Renewable energy carbon emission reduction accounting method and system considering electro-carbon coupling and medium

Abstract

The present application relates to the technical field of carbon management of power system, and more particularly to a renewable energy carbon emission reduction accounting method considering electric-carbon coupling, comprising: obtaining an electric-carbon coupling risk coefficient based on the net load-carbon emission joint time series data collected by the power grid dispatching system; obtaining an optimal power flow distribution result through electric-carbon coupling risk coefficient for electric-carbon collaborative optimization dispatching; obtaining a full-node carbon potential vector and a load node carbon flow rate vector through the optimal power flow distribution result for carbon emission flow dynamic tracking; obtaining the actual carbon reduction contribution value of each renewable energy unit through renewable energy carbon reduction contribution distribution according to the renewable energy unit grid-connected operation data; and coupling the full-node carbon potential vector, the load node carbon flow rate vector and the actual carbon reduction contribution value to generate the renewable energy carbon emission reduction amount. The present application realizes accurate real-time accounting of renewable energy carbon emission reduction amount through electric-carbon coupling risk quantification and full-chain carbon emission flow tracking.

Description

Technical Field

[0001] This invention relates to the field of carbon management technology for power systems, and in particular to a renewable energy carbon emission reduction accounting method, system, and medium that considers electrical-carbon coupling. Background Technology

[0002] With the large-scale grid connection of renewable energy, the existing carbon emission accounting methods of the power system have many problems. The current carbon market has a dual emission reduction attribution mechanism on the power generation side and the power consumption side. The lack of uniformity in this attribution mechanism may lead to the carbon emission reduction benefits of the same renewable energy source being calculated repeatedly or omitted, which seriously affects the accuracy of carbon emission reduction accounting results.

[0003] Traditional carbon emission accounting uses annual average carbon emission factors, which cannot reflect the dynamic changes in carbon emissions in the power system over time and space, especially the real-time correlation between renewable energy output fluctuations and grid carbon intensity. At the technical level, current carbon emission flow calculation models struggle to accurately establish the dynamic correlation of carbon footprints between power sources, the grid, and loads. Although some studies have proposed carbon flow tracking through nodal carbon potential, the impact of intermittent renewable energy output on system peak shaving and frequency regulation needs has not yet been addressed. Furthermore, existing carbon reduction contribution allocation methods fail to fully consider key parameters such as historical unit response capabilities and output stability, leading to an underestimation or misjudgment of the true emission reduction benefits of renewable energy. A more prominent problem is that existing electricity-carbon coupling models lack risk quantification capabilities, cannot assess the dynamic risk transmission relationship between net load fluctuations and carbon emissions, and cannot scientifically quantify the actual contribution of renewable energy units to grid decarbonization under different temporal and spatial conditions. Therefore, there is an urgent need to provide a renewable energy carbon emission reduction accounting method that considers electricity-carbon coupling. Summary of the Invention

[0004] To address the above technical problems, this invention provides a renewable energy carbon emission reduction accounting method, system, and medium that considers electrical-carbon coupling.

[0005] In a first aspect, the present invention provides a renewable energy carbon emission reduction accounting method considering electricity-carbon coupling, the method comprising the following steps: The risk of electricity-carbon coupling is quantified based on the joint time-series data of net load and carbon emissions collected by the power grid dispatching system, and the risk coefficient of electricity-carbon coupling is obtained. The optimal power flow distribution result is obtained by using real-time power flow distribution data of the power grid and the power-carbon coupling risk coefficient for power-carbon coordinated optimization scheduling. The optimal tidal flow distribution results are used to dynamically track carbon emission flows, resulting in the carbon potential vector of all nodes and the carbon flow rate vector of the load nodes. Based on the grid-connected operation data of new energy units, the operating characteristics of renewable energy units are extracted, and the carbon reduction contribution of renewable energy units is allocated through the operating characteristics of renewable energy units to obtain the actual carbon reduction contribution value of each renewable energy unit. The carbon potential vector of all nodes, the carbon flow rate vector of the load nodes, and the actual carbon reduction contribution value are coupled to generate renewable energy carbon emission reductions covering the entire chain from power generation to grid to user.

[0006] In a further implementation scheme, the step of quantifying the electricity-carbon coupling risk based on the net load-carbon emission joint time-series data collected by the power grid dispatching system to obtain the electricity-carbon coupling risk coefficient includes: The net load-carbon emission joint time series data collected by the power grid dispatch system are preprocessed to obtain standardized net load series and standardized carbon emission series; The relative rate of change between adjacent sampling points is calculated point by point based on the standardized net load sequence and the standardized carbon emission sequence to obtain the corresponding net load change rate sequence and carbon emission change rate sequence. First-order autoregressive filtering is performed on the net load change rate sequence and the carbon emission change rate sequence to extract the corresponding net load residual sequence and carbon emission residual sequence; Based on the net load residual sequence and the carbon emission residual sequence, a binary generalized autoregressive conditional heteroscedasticity model is constructed. The parameters of the binary generalized autoregressive conditional heteroscedasticity model are estimated using the maximum likelihood method, and the estimated model parameters include the long-term covariance autoregressive coefficient estimates and the short-term disturbance variance coefficient estimates. Calculate the net load fluctuation intensity and the level of carbon risk transmission based on the estimated values ​​of the model parameters. Based on the preset expected value of power grid dispatch decision risk attitude, the net load fluctuation intensity and the level of electricity carbon risk transmission are weighted and calculated to obtain the electricity carbon coupling risk coefficient.

[0007] In a further implementation, the step of constructing a binary generalized autoregressive conditional heteroscedasticity model based on the net load residual sequence and the carbon emission residual sequence includes: An autoregressive conditional heteroscedasticity test is performed on the net load residual sequence and the carbon emission residual sequence. If the autoregressive conditional heteroscedasticity test result is to reject the null hypothesis, the net load residual sequence and the carbon emission residual sequence are determined to have volatility clustering. Based on the net load residual sequence and carbon emission residual sequence with fluctuation clustering, a binary generalized autoregressive conditional heteroscedasticity model is constructed to describe the dynamic risk transmission relationship between net load and carbon emission flow.

[0008] In a further implementation, the step of performing coordinated power flow optimization scheduling using real-time power flow distribution data and the power-carbon coupling risk coefficient to obtain the optimal power flow distribution result includes: The power grid dispatching system synchronously collects the active power injection, reactive power injection, node voltage amplitude, and node voltage phase angle of all nodes at the same time section to form real-time power flow distribution data of the power grid. Multiple net load uncertainty scenarios are generated based on the real-time power flow distribution data of the power grid and its historical fluctuation range. The frequency of occurrence of each net load uncertainty scenario is normalized to obtain the probability of occurrence of each net load uncertainty scenario; Based on the probability of occurrence of the scenario and the risk coefficient of the electro-carbon coupling, a discrete conditional risk value optimization model is constructed. Solving the discrete conditional value at risk optimization model yields the optimal decision solution set for each net load uncertainty scenario; Extract the decision variable values ​​from the optimal decision solution set to generate the optimal power flow distribution result.

[0009] In a further implementation, the discrete conditional value at risk optimization model aims to minimize the sum of operating costs and conditional value at risk. The operating costs include at least the fuel costs of conventional generator sets and the penalty costs for the curtailment of renewable energy units; the conditional risk value transforms the electricity-carbon coupling risk coefficient into a risk cost weight by introducing a risk value auxiliary variable.

