Accounting method for marginal carbon emission factors of power grid

Abstract

The invention relates to the technical field of power grid planning, in particular to a power grid marginal carbon emission factor accounting method which comprises the steps that historical data in a region are acquired, and the historical data comprise power generation amount data per hour and corresponding carbon emission amount data; constructing a scheduling prediction model based on the historical data, and predicting the power generation amount and the carbon emission amount required per hour at present; and establishing a linear regression model, and obtaining marginal carbon emission factors in a preset period of the region based on a linear regression method. According to the method, on the basis of the hourly change, predicted by a scheduling model, of the demand-related emission amount and power generation amount of each region, the power grid marginal emission factors of the regions in the period are calculated according to the required period division change. The problems that day-by-day accounting cannot be carried out based on daily power consumption and various requirements are ignored in the existing power grid emission factor determination process are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid planning, and more specifically, to a method for calculating a marginal carbon emission factor of a power grid. Background Art

[0002] The grid emission factor is an important parameter for enterprises to calculate indirect carbon emissions. The grid emission factor can be used to estimate the carbon dioxide emissions generated by the company's purchased electricity. In particular, in the face of the Carbon Border Adjustment Mechanism (CBAM) proposed by the European Union and other regions, the grid emission factor is crucial for assessing the carbon footprint of export products. Currently, the average grid carbon emission factor is commonly used. However, with the increase in the proportion of renewable energy electricity and the increase in random loads, such as electric vehicles, air conditioners, and electrolysis of hydrogen, the marginal carbon emission factor of the grid may increase. These electrification behaviors may not achieve emission reductions, but instead lead to increased carbon emissions, but the method for determining the marginal carbon emission factor of the grid is still unclear.

[0003] The currently commonly used power grid carbon emission factor is the average power grid carbon emission factor, that is, the ratio of the total power supply in a year to its corresponding carbon emissions. For example, in the relevant patent, the patent document with announcement number CN116227822A discloses a power grid planning method and terminal based on the marginal emission factor of the provincial power grid. The patent document calculates the difference between the carbon emissions of the provincial power grid in each month and the carbon emissions of the next month, as well as the difference between the electricity consumption of the current month and the electricity consumption of the next month, and takes the ratio of the difference in carbon emissions to the difference in electricity consumption as the marginal emission factor of the provincial power grid for each month.

[0004] It can account for indirect emissions caused by purchasing electricity from outside the company or project, or the carbon footprint of products. As the proportion of renewable energy electricity increases, random loads increase, such as electric vehicles, air conditioners, and electrolysis for hydrogen production, which will cause repeated start and stop of thermal power or longer-term standby, which may lead to an increase in the marginal carbon emission factor of the power grid. These electrification behaviors may not achieve emission reductions, but instead lead to increased carbon emissions. The average carbon emission factor of the power grid cannot account for carbon emissions caused by this type of activity. Summary of the invention

[0005] The present invention provides a method for calculating the marginal carbon emission factor of a power grid, aiming to solve the problem of determining the existing power grid emission factor in the relevant technology, which is usually determined by the ratio of the total emissions of the power grid to the total power generation. This calculation process can only provide an estimate of the average emission factor, but a more refined estimate based on daily electricity consumption needs to take into account the power transmission between regions, the daily variation pattern of power demand, the calculation of transmission and distribution losses, and the problem of unmet demand due to rotating power outages.

[0006] In the first aspect, the present invention provides a method for calculating the marginal carbon emission factor of a power grid, obtaining historical data in a region, wherein the historical data includes hourly power generation data and corresponding carbon emission data; constructing a scheduling prediction model based on the historical data to predict the current hourly power generation and carbon emission required; establishing a linear regression model, and obtaining the marginal carbon emission factor of the region within a preset period based on the linear regression method, wherein the model calculation formula is: ; In the formula, represents the change in carbon emissions of region s on the dth day of period p compared with the previous day, represents the change in power generation in region s on the dth day of period p compared with the previous day, represents the marginal emission factor of region s in period p, The intercept term in the linear regression model, Represents the error term in the linear regression model.

[0007] Furthermore, the change in carbon emissions between the current day and the previous day is calculated using the following formula: ; In the formula, represents the change in carbon emissions of region s on the dth day of period p compared with the previous day, represents the carbon emissions of region s on the dth day of period p, represents the carbon emissions of region s on day d-1 of period p.

