Calculation method and device for uncertain parameters of power indirect carbon emission intensity and medium

Abstract

The invention provides a calculation method and device for uncertain parameters of electric power indirect carbon emission intensity and a medium. The method comprises the steps that the electric power indirect carbon emission intensity of a node of an electric power system is defined as the carbon emission transfer amount generated by a power generation link attached to the trading unit electric quantity; adopting a polyhedral uncertainty set to represent the uncertainty of the power indirect carbon emission intensity; according to the obtained source and load prediction values, system steady-state power flow distribution is obtained, and through carbon emission flow theoretical calculation, the prediction value of the power indirect carbon emission intensity of each node of the power grid is obtained; and predicting the maximum error value of the power indirect carbon emission intensity of each node of the power grid based on historical data by adopting a machine learning algorithm. After the prediction value and the maximum error value of the electric power indirect carbon emission intensity are obtained according to the method, day-ahead low-carbon optimization scheduling of the grid-connected energy system can be carried out under the condition that the uncertainty of the electric power indirect carbon emission of the grid-connected point is considered, and the carbon reduction benefit of the grid-connected energy system under the low-carbon target is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon emission calculation of power systems, and in particular to a method for calculating uncertain parameters of indirect carbon emission intensity of power. Background Art

[0002] As the core of energy production and consumption, the power system is an important source of carbon emissions, accounting for more than one-third of the total carbon emissions from fossil fuel combustion in the world and more than 40% of my country's carbon emissions. Therefore, promoting the low-carbon transformation of the power system and reducing its carbon emission level are crucial for my country to achieve its "dual carbon" goals and sustainable development.

[0003] The power system has the characteristic of source following load. It is the end users who create the demand for fossil fuel combustion and are the potential driving force behind carbon emissions. Direct settlement based on power generation cannot clearly allocate carbon emission responsibilities to the demand side, which may lead to incentive imbalance and fail to reduce carbon emissions in the power industry by promoting energy conservation on the consumer side. Under the principle of fair and equitable allocation of carbon emission responsibilities, if the carbon emissions caused by power users are calculated, measured and observed, and this signal is fed back to users, it can promote users to form a more efficient electricity consumption behavior pattern towards low-carbon goals.

[0004] In order to effectively guide users to save energy and reduce carbon emissions during the use of electricity, clarify the indirect carbon emissions generated by electricity consumption of different users, and clarify the allocation of carbon emission responsibilities, the power system carbon emission flow theory can intuitively analyze the source, flow direction and distribution of carbon emissions in the power system, and provide a new way to divide carbon emission responsibilities. It has been widely accepted. This method is based on real-time information such as power grid trends and unit carbon emissions. It can accurately calculate the carbon emissions generated by the equivalent power generation end of each node in the real-time operation of the power grid. Based on the power carbon emission flow theory, under the day-ahead scheduling time scale, due to the randomness of the output of new energy units, the unit electricity carbon emissions of the nodes connected to the power grid of the integrated energy system will have distinct uncertainty characteristics.

[0005] As the installed capacity of new energy sources increases year by year, the uncertainty of carbon emission intensity at power system nodes cannot be ignored in the low-carbon operation of grid-connected integrated energy systems. Therefore, a calculation method for the uncertain parameters of indirect carbon emissions of electricity is urgently needed to determine the range of values ​​of indirect carbon emission parameters at power system nodes on the day-ahead time scale. Summary of the invention

[0006] The present invention aims to solve at least one of the technical problems in the prior art, namely, the lack of a method for reliably calculating the uncertain parameters of indirect carbon emissions from electricity, and the difficulty in determining the value range of the indirect carbon emission parameters of power system nodes on a day-ahead time scale.

[0007] To this end, a first aspect of the present invention provides a method for calculating uncertain parameters of indirect carbon emission intensity of electricity.

[0008] A second aspect of the present invention provides a computer device.

[0009] A third aspect of the present invention provides a computer-readable storage medium.