[0010] In a further implementation, the step of using the optimal tidal flow distribution results to dynamically track carbon emission flows and obtain the total node carbon potential vector and the load node carbon flow rate vector includes: The sum of active power flowing into each node is calculated based on the optimal power flow distribution results to obtain the node active power flux matrix. Based on the active power flow direction of each line in the optimal power flow distribution results, a branch power flow distribution matrix containing only positive active power flow values ​​is constructed. Based on the optimal power flow distribution results, the active power injection of all conventional generator units at their respective grid connection nodes is extracted, and the unit injection distribution matrix is ​​constructed. Based on the optimal power flow distribution results, the active load values ​​of all actual load nodes are extracted, and the new energy units are regarded as virtual load nodes. The active load value of the virtual load node is taken as the opposite of the actual active power output of the corresponding new energy unit. A load distribution matrix is ​​constructed based on the active load values ​​of the actual load nodes and the virtual load nodes; Each conventional generator set is assigned a corresponding carbon emission coefficient based on its fuel type, forming a carbon emission coefficient vector for the conventional generator set. The carbon emission coefficient vector is then multiplied by the unit injection distribution matrix to obtain a node-level carbon emission injection distribution matrix. The total node carbon potential vector is calculated based on the node active power flux matrix, the branch power flow distribution matrix, and the node-level carbon emission injection distribution matrix. The load distribution matrix is ​​multiplied by the total node carbon potential vector to calculate the load node carbon flow rate vector.

[0011] In a further implementation, the step of allocating the carbon reduction contribution of renewable energy units based on their operating characteristics to obtain the actual carbon reduction contribution value of each renewable energy unit includes: The Shapley value method is used to calculate the combined carbon flow rate for all new energy unit combinations that do not include all new energy units being phased out. Based on the combined carbon flow rate, the carbon flow rate of each new energy unit after being removed from the corresponding new energy unit combination is calculated to obtain the initial value of carbon reduction contribution. A three-dimensional correction vector is constructed based on the operating characteristics of the renewable energy units; the three-dimensional correction vector includes an effective response correction factor, a stability correction factor, and a marginal benefit correction factor. Based on the three-dimensional correction vector and the average operating characteristics of all new energy generating units, the correction factor for each new energy generating unit is calculated. Calculate the system carbon flow rate difference between all new energy units that are not connected to the grid and those that are fully connected to the grid. Multiply the system carbon flow rate difference by the correction factor to obtain the carbon flow rate correction value. The real-time carbon reduction benefits of each renewable energy unit are quantified based on the initial carbon reduction contribution value and the carbon flow rate correction value to obtain the actual carbon reduction contribution value of each renewable energy unit.

[0012] In a further implementation, the step of coupling the full-node carbon potential vector, the load node carbon flow rate vector, and the actual carbon reduction contribution value to generate renewable energy carbon emission reductions covering the entire power generation-grid-user chain includes: The actual carbon reduction contribution values ​​of all new energy units are summarized to obtain the carbon reduction amount on the power generation side; The carbon emission reduction caused by the reduction in network loss is calculated based on the carbon potential vector of all nodes, and the carbon reduction data on the grid side is obtained. The equivalent carbon emission reduction of each load node on the user side due to the absorption of renewable energy is calculated based on the carbon flow rate vector of the load node, and the carbon reduction data on the user side is obtained. By combining the carbon reduction data from the power generation side, the carbon reduction data from the power grid side, and the carbon reduction data from the user side, we can obtain the total carbon emission reduction of renewable energy covering the entire chain from power generation to power grid to user.

[0013] Secondly, the present invention provides a renewable energy carbon emission reduction accounting system considering electricity-carbon coupling, the system comprising: The electric-carbon coupling module is used to quantify the electric-carbon coupling risk based on the net load-carbon emission joint time series data collected by the power grid dispatch system, and obtain the electric-carbon coupling risk coefficient. The power carbon optimization module is used to perform coordinated power carbon optimization scheduling based on real-time power flow distribution data of the power grid and the power carbon coupling risk coefficient to obtain the optimal power flow distribution result. The carbon emission tracking module is used to dynamically track carbon emission flows using the optimal tidal distribution results, and to obtain the carbon potential vector of all nodes and the carbon flow rate vector of the load nodes. The carbon reduction allocation module is used to extract the operating characteristics of renewable energy units based on the grid-connected operation data of renewable energy units, and to allocate the carbon reduction contribution of renewable energy units based on the operating characteristics of the renewable energy units, so as to obtain the actual carbon reduction contribution value of each renewable energy unit. The carbon emission reduction accounting module is used to couple the carbon potential vector of the whole node, the carbon flow rate vector of the load node, and the actual carbon reduction contribution value to generate renewable energy carbon emission reduction covering the entire chain of power generation-grid-user.

[0014] Thirdly, the present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described method.

[0015] This invention provides a method, system, and medium for calculating renewable energy carbon emission reduction considering electricity-carbon coupling. The method quantifies the electricity-carbon coupling risk based on joint time-series data of net load and carbon emissions collected by the power grid dispatch system, obtaining an electricity-carbon coupling risk coefficient. It then performs coordinated optimization scheduling of electricity and carbon emissions using real-time power flow distribution data and the electricity-carbon coupling risk coefficient to obtain the optimal power flow distribution result. The optimal power flow distribution result is used for dynamic tracking of carbon emission flows, obtaining the carbon potential vector of all nodes and the carbon flow rate vector of load nodes. Based on the grid-connected operation data of renewable energy units, the operating characteristics of renewable energy units are extracted, and the carbon reduction contribution of renewable energy units is allocated based on these characteristics, obtaining the actual carbon reduction contribution value of each renewable energy unit. Finally, the carbon potential vector of all nodes, the carbon flow rate vector of load nodes, and the actual carbon reduction contribution value are coupled to generate renewable energy carbon emission reductions covering the entire chain from generation to grid to users. Compared with existing technologies, this method achieves accurate real-time calculation of renewable energy carbon emission reductions through electricity-carbon coupling risk quantification and full-chain carbon emission flow tracking, providing comprehensive and accurate data support for power system carbon management. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the renewable energy carbon emission reduction accounting method considering electricity-carbon coupling provided in the embodiments of the present invention; Figure 2 This is a block diagram of a renewable energy carbon emission reduction accounting system considering electricity-carbon coupling, provided in an embodiment of the present invention.

[0017] Explanation of reference numerals in the attached diagram: 101, Electro-carbon coupling module; 102, Electro-carbon optimization module; 103, Carbon emission tracking module; 104, Carbon reduction allocation module; 105, Carbon emission reduction accounting module. Detailed Implementation

[0018] The embodiments of the present invention are described in detail below with reference to the accompanying drawings. The embodiments are given for illustrative purposes only and should not be construed as limiting the present invention. The accompanying drawings are for reference and illustration only and do not constitute a limitation on the scope of patent protection of the present invention, because many changes can be made to the present invention without departing from the spirit and scope of the present invention.

[0019] Figure 1 This is a schematic flowchart of a renewable energy carbon emission reduction accounting method considering electricity-carbon coupling provided by an embodiment of the present invention. The embodiment of the present invention provides a renewable energy carbon emission reduction accounting method considering electricity-carbon coupling, such as... Figure 1 As shown, the method includes the following steps: S1. Based on the net load-carbon emission joint time series data collected by the power grid dispatching system, the risk of electricity-carbon coupling is quantified to obtain the risk coefficient of electricity-carbon coupling.

