Carbon emission factor uncertainty analysis method and device and electronic equipment

Abstract

The invention discloses a carbon emission factor uncertainty analysis method and apparatus, and an electronic device. The method comprises the steps of obtaining target parameter data of a target power system; carrying out direct current load flow calculation based on the target parameter data so as to construct a carbon emission flow calculation model; uncertain sources are identified, random variables are constructed, a carbon emission flow calculation model is improved, and a probabilistic carbon emission flow model is obtained; clustering and grouping system operation state samples in the probabilistic carbon emission flow model based on the power flow direction of a system branch; aiming at each power flow working condition group, obtaining a piecewise linearization carbon flow calculation model according to a mapping relation between a node carbon emission factor and node power injection with uncertainty; and calculating a carbon emission factor of a target node, and further calculating an interval estimation result of the carbon emission factor of each node. According to the method, the random influence in system operation can be reflected, and timeliness and reliability are provided for carbon emission factor prediction and low-carbon scheduling.

Description

Technical Field

[0001] This application relates to the field of power system technology, and in particular to a method, apparatus and electronic equipment for analyzing the uncertainty of carbon emission factors. Background Technology

[0002] In new power systems with renewable energy integration, the uncertainty of wind and solar power output has a significantly enhanced impact on system operation. Its volatility is transmitted to the load side through power flow, causing the carbon emission factors of user nodes to exhibit uncertainty characteristics, thereby directly affecting users' low-carbon decisions and the overall carbon emission reduction efficiency of the system.

[0003] In related technologies, carbon emission flow theory can distribute carbon emissions generated by source-side units to the user side along the power flow path of the power grid by constructing a carbon flow transfer matrix, thereby achieving accurate tracking of nodal-level carbon emission factors. Based on day-ahead wind and solar power output and load forecasting, user carbon emission factors can be predicted by combining operational simulation models with carbon emission flow theory. However, existing methods are generally based on the assumption of deterministic inputs, ignoring key factors such as renewable energy fluctuations, load forecasting biases, and the stochasticity of power grid operation. This makes it difficult to directly characterize the uncertainty distribution of carbon emission factors, limiting their effectiveness as a risk-aware decision-making reference in user low-carbon response and system scheduling. Summary of the Invention

[0004] This disclosure provides a method, apparatus, and electronic device for analyzing the uncertainty of carbon emission factors, in order to at least solve the above-mentioned technical problems existing in the prior art.

[0005] According to a first aspect of this application, a method for analyzing the uncertainty of carbon emission factors is provided, comprising:

[0006] Obtain target parameter data for the target power system;

[0007] DC power flow calculations are performed based on the target parameter data to obtain the power flow calculation results;

[0008] Based on the tidal flow calculation results, a carbon emission flow calculation model is constructed;

[0009] Identify the sources of uncertainty in the target power system, generate random variables based on the probability distribution of the sources of uncertainty, and improve the carbon emission flow calculation model based on the random variables to obtain a probabilistic carbon emission flow model.

[0010] Based on the power flow direction of the system branches, the system operating state samples in the probabilistic carbon emission flow model are clustered and grouped to form multiple power flow condition groups; for each power flow condition group, the mapping relationship between the node carbon emission factor and the node power injection with uncertainty is approximated as a piecewise linear function to obtain a piecewise linearized carbon flow calculation model.

[0011] When the sample pool capacity of any tidal current condition group reaches a preset threshold, based on the sample data in the sample pool of any tidal current condition group, with the uncertain node power injection as the independent variable and the node carbon emission factor as the dependent variable, a linear regression analysis is performed to fit the piecewise linear coefficients of the corresponding tidal current condition group.

[0012] The carbon emission factor of the target node of the sample under each system operation scenario is calculated using a carbon flow calculation model with piecewise linear coefficients.

[0013] Based on the probability distribution of the carbon emission factors of the target node, the interval estimation results of the carbon emission factors of each node are calculated at a given confidence level.

[0014] In one possible implementation, it further includes:

[0015] Based on the confidence interval results of the node carbon emission factors, the uncertainty of carbon emission factors under different system operation scenarios is compared to assess the risk of carbon emission exceeding the standard of the target power system.

[0016] In one possible implementation, the probabilistic carbon emission flow model calculates the nodal stochastic carbon emission factor in the following manner:

[0017]

[0018] in, For carbon emission flow models; For node random carbon emission factors; A random vector injected into the power of random nodes.

[0019] In one possible implementation, for each of the power flow condition groups, the mapping relationship between the node carbon emission factor and the node power injection with uncertainty is approximated as a piecewise linear function to obtain a piecewise linearized carbon flow calculation model, including:

[0020] The probabilistic carbon emission flow model is subjected to Taylor expansion and higher-order terms are ignored. The power at the equilibrium node is eliminated by elimination method to obtain the linear coefficients and constant terms for each power flow condition group.

[0021] In one possible implementation, the elimination method for balancing node power includes:

[0022] The power of the balancing node is expressed as a linear combination of the power injected by other nodes;

[0023] Substitute the linear combination into the piecewise linear function to eliminate the power variable at the equilibrium node.

