Dynamic carbon emission factor prediction method and device, electronic equipment and storage medium

Abstract

The application relates to a dynamic carbon emission factor prediction method and device, electronic equipment and a storage medium. The method comprises the following steps: determining the probability distribution of a load and the probability distribution of a new energy unit output upper limit, sampling to obtain an actual load and an actual unit output, calculating network flow distribution of multiple time periods to obtain a flow calculation result, and generating a basic data matrix according to the flow calculation result; calculating a dynamic carbon emission factor according to the basic data matrix, resampling based on the probability distribution of the load and the probability distribution of the new energy unit output upper limit, accumulating the sampling times, obtaining a new dynamic carbon emission factor, until the sampling times reach a preset iteration number, generating a sampling result according to multiple dynamic carbon emission factors, and obtaining a prediction interval of the dynamic carbon emission factor according to the sampling result. Therefore, the problem that it is difficult to provide energy use guidance for users can be solved, the carbon emission responsibility of the user side can be tracked, and a guidance signal can be provided for demand response.

Description

Technical Field

[0001] This application relates to the field of low-carbon power system technology, and in particular to a dynamic carbon emission factor prediction method, device, electronic device and storage medium. Background Technology

[0002] Decarbonizing the power system is of paramount importance in achieving the "dual carbon" goals. The statistics and calculation of carbon emissions are key issues in the low-carbon development of the power industry. Traditional carbon emission calculations in the power industry are based on fuel data, attributing all carbon emission responsibility to the generation side. However, this is not conducive to reducing carbon emissions in the power industry by promoting adjustments on the user side.

[0003] Currently, carbon emission flow theory is mainly used in the accounting process and is difficult to provide users with energy use guidance a day-ahead. Moreover, with the rapid increase in the installed capacity of distributed new energy sources, the emergence of new loads such as data centers and electric vehicles, the gap in carbon emission intensity in different regions and at different times is widening, and the load adjustability is increasing. Under these circumstances, the day-ahead prediction of dynamic carbon emission factors at each node and at each time period has important emission reduction significance.

[0004] Currently, in the field of power system forecasting, forecasting methods based on historical data are commonly used. However, in the field of metering, dynamic emission factors have not been widely used before, and there is a lack of historical data available for forecasting, making it impossible to adopt data-driven methods. This issue urgently needs to be addressed. Summary of the Invention

[0005] This application provides a dynamic carbon emission factor prediction method, apparatus, electronic device, and storage medium to address the problem that related technologies have failed to reduce carbon emissions in the power industry through user-side adjustments. It can clarify users' responsibility for carbon emissions from electricity consumption and provide guidance signals for demand response.

[0006] The first aspect of this application provides a method for predicting dynamic carbon emission factors, including the following steps:

[0007] Determine the probability distribution of the load and the probability distribution of the upper limit of the output of the new energy units, and sample based on the probability distribution of the load and the probability distribution of the upper limit of the output of the new energy units to obtain the actual load and the actual unit output;

[0008] Calculate the network power flow distribution over multiple time periods based on the actual load and the actual unit output, obtain the power flow calculation results based on the network power flow distribution over multiple time periods, and generate a basic data matrix based on the power flow calculation results; and

[0009] The dynamic carbon emission factor is calculated based on the basic data matrix, and sampling is performed again based on the probability distribution of the load and the probability distribution of the upper limit of the output of the new energy unit. The number of samplings is accumulated to obtain a new dynamic carbon emission factor until the number of samplings reaches the preset number of iterations. Sampling results are generated based on multiple sets of dynamic carbon emission factors, and the prediction range of the dynamic carbon emission factor is obtained based on the sampling results.

[0010] Optionally, in some embodiments, after generating the sampling result based on the dynamic carbon emission factor, the method further includes:

[0011] Determine multiple sets of confidence levels for the power system;

[0012] The fluctuation range of the carbon emission factor under each set of confidence levels is determined based on the multiple sets of confidence levels.

[0013] Optionally, in some embodiments, calculating the dynamic carbon emission factor based on the basic data matrix includes:

[0014]

[0015] Among them, E N As a dynamic carbon emission factor, P N Let P be the active flux matrix of the nodes. B Let P be the branch power flow distribution matrix. G Inject the matrix into the unit, E G This represents the actual carbon emission intensity of the unit.

