Probability distribution determination method and device for marginal carbon emission factors, and computer equipment

Abstract

The invention relates to a probability distribution determination method and device for marginal carbon emission factors and computer equipment. The method comprises the following steps: sampling according to first probability distribution of renewable energy output and second probability distribution of a power system load to obtain a parameter sample of the sampling; matching the parameter sample with each critical domain in the critical domain list; in response to a result that the parameter sample is not matched with the critical domain, solving by utilizing a multi-period economic dispatching model to obtain a marginal carbon emission factor corresponding to the parameter sample, the constraint conditions of the multi-period economic dispatching model comprise the power balance constraint of each period and the inequality constraint formed by the line power flow constraint, the unit output constraint and the unit climbing constraint, and the constraint conditions are generated by utilizing the network topology information and the unit configuration information; and generating probability distribution of the marginal carbon emission factors by using the marginal carbon emission factors obtained by each sampling. By adopting the method, the calculation efficiency and accuracy can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of low-carbon power, in particular to a method and device for determining probability distribution of marginal carbon emission factor, computer equipment, computer readable storage medium and computer program product. BACKGROUND

[0002] Marginal carbon emission factor (MCI for short) is an effective index for measuring carbon intensity of user electricity, and its physical meaning is that the system carbon emission increment (unit: g / kWh) brought by the user increasing unit load. The marginal carbon emission factor can directly reflect the influence of load increase or reduction at different times and in different positions on system carbon emission, and can effectively represent the counteraction of user-side energy use behavior change on system carbon emission. In the technical field of low-carbon power, by analyzing the marginal carbon emission factor, the user can be guided to adjust his own electricity use behavior and carry out related carbon reduction work.

[0003] In the traditional technology, the method based on model or regression is generally used to study the system carbon emission change under the load change of power system in a single period. However, the actual operation of the unit in the power system is also affected by the time coupling constraint of unit climbing, which will lead to the multi-period coupling characteristics of marginal carbon emission factor. Thus, the traditional carbon emission change evaluation method has the problem of inaccuracy. SUMMARY

[0004] Therefore, it is necessary to provide a method and device for determining probability distribution of marginal carbon emission factor, computer equipment, computer readable storage medium and computer program product, which can improve the prediction accuracy and meet the multi-period coupling constraint demand in actual scene.

[0005] In a first aspect, the present application provides a method for determining probability distribution of marginal carbon emission factor, comprising:

[0006] obtaining network topology information, unit configuration information, first probability distribution of renewable energy output and second probability distribution of power system load;

[0007] repeating the following sampling operation multiple times until a preset sampling number is reached:

[0008] sampling according to the first probability distribution and the second probability distribution to obtain a parameter sample of this sampling;

[0009] matching the parameter sample with each critical domain in a critical domain list;

[0010] In response to the result of the parameter sample not matching the critical domain, the marginal carbon emission factor corresponding to the parameter sample is obtained by using a multi-period economic dispatch model. The constraints of the multi-period economic dispatch model include power balance constraints for each period and inequality constraints consisting of line power flow constraints, unit output constraints and unit ramping constraints. The constraints are generated using the network topology information and the unit configuration information.

[0011] The probability distribution of the marginal carbon emission factor is generated using the marginal carbon emission factor obtained from each sampling.

[0012] In one embodiment, the step of responding to the mismatch between the parameter sample and the critical domain by using a multi-period economic scheduling model to obtain the marginal carbon emission factor corresponding to the parameter sample includes:

[0013] The parameter samples are input into the multi-period economic dispatch model to obtain the optimal solution for unit output.

[0014] By applying equality to the inequality constraints in the multi-period economic dispatch model, the correlation between the optimal solution of the unit output and the node load parameters, line transmission capacity parameters, and renewable energy unit output parameters are obtained.

[0015] Based on the aforementioned correlation, the optimal solution of the unit output is differentiated with respect to the node load parameters to obtain the marginal carbon emission factor corresponding to each node in multiple time periods.