[0024] In one possible implementation, the piecewise linearized carbon flow calculation model is as follows:

[0025]

[0026]

[0027]

[0028] in, Let be the carbon emission factor of node i under the current flow condition group m. A set of nodes with uncertain power injection. The linear coefficients are given for power flow condition group m with uncertain node k. For constant terms, For power injection at node k with uncertainty, Let be the original linear coefficient of a certain node. These are the original linear coefficients of the equilibrium nodes. For constant terms, A set of nodes with uncertain power injection. Inject a power set into nodes of constant size.

[0029] In one possible implementation, the carbon flow calculation model with piecewise linear coefficients is:

[0030]

[0031]

[0032] Where E is the carbon emission factor of the node injection power, which has randomness. yes ×( An S+1 dimensional matrix.

[0033] In one possible implementation, the calculation of the target node carbon emission factor for samples under various system operating scenarios using a carbon flow calculation model with piecewise linear coefficients includes:

[0034] Determine whether the sample belongs to any power flow condition group;

[0035] In response to the fact that the sample belongs to any power flow condition group, the target node carbon emission factor of the sample is calculated based on the piecewise linear coefficients corresponding to the power flow condition group.

[0036] In response to the fact that the sample does not belong to any power flow condition group, the target node carbon emission factor of the sample is calculated using the probabilistic carbon emission flow model.

[0037] According to a second aspect of this application, a carbon emission factor uncertainty analysis apparatus is provided, comprising:

[0038] The data acquisition module is used to acquire target parameter data of the target power system;

[0039] The first calculation module is used to perform DC power flow calculation based on the target parameter data to obtain the power flow calculation result;

[0040] The model building module is used to build a carbon emission flow calculation model based on the tidal flow calculation results;

[0041] The model improvement module is used to identify the sources of uncertainty in the target power system, generate random variables based on the probability distribution of the sources of uncertainty, and improve the carbon emission flow calculation model based on the random variables to obtain a probabilistic carbon emission flow model.

[0042] The sample grouping module is used to cluster and group the system operating state samples in the probabilistic carbon emission flow model based on the power flow direction of the system branches, forming multiple power flow condition groups; for each power flow condition group, the mapping relationship between the node carbon emission factor and the node power injection with uncertainty is approximated as a piecewise linear function to obtain a piecewise linearized carbon flow calculation model.

[0043] The linear fitting module is used to perform linear regression analysis based on the sample data in the sample pool of any tidal current condition group when the sample pool capacity of any tidal current condition group reaches a preset threshold, with the node power injection with uncertainty as the independent variable and the node carbon emission factor as the dependent variable, to fit the piecewise linear coefficients of the corresponding tidal current condition group.

[0044] The second calculation module is used to calculate the target node carbon emission factor of the sample under each system operation scenario using a carbon flow calculation model with piecewise linear coefficients.

[0045] The third calculation module is used to calculate the interval estimation results of the carbon emission factors of each node at a given confidence level based on the probability distribution of the carbon emission factors of the target node.

[0046] According to a third aspect of this application, an electronic device is provided, comprising:

[0047] A memory communicatively connected to the at least one processor; wherein,

[0048] The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method described in any of the above embodiments.

[0049] According to a fourth aspect of this application, a non-transitory computer-readable storage medium is provided storing computer instructions for causing the computer to perform the methods described in this application.

[0050] According to a fifth aspect of this application, a computer program product is provided, comprising a computer program or instructions that, when executed by a processor, implement the method described in this application.

[0051] The technical solution of this application can provide timely and reliable technical support for carbon emission factor prediction and low-carbon scheduling.

[0052] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent from the following description. Attached Figure Description

[0053] The above and other objects, features, and advantages of exemplary embodiments of this application will become readily apparent from the following detailed description taken in conjunction with the accompanying drawings. Several embodiments of this application are illustrated in the drawings by way of example and not limitation, in which:

[0054] In the accompanying drawings, the same or corresponding reference numerals indicate the same or corresponding parts.

[0055] Figure 1 This paper illustrates the implementation flow of the carbon emission factor uncertainty analysis method in an embodiment of this application. Figure 1 ;

[0056] Figure 2 This paper illustrates the implementation flow of the carbon emission factor uncertainty analysis method in an embodiment of this application. Figure 2 ;

[0057] Figure 3 (a) shows a schematic diagram of the average carbon emission factor of each node within a day in the embodiments of this application;

[0058] Figure 3 (b) shows a schematic diagram of the standard deviation of carbon emission factors for each node within a day in the embodiments of this application;

[0059] Figure 4 A schematic diagram of the carbon emission factor uncertainty analysis device in an embodiment of this application is shown;

[0060] Figure 5 A schematic diagram of the composition structure of an electronic device according to an embodiment of this application is shown. Detailed Implementation

[0061] To make the objectives, features, and advantages of this application more apparent and understandable, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0062] In the following description, the terms "first" and "second" are used merely to distinguish similar objects and do not represent a specific ordering of objects. It is understood that "first" and "second" may be interchanged in a specific order or sequence where permitted, so that the embodiments of this application described herein can be implemented in an order other than that illustrated or described herein.