[0020] In some implementations, the step of quantifying the electricity-carbon coupling risk based on the net load-carbon emission joint time-series data collected by the power grid dispatching system to obtain the electricity-carbon coupling risk coefficient includes: The net load-carbon emission joint time series data collected by the power grid dispatch system are preprocessed to obtain standardized net load series and standardized carbon emission series; The relative rate of change between adjacent sampling points is calculated point by point based on the standardized net load sequence and the standardized carbon emission sequence to obtain the corresponding net load change rate sequence and carbon emission change rate sequence. First-order autoregressive filtering is performed on the net load change rate sequence and the carbon emission change rate sequence to extract the corresponding net load residual sequence and carbon emission residual sequence; Based on the net load residual sequence and the carbon emission residual sequence, a binary generalized autoregressive conditional heteroscedasticity model is constructed. The parameters of the binary generalized autoregressive conditional heteroscedasticity model are estimated using the maximum likelihood method, and the estimated model parameters include the long-term covariance autoregressive coefficient estimates and the short-term disturbance variance coefficient estimates. Calculate the net load fluctuation intensity and the level of carbon risk transmission based on the estimated values ​​of the model parameters. Based on the preset expected value of power grid dispatch decision risk attitude, the net load fluctuation intensity and the level of electricity carbon risk transmission are weighted and calculated to obtain the electricity carbon coupling risk coefficient.

[0021] With the increasing proportion of renewable energy, the uncertainty of load demand and renewable energy output will cause fluctuations in carbon emissions on both temporal (including intraday and seasonal) and spatial scales. The higher the proportion of renewable energy, the greater this fluctuation risk. Studying how such fluctuations are transmitted to system security (e.g., insufficient reserves) and economics (e.g., high carbon electricity costs) is an important prerequisite for building a resilient new power system. For example, when flexible resources are lacking, the superposition of large renewable energy generation and low load may lead to severe wind and solar curtailment, while the overlap of peak load and renewable energy trough may cause runaway carbon emissions and power supply shortages. Therefore, this embodiment uses the Generalized Auto-regressive Conditional Heteroskedasticity (GARCH) model to characterize the transmission risk impact of regional power grid net load fluctuations and carbon emission flow changes. This model measures the current regional power grid carbon emission intensity risk based on the fluctuation clustering and heteroskedasticity characteristics of historical data, obtains the electricity-carbon coupling risk coefficient, and transmits the electricity-carbon coupling risk coefficient to the optimal dispatch. The specific process of the electricity-carbon flow risk linkage coupling analysis method is as follows: This embodiment preprocesses the joint time-series data of net load and carbon emissions collected by the power grid dispatching system. The preprocessing process includes data cleaning, outlier removal, and standardization to obtain standardized net load and carbon emission sequences. Based on these standardized sequences, the relative rate of change between adjacent sampling points is calculated point-by-point to obtain the corresponding net load change rate sequence and carbon emission change rate sequence. First-order autoregressive filtering is applied to the net load change rate sequence and carbon emission change rate sequence to extract the corresponding net load residual sequence and carbon emission residual sequence. This step aims to remove autocorrelation components from the sequences and retain the random fluctuation portion. Next, this embodiment enters the time series verification process for the system's net load and carbon emission change rate, performing an autoregressive conditional heteroscedasticity (ARCH) effect test on the net load residual sequence and carbon emission residual sequence. The form of the autoregressive conditional heteroscedasticity effect test equation is: In the formula, This represents the net load residual or carbon emission residual for period t. This is the constant term in the autoregressive conditional heteroscedasticity effect test equation, which reflects the baseline fluctuation level and is dimensionless. is the ARCH coefficient of the squared residual of the j-th lag, which is used to measure the strength of the influence of historical volatility on current volatility; For the ( The residual sequence values ​​for the time period are used to capture the length of the fluctuation cluster memory. is the random error term of the autoregressive conditional heteroscedasticity effect test equation, which follows a zero-mean, homoscedastic distribution; n is the lag order index; q is the maximum lag order of the ARCH test. t This is a discrete-time index.

[0022] When the autoregressive conditional heteroscedasticity (ARCH) effect test result rejects the null hypothesis (i.e., ARCH effect exists), the net load residual series and carbon emission residual series are determined to have volatility clustering (i.e., large fluctuations are followed by sustained fluctuations). Based on the net load residual series and carbon emission residual series with volatility clustering, a bivariate generalized autoregressive conditional heteroscedasticity (GARCH) model describing the dynamic risk transmission relationship between net load and carbon emission flow is constructed, namely the bivariate GARCH(1,1)-BEKK model. The mean equation of the bivariate GARCH(1,1)-BEKK model is used to describe the dynamic relationship between net load and carbon emission flow, and the variance equation is used to quantify the risk transmission effect. Its mean equation and variance equation are expressed as follows: In the formula, This is the combined time-series data of net load and carbon emissions for period t. , Let be the rate of change of net load during period t. Let be the rate of change of carbon emission flow during time period t; This is the long-term mean constant matrix of the rate of change series, which represents the long-term average rate of change level; This is the historical rate of change influence coefficient matrix, which describes the impact of the rate of change in the previous period on the current mean. Let be the 2x2 conditional covariance matrix for time period t; To indicate The conditional distribution has a mean of zero and a covariance matrix of... The binary normal distribution; C is the transpose of the upper triangular constant matrix; C is the upper triangular constant matrix, which is used to guarantee Zhengding; This is the transpose of the long-term covariance autoregressive coefficient matrix; is the transpose of the random disturbance vector of the previous period; A is the long-term covariance autoregressive coefficient matrix, which characterizes the long-term memory of fluctuations or covariance and is used to reflect the continuous coupling effect between net load and carbon emission fluctuations. B is the transpose of the short-term disturbance variance coefficient matrix; B is the short-term disturbance variance coefficient matrix, which is used to reflect the short-term impact of the previous period's conditional covariance on the current value. The larger the matrix element value, the more sensitive the net load and carbon emission flow are to fluctuations, and the higher the level of risk transmission.

[0023] This embodiment uses the maximum likelihood method to estimate the parameter matrices A, B, and C of the bivariate GARCH(1,1)-BEKK model. It assumes that the conditional residual vector follows a bivariate conditional normal distribution, and the maximum likelihood function is in the form of: In the formula, The value is the log-likelihood function value. The vector of parameters to be estimated includes all unknown parameters such as the long-term covariance autoregressive coefficient, the short-term disturbance variance coefficient, the upper triangular constant, and the influence coefficient of the historical rate of change. This represents the total number of time periods in the sample. For natural logarithm operations; This is the transpose of the net load residual or carbon emission residual for time period t; the superscript -1 is the matrix inversion operator; Let be the determinant of the conditional covariance matrix.

[0024] Finally, this embodiment calculates the net load fluctuation intensity and the level of electricity carbon risk transmission by weighting the preset expected value of power grid dispatch decision risk attitude, and obtains the electricity carbon coupling risk coefficient. The specific implementation process is as follows: In this embodiment, the influence function of net load uncertainty on risk expectation attitude is defined based on a piecewise function: In the formula, is the risk coefficient for the electro-carbon coupling; k is the weighting coefficient, which is used to allocate weights between the net load's own fluctuation risk and the risk of electro-carbon coupling transmission. Let $\frac{ ... The intensity of net load fluctuation is estimated by the GARCH-BEKK model and reflects the conditional variance of the net load change rate itself. The level of carbon risk transmission in electricity is also estimated by the GARCH-BEKK model. It reflects the conditional covariance between the rate of change in net load and the rate of change in carbon emissions, and represents the fluctuation of cross-transmission between net load and carbon flow. This refers to the expected risk attitude value for power grid dispatching decisions, which is a pre-set benchmark risk aversion coefficient used to... and Mapping to actual risk attitude The scale; b is the comprehensive risk variable, which refers to... or It reflects the weighted overall volatility level during the current period; This represents the expected value of the combined risk variables; To control the margin of the impact of price risk.