[0016] Optionally, in some embodiments, determining the fluctuation range of the carbon emission factor under each set of confidence levels based on the multiple sets of confidence levels includes:

[0017]

[0018] Where α is the confidence level, E lower The upper limit for the set carbon emission factor, E upper The lower limit for the set carbon emission factor.

[0019] Optionally, in some embodiments, the probability distribution of the load is as follows:

[0020]

[0021] The probability distribution of the upper limit of the output of the new energy unit is as follows:

[0022]

[0023] Where σ is the variance of the load, P load,i,t Let P be the probability distribution of load i at time t. gen,i,tLet represent the probability distribution of the upper limit of the output of the new energy unit i at time t, and μ represent the expected load.

[0024] A second aspect of this application provides a dynamic carbon emission factor prediction device, comprising:

[0025] The determination module is used to determine the probability distribution of the load and the probability distribution of the upper limit of the output of the new energy unit, and to sample based on the probability distribution of the load and the probability distribution of the upper limit of the output of the new energy unit to obtain the actual load and the actual unit output.

[0026] The calculation module is used to calculate the network power flow distribution for multiple time periods based on the actual load and the actual unit output, obtain the power flow calculation results based on the network power flow distribution for multiple time periods, and generate a basic data matrix based on the power flow calculation results.

[0027] The prediction module is used to calculate the dynamic carbon emission factor based on the basic data matrix, and to resample based on the probability distribution of the load and the probability distribution of the upper limit of the output of the new energy unit, and to accumulate the number of samplings to obtain a new dynamic carbon emission factor until the number of samplings reaches a preset number of iterations. The module generates sampling results based on multiple sets of dynamic carbon emission factors, and obtains the prediction range of the dynamic carbon emission factor based on the sampling results.

[0028] Optionally, in some embodiments, after generating the sampling results based on the dynamic carbon emission factor, the prediction module further includes:

[0029] The first determining unit is used to determine multiple sets of confidence levels for the power system;

[0030] The second determining unit is used to determine the fluctuation range of the carbon emission factor under each set of confidence levels based on the multiple sets of confidence levels.

[0031] Optionally, in some embodiments, calculating the dynamic carbon emission factor based on the basic data matrix includes:

[0032]

[0033] Among them, E N As a dynamic carbon emission factor, P N Let P be the active flux matrix of the nodes. B Let P be the branch power flow distribution matrix. G Inject the matrix into the unit, E G This represents the actual carbon emission intensity of the unit.

[0034] Optionally, in some embodiments, determining the fluctuation range of the carbon emission factor under each set of confidence levels based on the multiple sets of confidence levels includes:

[0035]

[0036] Where α is the confidence level, E lower The upper limit for the set carbon emission factor, E upper The lower limit for the set carbon emission factor.

[0037] Optionally, in some embodiments, the probability distribution of the load is as follows:

[0038]

[0039] The probability distribution of the upper limit of the output of the new energy unit is as follows:

[0040]

[0041] Where σ is the variance of the load, P load,i,t Let P be the probability distribution of load i at time t. gen,i,t Let represent the probability distribution of the upper limit of the output of the new energy unit i at time t, and μ represent the expected load.

[0042] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the dynamic carbon emission factor prediction method as described in the above embodiments.

[0043] A fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement the dynamic carbon emission factor prediction method as described in the above embodiments.

[0044] Therefore, by determining the probability distribution of load and the probability distribution of the upper limit of renewable energy unit output, and sampling based on these probability distributions, the actual load and actual unit output are obtained. Then, based on the actual load and actual unit output, the network power flow distribution over multiple time periods is calculated, and the power flow calculation results are obtained. A basic data matrix is ​​generated based on the power flow calculation results. A dynamic carbon emission factor is calculated based on the basic data matrix, and sampling is performed again based on the probability distribution of load and the probability distribution of the upper limit of renewable energy unit output. The number of samplings is accumulated to obtain a new dynamic carbon emission factor until the number of samplings reaches a preset iteration number. Sampling results are generated based on multiple sets of dynamic carbon emission factors, and the prediction range of the dynamic carbon emission factor is obtained based on the sampling results. This addresses the difficulty of providing energy consumption guidance to users and the difficulty of reducing carbon emissions in the power industry through user-side adjustments. It clarifies users' responsibility for carbon emissions from electricity consumption and provides guidance signals for demand response.