[0063] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0064] The following description, in conjunction with the accompanying drawings, introduces a method, apparatus, and electronic device for analyzing the uncertainty of carbon emission factors provided in this application.

[0065] like Figure 1 As shown, this application provides a method for analyzing the uncertainty of carbon emission factors, including:

[0066] S101, Obtain target parameter data of the target power system.

[0067] The target parameter data includes network topology parameters, component parameters, load power prediction data, and new energy power generation prediction data.

[0068] Network topology parameters characterize the node connections and network structure of a power system, forming the fundamental framework for power flow calculations. These parameters include basic node information, branch connections, and the network topology diagram. Component parameters characterize the electrical characteristics of equipment such as transmission lines and transformers, directly affecting the accuracy of power flow calculations. Component parameters include transmission line parameters, transformer parameters, and generator parameters. Load power forecast data characterizes users' electricity demand over a future period and is one of the core inputs for uncertainty analysis. Renewable energy power generation forecast data characterizes the output fluctuation characteristics of wind power, photovoltaic, and other units, and is a key manifestation of uncertainties. Renewable energy power generation forecast data includes predicted power generation values ​​for wind farms and photovoltaic power plants. This forecast data is obtained from new energy plant monitoring systems or regional new energy dispatch platforms, and is typically generated by the plant-side forecasting system in conjunction with numerical weather forecasts (wind speed, solar irradiance).

[0069] S102, DC power flow calculation is performed based on the target parameter data to obtain power flow calculation results; the power flow calculation results include system power flow direction, branch power distribution, node injected power and node outflow power.

[0070] This application generates a node admittance matrix using the network topology parameters and component parameters described in the above steps, clarifying the connection relationships and reactance values ​​of each branch. Based on load power prediction data and new energy power generation prediction data, the net injected power of each node is determined, with the injected power of balanced nodes temporarily set as unknown. According to the node power balance principle, a system of linear equations with node phase angles as variables is established for each unbalanced node. The system of linear equations is solved using matrix inversion or iterative methods to obtain the voltage phase angle values ​​of all unbalanced nodes. The solved node phase angles are substituted into the branch power transmission formula to calculate the active power and direction of each branch, obtaining the system power flow direction and branch power distribution. The total injected power and total outflow power of each node are calculated based on the branch power to verify whether power balance is satisfied, ensuring the accuracy of the calculation results, and finally determining the node injected power and node outflow power.

[0071] S103, Based on the current flow calculation results, construct a carbon emission flow calculation model.

[0072] Specifically, constructing a carbon emission flow calculation model based on power flow calculation results transforms the physical power flow of the power system into a carbon emission transmission flow. By tracing the power source, the carbon emission intensity on the generation side is accurately transmitted to each load node according to the branch power distribution ratio, and finally the carbon emission factor of each node is quantified.

[0073] S104, Identify the sources of uncertainty in the target power system, generate random variables based on the probability distribution of the sources of uncertainty, and improve the carbon emission flow calculation model based on the random variables to obtain a probabilistic carbon emission flow model; the sources of uncertainty include wind and solar forecasting errors and load power fluctuations.

[0074] This application first needs to clarify the statistical characteristics of the two sources of uncertainty: wind and solar forecasting errors and load power fluctuations, to provide a basis for constructing random variables. Specifically, this includes constructing the probability distribution of wind and solar forecasting errors and the probability distribution of load power fluctuations to construct random variables. By substituting these random variables into the carbon emission flow calculation model, the output of the carbon emission flow calculation model changes from a fixed carbon emission factor to a probability distribution of the carbon emission factor. The core is to establish a mapping relationship from random input to random output, thus obtaining a probabilistic carbon emission flow model.

[0075] S105, based on the power flow direction of the system branches, the system operating state samples in the probabilistic carbon emission flow model are clustered and grouped to form multiple power flow condition groups; for each power flow condition group, the mapping relationship between the node carbon emission factor and the node power injection with uncertainty is approximated as a piecewise linear function to obtain a piecewise linearized carbon flow calculation model.

[0076] Specifically, grouping by power flow direction can accurately capture the characteristics of different operating modes. Combined with piecewise linearization, this can significantly simplify the complexity of uncertainty analysis. Based on the power flow direction grouping of system branches, a piecewise linearized carbon flow model is constructed. Specifically, the operating conditions are first divided according to power flow characteristics, and then the nonlinear mapping relationship between power injection and carbon emission factors is locally simplified into a linear function within each group, balancing computational accuracy and efficiency.

[0077] S106, when the sample pool capacity of any tidal current condition group reaches a preset threshold, based on the sample data in the sample pool of any tidal current condition group, with the node power injection with uncertainty as the independent variable and the node carbon emission factor as the dependent variable, perform linear regression analysis to fit the piecewise linear coefficients of the corresponding tidal current condition group.