[0010] The present invention provides a method for calculating uncertain parameters of indirect carbon emission intensity of electricity, comprising:

[0011] The indirect carbon emission intensity of power system nodes is defined as the carbon emission transfer amount generated by the power generation link attached to the transaction unit electricity; the uncertainty of the indirect carbon emission intensity of power is represented by a polyhedral uncertainty set;

[0012] The steady-state power flow distribution of the system is obtained based on the obtained source and load forecast values, and then the predicted value of the indirect carbon emission intensity of each node of the power grid is obtained through theoretical calculation of carbon emission flow;

[0013] A machine learning algorithm is used to predict the maximum error value of the indirect carbon emission intensity of electricity at each node in the power grid based on historical data.

[0014] The method for calculating the uncertain parameters of the indirect carbon emission intensity of electricity according to the above technical solution of the present invention may also have the following additional technical features:

[0015] In the above technical solution, the uncertainty of indirect carbon emission intensity of electricity is represented by a polyhedral uncertainty set, including:

[0016]

[0017] Among them, U2 represents the uncertain set of indirect carbon emission intensity of electricity at the grid connection point of the integrated energy system; represents the indirect carbon emission intensity of electricity at the grid connection point of the integrated energy system during period t; T represents the scheduling period; represents the predicted value of indirect carbon emission intensity of electricity during period t; It represents the deviation between the actual value and the predicted value of the indirect carbon emission intensity of electricity during period t; represents the maximum deviation between the actual value and the predicted value of the indirect carbon emission intensity of electricity during period t; Γ Grid Represents the uncertain adjustment parameter of indirect carbon emission intensity of electricity.

[0018] In the above technical solution, the source and load prediction values ​​include new energy power generation prediction value, fossil energy output prediction value and power load prediction value.

[0019] In the above technical solution, the steady-state power flow distribution of the system is obtained according to the obtained source and load prediction values, and then the indirect carbon emission intensity prediction value of each node of the power grid is obtained through theoretical calculation of carbon emission flow, including:

[0020]

[0021] in, It represents the indirect carbon emission intensity of electricity at the grid connection point of the integrated energy system during period t; represents the carbon flow from the kth branch to the node during period t; represents the power flowing from the kth branch to the node during period t; It represents the amount of electricity provided to the grid by the i-th fossil energy source flowing to the node during period t; It represents the amount of electricity provided to the grid by the jth clean energy flowing to the node during period t; It represents the energy quality consumed by the i-th fossil energy to provide unit electricity to the power grid during period t; It represents the carbon emission per unit mass of the i-th fossil energy consumption in period t; It represents the network loss rate of the electric energy provided by the i-th fossil energy to the power grid from the power generation end to the point where the integrated energy system is connected to the power grid during period t; It represents the network loss rate of the electric energy provided by the jth clean energy to the power grid from the power generation end to the point where the integrated energy system is connected to the power grid during period t.

[0022] In the above technical solution, the historical data includes power grid topology data, photovoltaic power generation forecast data, wind power generation forecast data, load forecast data, generator set carbon emission power generation data, and node power indirect carbon emission intensity historical data.

[0023] In the above technical solution, the machine learning algorithm includes a neural network algorithm.

[0024] In the above technical solution, a neural network algorithm is used to predict the maximum error value of the indirect carbon emission intensity of each node of the power grid based on historical data, including:

[0025] Collect grid topology data, including photovoltaic power generation forecast data, wind power generation forecast data, load forecast data, carbon emission data of each generator set, and historical data of indirect carbon emission intensity of node power;

[0026] Use MATLAB neural network toolbox to establish BP neural network;

[0027] The collected data is used to train the BP neural network and establish a maximum error value prediction model for indirect carbon emission intensity of electricity;

[0028] The real-time data of the power grid is input into the maximum error value prediction model of the indirect carbon emission intensity of electricity to obtain the maximum error value of the indirect carbon emission intensity of electricity.

[0029] In the above technical solution, the use of a neural network algorithm to predict the maximum error value of the indirect carbon emission intensity of each node of the power grid based on historical data also includes:

[0030] Conduct error analysis on the calculation results of the maximum error value of indirect carbon emission intensity of electricity;

[0031] The calculation results that meet the error convergence are determined as the predicted values, and the prediction curve of the maximum error value of the indirect carbon emission intensity of electricity is obtained.

[0032] The present invention provides a computer device, comprising a processor and a memory, wherein the memory stores a computer program, and when the computer program is loaded and executed by the processor, a method for calculating uncertain parameters of indirect carbon emission intensity of electricity as described in any one of the above technical solutions is implemented.