[0045] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0046] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0047] Figure 1 This is a flowchart of the dynamic carbon emission factor prediction method provided according to the embodiments of this application;

[0048] Figure 2 This is a schematic diagram illustrating the daily dynamic carbon emission factor prediction for each node according to an embodiment of this application;

[0049] Figure 3 This is a schematic diagram illustrating the dynamic carbon emission factor fluctuation range at different confidence levels for one node according to an embodiment of this application;

[0050] Figure 4 This is a flowchart of a dynamic carbon emission factor prediction method provided according to an embodiment of this application;

[0051] Figure 5 This is a block diagram of a dynamic carbon emission factor prediction device provided according to an embodiment of this application;

[0052] Figure 6 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application. Detailed Implementation

[0053] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0054] The following describes a dynamic carbon emission factor prediction method, apparatus, electronic device, and storage medium according to embodiments of this application with reference to the accompanying drawings. Addressing the limitations of the aforementioned background technologies in providing energy consumption guidance to users and reducing carbon emissions in the power industry through user-side adjustments, this application provides a dynamic carbon emission factor prediction method. In this method, the probability distribution of load and the probability distribution of the upper limit of output from renewable energy units are determined. Based on these probability distributions, actual load and actual unit output are obtained through sampling. Network power flow distributions for multiple time periods are calculated based on the actual load and actual unit output. Power flow calculation results are obtained based on these distributions, and a basic data matrix is ​​generated based on the power flow calculation results. Dynamic carbon emission factors are calculated based on the basic data matrix. Sampling is then performed again based on the probability distribution of load and the probability distribution of the upper limit of output from renewable energy units, and the number of samplings is accumulated to obtain new dynamic carbon emission factors. This process continues until the number of samplings reaches a preset iteration count. Sampling results are generated based on multiple sets of dynamic carbon emission factors, and the prediction range of the dynamic carbon emission factors is obtained based on the sampling results. Therefore, addressing the difficulty of providing energy usage guidance to users through relevant technologies and reducing carbon emissions in the power industry through user-side adjustments can clarify users' responsibility for carbon emissions from electricity consumption and provide guidance signals for demand response.

[0055] Specifically, Figure 1 This is a flowchart illustrating a dynamic carbon emission factor prediction method provided in an embodiment of this application.

[0056] like Figure 1 As shown, the dynamic carbon emission factor prediction method includes the following steps:

[0057] In step S101, the probability distribution of the load and the probability distribution of the upper limit of the output of the new energy unit are determined, and the actual load and the actual unit output are obtained by sampling based on the probability distribution of the load and the probability distribution of the upper limit of the output of the new energy unit.

[0058] Those skilled in the art will understand that the embodiments of this application can use a normal distribution to describe the randomness of load and the upper limit of output of new energy units, and determine the expected value and variance of load and corresponding new energy units at each node and time period.

[0059] 1. Describe the randomness of the load.

[0060] Because loads are random and fluctuating, there may be discrepancies between the actual load and the predicted load. The probability distribution of load i at time t can be described using a normal distribution:

[0061]

[0062] 2. Describe the randomness of new energy output.

[0063] The upper limit of the output of new energy units such as wind power and photovoltaic power is random and fluctuating. Therefore, a normal distribution is used to describe the probability distribution of the upper limit of the output of new energy unit i at time t:

[0064]

[0065] Based on the probability distribution of load and the upper limit of output of new energy units, the actual load and unit output are obtained by random sampling.

[0066] In step S102, the network power flow distribution for multiple time periods is calculated based on the actual load and actual unit output, and the power flow calculation results are obtained based on the network power flow distribution for multiple time periods. A basic data matrix is ​​then generated based on the power flow calculation results.

[0067] This application embodiment can calculate the network power flow distribution for each time period based on the load and unit output data obtained in step S101. Specifically:

[0068] 1. System power flow calculation:

[0069] Based on the predicted load and output of new energy units, the economic dispatch results are obtained. The economic dispatch model aims to minimize the total system cost.