[0078] Specifically, triggering regression analysis by setting a sample pool capacity threshold ensures sufficient data for fitting and improves the reliability of linear coefficients. When the sample pool for the power flow condition group meets the threshold, linear regression is performed with uncertain node power injection as the independent variable and carbon emission factor as the dependent variable. The core is to use sample data to solve for the coefficients (slope k and intercept b) of the piecewise linear function, allowing the linear model to accurately map the variable relationships within the condition group.

[0079] S107 uses a carbon flow calculation model with piecewise linear coefficients to calculate the target node carbon emission factor of samples under various system operation scenarios.

[0080] This application improves computational efficiency by mapping the relationship between uncertain power injection and carbon emission factors, thus avoiding the repeated solution of complex probabilistic models.

[0081] S108, Based on the probability distribution of the carbon emission factors of the target node, calculate the interval estimation results of the carbon emission factors of each node at a given confidence level.

[0082] Finally, based on the probability distribution of the carbon emission factors of the target nodes, the interval estimation results of the carbon emission factors of each node at a given confidence level are calculated.

[0083] This application relates to a method for analyzing the uncertainty of carbon emission factors. By systematically integrating power system operation data and probabilistic modeling, it addresses the stochasticity problem in the uncertainty analysis of nodal carbon emission factors. First, target parameter data of the target power system is acquired, which may include network topology parameters, component parameters, load power prediction data, and renewable energy power generation prediction data, and may also include other data, which are not limited herein. The target parameter data provides a comprehensive and realistic system input basis for subsequent analysis, ensuring accurate reflection of the actual power grid structure and operating conditions. Based on the target parameter data, DC power flow calculations are performed, generating results such as system power flow direction, branch power allocation, node injection power, and node outflow power. The power flow calculation results provide a power distribution basis for constructing the carbon emission flow model. After the carbon emission flow calculation model is constructed, the direct correlation between power flow and carbon emissions can be obtained, thus transforming physical power flow into carbon emission flow. Specifically, uncertainties such as wind and solar forecast errors and load power fluctuations are constructed as random variables according to their corresponding probability distributions. These random variables are then used to improve the carbon emission flow calculation model, resulting in a probabilistic carbon emission flow model that incorporates random fluctuation characteristics. Furthermore, based on the power flow direction of the system branches, the system operating states are grouped into multiple power flow condition groups. For each condition group, the mapping relationship between the node carbon emission factor and the uncertain node power injection can be approximated as a piecewise linear function, resulting in a piecewise linearized carbon flow calculation model. This model can locally linearize complex nonlinear relationships by identifying behavioral differences under different power flow modes. When the sample pool capacity of any power flow condition group reaches a preset threshold, linear regression analysis is performed based on the sample data in the sample pool of that power flow condition group, with the uncertain node power injection as the independent variable and the node carbon emission factor as the dependent variable, to fit and obtain the piecewise linear coefficients of the corresponding condition group. Using the carbon flow calculation model with piecewise linear coefficients, the target node carbon emission factor of the samples under each system operating scenario is calculated, which avoids redundant probability calculations when matching samples to grouped conditions. Finally, based on the probability distribution of the target node carbon emission factor, the interval estimation results of the carbon emission factor of each node at a given confidence level are calculated, providing an intuitive basis for uncertainty quantification.

[0084] This application improves efficiency in handling large-scale scenarios by preserving key dynamic features while maintaining computational lightness through piecewise linearization. The probabilistic carbon emission flow model in this application realistically simulates the impact of random factors such as wind and solar forecasting errors and load power fluctuations, making carbon emission factor analysis closely coupled with the real-time operating status of the system. The interval estimation results provide a reliable basis for uncertainty quantification, effectively solving the accuracy problem of uncertainty analysis of node carbon emission factors in power systems, and laying a technical foundation for low-carbon operation management.

[0085] In some embodiments, it also includes:

[0086] Based on the confidence interval results of the node carbon emission factors, the uncertainty of carbon emission factors under different system operation scenarios is compared to assess the risk of carbon emission exceeding the standard of the target power system.

[0087] Specifically, after completing the rapid calculation and interval estimation of the probability distribution of carbon emission factors, this application can also conduct uncertainty analysis and applied research. By comparing the confidence intervals of node carbon emission factors under different operating scenarios, the carbon emission risk level of the system under the uncertainty conditions of new energy fluctuations can be assessed, thus providing an interval-based reference for low-carbon dispatching and market settlement.

[0088] It should be noted that traditional probabilistic power flow problems typically treat nodal power injection as a random variable, and solve for the probability distribution of branch power flow or nodal voltage given its joint probability distribution. The expression for probabilistic power flow is given by the following equation:

[0089] (1)

[0090] Where, vector This represents the output variables of probabilistic power flow, which may include node voltages or branch power flows. System function This refers to the trend model. It is a random vector representing the power injection of random nodes, including the volatility of renewable energy and load. The joint probability density function is expressed as ,in It represents the number of power injections to random nodes.

[0091] For the probabilistic carbon emission flow problem, this application obtains the probability distribution of the node carbon emission factor given the probability distribution of node power injection.