[0033] The present invention provides a computer-readable storage medium storing a program, which, when loaded by a processor, implements a method for calculating uncertain parameters of indirect carbon emission intensity of electricity as described in any one of the above technical solutions.

[0034] In summary, due to the adoption of the above technical features, the beneficial effects of the present invention are:

[0035] The present invention can effectively calculate the predicted value of the indirect carbon emission intensity of electricity and the maximum error value, and can reliably determine the value range of the indirect carbon emission parameters of the power system node at the day-ahead time scale. The calculation method provided by the present invention is simple and efficient. Moreover, after obtaining the predicted value of the indirect carbon emission intensity of electricity and the maximum error value according to the present invention, the day-ahead low-carbon optimization scheduling of the grid-connected energy system can be carried out under the consideration of the uncertainty of the indirect carbon emission of electricity at the grid connection point, thereby improving the carbon reduction and carbon reduction benefits of the grid-connected energy system under the low-carbon target.

[0036] Additional aspects and advantages of the present invention will become apparent from the following description or may be learned by practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0038] Figure 1 It is a schematic diagram of the calculation principle of the predicted value of indirect carbon emission intensity of electricity in a method for calculating uncertain parameters of indirect carbon emission intensity of electricity in one embodiment of the present invention;

[0039] Figure 2It is a schematic diagram of the calculation principle of the maximum error value of the indirect carbon emission intensity of electricity in a method for calculating the uncertain parameters of the indirect carbon emission intensity of electricity in one embodiment of the present invention;

[0040] Figure 3 This is the schematic diagram of the IEEE five-machine fourteen-node system;

[0041] Figure 4 It is a schematic diagram of the calculation results of the predicted value and maximum error value of the indirect carbon emission intensity of electricity according to an embodiment of the present invention. DETAILED DESCRIPTION

[0042] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0043] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0044] Refer to the following Figures 1 to 4 The following describes a method, device and medium for calculating uncertain parameters of indirect carbon emission intensity of electricity provided in some embodiments of the present invention.

[0045] Some embodiments of the present application provide a method for calculating uncertain parameters of indirect carbon emission intensity of electricity.

[0046] The first embodiment of the present invention proposes a method for calculating uncertain parameters of indirect carbon emission intensity of electricity, and the method for calculating uncertain parameters of indirect carbon emission intensity of electricity includes the following steps S1-S3.

[0047] S1. The indirect carbon emission intensity of power system nodes is defined as the carbon emission transfer generated by the power generation link attached to the trading unit electricity; the uncertainty of the indirect carbon emission intensity of power is represented by a polyhedron uncertainty set.

[0048] Specifically, the indirect carbon emission intensity of electricity is not a real physical parameter, but is related to the carbon emission parameters of each node in the power grid topology and the power flow distribution. Therefore, the indirect carbon emission intensity of electricity cannot be directly predicted like the output of new energy and load power. The present disclosure first defines the indirect carbon emission intensity of electricity, and then gives the calculation method of the predicted value of the indirect carbon emission intensity of electricity and the maximum error value of the indirect carbon emission intensity of electricity. To calculate the transfer of carbon emission costs caused by the interaction of electricity between the integrated energy system and the power grid, it is necessary to obtain the indirect carbon emission intensity of electricity at the point where the integrated energy system and the power grid are connected at the current moment. Therefore, the indirect carbon emission intensity of electricity at the power system node is defined as the amount of carbon emission transfer generated by the power generation link attached to the trading unit electricity. Among them, the integrated energy system is a comprehensive power supply system that provides electricity through wind, light, water, fire and other energy sources. Specifically, wind power generation, hydropower generation and photovoltaic power generation are new energy systems, and thermal power generation is a fossil energy system.

[0049] Specifically, the polyhedral uncertainty set is used to represent the uncertainty of indirect carbon emission intensity of electricity, including:

[0050]

[0051] Among them, U2 represents the uncertain set of indirect carbon emission intensity of electricity at the grid connection point of the integrated energy system; represents the indirect carbon emission intensity of electricity at the grid-connected point of the integrated energy system during period t; T represents the scheduling period, and in a specific embodiment, the value is 24h; represents the predicted value of indirect carbon emission intensity of electricity during period t; It represents the deviation between the actual value and the predicted value of the indirect carbon emission intensity of electricity during period t; represents the maximum deviation between the actual value and the predicted value of the indirect carbon emission intensity of electricity during period t; Γ Grid Represents the uncertain adjustment parameter of indirect carbon emission intensity of electricity.