[0070] min C = c T P G Δt;

[0071] The model must satisfy the upper and lower limits of unit output and power flow safety constraints:

[0072]

[0073]

[0074] in, For generator sets, For the collection of transmission lines, and These are the upper and lower limits of the output of the i-th unit, respectively. and Δt represents the upper and lower limits of the power flow for the l-th line, Δt represents the time period length, and C represents the total cost.

[0075] Therefore, based on the economic dispatch model, the power output of each unit and the power flow of the lines can be obtained.

[0076] 2. Generate the branch power flow distribution matrix:

[0077] To describe the active power flow distribution of a power system, a branch power flow distribution matrix P is defined. B It contains system network topology information and power flow distribution information, where P BThe specific definition is as follows: If node i is connected to node j, and the power flowing from j into i is p (p>0), then If p < 0, then In other cases Furthermore, all diagonal elements of the matrix are 0.

[0078] 3. Generate the unit injection matrix:

[0079] The generator injection matrix describes the connection relationships between all generator sets and the power system, as well as the active power injected into the system by the generator sets. Specifically, it is defined as follows: If the k-th generator set is connected to node j and injects active power p > 0, then... Otherwise, it is 0.

[0080] 4. Generate the active flux matrix of the nodes:

[0081] In carbon emission flow calculations, the nodal carbon emission intensity (carbon potential) is only affected by the injected power flow; therefore, the positive active power flow flowing into the node is called the nodal active flux. The nodal active flux matrix is ​​specifically defined as follows: Define a diagonal matrix P. N The i-th diagonal element is the sum of the power generation at node i and the positive flow into node i.

[0082] 5. Generate the unit's carbon emission intensity vector:

[0083] Based on the actual carbon emission intensity data of the unit, a vector E is generated. G The k-th element of this vector represents the carbon emission intensity of the k-th unit.

[0084] Based on the power flow calculation results above, the basic data matrices of branch power flow distribution matrix, unit injection matrix, node active power flux matrix, and unit carbon emission intensity vector are generated for each time period.

[0085] In step S103, the dynamic carbon emission factor is calculated based on the basic data matrix, and sampling is performed again based on the probability distribution of the load and the probability distribution of the upper limit of the output of the new energy unit. The number of samplings is accumulated to obtain a new dynamic carbon emission factor until the number of samplings reaches the preset number of iterations. Sampling results are generated based on multiple sets of dynamic carbon emission factors, and the prediction range of the dynamic carbon emission factor is obtained based on the sampling results.

[0086] The preset number of iterations can be set by the user, obtained through a limited number of experiments, or obtained through a limited number of computer simulations; no specific limitation is made here.

[0087] Specifically, based on the basic data matrix generated above, the dynamic carbon emission factor of each node can be calculated, and the specific calculation formula is as follows:

[0088]

[0089] Among them, E N As a dynamic carbon emission factor, P N Let P be the active flux matrix of the nodes. B Let P be the branch power flow distribution matrix. G Inject the matrix into the unit, E G This represents the actual carbon emission intensity of the unit.

[0090] Random sampling is performed based on the probability distribution of load and upper limit of renewable energy output to obtain the actual load and unit output of a day. Based on this, the dynamic carbon emission factor of each node and time period is calculated. The sampling is repeated N times to calculate the expected value and obtain the upper and lower limits of the fluctuation of the dynamic emission factor.

[0091] Optionally, in some embodiments, after generating sampling results based on dynamic carbon emission factors, the method further includes: determining multiple sets of confidence levels for the power system; and determining the fluctuation range of the carbon emission factors under each set of confidence levels.

[0092] Specifically, the embodiments of this application can use the Monte Carlo method to calculate the fluctuation range of the node dynamic carbon emission factor under different confidence levels.

[0093] In this embodiment of the application, after obtaining the upper and lower limits of the dynamic emission factor fluctuation, the corresponding carbon emission factor E fluctuation range is defined based on the confidence level α:

[0094]

[0095] Where α is the confidence level, E lower The upper limit for the set carbon emission factor, E upper The lower limit for the set carbon emission factor.

[0096] Therefore, based on the sampling results, upper and lower limits for the predicted dynamic carbon emission factors of each node at each time period are given. Furthermore, according to the confidence level requirements, the fluctuation range of the dynamic carbon emission factors of each node at each time period under different confidence levels is given, serving as a guiding signal for the low-carbon demand response of the load in the next day.