[0025] S2. By using real-time power flow distribution data of the power grid and the aforementioned power-carbon coupling risk coefficient, power-carbon coordinated optimization scheduling is performed to obtain the optimal power flow distribution result.

[0026] In some implementations, the step of performing coordinated power flow optimization scheduling using real-time power flow distribution data and the power-carbon coupling risk coefficient to obtain the optimal power flow distribution result includes: The power grid dispatching system synchronously collects the active power injection, reactive power injection, node voltage amplitude, and node voltage phase angle of all nodes at the same time section to form real-time power flow distribution data of the power grid. Multiple net load uncertainty scenarios are generated based on the real-time power flow distribution data of the power grid and its historical fluctuation range. The frequency of occurrence of each net load uncertainty scenario is normalized to obtain the probability of occurrence of each net load uncertainty scenario; Based on the probability of occurrence of the scenario and the risk coefficient of the electro-carbon coupling, a discrete conditional risk value optimization model is constructed. Solving the discrete conditional value at risk optimization model yields the optimal decision solution set for each net load uncertainty scenario; Extract the decision variable values ​​from the optimal decision solution set to generate the optimal power flow distribution result.

[0027] Traditional carbon emission accounting in power systems often employs top-down macro-statistical methods. These methods rely on the national average carbon factor and can only provide rough estimates of carbon emissions, failing to reflect the complex carbon flow characteristics within the power grid. Their limitations primarily lie in neglecting the dynamic interactions across the entire power system—from power generation to grid, load, and storage. This directly leads to ambiguity in carbon emission responsibility attribution, making it difficult to effectively incentivize stakeholders at each stage to implement precise emission reduction efforts. With the continuous development of smart grid technology, improved carbon emission flow theory has emerged. This theory treats carbon emissions as a virtual flow dependent on electricity flow. By establishing a carbon flow model corresponding to power flow, it achieves full-process tracking of carbon emissions from power plants to user terminals. Furthermore, it uses concepts such as node carbon potential and branch carbon flow rate to provide a refined description of the spatial distribution of carbon emissions, effectively solving the problem of unfair carbon responsibility allocation caused by regional averaging in traditional methods. In addition, the improved carbon emission flow theory establishes a quantitative correlation between direct emissions from the generation side and indirect emissions from the user side, providing a scientific basis for the principle of "whoever uses electricity, bears the responsibility" in carbon responsibility allocation.

[0028] Since the calculation of carbon emission flows depends on the system's power flow information, an optimal scheduling model for the system needs to be constructed. To quantify the risk impact of net load uncertainty on the system, this embodiment uses Conditional Value at Risk (CVaR) theory to comprehensively analyze the system's operating costs and potential risk costs. Considering that the scheduling model in this embodiment is a discrete optimization problem, this embodiment introduces auxiliary variables to construct a discrete Conditional Value at Risk (CVaR) model. Specifically, this embodiment uses real-time node active power, reactive power, node voltage amplitude, and phase angle data synchronously collected by the power grid dispatching system at the same time section to form real-time power flow distribution data. Based on the measured net load values ​​and their historical fluctuation range in the real-time power flow distribution data, multiple net load uncertainty scenarios are generated using the Monte Carlo simulation method. Each scenario corresponds to a possible load fluctuation situation. For the generated net load uncertainty scenarios, this embodiment uses the maximum likelihood estimation method to calculate the probability of occurrence of each scenario based on the statistical relationship between the load fluctuation amplitude and the historical frequency of occurrence, and then normalizes the probability values. To ensure the sum of probabilities for all scenarios is 1, the probability of occurrence for each net load uncertainty scenario is obtained. This embodiment quantifies the combined cost of system operating costs and risk costs based on the normalized scenario occurrence probabilities and the electricity-carbon coupling risk coefficient. This leads to the construction of a discrete conditional risk value optimization model with the objective of minimizing the sum of operating costs and conditional risk value. The operating costs include at least the fuel costs of conventional generator sets and the penalty costs for curtailed power from renewable energy units. With a confidence level of 0.9, the conditional risk value is transformed from the electricity-carbon coupling risk coefficient into a risk cost weight by introducing a risk value auxiliary variable, thus achieving a balance between operating costs and potential risks. The specific objective function is as follows: In the formula, F is the objective value of the discrete conditional value at risk optimization model; is the risk coefficient for the coupling of electricity and carbon; s is the index of the net load uncertainty scenario, which represents the s-th net load uncertainty scenario; This is a set of scenarios with uncertain net load. Let s be the probability of the uncertain scenario s occurring. The operating cost under the uncertain scenario s; It serves as an auxiliary variable for Value at Risk (VaR) and is used to construct VaR. For the value at risk (VaR) variable, the excess loss under uncertainty scenario s; For confidence level, this embodiment uses 0.9; T is the total number of optimization periods; m is the index of conventional generator sets; This is a set of nodes for conventional generator sets; Let be the coefficient of the quadratic term of the power generation cost of conventional generator unit m, and its dimensions are: ; The active power output of a conventional generator unit m in scenario s and time period t; Let m be the coefficient for the primary term of the power generation cost of a conventional generator unit, and its dimensions are... ; For conventional generator set m, this is a constant term representing the power generation cost. This represents the secondary cost component generated by the square of the output of a conventional generator unit m during time period t. A set of nodes for new energy generator sets; This is the penalty coefficient for curtailment of renewable energy, which is used to adjust the severity of the curtailment penalty. This represents the primary cost component that is linearly related to the output of a conventional generator unit m during time period t. Let m be the power curtailment of a new energy unit in scenario s and time period t, and its dimension is MW; The duration of a single time period is used to convert power into energy, and its dimension is h. It should be noted that the dimension of all power variables is MW, and the dimension of all energy variables is MWh.

[0029] Meanwhile, this embodiment constructs constraints for each scenario based on real-time power flow distribution data of the power grid. The discrete conditional value-at-risk optimization model must at least satisfy power balance constraints, voltage and phase angle constraints, energy storage device constraints, and reserve capacity constraints. The specific expressions for power balance constraints and voltage and phase angle constraints are as follows: In the formula, is the voltage magnitude of node i; j is the number of the adjacent node directly connected to node i; It is the set of all nodes in the system; Let be the voltage amplitude at node j; Let be the real part of the i-th row and j-th column of the nodal admittance matrix; The voltage phase angle difference between node i and node j; Let be the imaginary part of the i-th row and j-th column of the nodal admittance matrix; Let be the active load power of node j; The energy storage charging power for node i; Let i be the active power output of the conventional generator set at node i; The active power output of the new energy generator unit at node i; Let be the energy storage discharge power of node i; i is the node number index in the power grid topology. Let be the reactive load power of node i; Let i be the reactive power output of the conventional generator set; The reactive power output of the new energy generator set at node i; Let be the voltage phase angle at node i; Let be the voltage phase angle at node j; This represents the lower limit of the voltage amplitude at node i; This represents the upper limit of the voltage amplitude at node i; This represents the lower limit of the active power output of the conventional generator set at node i. Let be the upper limit of the active power output of the conventional generator set at node i; Let be the lower limit of the reactive power output of the conventional generator set at node i; The upper limit of reactive power output of the conventional generator set at node i; Let be the apparent power (complex power magnitude) of line ij (from node i to node j), in MVA. This represents the lower limit of the apparent power of line ij; The apparent power limit of line ij; This is the set of all lines in the system.