[0016] The marginal carbon emission factors corresponding to each node in the multi-time period are decomposed to obtain the marginal carbon emission factors of the equilibrium node and the marginal carbon emission factors of the congested line in each time period, which are used as the marginal carbon emission factors corresponding to the parameter sample.

[0017] In one embodiment, the method further includes:

[0018] Based on the inequality constraints that are not equal in the multi-period economic scheduling model, the target critical domain corresponding to the parameter sample is determined;

[0019] Add the target critical region corresponding to the parameter sample to the critical region list, and record the marginal carbon emission factor corresponding to the target critical region.

[0020] In one embodiment, the method further includes:

[0021] In response to the result of matching the parameter sample with the critical domain, the marginal carbon emission factor corresponding to the critical domain is used as the marginal carbon emission factor corresponding to the parameter sample.

[0022] In one embodiment, the method further includes:

[0023] Record the number of parameter samples that match each of the aforementioned critical regions;

[0024] The critical regions in the critical region list are sorted according to the number of parameter samples to obtain a sorted critical region list.

[0025] According to the order of the critical regions in the sorted critical region list, each critical region is matched with the parameter sample in turn.

[0026] In one embodiment, sorting the critical regions in the critical region list according to the number of parameter samples to obtain the sorted critical region list includes:

[0027] The critical regions in the critical region list are sorted in descending order according to the number of parameter samples to obtain the sorting result of each critical region.

[0028] The sorted critical region list is constructed using several critical regions before a preset threshold based on the sorting results.

[0029] Secondly, this application also provides a device for determining the probability distribution of marginal carbon emission factors, comprising:

[0030] The data acquisition module is used to acquire network topology information, generator configuration information, the first probability distribution of renewable energy output, and the second probability distribution of power system load.

[0031] The parameter sampling module is used to repeatedly perform the following sampling operation multiple times until a preset number of samplings is reached: sampling is performed according to the first probability distribution and the second probability distribution to obtain the parameter sample of this sampling.

[0032] The critical region matching module is used to match the parameter sample with each critical region in the critical region list;

[0033] The economic dispatch module is used to respond to the result of the mismatch between the parameter sample and the critical domain by using a multi-period economic dispatch model to obtain the marginal carbon emission factor corresponding to the parameter sample. The constraints of the multi-period economic dispatch model include power balance constraints for each period and inequality constraints composed of line power flow constraints, unit output constraints and unit ramping constraints. The constraints are generated using the network topology information and the unit configuration information.

[0034] The distribution generation module is used to generate a probability distribution of the marginal carbon emission factor using the marginal carbon emission factor obtained from each sampling.

[0035] Thirdly, this application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method for determining the probability distribution of marginal carbon emission factors as described in any of the embodiments of the first aspect.

[0036] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, implements the method for determining the probability distribution of marginal carbon emission factors as described in any of the embodiments of the first aspect.

[0037] Fifthly, this application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the method for determining the probability distribution of marginal carbon emission factors as described in any of the embodiments of the first aspect.

[0038] The aforementioned method, apparatus, computer equipment, computer-readable storage medium, and computer program product for determining the probability distribution of marginal carbon emission factors construct a multi-period economic dispatch model for solving multi-parameter linear programming by using the power balance constraints of the power system in each time period and the inequality constraints composed of line flow constraints, unit output constraints, and unit ramping constraints in the power system as constraints. By using the multi-period economic dispatch model to solve for the marginal carbon emission factors corresponding to the parameter samples, it can consider the distribution results of marginal carbon emission factors under the time-series influence factors such as unit ramping in multiple time periods (i.e., multi-period coupling constraints), more accurately characterizing the marginal carbon emission factors in multiple time periods under the time-series changes of the power system's operating state, and improving the solution speed and accuracy of marginal carbon emission factors. Attached Figure Description

[0039] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 This is a flowchart illustrating a method for determining the probability distribution of marginal carbon emission factors in one embodiment.