[0052] S2. Obtain the steady-state power flow distribution of the system based on the acquired source and load forecast values, and then obtain the forecast value of the indirect carbon emission intensity of each node of the power grid through theoretical calculation of carbon emission flow. The source and load forecast values ​​include the new energy power generation forecast value, the fossil energy output forecast value and the power load forecast value.

[0053] For the output of new energy and load power, predictions can be made based on meteorological conditions and electricity consumption habits. However, the indirect carbon emission intensity of electricity is not a real physical parameter. It is related to the carbon emission parameters of each node in the power grid topology and the power flow distribution. Therefore, the indirect carbon emission intensity of electricity cannot be directly predicted like the output of new energy and load power. In other words, unlike the traditional method of obtaining the prediction value of uncertain parameters of new energy output and load power, the prediction value of the indirect carbon emission intensity of electricity is not directly predicted based on relevant parameters and historical data, but based on the obtained source and load prediction values ​​to obtain the steady-state power flow distribution of the system, and then through the carbon emission flow theory calculation, finally the prediction value of the indirect carbon emission intensity of electricity at each node of the power grid is obtained.

[0054] That is to say, unlike the traditional method of obtaining the prediction value of uncertain parameters of new energy output and load power, the prediction value of indirect carbon emission intensity of electricity is not directly predicted based on relevant parameters and historical data, but rather Figure 1 As shown in the figure, the steady-state power flow distribution of the system is obtained based on the obtained source and load forecast values, and then the carbon emission flow theory is calculated to finally obtain the forecast value of the indirect carbon emission intensity of each node in the power grid.

[0055] Among them, the carbon emission flow is a virtual network flow formed by carbon emissions that exist in dependence on the power flow and are used to characterize the carbon emissions that maintain the power flow of any branch in the power system. Without causing ambiguity, it can be referred to as carbon emission flow or carbon flow in the field of power systems. Intuitively, the carbon emission flow of the power system is equivalent to adding a carbon emission label to the power flow on each branch. Since there is a dependency relationship between the carbon emission flow and the power flow, it can be considered that: in the power system, the carbon emission flow starts from the power plant (power plant node), enters the power system with the power plant's grid-connected power, flows in the power grid following the power flow in the system, and finally flows into the consumer terminal (load node) on the user side. On the surface, carbon emissions are discharged into the atmosphere through power plants, but in reality, carbon emissions are consumed by power users through carbon emission flows. The carbon flow density of all power flows flowing out of a node is equal to the carbon potential of the node. The carbon flow rate and carbon flow of the branch in the carbon flow analysis correspond to the active power and power of the branch in the power flow analysis, and are associated with the carbon flow density, and both have clear physical meanings. The specific carbon emission flow theory is known to those skilled in the art and will not be elaborated here.

[0056] In some embodiments, the calculation method of the indirect carbon emission intensity of power system node electricity is as follows:

[0057]

[0058] in, It represents the indirect carbon emission intensity of electricity at the grid connection point of the integrated energy system during period t; represents the carbon flow from the kth branch to the node during period t; represents the power flowing from the kth branch to the node during period t; It represents the amount of electricity provided to the grid by the i-th fossil energy source flowing to the node during period t; It represents the amount of electricity provided to the grid by the jth clean energy flowing to the node during period t; It represents the energy quality consumed by the i-th fossil energy to provide unit electricity to the power grid during period t; It represents the carbon emission per unit mass of the i-th fossil energy consumption in period t; It represents the network loss rate of the electric energy provided by the i-th fossil energy to the power grid from the power generation end to the point where the integrated energy system is connected to the power grid during period t; It represents the network loss rate of the electric energy provided by the jth clean energy to the power grid from the power generation end to the point where the integrated energy system is connected to the power grid during period t.