[0008] Furthermore, it is characterized in that the change in power generation between the current day and the previous day is calculated, and the calculation formula is: ; In the formula, represents the change in power generation in region s on the dth day of period p compared with the previous day, represents the power generation of region s on the dth day of period p, represents the power generation of region s on day d-1 of period p.

[0009] Furthermore, based on the linear regression method, the marginal carbon emission factor of the region within a preset period is obtained, including: using the least fit squares method to calculate the marginal emission factor in the linear regression model.

[0010] Furthermore, constructing a scheduling prediction model based on the historical data also includes: detecting abnormal values ​​in the historical data and smoothing the abnormal values.

[0011] Furthermore, detecting outliers in the historical data includes: detecting outliers in the historical data by a box plot method or a cluster analysis method.

[0012] Further, the abnormal value is smoothed, including: establishing a window of a preset size with the abnormal value as the center, calculating the average value of all data in the window and smoothing the abnormal value. Further, the size of the window is 10 bytes.

[0013] Beneficial effects: Based on the hourly changes in emissions and power generation related to the demand predicted by the dispatch model for each region, the changes are divided according to the required period, and the marginal emission factors of the power grid in the region within the period are calculated. This solves the problem that the existing power grid emission factor determination process cannot be based on daily calculations based on daily electricity consumption, ignores the power transmission between regions, the daily change pattern of power demand, the calculation of transmission and distribution losses, and the problem of unmet demand due to rotating power outages. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The following detailed description is read with reference to the accompanying drawings, which illustrate several embodiments of the present invention in an exemplary and non-limiting manner, and in which like or corresponding reference numerals represent like or corresponding parts, wherein: Figure 1 is a flow chart schematically illustrating the calculation of the marginal carbon emission factor according to an embodiment of the present invention.

[0015] Reference numerals 1. Processor; 2. Communication bus; 3. Memory; 4. Communication interface. DETAILED DESCRIPTION

[0016] 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 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 those skilled in the art without creative work are within the scope of protection of the present invention.

[0017] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0018] In one embodiment, the currently commonly used grid carbon emission factor is the grid average carbon emission factor, that is, the ratio of the total power supply in a year to its corresponding carbon emissions. It can calculate the indirect emissions caused by the purchase of electricity by enterprises or projects, or the carbon footprint of products. As the proportion of renewable energy power increases, random loads increase, such as electric vehicles, air conditioners, and electrolytic hydrogen production, which will cause repeated start and stop or long-term standby of thermal power, which may lead to an increase in the marginal carbon emission factor of the grid. These electrification behaviors may not achieve emission reductions, but instead lead to increased carbon emissions. The grid average carbon emission factor cannot calculate the carbon emissions caused by this type of activity.

[0019] Therefore, the existing grid emission factor is usually determined by the ratio of total grid emissions to total power generation. This calculation process can only provide an estimate of the average emission factor, but a more refined estimate based on daily electricity consumption needs to take into account power transmission between regions, daily variation patterns in power demand, accounting for transmission and distribution losses, and the problem of unmet demand due to rotating power outages.

[0020] S101: Obtain historical data in a region.

[0021] In one embodiment, historical data in each region is obtained, and the historical data includes hourly power generation data and corresponding carbon emission data. Specifically, it can usually be obtained from the public database of the power company, the government statistics bureau or the energy regulatory agency. These data may include the power generation of different types of power plants, such as coal, gas, hydropower, wind power and solar energy. The collected power generation data and carbon emission data are matched and integrated according to time. In this embodiment, the time is one hour. In other embodiments, the time can be three hours or five hours, etc., which can be adjusted according to the specific actual situation.

[0022] S102: Predict the power generation and carbon emissions required per hour.

[0023] In one embodiment, a scheduling prediction model is constructed based on the historical data to predict the current power generation and carbon emissions required per hour. Constructing a scheduling prediction model includes: selecting a suitable machine learning or statistical model to construct a scheduling prediction model, such as time series analysis or a deep learning model. The historical data is divided into a training set and a test set, the training set is used to train the model, and the test set is used to evaluate the performance of the model. According to the prediction results of the model, the parameters of the model are adjusted or other models are selected for improvement. Once the model has been trained and verified, it can be deployed in a production environment for real-time prediction of the current power generation and carbon emissions required per hour.