[0041] Figure 2 This is a flowchart illustrating the steps for solving the marginal carbon emission factor in one embodiment;

[0042] Figure 3 This is a flowchart illustrating the critical region sorting steps in one embodiment;

[0043] Figure 4 A structural block diagram of a probability distribution determination device 400 for marginal carbon emission factors in one embodiment;

[0044] Figure 5 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0045] In related technologies, since carbon emissions in the power system mainly originate from the power generation process, the total direct carbon emissions can be calculated using the following formula:

[0046] ,

[0047] in, express Total direct carbon emissions from the power system over a period of time. Indicates the unit carbon emission factors, Indicates the unit Power generation output, It represents the set of system units.

[0048] The marginal carbon emission factor refers to the increase in system carbon emissions caused by a unit increase in node load, and can be calculated using the following formula:

[0049] ,

[0050] Among them, row vector The marginal carbon emission factor of each node is represented by the dimension of... , This represents the number of system nodes. It is composed of the load of each node. A column vector of dimension N, where N is the number of system nodes. You can expand your writing , This represents a node. The load.

[0051] However, the actual operation of the generating units is also affected by time-dependent constraints such as unit ramp-up, leading to a multi-time-dependent coupling characteristic in the marginal carbon emission factor. Furthermore, to meet the "carbon response" requirement, the marginal carbon emission factor needs to be predicted based on the day-ahead simulation results of the system. Therefore, in the prediction of the marginal carbon emission factor, the uncertainty in renewable energy forecasts will be transmitted to the simulation results, thus resulting in uncertainty in the marginal carbon emission factor.

[0052] To address the aforementioned problems, this application proposes a method for determining the probability distribution of marginal carbon emission factors. To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description, in conjunction with the accompanying drawings and embodiments, is provided. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0053] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the solutions, or any combination of multiple solutions. The user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of related data must comply with relevant regulations.

[0054] In one embodiment, such as Figure 1 As shown, a method for determining the probability distribution of marginal carbon emission factors is provided. This embodiment illustrates the application of this method to a server. It is understood that this method can also be applied to terminals, and further to systems including both terminals and servers, and implemented through interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0055] Step S102: Obtain network topology information, generator configuration information, the first probability distribution of renewable energy output, and the second probability distribution of power system load.

[0056] For example, the server can obtain network topology information between nodes in the power system based on the physical or logical connections between nodes (such as power plants, substations, load centers, etc.) and the structural model of energy flow paths. Based on the configuration information of various generating units in the power system (such as fossil fuel units, renewable energy units, hydropower units, nuclear power units, energy storage units, hybrid power units, etc.), the server calculates a first probability distribution of renewable energy output using the output data of renewable energy units in the power system. Finally, it generates a second probability distribution of the power system load using the load data of the power system.

[0057] Step S104: Determine whether the preset number of sampling times has been reached.

[0058] For example, the server may store a preset number of sampling times. If the preset number of sampling times is not reached, the sampling operations of steps S106 to S110 are repeated. If the preset number of sampling times is reached, the operation of step S112 is performed.

[0059] Step S106: Sample the parameters for this sampling based on the first probability distribution and the second probability distribution.

[0060] For example, the server can use various methods such as simple random sampling, Markov chain Monte Carlo sampling, hierarchical random sampling, and system random sampling to sample the first probability distribution and the second probability distribution, thereby obtaining sampling parameters of renewable energy output that satisfy the first probability distribution and sampling parameters of power system load that satisfy the second probability distribution. The sampling parameters of renewable energy output and power system load are then used to construct the parameter sample for this test.

[0061] Optionally, in some implementations, the server may also store the number of samples corresponding to a single sampling operation. In each sampling operation, parameter samples matching the sample number are generated. This increases the amount and richness of the parameter samples.

[0062] Step S108: Match the parameter samples with each critical region in the critical region list.