[0097] To enable those skilled in the art to further understand the dynamic carbon emission factor prediction method of the embodiments of this application, the following detailed description is provided in conjunction with specific embodiments.

[0098] This application uses the IEEE 5-node example, and the information for each unit is as follows:

[0099]

[0100] Based on the measured values ​​of load and output of new energy units, the carbon emission factors for each node and time period are calculated as follows: Figure 2As shown, taking node 1 as an example, after Monte Carlo sampling with different confidence levels, the sampling results and fluctuation range of the carbon emission factor of node 1 are as follows. Figure 3 As shown.

[0101] In summary, as Figure 4 As shown, this application provides a method for predicting dynamic carbon emission factors of power systems based on carbon emission flow theory. Based on baseline load and unit output, the method uses carbon emission flow theory to predict the dynamic emission factors of each node in the power system at each time period. The method also uses the Monte Carlo method to characterize the uncertainty of load and renewable energy output, and gives the fluctuation range of dynamic carbon emission factors under different confidence requirements.

[0102] The dynamic carbon emission factor prediction method proposed in this application determines the probability distribution of load and the probability distribution of the upper limit of renewable energy unit output. Based on these probability distributions, actual load and actual unit output are obtained through sampling. Network power flow distributions for multiple time periods are calculated based on the actual load and output, and power flow calculation results are obtained. A basic data matrix is ​​generated based on the power flow calculation results. The dynamic carbon emission factor is calculated based on the basic data matrix. Sampling is then performed again based on the probability distribution of load and the probability distribution of the upper limit of renewable energy unit output, and the number of samplings is accumulated to obtain a new dynamic carbon emission factor. This process continues until the number of samplings reaches a preset iteration number. Sampling results are generated based on multiple sets of dynamic carbon emission factors, and the prediction range of the dynamic carbon emission factor is obtained based on the sampling results. This addresses the difficulty of providing energy consumption guidance to users and reducing carbon emissions in the power industry through user-side adjustments in related technologies. It clarifies users' responsibility for carbon emissions from electricity consumption and provides guidance signals for demand response.

[0103] Next, the dynamic carbon emission factor prediction device proposed according to the embodiments of this application is described with reference to the accompanying drawings.

[0104] Figure 5 This is a block diagram of a dynamic carbon emission factor prediction device according to an embodiment of this application.

[0105] like Figure 5 As shown, the dynamic carbon emission factor prediction device 10 includes: a determination module 100, a calculation module 200, and a prediction module 300.

[0106] The determination module 100 is used to determine the probability distribution of the load and the probability distribution of the upper limit of the output of the new energy unit, and to sample the actual load and the actual unit output based on the probability distribution of the load and the probability distribution of the upper limit of the output of the new energy unit.

[0107] The calculation module 200 is used to calculate the network power flow distribution over multiple time periods based on the actual load and actual unit output, obtain the power flow calculation results based on the network power flow distribution over multiple time periods, and generate a basic data matrix based on the power flow calculation results.

[0108] The prediction module 300 is used to calculate the dynamic carbon emission factor based on the basic data matrix, and to resample based on the probability distribution of the load and the probability distribution of the upper limit of the output of the new energy unit, and accumulate the number of samplings to obtain a new dynamic carbon emission factor until the number of samplings reaches the preset number of iterations. It generates sampling results based on multiple sets of dynamic carbon emission factors and obtains the prediction range of the dynamic carbon emission factor based on the sampling results.

[0109] Optionally, in some embodiments, after generating sampling results based on dynamic carbon emission factors, the prediction module 300 further includes: a first determining unit and a second determining unit.

[0110] The first determining unit is used to determine multiple sets of confidence levels for the power system.

[0111] The second determining unit is used to determine the fluctuation range of the carbon emission factor under each set of confidence levels based on multiple sets of confidence levels.

[0112] Optionally, in some embodiments, calculating the dynamic carbon emission factor based on the underlying data matrix includes:

[0113]

[0114] Among them, E N As a dynamic carbon emission factor, P N Let P be the active flux matrix of the nodes. B Let P be the branch power flow distribution matrix. G Inject the matrix into the unit, E G This represents the actual carbon emission intensity of the unit.