[0030] The specific expression for the constraints of energy storage devices is as follows: In the formula, The charging power of the energy storage at node i during time period t (power flows into the energy storage). For node i, store energy in the binary variable representing the charging state during time period t; The maximum allowable charging power for energy storage at node i; Let be the discharge power (power outflow from energy storage) of the energy stored at node i during time period t. Store energy for node i in time period t as a binary variable representing the discharge state. The maximum allowable discharge power for energy storage at node i; Let be the remaining energy stored at node i at the end of time period t; The self-discharge coefficient for energy stored at node i; Charging efficiency for energy storage at node i; The discharge efficiency for storing energy at node i; The minimum allowable amount of energy to be stored for node i; The maximum allowable amount of energy to be stored for node i; This represents the maximum descending (downward) ramp power of conventional generator set i, i.e., the upper limit of output that can be reduced within a single time period. This represents the maximum upward (climbing) power of conventional generator set i, i.e., the upper limit of the power output that can be increased within a single time period.

[0031] The standby capacity constraint is: In the formula, The active power output of conventional generator unit i during time period t; The system's spinning reserve rate; Let be the active load power of node j in time period t; This refers to the system's spinning reserve rate.

[0032] In this embodiment, based on the above objective function and constraints, a mixed integer programming algorithm is used to solve the discrete CVaR model to obtain the optimal decision solution set that satisfies all constraints under each scenario. The decision variables in the optimal decision solution set are extracted, and the node voltage amplitude, node voltage phase angle, line active power, line reactive power, and energy storage charging and discharging power are used as the real-time executable values ​​of the power grid to generate the optimal power flow distribution result. The optimal power flow distribution result includes at least the node voltage amplitude, node voltage phase angle, line active power, line reactive power, and energy storage charging and discharging power.

[0033] S3. Use the optimal tidal flow distribution results to perform dynamic tracking of carbon emission flows, and obtain the carbon potential vector of all nodes and the carbon flow rate vector of the load nodes.

[0034] In some implementations, the step of dynamically tracking carbon emission flows using the optimal tidal current distribution results to obtain the total node carbon potential vector and the load node carbon flow rate vector includes: The sum of active power flowing into each node is calculated based on the optimal power flow distribution results to obtain the node active power flux matrix. Based on the active power flow direction of each line in the optimal power flow distribution results, a branch power flow distribution matrix containing only positive active power flow values ​​is constructed. Based on the optimal power flow distribution results, the active power injection of all conventional generator units at their respective grid connection nodes is extracted, and the unit injection distribution matrix is ​​constructed. Based on the optimal power flow distribution results, the active load values ​​of all actual load nodes are extracted, and the new energy units are regarded as virtual load nodes. The active load value of the virtual load node is taken as the opposite of the actual active power output of the corresponding new energy unit. A load distribution matrix is ​​constructed based on the active load values ​​of the actual load nodes and the virtual load nodes; Each conventional generator set is assigned a corresponding carbon emission coefficient based on its fuel type, forming a carbon emission coefficient vector for the conventional generator set. The carbon emission coefficient vector is then multiplied by the unit injection distribution matrix to obtain a node-level carbon emission injection distribution matrix. The total node carbon potential vector is calculated based on the node active power flux matrix, the branch power flow distribution matrix, and the node-level carbon emission injection distribution matrix. The load distribution matrix is ​​multiplied by the total node carbon potential vector to calculate the load node carbon flow rate vector.

[0035] Specifically, after obtaining the optimal power flow distribution information through coordinated power and carbon scheduling in this embodiment, the carbon potential vector and load carbon flow rate of each node can be further calculated based on the carbon emission flow theory. It should be noted that, in order to characterize the carbon reduction effect of renewable energy, this embodiment needs to treat the new energy units as virtual loads, and the power of the virtual load is the negative of the unit power. In the formula, This is the carbon potential vector for all nodes, and its elements represent the equivalent carbon emissions on the generation side for each 1 kWh of electricity consumed by each node. The active flux matrix of the nodes; This is the branch power flow distribution matrix; the superscript T is the transpose symbol for the matrix or vector. Inject the distribution matrix into the generator unit; For the carbon emission coefficient vector of conventional generator sets; The carbon flow rate vector at the load node; This is the load distribution matrix; The total carbon flow rate of the system is equal to the sum of the carbon flow rates of all loads, reflecting the total carbon emission intensity of the system; M is the total number of nodes with actual loads or virtual loads (including new energy sources); is the i-th component of the carbon flow rate vector at the load node; is the element symbol of the node active flux matrix; K is the total number of nodes in the system; The element symbols of the branch power flow distribution matrix; Inject element symbols into the distribution matrix for the generator units; is the element symbol of the load distribution matrix; R is the total number of conventional generator sets.

[0036] Based on the optimal power flow distribution results, this embodiment constructs a node active power flux matrix describing the active power flow between nodes, a unit injection distribution matrix describing the power injected by conventional units at each node, and a load distribution matrix including actual load and virtual load (the power of new energy units is the opposite). In this embodiment, the diagonal elements of the node active power flux matrix are the sum of the active power flows of all branches flowing into each node, and the off-diagonal elements of the node active power flux matrix are zero; the off-diagonal elements of the branch power flow distribution matrix are the positive active power flow values ​​from node i to node j (0 if the flow is reversed), and the diagonal elements of the branch power flow distribution matrix are 0; the elements of the unit injection distribution matrix are the active power injected by the r-th conventional unit at node j; the load distribution... The element value of the distribution matrix is ​​the active power (including actual load and virtual load) of the m-th load at node j. In this embodiment, the renewable energy units are regarded as virtual loads, and their power value is the inverse of the actual output to accurately characterize the effect of renewable energy grid connection on the reduction of system carbon intensity. At the same time, this embodiment assigns carbon emission coefficients according to the fuel type of conventional units to form a carbon emission coefficient vector. The carbon emission coefficient vector is multiplied by the unit injection distribution matrix to obtain a node-level carbon emission injection distribution matrix with the dimension of the total number of nodes. Each component is the carbon emission intensity contribution value of the corresponding node from conventional units. Then, the node active flux matrix, branch power flow distribution matrix and carbon emission injection matrix are combined to calculate the carbon potential vector of all nodes by using the inverse matrix operation method in the improved carbon emission flow theory. Multiplying the load distribution matrix by the total node carbon potential vector yields the load node carbon flow rate vector. In this context, each component of the load node carbon flow rate vector represents the carbon emission flow generated by the corresponding load node due to electricity consumption per unit time. The carbon potential vector of the system nodes and the load carbon flow rate results can reflect the real-time carbon emission intensity of the power plant and the equivalent carbon emission value on the generation side caused by the load consuming a unit of electricity, quantitatively characterizing the impact of each new energy unit on the system's carbon emissions. The total system load carbon flow rate, on the other hand, reflects the carbon emission intensity generated by the regional power system to meet load demand within a given time period.

[0037] S4. Extract the operating characteristics of renewable energy units based on the grid-connected operation data of renewable energy units, and allocate the carbon reduction contribution of renewable energy units through the operating characteristics of renewable energy units to obtain the actual carbon reduction contribution value of each renewable energy unit.

[0038] In some embodiments, the step of allocating the carbon reduction contribution of renewable energy units based on their operating characteristics to obtain the actual carbon reduction contribution value of each renewable energy unit includes: The Shapley value method is used to calculate the combined carbon flow rate for all new energy unit combinations that do not include all new energy units being phased out. Based on the combined carbon flow rate, the carbon flow rate of each new energy unit after being removed from the corresponding new energy unit combination is calculated to obtain the initial value of carbon reduction contribution. A three-dimensional correction vector is constructed based on the operating characteristics of the renewable energy units; the three-dimensional correction vector includes an effective response correction factor, a stability correction factor, and a marginal benefit correction factor. Based on the three-dimensional correction vector and the average operating characteristics of all new energy generating units, the correction factor for each new energy generating unit is calculated. Calculate the system carbon flow rate difference between all new energy units that are not connected to the grid and those that are fully connected to the grid. Multiply the system carbon flow rate difference by the correction factor to obtain the carbon flow rate correction value. The real-time carbon reduction benefits of each renewable energy unit are quantified based on the initial carbon reduction contribution value and the carbon flow rate correction value to obtain the actual carbon reduction contribution value of each renewable energy unit.