[0063] In linear programming, equality constraints typically correspond to vertices of the feasible region where the optimal solution of the linear programming problem lies. When the input parameters of a linear programming problem change, the vertices corresponding to the optimal solution, i.e., the equality inequality constraints, may also change. Based on this characteristic, multi-parameter linear programming theory can be used to divide the parameter space containing the input parameters into several critical regions. Input parameters within the same critical region have the same equality and inequality constraints. When input parameters move within the same critical region, the equality inequality constraints corresponding to the input parameters do not change. That is, in the scenario of solving multi-parameter linear programming problems in multi-period economic scheduling models, each critical region can correspond to a specific marginal carbon emission factor.

[0064] For example, the server may store a list of critical regions, which may include several critical regions. The server may match the parameter sample obtained in this sampling with each critical region in the list of critical regions. If no critical region in the list matches the parameter sample, the operation of step S110 is performed.

[0065] Step S110: In response to the mismatch between the parameter sample and the critical domain, the marginal carbon emission factor corresponding to the parameter sample is obtained by using a multi-period economic scheduling model.

[0066] The constraints of the multi-period economic dispatch model can include power balance constraints of the power system in each period, as well as inequality constraints consisting of line power flow constraints, generator output constraints, and generator ramping constraints. These constraints are generated using the power system's network topology and generator configuration information. In the application scenario of this application, the critical region can be constructed based on the inequality constraints in the multi-period economic dispatch model.

[0067] The marginal carbon emission factor in this application incorporates unit output. This intermediate variable, based on the chain rule for differentiating the marginal carbon emission factor in the aforementioned related technologies, can be used to determine the marginal carbon emission factor in this application. Represented as:

[0068] ,

[0069] Among them, the two terms multiplied on the right side of the equation have clear physical meaning; for The column vector of carbon emission factors of the generator unit is denoted as... ,in for Dimensional generator output column vector Number of generating units; for The marginal unit matrix is ​​denoted as The first in the matrix Column elements represent nodes Increase the output increment of each unit corresponding to the unit load.

[0070] For example, the server can store a pre-constructed multi-period economic dispatch model. Substituting the parameter samples into the multi-period economic dispatch model, the optimal output of each unit is obtained while minimizing the system's power generation cost. Using the pre-defined data relationship between the optimal unit output and the marginal carbon emission factor, the currently obtained optimal unit output solution is processed to obtain the marginal carbon emission factor corresponding to the parameter samples.

[0071] Optionally, in some implementations, the multi-period economic dispatch model can be a model that minimizes the system generation cost of the power system by constructing decision variables of the cost and output of each unit. Power balance constraints can be constructed based on the decision variables of the output of each unit and the node load. The inequality constraints constructed by the line power flow constraints, unit output constraints, and unit ramping constraints can be combined by multiplying the matrix constructed by the power flow transfer distribution factor matrix and the node-unit correlation matrix by the decision variables of the output of each unit as the decision variable combination on the left side of the inequality constraints. The externally imposed constraints on the right side of the inequality constraints are the matrix constructed by the unit-thermal power unit correlation matrix and the maximum and minimum output of the thermal power units, the product of the power flow transfer distribution factor matrix and the node load, the product of the unit-renewable energy unit correlation matrix and the output of the renewable energy units, and the matrix of line transmission capacity.

[0072] Step S112: Use the marginal carbon emission factors obtained from each sampling to generate the probability distribution of the marginal carbon emission factors.

[0073] For example, the server can perform statistical processing on the marginal carbon emission factors of each parameter sample obtained in each sampling, thereby generating a probability distribution of the marginal carbon emission factors. The probability distribution of the marginal carbon emission factors can be used to reflect the impact of load increases or decreases at different times and locations on the system's carbon emissions, effectively characterizing the counter-effect of changes in user-side energy consumption behavior on system carbon emissions, and helping to guide users to adjust their own electricity consumption behavior to achieve carbon reduction and emission reduction in electricity dispatching.

