When When When the carbon emission control efficiency of the region is poor, it is judged as very poor, so as to conduct a graded assessment on the carbon emission control efficiency of the region, so as to facilitate the subsequent corresponding rectification according to the grade.

[0074] In the above technical solution, the carbon emission threshold Car preset for each branch th , and the set judgment thresholds b1, b2, and b3 are all determined based on empirical data.

[0075] As an implementation mode of the present invention, the method for judging the potential abnormal emission risk of the branch according to the risk judgment coefficient in step 5 is:

[0076] When the carbon emission of the branch is normal, the carbon emission value Car of the branch is obtained over time every detection period ΔT, and the historical carbon emission average Car in the next detection period is obtained. v ;

[0077] By formula The risk judgment coefficient Dr is obtained;

[0078] When Dr>1, it is judged that the branch has potential abnormal emission risk;

[0079] Among them, Car0(t) is the preset standard curve of carbon emission value changing with time, t1 is the start time of the detection cycle, and t2 is the end time of the detection cycle. is the maximum slope value of Car(t) during the detection period, is the minimum slope of Car(t) within the detection period, ΔCar is the preset reference value, and Z th is the preset risk judgment threshold, γ is the weather influence coefficient;

[0080] The method for obtaining the weather influence coefficient γ is: obtain the temperature value T of the next detection period from the weather forecast system emp , and compare it with the preset temperature threshold range Compare: The weather influence coefficient γ is obtained by the following formula;

[0081]

[0082] Through the above technical solution, this embodiment provides a method for judging the potential abnormal emission risk of the branch according to the risk judgment coefficient. First, a detection period ΔT is set artificially to obtain the carbon emission value Car of the branch over time during the detection period and the historical carbon emission average Car in the next detection period. v , and then by the formula The risk judgment coefficient Dr is obtained, in the formula It indicates the cumulative change of carbon emissions obtained during the detection period relative to the standard carbon emissions change. It indicates the difference between the maximum slope value and the minimum slope value of the carbon emission change during the detection period. Since the carbon emission threshold is generally a fixed value set artificially, if the carbon emission value has been near the set threshold for a long time, although it is judged that it has not exceeded the emission value, its risk of exceeding the value is relatively large. Similarly, if the carbon emission value is in a state of rapid growth over a period of time, it means that there is a greater risk of exceeding the value. Therefore, when as well as The larger the value, the greater the possibility of abnormal carbon emission risk; similarly, since carbon emissions are also related to historical carbon emissions, the historical carbon emission average Car in the next detection period is obtained. v , when its value is larger, it means that the risk of abnormal carbon emissions in the next detection cycle is greater; in addition, the power consumption of the next detection cycle is also related to weather conditions, mainly related to temperature changes. When the temperature is too high or too low, the more power it needs to consume, so the temperature value T of the next detection cycle is obtained from the weather forecast system. emp , and compare it with the preset temperature threshold range For comparison: When , it means that the weather temperature has little effect on the electricity consumption, so let γ=1. When the temperature is low, it means that the power consumption is affected. when When the temperature is too high, it means that the power consumption is affected. It can be seen that the greater the temperature deviation, the greater the weather impact coefficient; a comprehensive analysis is conducted based on the weather impact, the cumulative change in carbon emissions, and the electricity consumption, and the formula The risk judgment coefficient Dr is obtained. When Dr>1, it means that the risk value has exceeded the preset risk judgment threshold. Then it is judged that the branch has potential abnormal emission risk, and a corresponding warning is issued to remind the management personnel to deal with it accordingly. In this way, the potential abnormal carbon emission risk of the branch can be predicted, so as to timely reduce the occurrence of excessive carbon emissions, so as to better manage carbon emissions in the region.

[0083] In the above technical solution, the preset standard curve of carbon emission value changing with time Car0(t), ΔCar is the preset reference value, and the preset risk judgment threshold Z th And the preset temperature threshold range All of them can be determined based on the corresponding historical data and experience data, so I will not elaborate on them here.

[0084] It should be noted that the calculation formulas and various parameters involved in the calculations in the present invention have been dimensionally processed in advance, and the process of dimensionless processing is well known in the industry and will not be described here.