[0115] Optionally, in some embodiments, determining the fluctuation range of the carbon emission factor under each confidence level based on multiple confidence levels includes:

[0116]

[0117] Where α is the confidence level, E lower The upper limit for the set carbon emission factor, E upper The lower limit for the set carbon emission factor.

[0118] Optionally, in some embodiments, the probability distribution of the load is as follows:

[0119]

[0120] The probability distribution of the upper limit of output of new energy units is as follows:

[0121]

[0122] Where σ is the variance of the load, P load,i,t Let P be the probability distribution of load i at time t. gen,i,t Let represent the probability distribution of the upper limit of the output of the new energy unit i at time t, and μ represent the expected load.

[0123] It should be noted that the foregoing explanation of the embodiment of the dynamic carbon emission factor prediction method also applies to the dynamic carbon emission factor prediction device of this embodiment, and will not be repeated here.

[0124] The dynamic carbon emission factor prediction device proposed in this application determines the probability distribution of load and the probability distribution of the upper limit of output of new energy units, and obtains the actual load and actual unit output by sampling based on the probability distribution of load and the upper limit of output of new energy units. It then calculates the network power flow distribution over multiple time periods based on the actual load and actual unit output, obtains the power flow calculation results based on the network power flow distribution over multiple time periods, and generates a basic data matrix based on the power flow calculation results. The device then calculates the dynamic carbon emission factor based on the basic data matrix, and re-samples based on the probability distribution of load and the probability distribution of the upper limit of output of new energy units, accumulating the number of samplings to obtain a new dynamic carbon emission factor until the number of samplings reaches a preset iteration number. Sampling results are generated based on multiple sets of dynamic carbon emission factors, and the prediction range of the dynamic carbon emission factor is obtained based on the sampling results. This addresses the difficulty of related technologies in providing energy consumption guidance to users and reducing carbon emissions in the power industry through user-side adjustments. It clarifies users' responsibility for carbon emissions from electricity consumption and provides guidance signals for demand response.

[0125] Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include:

[0126] The memory 601, the processor 602, and the computer program stored on the memory 601 and capable of running on the processor 602.

[0127] When the processor 602 executes the program, it implements the dynamic carbon emission factor prediction method provided in the above embodiments.

[0128] Furthermore, electronic devices also include:

[0129] Communication interface 603 is used for communication between memory 601 and processor 602.

[0130] The memory 601 is used to store computer programs that can run on the processor 602.

[0131] The memory 601 may include high-speed RAM (Random Access Memory) memory, and may also include non-volatile memory, such as at least one disk storage.

[0132] If the memory 601, processor 602, and communication interface 603 are implemented independently, then the communication interface 603, memory 601, and processor 602 can be interconnected via a bus to complete communication between them. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 6 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0133] Optionally, in a specific implementation, if the memory 601, processor 602, and communication interface 603 are integrated on a single chip, then the memory 601, processor 602, and communication interface 603 can communicate with each other through an internal interface.

[0134] The processor 602 may be a CPU (Central Processing Unit), an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of this application.

[0135] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described dynamic carbon emission factor prediction method.

[0136] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0137] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0138] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0139] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (FPGAs), field-programmable gate arrays (FPGAs), etc.