[0074] The aforementioned method for determining the probability distribution of marginal carbon emission factors uses the power balance constraints of the power system in each time period and the inequality constraints composed of line power flow constraints, unit output constraints, and unit ramping constraints in the power system as constraints to construct a multi-time period economic dispatch model for solving multi-parameter linear programming. By using the multi-time period economic dispatch model to solve for the marginal carbon emission factors corresponding to the parameter samples, it can take into account the distribution results of marginal carbon emission factors under time-series influencing factors such as unit ramping in multiple time periods (i.e., multi-time period coupling constraints), thereby improving the solution speed and accuracy of marginal carbon emission factors.

[0075] In one exemplary embodiment, such as Figure 2 As shown, step S110 may include steps S202 to S208. Wherein:

[0076] Step S202: Input the parameter samples into the multi-period economic dispatch model to obtain the optimal solution for unit output.

[0077] For example, the server can input parameter samples into the multi-period economic dispatch model, take minimizing the system power generation cost as the objective function, and perform multi-parameter linear programming based on the constraints to obtain the optimal solution of unit output when the system power generation cost is minimized under the parameter sample conditions.

[0078] Alternatively, in some implementations, the server can use the following formula to construct the objective function of the multi-period economic dispatch model to minimize the system's power generation cost:

[0079] ,

[0080] Among them, X GM The decision variable for the unit's output during the day has a dimension of N. G T×1, N G X represents the number of generating units, and T represents the number of time periods. GM It can be expanded to represent as , Let C be the decision variable representing the unit's output during time period t. BM This refers to the system's power generation cost. H G It is a dimension of N G T×N G The identity matrix can be expanded as follows: , It is a dimension N G ×N G The identity matrix. Vector c represents the cost of each unit, with dimension N. G ×1. Indicates a time interval.

[0081] Alternatively, in other implementations, the server can use the following formula to construct the power balance constraints of the multi-period economic dispatch model for each period:

[0082] ,

[0083] Among them, P GS It is a dimension T×N G The matrix T, whose expansion is expressed as u G It is a dimension of N G A column vector of all 1s of size ×1. P LS It is a matrix of dimension T×NT, and its expanded form is: u N It is an N×1 column vector consisting entirely of 1s. LM These are node load parameters, which are parameters that have uncertainties or require sensitivity analysis during the optimization and solution of multi-period economic dispatch models.

[0084] Alternatively, in other implementations, the server can construct inequality constraints consisting of line power flow constraints, unit output constraints, and unit ramping constraints using the following formula:

[0085] ,

[0086] ,

[0087] ,

[0088] ,

[0089] Where, matrix A M P in G It is a dimension of N B T×N G The matrix T has the following expansion: , This represents the power transfer distribution factor (PTDF) matrix. This is a node-unit correlation matrix with dimensions N×N. G ; It is a dimension of N G T×N G The identity matrix of T; H D It is a dimension of N G (T-1)×N G The matrix T has the following expansion: , It is a dimension of N G ×N G The identity matrix.

[0090] Matrix b M H in G It is a dimension N G T×N G The matrix, whose expansion is: A G,th This is the relationship matrix between generating units and thermal power units, with dimension N. G ×N G,th ; Indicates the maximum and minimum output parameters of the thermal power unit; H DG It is a dimension of N G (T-1)×N G The matrix, whose expansion is: ; It is a dimension of N G The climbing speed vector of a thermal power unit is ×1. It is a dimension of N GThe downhill climbing rate vector of a thermal power unit is ×1.

[0091] Matrix E LM P in L It is a dimension of N B A T×NT matrix has the following expansion: .

[0092] Matrix E GRM P in R It is a dimension of N G T×N G，res The matrix T has the following expansion: A G,res It is the correlation matrix between generating units and renewable energy generating units.

[0093] Matrix E FM In It is a dimension of N G T×N G The identity matrix of T.

[0094] S GRM These are renewable energy output parameters, which are parameters that have uncertainties or require sensitivity analysis during the optimization and solution of multi-period economic dispatch models.

[0095] S FM These are line capacity parameters, which are parameters that have uncertainties or require sensitivity analysis during the optimization and solution of multi-time period economic dispatch models.