[0024] It should be noted that when using historical data to establish a prediction model, there may be outliers in the historical data. If the outliers are not processed, it may affect the subsequent establishment of the prediction model. Therefore, in this embodiment, it is also necessary to detect the outliers in the historical data and smooth the outliers.

[0025] In one embodiment, detecting outliers in the historical data includes: detecting outliers in the historical data by a box plot method or a cluster analysis method. For the box plot method to detect outliers, it includes: the box plot displays a five-number summary of the data (minimum value, lower quartile, median, upper quartile, maximum value) and outliers, so that it can be intuitively seen which values ​​are outliers. Generally, data less than the lower quartile -1.5 times the interquartile range (IQR) or greater than the upper quartile +1.5 times the IQR is considered an outlier. For the cluster analysis method to detect outliers in the historical data, it includes: using clustering algorithms such as K-means and DBSCAN to classify the historical data, the historical data can be divided into several groups, and data points that do not belong to any group are considered outliers.

[0026] In one embodiment, the abnormal value is smoothed, including: establishing a window of a preset size with the abnormal value as the center, calculating the average value of all data in the window, and smoothing the abnormal value. In this embodiment, the size of the window is 10 bytes, and in other embodiments, the size of the window is 15 bytes and 20 bytes. The size of the window can be adjusted according to the specific implementation.

[0027] In another embodiment, the outliers are smoothed, and further include: replacing the outliers with the median in the historical data: replacing the outliers with the median is particularly suitable for the case of asymmetric data distribution. The median is not sensitive to outliers, and thus can better reflect the central trend of the data.

[0028] S103: Calculate the marginal carbon emission factor within a preset period.

[0029] In one embodiment, the marginal carbon emission factor within a preset period is calculated. In this embodiment, the preset period is 1 month. In other embodiments, the preset period may be two months or three months, etc., and may be adjusted according to specific actual conditions.

[0030] In one embodiment, a linear regression model is established, and based on the linear regression method, the marginal carbon emission factor of the region within a preset period is obtained. The model calculation formula is: ; In the formula, represents the change in carbon emissions of region s on the dth day of period p compared with the previous day, represents the change in power generation in region s on the dth day of period p compared with the previous day, represents the marginal emission factor of region s in period p, The intercept term in the linear regression model, Represents the error term in the linear regression model.

[0031] It should be noted that the above linear regression model describes the relationship between marginal emissions and marginal power generation, rather than being used directly to calculate daily changes. The linear regression model can be used to determine the impact of power generation changes on emissions changes.

[0032] In one embodiment, the change in carbon emissions between the current day and the previous day is calculated using the following formula: ; In the formula, represents the change in carbon emissions of region s on the dth day of period p compared with the previous day, represents the carbon emissions of region s on the dth day of period p, represents the carbon emissions of region s on day d-1 of period p.

[0033] For example, for region A, the preset period corresponding to region A is one month, the carbon emissions of region A on the 3rd day of the month is E1, and the carbon emissions of region A on the 4th day of the month is E2. The change in carbon emissions between the 4th day and the 3rd day is calculated as , .

[0034] In one embodiment, the change in power generation between the current day and the previous day is calculated using the following formula: ; In the formula, represents the change in power generation in region s on the dth day of period p compared with the previous day, represents the power generation of region s on the dth day of period p, represents the power generation of region s on day d-1 of period p.

[0035] For example, for region A, the preset period corresponding to region A is one month, the power generation of region A on the third day of the month is G1, and the carbon emissions of region A on the fourth day of the month is G2. Calculate the change in power generation between the fourth day and the third day. , .

[0036] Through the above steps, based on the hourly changes in emissions and power generation related to the demand predicted by the dispatch model for each region, the changes are divided according to the required period, and the marginal emission factors of the power grid in the region within the period are calculated. This solves the problem that the existing power grid emission factor determination process cannot be based on daily calculations based on daily electricity consumption, ignores the power transmission between regions, the daily change pattern of power demand, the calculation of transmission and distribution losses, and the problem of unmet demand due to rotating power outages.