[0092] In some embodiments, the probabilistic carbon emission flow model calculates the nodal stochastic carbon emission factor in the following manner:

[0093]

[0094] in, For carbon emission flow models; For node random carbon emission factors; A random vector is injected into the power of random nodes. Output random variables. It is the vector representation of the nodal random carbon emission factor, which can be expanded into writing... .

[0095] It should be noted that the biggest difference between probabilistic power flow and probabilistic carbon emission flow lies in the linearity of the system function. In probabilistic power flow problems, the mapping relationship between branch power flow and node injected power can be represented by a DC power flow model, or the AC power flow can be linearized near the operating point. Therefore, probabilistic power flow problems can be calculated based on the approximate linear relationship between input and output variables. However, for probabilistic carbon flow problems, the system function includes both the power flow model and the carbon flow model. As shown in equation (1), the carbon emission flow model has a nonlinear matrix inversion step, which makes it difficult to linearize the carbon flow model locally. The nonlinear system function makes analytical methods difficult to apply and increases the computational complexity of the sampling method, hindering the relevant factor calculation method when facing high time resolution application scenarios such as low-carbon dispatch and carbon market trading decisions. To this end, this application proposes a piecewise linear approximation method for carbon emission flow by combining power system power flow operating condition grouping, Taylor expansion of the carbon emission factor calculation model, and linear regression analysis to support the computational needs of application scenarios with high spatiotemporal resolution and high timeliness requirements.

[0096] This application divides carbon emission flows into several tidal current condition groups with clear physical meanings under different tidal current conditions. Within each tidal current condition group, the relationship between nodal carbon emission factors and nodal power injection can be approximated as linear, thus transforming the complex nonlinear problem into a piecewise processing method. This significantly reduces the computational complexity of carbon emission factor uncertainty analysis and improves the solution efficiency of uncertainty analysis.

[0097] Specifically, the direction of tidal currents plays a decisive role in the transmission of carbon emission flows. Therefore, this application groups carbon emission flows with the same tidal current direction into the same tidal current condition group, and simplifies the carbon flow formula within this group, making the overall calculation expression more concise and reducing computational complexity.

[0098] It is understandable that the carbon emission factor itself naturally exhibits piecewise characteristics as the tidal current direction changes. Under actual operating conditions, the power injection fluctuation range within each tidal current group is limited. Therefore, when performing matrix inversion and Taylor expansion on the carbon flow formula, the influence of higher-order terms (such as second-order and higher-order terms) can be ignored. Under this condition, the mapping relationship between the nodal carbon emission factor and the nodal power injection can be approximated as a linear function.

[0099] In some embodiments, for each of the power flow condition groups, the mapping relationship between the node carbon emission factor and the node power injection with uncertainty is approximated as a piecewise linear function to obtain a piecewise linearized carbon flow calculation model, including:

[0100] The probabilistic carbon emission flow model is subjected to Taylor expansion and higher-order terms are ignored. The power at the equilibrium node is eliminated by elimination method to obtain the linear coefficients and constant terms for each power flow condition group.

[0101] Specifically, for the operating condition group, the node carbon emission factor can be approximately calculated as follows:

[0102] (3)

[0103] in, This represents the node injection power of node 𝑘. and These represent the power flow condition groups respectively. Next, node Linear coefficients and constant terms in the approximate calculation expression for carbon emission factors.

[0104] For probabilistic carbon emission flow problems, this application only requires node power injection variables with randomness, without involving the power injection amount of all nodes in the system. In order to directly give the expressions for node carbon emission factors and node injection power, formula (3) is rewritten as (4).

[0105] (4)

[0106] in, This represents the set of node injection power with uncertainty. and These are the linear coefficients and constant terms in the rewritten calculation formula.

[0107] Below, the derivation , The mapping relationship between the coefficients and the original formula. First, consider the elimination of the slack node. This paper adopts the DC power flow model and assumes that the network is lossless. Therefore, the power of the slack node can be injected by the power of other nodes as shown in formula (5).

[0108] (5)

[0109] in, It represents the set of node injection power of constant size.

[0110] Substituting equation (5) into the original equation (3) eliminates the influence of the injected power at the equilibrium node. The mapping relationship between the linear coefficients in the rewritten equation (4) and the linear coefficients in the original equation is shown in equation (6). Linear coefficients It is the difference between the original linear coefficient of a node and the original linear coefficient of the equilibrium node.

[0111] (6)

[0112] The node power with a constant value in the original formula (3) Eliminate. Specifically, the constant node power injection can be incorporated into the constant term of the formula, thus obtaining the expression of formula (7). At this time, the new constant term in formula (4) includes both the part related to the constant node power injection and the constant part in the original formula.

[0113] (7)

[0114] Similarly, the rewritten coefficient matrix in the probabilistic carbon flow problem can be rewritten as (8). In equation (8)... yes ×( An S+1 dimensional matrix.

[0115] (8)

[0116] The formula for calculating the nodal carbon emission factor in probabilistic carbon flow problems can be written as (9). This formula only considers the influence of the random node injection power in its expression form, and is more suitable for probabilistic carbon flow problems.