[0096] Step S204: Equalize the inequality constraints in the multi-period economic dispatch model to obtain the correlation between the optimal solution of unit output and the node load parameters, line transmission capacity parameters, and renewable energy unit output parameters.

[0097] For example, the server can perform equality checks on the inequality constraints in the above embodiments, that is, use the condition that the left-hand side parameter and the right-hand side condition are equal as the constraint condition, and perform multi-parameter linear programming to solve the multi-time period economic dispatch model to obtain the following correlations between the optimal solution of unit output and the node load parameters, line transmission capacity parameters, and renewable energy unit output parameters:

[0098] ,

[0099] in, It is the optimal solution for unit output across multiple time periods. This represents the coefficient matrix / vector corresponding to equality and inequality constraints.

[0100] Step S206: Based on the correlation, differentiate the optimal solution of unit output with respect to the node load parameters to obtain the marginal carbon emission factor corresponding to each node in multiple time periods.

[0101] For example, based on the above correlation, the server can differentiate the node load parameters using the optimal solution of unit output to construct the following marginal carbon emission factor calculation expression:

[0102] ,

[0103] in, To represent the marginal carbon emission factor at each time point A 3D row vector can be represented as ,in It is a time period Marginal carbon emission factor vector for each node.

[0104] Step S208: Perform component decomposition on the marginal carbon emission factors corresponding to each node in multiple time periods to obtain the marginal carbon emission factors of the equilibrium node and the marginal carbon emission factors of the congested line in each time period as the marginal carbon emission factors corresponding to the parameter samples.

[0105] The marginal carbon emission factor of a congested line can include the marginal carbon emission factor caused by forward congestion and the marginal carbon emission factor caused by reverse congestion.

[0106] For example, the server can use the following formula to perform component decomposition:

[0107] ,

[0108] ,

[0109] in, This indicates a time period. The marginal carbon emission factor at the equilibrium node is a vector. The first in Each element. They represent time periods respectively. Each line experiences marginal carbon emission factors due to both forward and reverse congestion. and These represent the sets of branches where forward and reverse congestion occurs during time period t, respectively. , They represent time periods respectively. branch road The sequence number in all forward (or reverse) blocked branches; This represents the total number of forward congested branch roads throughout the entire time period.

[0110] Optionally, in some implementations, the above-mentioned multi-period economic scheduling model can be derived based on the following single-period economic scheduling model, combined with the multi-period coupling constraints in the application scenario of this application:

[0111] ,

[0112] ,

[0113] ,

[0114] Where, vector This represents the cost of each unit, with the following dimensions: . The decision variables representing the output of each generating unit have the following dimensions: . These represent node load, line transmission capacity, and renewable energy unit output, respectively. These are parameters that present uncertainties in the optimization process or require sensitivity analysis. Their dimensions are as follows: , , , and These represent the number of system branches and the number of renewable energy units, respectively. (Vector) , This represents a column vector consisting entirely of 1s, with dimensions of [dimensions missing]. , .

[0115] Among them, matrix sum vector The calculation formula is as follows:

[0116] ,

[0117] ,

[0118] ,

[0119] ,

[0120] ,

[0121] in, This represents the power flow transfer distribution factor matrix. It is an incidence matrix; This is a node-unit correlation matrix with dimension 1. Its elements Defined as: if the unit Connected to the node Above, then =1, otherwise =0; These are the correlation matrices between generating units and renewable energy units, and between generating units and thermal power units, with dimensions of [missing information]. , ,That The element of location is defined as: if renewable energy (or thermal power) unit The unit is numbered as follows: ,but The position element takes a value of 1, and vice versa. Let represent the maximum and minimum output of the thermal power unit, which are invariant parameters in the optimization problem, and their dimensions are . , This represents the number of thermal power units. , They are respectively , An identity matrix of dimension 1.

[0122] The inequality constraints in the above single-period economic scheduling model are evaluated by equality, and the inequality constraint coefficients are determined accordingly. The following correlation between the optimal solution for unit output in a single time period and the coefficient matrix can be obtained:

[0123] ,

[0124] in, This represents the optimal solution for the unit's output.