[0085] The above contents are merely examples and explanations of the concept of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

Claims

1. A carbon flow tracking method for a power system based on carbon emission flow theory, characterized in that: The method comprises: Step 1: Determine the carbon emission factor based on the energy consumption of regional power plants and obtain the power operation data information of each branch of the power system; Step 2: Perform a comprehensive analysis based on the power operation data information combined with the carbon emission factor to obtain the carbon emission value of each branch of the power system, and combine the carbon emission value with the power system flow analysis to establish a carbon flow model of the power system; Step 3: According to the distribution of carbon emission values ​​in the carbon flow model, determine whether the carbon emission of each branch in the power system is abnormal, and optimize the carbon emission of the branch with abnormal carbon emission; Step 4: Evaluate the efficiency of regional carbon emission management based on the carbon emission of each branch. Step 5: At the same time, relevant parameter information of the branch with normal carbon emissions is obtained, and a risk judgment coefficient is generated based on a comprehensive analysis of the relevant parameter information, so as to judge the potential abnormal emission risk of the branch based on the risk judgment coefficient.

2. A method for tracking carbon flow in a power system based on carbon emission flow theory according to claim 1, characterized in that: The method for obtaining the carbon emission value in step 2 is: The power operation data information includes resistance information of the branch circuit during operation, line temperature information, and power consumption obtained by the branch circuit; Obtain the resistance information, line temperature information and power consumption Q of the branch in real time, so as to construct a mathematical model to obtain the carbon emission value Car; The mathematical model expression is: Among them, ρ is the distribution loss rate, μ is the carbon emission factor, τ1 and τ2 are proportional coefficients, Y is the service life of the branch, Y0 is the reference value of the service life of the branch, L is the length of the transmission line of the branch, L0 is the reference value of the length of the branch transmission line, Rv is the resistance change value, and Tv is the temperature change value.

3. A method for tracking carbon flow in a power system based on carbon emission flow theory according to claim 2, characterized in that: The resistance change value Rv and the temperature change value Tv are determined as follows: Obtain the branch resistance value R and the line temperature value T in real time, and compare them with their respective preset standard thresholds R th , T th To compare: When R≤R th When R th =0, when R>R th When R th =RR th ; When T≤T th When R th =0, when T>T th When Tv=TT th .

4. A method for tracking carbon flow in a power system based on carbon emission flow theory according to claim 2, characterized in that: The method for determining whether the carbon emissions of each branch of the power system are abnormal in step 3 is: The obtained carbon emission value Car is compared with the preset carbon emission threshold Car of each branch th To compare: When Car>Car th When the carbon emission is abnormal.

5. A method for tracking carbon flow in a power system based on carbon emission flow theory according to claim 4, characterized in that: The method for judging the potential abnormal emission risk of the branch according to the risk judgment coefficient in step 5 is: When the carbon emission of the branch is normal, the carbon emission value Car of the branch is obtained over time every detection period ΔT, and the historical carbon emission average Car in the next detection period is obtained. v ; By formula The risk judgment coefficient Dr is obtained; When Dr>1, it is judged that the branch has potential abnormal emission risk; Among them, Car0(t) is the preset standard curve of carbon emission value changing with time, t1 is the start time of the detection cycle, and t2 is the end time of the detection cycle. is the maximum slope value of Car(t) during the detection period, is the minimum slope of Car(t) within the detection period, ΔCar is the preset reference value, and Z th is the preset risk judgment threshold, and γ is the weather influence coefficient.

6. A method for tracking carbon flow in a power system based on carbon emission flow theory according to claim 5, characterized in that: The method for obtaining the weather influence coefficient γ is: Get the temperature value T of the next detection period from the weather forecast system emp , and compare it with the preset temperature threshold range By comparison, the weather influence coefficient γ is obtained by the following formula; 7. A method for tracking carbon flow in a power system based on carbon emission flow theory according to claim 4, characterized in that: The method for evaluating the efficiency of regional carbon emission management in step 4 is: The efficiency of carbon emission management in the region is divided into four levels, namely excellent, good, poor and very poor; Get the number of abnormal carbon emissions in each branch in this area within a period of time w i ; when , it is judged as excellent; when , it is judged as good; when When , it is judged as poor; when , it is judged as very poor; Among them, m is the total number of branches, and i∈(1,m), b1, b2, and b3 are all set judgment thresholds.

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