[0039] The traditional Shapley value method determines the contribution of each renewable energy unit to reducing the total carbon emissions of the system by calculating the impact of each renewable energy unit across all possible unit combinations. In a system with U renewable energy units, if they are not averaged or participate in dispatch, the total contribution is... This embodiment uses various combinations of methods. Based on real-time measurement data of each renewable energy unit from the power grid dispatch system over the past year, it collects and records data such as the declared output, actual output, actual grid connection time, and dispatch response instructions for each unit, forming renewable energy unit grid-connected operation data. Based on this data, it extracts renewable energy unit operation characteristics, including effective response rate, output stability, and marginal carbon reduction benefits. The effective response rate directly affects the capacity reliability of renewable energy units and the synergistic efficiency of system operation optimization. Generally, the higher the historical compliance rate, the stronger the renewable energy unit's absorption capacity and carbon reduction potential. In this embodiment, the effective response rate is calculated by statistically analyzing the ratio of the historical effective response counts of each renewable energy unit to the total number of responses. In the formula, The effective response rate of the new energy unit u; This represents the actual amount of electricity generated by the new energy unit u in the e-th response; This represents the total number of historical responses for the new energy unit u. The electricity volume declared in advance by unit u in the e-th response; u is the index of the new energy unit; e represents the e-th historical response event.

[0040] The impact of power output forecast deviation on the calculation of carbon reduction of new energy units is mainly achieved by changing the operation mode and energy structure of the power system. This process involves a complex electricity-carbon coupling relationship. When there is a deviation in the power output forecast of new energy, the system operator must take corresponding balancing measures. These measures directly determine the actual carbon emission level of the system. In this embodiment, the power output stability is defined by collecting the deviation values ​​between the predicted power output and the actual power output at each time period, and calculating the standard deviation to characterize the volatility. The expression for the power output stability is: In the formula, The output stability of the new energy unit u; T is the total number of optimization periods; The predicted output of the new energy unit u during time period t; The actual output of the new energy unit u during time period t.

[0041] In calculating carbon reduction contributions, this embodiment considers not only the absolute carbon emission reduction of the new energy units themselves in the traditional Shapley Value method, but also the relative contribution of each unit to the overall carbon reduction of the system. This is to characterize the relative weight of each new energy unit in the system's carbon reduction. Therefore, based on the absolute value of carbon reduction contribution, this embodiment further incorporates the marginal benefit of carbon reduction into the calculation of carbon reduction contribution. Specifically, it means the ratio of the sum of the overall carbon emission reduction of the whole society before and after the participation of new energy unit u in the dispatch under various unit combinations to the system's carbon emission reduction. It characterizes the relative contribution weight of the unit to the system's carbon emission reduction in various combinations. The expression for the marginal benefit of carbon reduction is: In the formula, The marginal carbon reduction benefit of the new energy unit u is represented by the proportion of the marginal contribution of the new energy unit u to the system's carbon reduction under various combinations to its individual contribution. Let represent the change in carbon emissions of the system before and after the participation of the renewable energy unit u in the scheduling; S represents the subset of all combinations of renewable energy units U that do not contain renewable energy unit u. The system carbon flow rate when combination S participates in scheduling; The system carbon flow rate when the new energy unit u is running alone.

[0042] Then, this embodiment uses the Shapley value method to calculate the initial carbon reduction contribution of each renewable energy unit. Specifically, it enumerates all possible combinations of renewable energy units (total... (where U is the total number of new energy generating units), for the total number of U new energy generating units, this embodiment traverses... For each non-empty combination S, the combined carbon flow rate is calculated, and based on the combined carbon flow rate, the initial value of the carbon reduction contribution of each unit is calculated. The mathematical expression for the initial value of the carbon reduction contribution is: In the formula, Let S be the initial value of the carbon reduction contribution of the new energy unit u; S is the subset of all combinations of new energy units U that do not contain unit u, that is, the set of a subset of units selected from all new energy units; ! is the factorial symbol; U is the total number of new energy units in the system; U! is the factorial of U; The carbon flow rate of the system after removing the new energy unit u from the combination S; Let S be the number of elements in combination S.

[0043] The classic Shapley value method relies on actual power flow results to calculate the carbon reduction of a unit. However, in reality, the historical operating conditions of the unit, the output deviation between the day and day, and the marginal benefit of carbon reduction all directly or indirectly affect the system's carbon emissions. Therefore, this embodiment proposes three evaluation indicators: effective response capability, output stability, and marginal benefit of carbon reduction, which are used as correction factors to adjust the calculation results of the Shapley value method. This embodiment constructs a three-dimensional correction vector based on the operating characteristics of the renewable energy units. The three-dimensional correction vector includes an effective response correction factor, a stability correction factor, and a marginal benefit correction factor. Based on the three-dimensional correction vector and the average operating characteristics of all renewable energy units, the correction factor for each renewable energy unit is calculated. In this embodiment, the higher the effective response rate, the larger the effective response correction factor; the smaller the standard deviation of output fluctuation, the larger the stability correction factor; and the higher the carbon emission reduction efficiency per unit output, the larger the marginal benefit correction factor. The formula for calculating the correction factor for each renewable energy unit is as follows: In the formula, The correction factor for u in new energy generating units is dimensionless; The effective response weighting coefficients are dimensionless. The stability weighting coefficient is dimensionless. This is the marginal benefit weighting coefficient, which is dimensionless.

[0044] This embodiment calculates the system carbon flow rate difference between all renewable energy units not connected to the grid and those fully connected to the grid. The system carbon flow rate difference is multiplied by the correction factor to obtain the carbon flow rate correction value. The initial carbon reduction contribution is then added to the carbon flow rate correction value to quantify the real-time carbon reduction benefit of each renewable energy unit. The carbon reduction contribution of the renewable energy unit at a certain time series section, considering the correction factor, is as follows: In the formula, Real-time carbon reduction benefits for new energy units; Initial value for the carbon reduction contribution of new energy unit u; This is a correction factor for the new energy unit u; This represents the difference in carbon flow rate within the system.

[0045] S5. Couple the carbon potential vector of the whole node, the carbon flow rate vector of the load node and the actual carbon reduction contribution value to generate renewable energy carbon emission reduction covering the entire chain of power generation-grid-user.

[0046] In some implementations, the step of coupling the full-node carbon potential vector, the load node carbon flow rate vector, and the actual carbon reduction contribution value to generate renewable energy carbon emission reductions covering the entire power generation-grid-user chain includes: The actual carbon reduction contribution values ​​of all new energy units are summarized to obtain the carbon reduction amount on the power generation side; The carbon emission reduction caused by the reduction in network loss is calculated based on the carbon potential vector of all nodes, and the carbon reduction data on the grid side is obtained. The equivalent carbon emission reduction of each load node on the user side due to the absorption of renewable energy is calculated based on the carbon flow rate vector of the load node, and the carbon reduction data on the user side is obtained. By combining the carbon reduction data from the power generation side, the carbon reduction data from the power grid side, and the carbon reduction data from the user side, we can obtain the total carbon emission reduction of renewable energy covering the entire chain from power generation to power grid to user.