[0059] On the day-ahead time scale, the random fluctuation characteristics of wind power and photovoltaic power make the proportion of power sources flowing from the power grid to the grid connection point of the integrated energy system have a certain randomness. The randomness and volatility of the grid will eventually lead to indirect carbon emission intensity at the grid nodes. is an uncertain parameter. That is, the indirect carbon emission intensity of the power system node will inherit the uncertainty characteristics of the output of new energy sources such as wind and solar in the power grid.

[0060] S3. Use machine learning algorithms to predict the maximum error value of the indirect carbon emission intensity of electricity at each node in the power grid based on historical data.

[0061] In some embodiments, the maximum error value of the indirect carbon emission intensity of electricity at each node in the power grid is predicted based on multiple historical data, and the historical data include power grid topology data, photovoltaic power generation prediction data, wind power generation prediction data, load prediction data, generator set carbon emission power generation data, and node power indirect carbon emission intensity historical data.

[0062] It can be understood that the machine learning algorithm can adopt any suitable machine learning algorithm. For better explanation, a neural network algorithm is used for training in the present disclosure.

[0063] In a specific embodiment, Figure 2 As shown in the figure, a neural network algorithm is used to predict the maximum error value of the indirect carbon emission intensity of each node in the power grid based on historical data, including:

[0064] Collect grid topology data, including photovoltaic power generation forecast data, wind power generation forecast data, load forecast data, carbon emission data of each generator set, and historical data of indirect carbon emission intensity of node power;

[0065] Use MATLAB neural network toolbox to establish BP neural network;

[0066] The collected data is used to train the BP neural network and establish a maximum error value prediction model for indirect carbon emission intensity of electricity;

[0067] Inputting the real-time data of the power grid into the maximum error value prediction model of the indirect carbon emission intensity of electricity to obtain the maximum error value of the indirect carbon emission intensity of electricity;

[0068] Conduct error analysis on the calculation results of the maximum error value of indirect carbon emission intensity of electricity;

[0069] The calculation results that meet the error convergence are determined as the predicted values, and the prediction curve of the maximum error value of the indirect carbon emission intensity of electricity is obtained.

[0070] In a specific embodiment, Figure 3 The IEEE five-machine fourteen-node system shown performs simulation calculations on the predicted value and maximum error value of the indirect carbon emission intensity of the power grid nodes.

[0071] Figure 3 The generators connected to nodes 2 and 6 are renewable energy generators, and the rest are thermal power generators. The calculation method described in this disclosure is used to obtain the predicted value and maximum error value of the indirect carbon emission intensity of power for node 4 of the IEEE 5-machine 14 nodes during the time period of 0:00 to 24:00, as shown in the following figure: Figure 4 shown.

[0072] Figure 4 The black dotted line in the middle is the predicted value of the indirect carbon emission intensity of electricity at node 4 in the time period of 0:00-24:00. The gray interval is the error interval of the indirect carbon emission intensity of electricity at node 4 in the time period of 0:00-24:00, and the interval boundary is the maximum error value.

[0073] Some other embodiments of the present invention provide a computer device, including a processor and a memory, wherein the memory stores a computer program, and when the computer program is loaded and executed by the processor, the method for calculating the uncertainty parameters of the indirect carbon emission intensity of electricity as described in any of the above embodiments is implemented.

[0074] Still other embodiments of the present invention provide a computer-readable storage medium storing a program, which, when loaded by a processor, implements the method for calculating the uncertainty parameters of the indirect carbon emission intensity of electricity as described in any of the above embodiments.

[0075] In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0076] Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for calculating uncertain parameters of indirect carbon emission intensity of electricity, characterized in that: include: The indirect carbon emission intensity of power system nodes is defined as the carbon emission transfer generated by the power generation link attached to the transaction unit electricity; The uncertainty of indirect carbon emission intensity of electricity is represented by a polyhedral uncertainty set. The steady-state power flow distribution of the system is obtained based on the obtained source and load forecast values, and then the forecast value of the indirect carbon emission intensity of each node of the power grid is obtained through theoretical calculation of carbon emission flow; A machine learning algorithm is used to predict the maximum error value of the indirect carbon emission intensity of electricity at each node in the power grid based on historical data.