[0117] (9)

[0118] In some embodiments, the calculation of the target node carbon emission factor of samples under various system operating scenarios using a carbon flow calculation model with piecewise linear coefficients includes:

[0119] Determine whether the sample belongs to any power flow condition group;

[0120] In response to the fact that the sample belongs to any power flow condition group, the target node carbon emission factor of the sample is calculated based on the piecewise linear coefficients corresponding to the power flow condition group.

[0121] In response to the fact that the sample does not belong to any power flow condition group, the target node carbon emission factor of the sample is calculated using the probabilistic carbon emission flow model.

[0122] Specifically, to improve the feasibility and timeliness of probabilistic carbon emission flow calculation, this application proposes a calculation process based on sampling and regression, building upon piecewise linearization. This process reduces computational complexity by approximating matrix inversion in the original carbon flow formula as matrix multiplication, and simplifies the solution process while maintaining accuracy. Since the analytical form of matrix inversion is complex and difficult to directly derive piecewise linear functions, this application utilizes sampling results and regression modeling methods to gradually establish approximate linear relationship coefficients under different tidal flow conditions, thereby achieving efficient solutions for the probability distribution of carbon emission factors. The calculation framework mainly includes the following four steps, detailed as follows: Figure 2 As shown, the details are as follows:

[0123] Sampling: Acquiring target parameter data for the target power system can be achieved by randomly sampling node power injection based on the probability distribution of renewable energy output prediction errors and load fluctuations. Various methods can be employed, such as Monte Carlo sampling and Latin hypercube sampling.

[0124] Flow calculation and operating condition group determination: Perform DC flow calculation on each collected sample, determine the flow operating condition group to which it belongs based on the branch flow direction, and check whether the group has linearized coefficients obtained from regression.

[0125] Carbon flow calculation and sample accumulation: If the current flow condition group already has linear coefficients, the node carbon emission factor can be calculated directly using the piecewise linear formula; if there are no coefficients, the complete carbon flow formula is called to perform the calculation, and the sample result is stored in the regression sample pool.

[0126] Regression Modeling and Coefficient Update: When the sample pool for a certain power flow condition group reaches a set threshold, i.e., when the regression sample pool has sufficient samples, linear regression is performed to fit the piecewise linear coefficients. The dependent variable is the node carbon emission factor, and the independent variable is the node power injection with randomness. If the regression sample pool is insufficient, the DC power flow is recalculated using the newly obtained samples to obtain the power flow calculation results.

[0127] To improve the computational efficiency of uncertainty analysis, this application establishes a power flow condition group parameter storage structure to record the approximate coefficients corresponding to each condition group. During sampling, the calculated coefficients under that condition group can be quickly matched and retrieved based on the power flow direction of the sample. This application can dynamically maintain the power flow condition group parameter storage structure. It is necessary to determine whether sampling is complete, and output the uncertainty of carbon emission factors for each node after confirming completion.

[0128] After obtaining the carbon emission factor calculation results for a large number of samples, this application further models the probability distribution characteristics of each node through regression analysis, and outputs interval estimates at different confidence levels based on this. For example, by sorting the result samples and truncating specific quantiles, the factor interval range at different confidence levels can be obtained, thereby providing risk-aware decision-making basis for users and system scheduling.

[0129] After completing the rapid calculation and interval estimation of the probability distribution of carbon emission factors, this application further conducts uncertainty analysis and application research. The goal of this step is to reveal the carbon emission risk level of the system under the uncertainty conditions of new energy fluctuations by comparing the uncertainty intervals of node carbon emission factors under different operating scenarios, thereby providing an interval-based reference for low-carbon dispatching and market settlement.

[0130] Specifically, carbon flow analysis is first conducted under uncertain conditions such as different renewable energy penetration rates and different load fluctuation levels to obtain the probability distribution and confidence interval of carbon emission factors at each node. By comparing the width of the interval range and the shift in the center position under different scenarios, the carbon emission risk level of the system under the influence of various uncertainties can be quantitatively reflected. For example, when the factor interval of a certain node expands significantly, it indicates that the node is more sensitive to renewable energy fluctuations or load forecasting errors, and its carbon emission responsibility has a higher degree of uncertainty.

[0131] For example, the method provided in this application is applied to a 39-node power system to conduct intraday carbon emission factor prediction and uncertainty analysis. Based on different renewable energy penetration rates, the mean and standard deviation of the carbon emission factor for each node within a day are calculated, and the results are as follows. Figure 3 As shown.

[0132] Figure 3 In a, with a renewable energy penetration rate of 20%, and Figure 3 In scenario b, with 55% of the comparison, the results show that as penetration increases, the mean carbon emission factor of most nodes decreases, but its uncertainty level increases significantly. This indicates that while a high proportion of renewable energy integration can reduce overall carbon intensity, it also amplifies the uncertainty in the calculation and prediction of carbon emission factors.