[0047] Specifically, this embodiment superimposes the actual carbon reduction contributions of all new energy units across time sections to obtain the total carbon reduction on the power generation side directly reduced by the grid connection of renewable energy. Simultaneously, this embodiment calculates the difference between the baseline carbon potential of nodes without new energy and the carbon potential vector of all nodes, and multiplies this difference with the reduction in grid losses after the grid connection of new energy to calculate the carbon emission reduction resulting from the reduction in grid losses. The reduction in grid losses can be obtained by comparing the difference in line losses before and after optimization. This embodiment directly sums the carbon flow rate vectors of load nodes to obtain the equivalent carbon emission reduction on the user side due to the absorption of renewable energy. This embodiment weights and combines the carbon reduction data from the power generation side, grid side, and user side to obtain the carbon emission reduction of the entire renewable energy chain, realizing the quantification from the carbon reduction contribution of a single unit to the carbon emission reduction of the entire chain.

[0048] This invention provides a renewable energy carbon emission reduction accounting method considering electricity-carbon coupling. The method quantifies the electricity-carbon coupling risk based on joint time-series data of net load and carbon emissions collected by the power grid dispatch system, obtaining an electricity-carbon coupling risk coefficient. It then performs electricity-carbon coordinated optimization scheduling using real-time power flow distribution data and the electricity-carbon coupling risk coefficient to obtain the optimal power flow distribution result. Using the optimal power flow distribution result, it dynamically tracks carbon emission flows to obtain the carbon potential vector of all nodes and the carbon flow rate vector of load nodes. Based on the grid-connected operation data of renewable energy units, it extracts the operating characteristics of renewable energy units and allocates the carbon reduction contribution of renewable energy units based on these characteristics, obtaining the actual carbon reduction contribution value of each renewable energy unit. Finally, it couples the carbon potential vector of all nodes, the carbon flow rate vector of load nodes, and the actual carbon reduction contribution value to generate renewable energy carbon emission reductions covering the entire chain from generation to grid to users. Compared with existing technologies, this method achieves accurate real-time accounting of renewable energy carbon emission reductions through electricity-carbon coupling risk quantification and full-chain carbon emission flow tracking, providing comprehensive and accurate data support for power system carbon management.

[0049] It should be noted that the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0050] In one embodiment, such as Figure 2 As shown, this embodiment of the invention provides a renewable energy carbon emission reduction accounting system considering electricity-carbon coupling, the system comprising: The electric carbon coupling module 101 is used to quantify the electric carbon coupling risk based on the net load-carbon emission joint time series data collected by the power grid dispatch system, and obtain the electric carbon coupling risk coefficient. The power carbon optimization module 102 is used to perform power carbon collaborative optimization scheduling through real-time power flow distribution data of the power grid and the power carbon coupling risk coefficient to obtain the optimal power flow distribution result; Carbon emission tracking module 103 is used to dynamically track carbon emission flows using the optimal tidal distribution results, and to obtain the carbon potential vector of all nodes and the carbon flow rate vector of the load nodes. The carbon reduction allocation module 104 is used to extract the operating characteristics of renewable energy units based on the grid-connected operation data of new energy units, and to allocate the carbon reduction contribution of renewable energy units through the operating characteristics of renewable energy units to obtain the actual carbon reduction contribution value of each renewable energy unit. The carbon emission reduction accounting module 105 is used to couple the carbon potential vector of the whole node, the carbon flow rate vector of the load node and the actual carbon reduction contribution value to generate renewable energy carbon emission reduction covering the entire chain of power generation-grid-user.

[0051] Specific limitations regarding a renewable energy carbon reduction accounting system considering electro-carbon coupling can be found in the above-described limitations regarding a renewable energy carbon reduction accounting method considering electro-carbon coupling, and will not be repeated here. Those skilled in the art will recognize that the various modules and steps described in conjunction with the embodiments disclosed in this application can be implemented in hardware, software, or a combination of both. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0052] This invention provides a renewable energy carbon emission reduction accounting system considering electricity-carbon coupling. The system's electricity-carbon coupling module quantifies the electricity-carbon coupling risk based on joint time-series data of net load and carbon emissions collected by the power grid dispatch system, obtaining an electricity-carbon coupling risk coefficient. The electricity-carbon optimization module performs coordinated electricity-carbon optimization scheduling using real-time power flow distribution data and the electricity-carbon coupling risk coefficient, obtaining the optimal power flow distribution result. The carbon emission tracking module uses the optimal power flow distribution result to dynamically track carbon emission flows, obtaining the carbon potential vector of all nodes and the carbon flow rate vector of load nodes. The carbon reduction allocation module extracts the operating characteristics of renewable energy units based on grid-connected operation data and allocates the carbon reduction contribution of renewable energy units based on these characteristics, obtaining the actual carbon reduction contribution value of each renewable energy unit. The carbon emission reduction accounting module couples the carbon potential vector of all nodes, the carbon flow rate vector of load nodes, and the actual carbon reduction contribution value to generate renewable energy carbon emission reductions covering the entire chain from power generation to the grid to the user. Compared with existing technologies, this system achieves accurate real-time accounting of renewable energy carbon emission reductions through electricity-carbon coupling risk quantification and full-chain carbon emission flow tracking, providing comprehensive and accurate data support for power system carbon management.

[0053] In one embodiment, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0054] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., SSD), etc.

[0055] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed, it can include the processes of the embodiments of the above methods.

[0056] The embodiments described above are merely preferred embodiments of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various improvements and substitutions without departing from the technical principles of this invention, and these improvements and substitutions should also be considered within the scope of protection of this application. Therefore, the scope of protection of this patent application should be determined by the scope of the claims.

Claims

1. A renewable energy carbon emission reduction accounting method considering electricity-carbon coupling, characterized in that, Includes the following steps: The risk of electricity-carbon coupling is quantified based on the joint time-series data of net load and carbon emissions collected by the power grid dispatching system, and the risk coefficient of electricity-carbon coupling is obtained. The optimal power flow distribution result is obtained by using real-time power flow distribution data of the power grid and the power-carbon coupling risk coefficient for power-carbon coordinated optimization scheduling. The optimal tidal flow distribution results are used to dynamically track carbon emission flows, resulting in the carbon potential vector of all nodes and the carbon flow rate vector of the load nodes. Based on the grid-connected operation data of new energy units, the operating characteristics of renewable energy units are extracted, and the carbon reduction contribution of renewable energy units is allocated through the operating characteristics of renewable energy units to obtain the actual carbon reduction contribution value of each renewable energy unit. The carbon potential vector of all nodes, the carbon flow rate vector of the load nodes, and the actual carbon reduction contribution value are coupled to generate renewable energy carbon emission reductions covering the entire chain from power generation to grid to user.

2. The renewable energy carbon emission reduction accounting method considering electricity-carbon coupling as described in claim 1, characterized in that, The steps for quantifying the electricity-carbon coupling risk based on the joint time-series data of net load and carbon emissions collected by the power grid dispatching system, and obtaining the electricity-carbon coupling risk coefficient, include: The net load-carbon emission joint time series data collected by the power grid dispatch system are preprocessed to obtain standardized net load series and standardized carbon emission series; The relative rate of change between adjacent sampling points is calculated point by point based on the standardized net load sequence and the standardized carbon emission sequence to obtain the corresponding net load change rate sequence and carbon emission change rate sequence. First-order autoregressive filtering is performed on the net load change rate sequence and the carbon emission change rate sequence to extract the corresponding net load residual sequence and carbon emission residual sequence; Based on the net load residual sequence and the carbon emission residual sequence, a binary generalized autoregressive conditional heteroscedasticity model is constructed. The parameters of the binary generalized autoregressive conditional heteroscedasticity model are estimated using the maximum likelihood method, and the estimated model parameters include the long-term covariance autoregressive coefficient estimates and the short-term disturbance variance coefficient estimates. Calculate the net load fluctuation intensity and the level of carbon risk transmission based on the estimated values ​​of the model parameters. Based on the preset expected value of power grid dispatch decision risk attitude, the net load fluctuation intensity and the level of electricity carbon risk transmission are weighted and calculated to obtain the electricity carbon coupling risk coefficient.