2. The method for calculating the uncertainty parameters of indirect carbon emission intensity of electricity according to claim 1 is characterized in that: The polyhedral uncertainty set is used to represent the uncertainty of indirect carbon emission intensity of electricity, including: Among them, U2 represents the uncertain set of indirect carbon emission intensity of electricity at the grid connection point of the integrated energy system; represents the indirect carbon emission intensity of electricity at the grid connection point of the integrated energy system during period t; T represents the scheduling period; represents the predicted value of indirect carbon emission intensity of electricity during period t; It represents the deviation between the actual value and the predicted value of the indirect carbon emission intensity of electricity during period t; represents the maximum deviation between the actual value and the predicted value of the indirect carbon emission intensity of electricity during period t; Γ Grid Represents the uncertain adjustment parameter of indirect carbon emission intensity of electricity.

3. The method for calculating the uncertainty parameters of indirect carbon emission intensity of electricity according to claim 1 is characterized in that: The source and load forecast values ​​include new energy power generation forecast values, fossil energy output forecast values ​​and power load forecast values.

4. The method for calculating the uncertainty parameters of indirect carbon emission intensity of electricity according to claim 3 is characterized in that: The method of obtaining the steady-state power flow distribution of the system based on the obtained source and load prediction values, and then calculating the indirect carbon emission intensity prediction value of each node of the power grid through carbon emission flow theory, includes: in, It represents the indirect carbon emission intensity of electricity at the grid connection point of the integrated energy system during period t; represents the carbon flow from the kth branch to the node during period t; represents the power flowing from the kth branch to the node during period t; It represents the amount of electricity provided to the grid by the i-th fossil energy source flowing to the node during period t; It represents the amount of electricity provided to the grid by the jth clean energy flowing to the node during period t; It represents the energy quality consumed by the i-th fossil energy to provide unit electricity to the power grid during period t; It represents the carbon emission per unit mass of the i-th fossil energy consumption in period t; It represents the network loss rate of the electric energy provided by the i-th fossil energy to the power grid from the power generation end to the point where the integrated energy system is connected to the power grid during period t; It represents the network loss rate of the electric energy provided by the jth clean energy to the power grid from the power generation end to the point where the integrated energy system is connected to the power grid during period t.

5. The method for calculating the uncertainty parameters of indirect carbon emission intensity of electricity according to claim 2 is characterized in that: The historical data includes power grid topology data, photovoltaic power generation forecast data, wind power generation forecast data, load forecast data, generator set carbon emission power generation data, and node power indirect carbon emission intensity historical data.

6. The method for calculating the uncertainty parameters of indirect carbon emission intensity of electricity according to claim 5 is characterized in that: The machine learning algorithm includes a neural network algorithm.

7. The method for calculating the uncertainty parameters of indirect carbon emission intensity of electricity according to claim 6 is characterized in that: The neural network algorithm is used to predict the maximum error value of the indirect carbon emission intensity of each node of the power grid based on historical data, including: Collect grid topology data, including photovoltaic power generation forecast data, wind power generation forecast data, load forecast data, carbon emission data of each generator set, and historical data of indirect carbon emission intensity of node power; Use MATLAB neural network toolbox to establish BP neural network; The collected data is used to train the BP neural network and establish a maximum error value prediction model for indirect carbon emission intensity of electricity; The real-time data of the power grid is input into the maximum error value prediction model of the indirect carbon emission intensity of electricity to obtain the maximum error value of the indirect carbon emission intensity of electricity.

8. The method for calculating the uncertainty parameters of indirect carbon emission intensity of electricity according to claim 7 is characterized in that: The method of using a neural network algorithm to predict the maximum error value of the indirect carbon emission intensity of electricity at each node of the power grid based on historical data also includes: Conduct error analysis on the calculation results of the maximum error value of indirect carbon emission intensity of electricity; The calculation results that meet the error convergence are determined as the predicted values, and the prediction curve of the maximum error value of the indirect carbon emission intensity of electricity is obtained.

9. A computer device, characterized in that: It comprises a processor and a memory, wherein a computer program is stored in the memory, and when the computer program is loaded and executed by the processor, the method for calculating the uncertainty parameters of the indirect carbon emission intensity of electricity as described in any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium, characterized in that: A program is stored, and when the program is loaded by a processor, a method for calculating uncertain parameters of indirect carbon emission intensity of electricity as described in any one of claims 1 to 8 is implemented.

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