[0140] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0141] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A method for predicting dynamic carbon emission factors, characterized in that, Includes the following steps: The probability distribution of load and the probability distribution of the upper limit of output of new energy units are determined, and the actual load and actual unit output are obtained by sampling based on the probability distribution of load and the probability distribution of the upper limit of output of new energy units; wherein, The probability distribution of the load is as follows: ； The probability distribution of the upper limit of the output of the new energy unit is as follows: ； in, The variance of the load, Let be the probability distribution of load i at time t. Let be the probability distribution of the upper limit of the output of the new energy unit i at time t. The expected load; Based on the actual load and the actual unit output, the network power flow distribution for multiple time periods is calculated under the constraints of the economic dispatch model. The economic dispatch model aims to minimize the total system cost and satisfies the upper and lower limits of unit output and power flow safety constraints. The economic dispatch model is as follows: The upper and lower limits of unit output and the power flow safety constraints are as follows: in, For generator sets, For the collection of transmission lines, and These are the upper and lower limits of the output of the i-th unit, respectively. and These are the upper and lower limits of the power flow for the l-th line, respectively. The duration is the length of the time period. The total cost is calculated, and the power flow calculation results are obtained based on the network power flow distribution over the multiple time periods. A basic data matrix is ​​then generated based on the power flow calculation results. The dynamic carbon emission factor is calculated based on the aforementioned basic data matrix, and sampling is performed again based on the probability distribution of the load and the probability distribution of the upper limit of the output of the new energy units. The number of samplings is accumulated to obtain a new dynamic carbon emission factor, until the number of samplings reaches a preset number of iterations. Sampling results are generated based on multiple sets of dynamic carbon emission factors, and the prediction range of the dynamic carbon emission factor is obtained based on the sampling results. The calculation of the dynamic carbon emission factor based on the basic data matrix includes: in, As a dynamic carbon emission factor, Let be the active flux matrix of the nodes. This is the branch power flow distribution matrix. Inject the matrix into the unit, This represents the actual carbon emission intensity of the unit. After generating the sampling results based on the dynamic carbon emission factor, the method further includes: Determine multiple sets of confidence levels for the power system; The fluctuation range of the carbon emission factor under each confidence level is determined based on the multiple confidence levels; wherein, The step of determining the fluctuation range of the carbon emission factor under each set of confidence levels based on the multiple sets of confidence levels includes: ； in, For confidence level, The upper limit for the set carbon emission factor fluctuation. E is the set lower limit for the carbon emission factor.

2. A dynamic carbon emission factor prediction device, characterized in that, include: The determination module is used to determine the probability distribution of the load and the probability distribution of the upper limit of the output of the new energy units, and to sample the actual load and the actual unit output based on the probability distribution of the load and the probability distribution of the upper limit of the output of the new energy units; wherein, the probability distribution of the load is: ； The probability distribution of the upper limit of the output of the new energy unit is as follows: ； in, The variance of the load, Let be the probability distribution of load i at time t. Let represent the probability distribution of the upper limit of the output of the new energy unit i at time t. The expected load; The calculation module is used to calculate the network power flow distribution over multiple time periods based on the actual load and the actual unit output, under the constraints of an economic dispatch model. The economic dispatch model aims to minimize the total system cost and satisfies upper and lower limits for unit output and power flow safety constraints. The economic dispatch model is as follows: The upper and lower limits of unit output and the power flow safety constraints are as follows: in, For generator sets, For the collection of transmission lines, and These are the upper and lower limits of the output of the i-th unit, respectively. and These are the upper and lower limits of the power flow for the l-th line, respectively. The duration is the length of the time period. The total cost is calculated, and the power flow calculation results are obtained based on the network power flow distribution of the multiple time periods. A basic data matrix is ​​then generated based on the power flow calculation results. The prediction module is used to calculate the dynamic carbon emission factor based on the basic data matrix, and to resample based on the probability distribution of the load and the probability distribution of the upper limit of the output of the new energy units, and to accumulate the number of samplings to obtain a new dynamic carbon emission factor, until the number of samplings reaches a preset number of iterations, and to generate sampling results based on multiple sets of dynamic carbon emission factors, and to obtain the prediction range of the dynamic carbon emission factor based on the sampling results; wherein, the calculation of the dynamic carbon emission factor based on the basic data matrix includes: in, As a dynamic carbon emission factor, Let be the active flux matrix of the nodes. This is the branch power flow distribution matrix. Inject the matrix into the unit, This represents the actual carbon emission intensity of the unit. After generating the sampling results based on the dynamic carbon emission factor, the prediction module further includes: The first determining unit is used to determine multiple sets of confidence levels for the power system; The second determining unit is configured to determine the fluctuation range of the carbon emission factor under each set of confidence levels based on the multiple sets of confidence levels, wherein determining the fluctuation range of the carbon emission factor under each set of confidence levels based on the multiple sets of confidence levels includes: ； in, For confidence level, The upper limit for the set carbon emission factor fluctuation. E is the set lower limit for the carbon emission factor.

3. An electronic device, characterized in that, include: The memory, the processor, and the computer program stored in the memory and executable on the processor, the processor executing the program to implement the dynamic carbon emission factor prediction method as described in claim 1.

4. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the dynamic carbon emission factor prediction method as described in claim 1.

5. A computer program product, comprising computer program instructions, characterized in that, When the computer program / instructions are executed by the processor, they are used to implement the dynamic carbon emission factor prediction method as described in claim 1.

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