[0133] Furthermore, in a scenario with a penetration rate of 55%, the average carbon emission factor of node 3 during the 10:00–16:00 period is higher than the corresponding value under the 20% scenario, presenting a result inconsistent with intuitive expectations. Analysis indicates that this phenomenon stems from a change in power flow direction: under high renewable energy conditions, the power connected to node 3 mainly comes from high-carbon sources, leading to an increase in its carbon emission factor level. This example verifies that the method in this application can reveal system operating characteristics that are difficult to reflect by traditional point value estimation, providing an effective tool for understanding the distribution of carbon emission risks under increasing renewable energy penetration.

[0134] like Figure 4 As shown, this application provides a carbon emission factor uncertainty analysis device, comprising:

[0135] Data acquisition module 401 is used to acquire target parameter data of the target power system;

[0136] The first calculation module 402 is used to perform DC power flow calculation based on the target parameter data to obtain power flow calculation results; the power flow calculation results include system power flow direction, branch power distribution, node injected power and node outflow power.

[0137] The model building module 403 is used to build a carbon emission flow calculation model based on the tidal flow calculation results;

[0138] The model improvement module 404 is used to identify sources of uncertainty in the target power system, generate random variables based on the probability distribution of the sources of uncertainty, and improve the carbon emission flow calculation model based on the random variables to obtain a probabilistic carbon emission flow model; the sources of uncertainty include wind and solar forecasting errors and load power fluctuations;

[0139] The sample grouping module 405 is used to cluster and group the system operating state samples in the probabilistic carbon emission flow model based on the power flow direction of the system branches to form multiple power flow condition groups; for each power flow condition group, the mapping relationship between the node carbon emission factor and the node power injection with uncertainty is approximated as a piecewise linear function to obtain a piecewise linearized carbon flow calculation model.

[0140] The linear fitting module 406 is used to perform linear regression analysis based on the sample data in the sample pool of any tidal current condition group when the sample pool capacity of any tidal current condition group reaches a preset threshold, with the node power injection with uncertainty as the independent variable and the node carbon emission factor as the dependent variable, to fit the piecewise linear coefficients of the corresponding tidal current condition group.

[0141] The second calculation module 407 is used to calculate the target node carbon emission factor of the sample under each system operation scenario using a carbon flow calculation model with piecewise linear coefficients.

[0142] The third calculation module 408 is used to calculate the interval estimation results of the carbon emission factors of each node at a given confidence level based on the probability distribution of the carbon emission factors of the target node.

[0143] According to embodiments of this application, this application also provides an electronic device and a readable storage medium.

[0144] The electronic device includes at least one processor and a memory communicatively connected to the at least one processor. The memory stores instructions executable by the at least one processor, which, when executed, enable the at least one processor to perform the carbon emission factor uncertainty analysis method described in this application. The computer instructions are used to cause the computer to perform the carbon emission factor uncertainty analysis method described in this application.

[0145] This application also provides a computer program product, including a computer program / instructions, which, when executed by a processor, implements the carbon emission factor uncertainty analysis method of this application.

[0146] Figure 5A schematic block diagram of an example electronic device 800 that can be used to implement embodiments of this application is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the application described and / or claimed herein.

[0147] like Figure 5 As shown, device 800 includes a computing unit 801, which can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) 802 or a computer program loaded from storage unit 808 into random access memory (RAM) 803. RAM 803 may also store various programs and data required for the operation of device 800. The computing unit 801, ROM 802, and RAM 803 are interconnected via bus 804. Input / output (I / O) interface 805 is also connected to bus 804.

[0148] Multiple components in device 800 are connected to I / O interface 805, including: input unit 806, such as keyboard, mouse, etc.; output unit 807, such as various types of monitors, speakers, etc.; storage unit 808, such as disk, optical disk, etc.; and communication unit 809, such as network card, modem, wireless transceiver, etc. Communication unit 809 allows device 800 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0149] The computing unit 801 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 801 performs the various methods and processes described above, such as the carbon emission factor uncertainty analysis method. For example, in some embodiments, the carbon emission factor uncertainty analysis method can be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 808. In some embodiments, part or all of the computer program can be loaded and / or installed on device 800 via ROM 802 and / or communication unit 809. When the computer program is loaded into RAM 803 and executed by the computing unit 801, one or more steps of the carbon emission factor uncertainty analysis method described above can be performed. Alternatively, in other embodiments, the computing unit 801 can be configured to perform the carbon emission factor uncertainty analysis method by any other suitable means (e.g., by means of firmware).