[0037] The present invention also provides a system for calculating the marginal carbon emission factor of a power grid. The system includes a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a method for calculating the marginal carbon emission factor of a power grid according to the first aspect of the present invention is implemented.

[0038] The system also includes other components familiar to those skilled in the art, such as a communication bus and a communication interface, whose configuration and functions are known in the art and thus will not be described in detail here.

[0039] In the present invention, the aforementioned memory may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, apparatus or device. For example, a computer-readable storage medium may be any appropriate magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory RRAM (Resistive Random Access Memory), a dynamic random access memory DRAM (Dynamic Random Access Memory), a static random access memory SRAM (Static Random-Access Memory), an enhanced dynamic random access memory EDRAM (Enhanced Dynamic Random Access Memory), a high-bandwidth memory HBM (High-Bandwidth Memory), a hybrid memory cube HMC (Hybrid Memory Cube), etc., or any other medium that can be used to store the required information and can be accessed by an application, a module, or both. Any such computer storage medium may be part of a device or accessible or connectable to a device. Any application or module described in the present invention may be implemented using computer-readable / executable instructions that may be stored or otherwise maintained by such a computer-readable medium.

[0040] In one embodiment, the present invention provides a computer device. The computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities, and various varieties such as CPU, single-chip microcomputer, DSP or FPGA can be selected. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. When the computer program is executed, the steps described in the above method embodiment can be completed, such as S11-step S14. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, an operator network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a method for calculating the marginal carbon emission factor of a power grid is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a key, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.

[0041] In the description of this specification, "plurality" or "several" means at least two, such as two, three or more, etc., unless otherwise clearly and specifically defined.

[0042] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0043] The above-mentioned embodiments only express several implementation methods of the present invention, and the description is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent application. It should be pointed out that for ordinary technicians in this field, several modifications and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention.

Claims

1. A method for calculating the marginal carbon emission factor of a power grid, characterized in that: include: Acquire historical data in the region, the historical data including hourly power generation data and corresponding carbon emission data; Building a scheduling prediction model based on the historical data to predict the power generation and carbon emissions required per hour; A linear regression model is established, and based on the linear regression method, the marginal carbon emission factor within the preset period of the region is obtained. The model calculation formula is: ； In the formula, represents the change in carbon emissions of region s on the dth day of period p compared with the previous day, represents the change in power generation in region s on the dth day of period p compared with the previous day, represents the marginal emission factor of region s in period p, The intercept term in the linear regression model, Represents the error term in the linear regression model.

2. The method for calculating the marginal carbon emission factor of the power grid according to claim 1, characterized in that: Calculate the change in carbon emissions between the current day and the previous day using the following formula: ； In the formula, represents the change in carbon emissions of region s on the dth day of period p compared with the previous day, represents the carbon emissions of region s on the dth day of period p, represents the carbon emissions of region s on day d-1 of period p.

3. The method for calculating the marginal carbon emission factor of the power grid according to claim 1 is characterized in that Calculate the change in power generation between the current day and the previous day using the following formula: ； In the formula, represents the change in power generation in region s on the dth day of period p compared with the previous day, represents the power generation of region s on the dth day of period p, represents the power generation of region s on day d-1 of period p.

4. The method for calculating the marginal carbon emission factor of the power grid according to claim 1, characterized in that: Based on the linear regression method, the marginal carbon emission factor of the region within the preset period is obtained, including: The marginal emission factors in the linear regression model were calculated using the least-fit squares method.

5. The method for calculating the marginal carbon emission factor of the power grid according to claim 1, characterized in that: Building a scheduling prediction model based on the historical data also includes: Detecting abnormal values ​​in the historical data and performing smoothing on the abnormal values.

6. The method for calculating the marginal carbon emission factor of the power grid according to claim 2, characterized in that: Detecting outliers in the historical data, including: Outliers in the historical data are detected by a box plot method or a cluster analysis method.

7. The method for calculating the marginal carbon emission factor of the power grid according to claim 5, characterized in that: The abnormal value is smoothed, comprising: A window of a preset size is established with the abnormal value as the center, and the average value of all data in the window is calculated to smooth the abnormal value.

8. The method for calculating the marginal carbon emission factor of the power grid according to claim 7, characterized in that: The size of the window is 10 bytes.

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