3. The renewable energy carbon emission reduction accounting method considering electricity-carbon coupling as described in claim 2, characterized in that, The steps for constructing a binary generalized autoregressive conditional heteroscedasticity model based on the net load residual sequence and the carbon emission residual sequence include: An autoregressive conditional heteroscedasticity test is performed on the net load residual sequence and the carbon emission residual sequence. If the autoregressive conditional heteroscedasticity test result is to reject the null hypothesis, the net load residual sequence and the carbon emission residual sequence are determined to have volatility clustering. Based on the net load residual sequence and carbon emission residual sequence with fluctuation clustering, a binary generalized autoregressive conditional heteroscedasticity model is constructed to describe the dynamic risk transmission relationship between net load and carbon emission flow.

4. The renewable energy carbon emission reduction accounting method considering electricity-carbon coupling as described in claim 1, characterized in that, The step of performing coordinated optimization scheduling of power grid and carbon coupling using real-time power grid power flow distribution data and the power-carbon coupling risk coefficient to obtain the optimal power flow distribution result includes: The power grid dispatching system synchronously collects the active power injection, reactive power injection, node voltage amplitude, and node voltage phase angle of all nodes at the same time section to form real-time power flow distribution data of the power grid. Multiple net load uncertainty scenarios are generated based on the real-time power flow distribution data of the power grid and its historical fluctuation range. The frequency of occurrence of each net load uncertainty scenario is normalized to obtain the probability of occurrence of each net load uncertainty scenario; Based on the probability of occurrence of the scenario and the risk coefficient of the electro-carbon coupling, a discrete conditional risk value optimization model is constructed. Solving the discrete conditional value at risk optimization model yields the optimal decision solution set for each net load uncertainty scenario; Extract the decision variable values ​​from the optimal decision solution set to generate the optimal power flow distribution result.

5. The renewable energy carbon emission reduction accounting method considering electrical-carbon coupling as described in claim 4, characterized in that: The discrete conditional value at risk optimization model aims to minimize the sum of operating costs and conditional value at risk. The operating costs include at least the fuel costs of conventional generator sets and the penalty costs for the curtailment of renewable energy units; the conditional risk value transforms the electricity-carbon coupling risk coefficient into a risk cost weight by introducing a risk value auxiliary variable.

6. The renewable energy carbon emission reduction accounting method considering electricity-carbon coupling as described in claim 1, characterized in that, The step of dynamically tracking carbon emission flows using the optimal tidal distribution results to obtain the total node carbon potential vector and the load node carbon flow rate vector includes: The sum of active power flowing into each node is calculated based on the optimal power flow distribution results to obtain the node active power flux matrix. Based on the active power flow direction of each line in the optimal power flow distribution results, a branch power flow distribution matrix containing only positive active power flow values ​​is constructed. Based on the optimal power flow distribution results, the active power injection of all conventional generator units at their respective grid connection nodes is extracted, and the unit injection distribution matrix is ​​constructed. Based on the optimal power flow distribution results, the active load values ​​of all actual load nodes are extracted, and the new energy units are regarded as virtual load nodes. The active load value of the virtual load node is taken as the opposite of the actual active power output of the corresponding new energy unit. A load distribution matrix is ​​constructed based on the active load values ​​of the actual load nodes and the virtual load nodes; Each conventional generator set is assigned a corresponding carbon emission coefficient based on its fuel type, forming a carbon emission coefficient vector for the conventional generator set. The carbon emission coefficient vector is then multiplied by the unit injection distribution matrix to obtain a node-level carbon emission injection distribution matrix. The total node carbon potential vector is calculated based on the node active power flux matrix, the branch power flow distribution matrix, and the node-level carbon emission injection distribution matrix. The load distribution matrix is ​​multiplied by the total node carbon potential vector to calculate the load node carbon flow rate vector.

7. The renewable energy carbon emission reduction accounting method considering electricity-carbon coupling as described in claim 1, characterized in that, The step of allocating the carbon reduction contribution of renewable energy units based on their operating characteristics to obtain the actual carbon reduction contribution value of each renewable energy unit includes: The Shapley value method is used to calculate the combined carbon flow rate for all new energy unit combinations that do not include all new energy units being phased out. Based on the combined carbon flow rate, the carbon flow rate of each new energy unit after being removed from the corresponding new energy unit combination is calculated to obtain the initial value of carbon reduction contribution. A three-dimensional correction vector is constructed based on the operating characteristics of the renewable energy units; the three-dimensional correction vector includes an effective response correction factor, a stability correction factor, and a marginal benefit correction factor. Based on the three-dimensional correction vector and the average operating characteristics of all new energy generating units, the correction factor for each new energy generating unit is calculated. Calculate the system carbon flow rate difference between all new energy units that are not connected to the grid and those that are fully connected to the grid. Multiply the system carbon flow rate difference by the correction factor to obtain the carbon flow rate correction value. The real-time carbon reduction benefits of each renewable energy unit are quantified based on the initial carbon reduction contribution value and the carbon flow rate correction value to obtain the actual carbon reduction contribution value of each renewable energy unit.

8. The renewable energy carbon emission reduction accounting method considering electricity-carbon coupling as described in claim 1, characterized in that, The step of coupling the full-node carbon potential vector, the load node carbon flow rate vector, and the actual carbon reduction contribution value to generate renewable energy carbon emission reductions covering the entire power generation-grid-user chain includes: The actual carbon reduction contribution values ​​of all new energy units are summarized to obtain the carbon reduction amount on the power generation side; The carbon emission reduction caused by the reduction in network loss is calculated based on the carbon potential vector of all nodes, and the carbon reduction data on the grid side is obtained. The equivalent carbon emission reduction of each load node on the user side due to the absorption of renewable energy is calculated based on the carbon flow rate vector of the load node, and the carbon reduction data on the user side is obtained. By combining the carbon reduction data from the power generation side, the carbon reduction data from the power grid side, and the carbon reduction data from the user side, we can obtain the total carbon emission reduction of renewable energy covering the entire chain from power generation to power grid to user.

9. A renewable energy carbon emission reduction accounting system considering electro-carbon coupling, characterized in that, The system includes: The electric-carbon coupling module is used to quantify the electric-carbon coupling risk based on the net load-carbon emission joint time series data collected by the power grid dispatch system, and obtain the electric-carbon coupling risk coefficient. The power carbon optimization module is used to perform coordinated power carbon optimization scheduling based on real-time power flow distribution data of the power grid and the power carbon coupling risk coefficient to obtain the optimal power flow distribution result. The carbon emission tracking module is used to dynamically track carbon emission flows using the optimal tidal distribution results, and to obtain the carbon potential vector of all nodes and the carbon flow rate vector of the load nodes. The carbon reduction allocation module is used to extract the operating characteristics of renewable energy units based on the grid-connected operation data of renewable energy units, and to allocate the carbon reduction contribution of renewable energy units based on the operating characteristics of the renewable energy units, so as to obtain the actual carbon reduction contribution value of each renewable energy unit. The carbon emission reduction accounting module is used to couple the carbon potential vector of the whole node, the carbon flow rate vector of the load node, and the actual carbon reduction contribution value to generate renewable energy carbon emission reduction covering the entire chain of power generation-grid-user.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method as described in any one of claims 1 to 8.

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