[0150] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0151] The program code used to implement the methods of this application may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0152] In the context of this application, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0153] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0154] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0155] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. A client-server mapping is created by computer programs running on the respective computers and having client-server relationships with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0156] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method of carbon emission factor uncertainty analysis, characterized by, include: Obtain target parameter data for the target power system; DC power flow calculations are performed based on the target parameter data to obtain the power flow calculation results; Based on the tidal flow calculation results, a carbon emission flow calculation model is constructed; Identify the sources of uncertainty in the target power system, generate random variables based on the probability distribution of the sources of uncertainty, and improve the carbon emission flow calculation model based on the random variables to obtain a probabilistic carbon emission flow model. Based on the power flow direction of the system branches, the system operating state samples in the probabilistic carbon emission flow model are clustered and grouped to form multiple power flow condition groups. For each of the aforementioned power flow conditions, the mapping relationship between the node carbon emission factor and the node power injection with uncertainty is approximated as a piecewise linear function, resulting in a piecewise linearized carbon flow calculation model. When the sample pool capacity of any tidal current condition group reaches a preset threshold, based on the sample data in the sample pool of any tidal current condition group, with the uncertain node power injection as the independent variable and the node carbon emission factor as the dependent variable, a linear regression analysis is performed to fit the piecewise linear coefficients of the corresponding tidal current condition group. The carbon emission factor of the target node of the sample under each system operation scenario is calculated using a carbon flow calculation model with piecewise linear coefficients. Based on the probability distribution of the carbon emission factors of the target node, the interval estimation results of the carbon emission factors of each node are calculated at a given confidence level.

2. The method of claim 1, wherein, Also includes: Based on the confidence interval results of the node carbon emission factors, the uncertainty of carbon emission factors under different system operation scenarios is compared to assess the risk of carbon emission exceeding the standard of the target power system.

3. The method of claim 1, wherein, The probabilistic carbon emission flow model calculates the nodal stochastic carbon emission factor in the following manner. ； wherein, is a carbon emission flow model; is a random node carbon emission factor; is a random vector of random node power injections.

4. The method of claim 1, wherein, For each of the aforementioned power flow condition groups, the mapping relationship between the node carbon emission factor and the node power injection with uncertainty is approximated as a piecewise linear function, resulting in a piecewise linearized carbon flow calculation model, including: The probabilistic carbon emission flow model is subjected to Taylor expansion and higher-order terms are ignored. The power at the equilibrium node is eliminated by elimination method to obtain the linear coefficients and constant terms for each power flow condition group.

5. The method of claim 4, wherein, The elimination method for removing power at the balancing node includes: The power of the balancing node is expressed as a linear combination of the power injected by other nodes; Substitute the linear combination into the piecewise linear function to eliminate the power variable at the equilibrium node.

6. The method of claim 5, wherein, The piecewise linearized carbon flow calculation model is as follows: ； ； ； wherein, is the carbon emission factor of node i under power flow scenario group m, is the set of nodal injection power with uncertainty, is the linear coefficient of node k under power flow scenario group m with uncertainty, is the constant term, is the power injection of node k with uncertainty, is the original linear coefficient of a certain node, is the original linear coefficient of the slack node, is the constant term, is the set of nodal injection power with uncertainty, is the set of nodal injection power with constant size.

7. The method of claim 1, wherein, The carbon flow calculation model with piecewise linear coefficients is as follows: ； ； wherein E is a carbon emission factor of the node-injected power with randomness, is ×( S + 1) -dimensional matrix.

8. The method of claim 1, wherein, The calculation of the target node carbon emission factor for samples under various system operating scenarios using a carbon flow calculation model with piecewise linear coefficients includes: Determine whether the sample belongs to any power flow condition group; In response to the fact that the sample belongs to any power flow condition group, the target node carbon emission factor of the sample is calculated based on the piecewise linear coefficients corresponding to the power flow condition group. In response to the fact that the sample does not belong to any power flow condition group, the target node carbon emission factor of the sample is calculated using the probabilistic carbon emission flow model.

9. A carbon emission factor uncertainty analysis apparatus, characterized by, include: The data acquisition module is used to acquire target parameter data of the target power system; The first calculation module is used to perform DC power flow calculation based on the target parameter data to obtain the power flow calculation result; a model construction module, configured to construct a carbon emission flow calculation model based on the power flow calculation result; a model improvement module, configured to identify an uncertainty source of the target power system, generate a random variable according to a probability distribution of the uncertainty source, and improve the carbon emission flow calculation model based on the random variable to obtain a probabilistic carbon emission flow model; a sample grouping module, configured to cluster and group system operating state samples in the probabilistic carbon emission flow model based on power flow directions of system branches to form a plurality of power flow condition groups; for each power flow condition group, the mapping relationship between a node carbon emission factor and a node power injection with uncertainty is approximated as a piecewise linear function to obtain a piecewise linearized carbon flow calculation model; a linear fitting module, configured to, when a sample pool capacity of any power flow condition group reaches a preset threshold, perform linear regression analysis based on sample data in the sample pool of the any power flow condition group, with the node power injection with uncertainty as an independent variable and the node carbon emission factor as a dependent variable, to fit to obtain piecewise linear coefficients of the corresponding power flow condition group; a second calculation module, configured to calculate target node carbon emission factors of samples in each system operating scenario by using the carbon flow calculation model with the piecewise linear coefficients; a third calculation module, configured to calculate interval estimation results of each node carbon emission factor under a given confidence level based on a probability distribution of the target node carbon emission factors.

10. An electronic device, comprising: at least one processor; and a memory connected in communication with the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 8.

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