[0125] Based on the optimal solution and optimization parameters of unit output in the above single-time period correlation relationship Based on the correlation and the definition of marginal units, by differentiating the unit output with respect to the nodal load, we can obtain the following expression for calculating the marginal unit output matrix for a single time period:

[0126] .

[0127] Furthermore, the analytical calculation expression for the marginal carbon emission factor of each node in a single time period can be derived:

[0128] .

[0129] Furthermore, similar to the nodal marginal electricity price, the marginal carbon emission factor can be decomposed into its components. For example, for any node k in the power system, its marginal carbon emission factor can be expanded into the following form:

[0130] ,

[0131] in, Indicates the first One element is 1, and the rest are 0. Dimensional column vector.

[0132] The calculation method for the marginal carbon emission factor at the equilibrium node can be simplified as follows:

[0133] .

[0134] The formula for calculating the marginal carbon emission factor of a line caused by congestion can be expressed as follows:

[0135] ,

[0136] Among them, row vector The dimension is This represents the marginal carbon emission factor of the line. Note that... The non-zero element corresponds one-to-one with the line that experiences forward or reverse blockage.

[0137] Furthermore, since the marginal carbon emission factor of a line due to congestion includes both forward and reverse congestion of branches, a forward line marginal carbon emission factor can be defined to separately consider the forward and reverse congestion scenarios. and the marginal carbon emission factor of the reverse route The following formula can be used to calculate the branches. Corresponding positive (or negative) branch marginal carbon emission factors:

[0138] ,

[0139] ,

[0140] in, and These represent the marginal carbon emission factors of the line considering only forward or reverse congestion, respectively; operators and Representing branch roads The number of the branch that is blocked in either the forward or reverse direction; , , These represent the sets of branches that experience forward congestion, reverse congestion, and no congestion, respectively. This indicates the number of branches blocked by the forward current.

[0141] In other words, the blocking component of the marginal carbon emission factor can be expressed in the following form:

[0142] ,

[0143] in, It represents a matrix The List.

[0144] Substituting the congestion components corresponding to the marginal carbon emission factors of the above-mentioned equilibrium nodes and the line marginal carbon emission factors into the calculation formula of the marginal carbon emission factor, we can obtain the following component decomposition expression:

[0145] ,

[0146] in, is the equilibrium component in the marginal carbon emission factor, and is the marginal carbon emission factor at the equilibrium node. This is the blocking component in the marginal carbon emission factor, and it is related to the marginal carbon emission factor of the line.

[0147] In this embodiment, by using the marginal carbon emission factors of the balancing nodes and the marginal carbon emission factors of the congested lines at each time period as the marginal carbon emission factors corresponding to the parameter samples, it is possible to achieve uncertainty analysis of marginal carbon emission factors in multiple time periods to address the impact of uncertainty in future renewable energy output. This provides the power system with more comprehensive marginal carbon emission factor analysis results to cope with the uncertain operating conditions of the power system.

[0148] In an exemplary embodiment, the method provided in this application further includes: determining the target critical domain corresponding to the parameter sample based on the inequality constraints that are not equal in the multi-period economic scheduling model; adding the target critical domain corresponding to the parameter sample to the critical domain list, and recording the marginal carbon emission factor corresponding to the target critical domain.

[0149] Alternatively, in some implementations, since general linear programming problems can be solved in the following way:

[0150] ,

[0151] in, As decision variables, For parameter vectors, The possible range of variation is within the parameter space. . For the coefficient matrix / vector. Assume that for the parameter space... Any given set of parameters Each optimization model has a unique optimal solution. .

[0152] Therefore, for a given parameter Its optimal solution The corresponding inequality constraints can be divided into two categories: equality inequality constraints and non-equality inequality constraints.

[0153] ,

[0154] ,

[0155] in, To obtain the coefficient matrix corresponding to the equality constraint, This is the coefficient matrix corresponding to the lack of equality constraints.

[0156] For linear programming, equality constraints correspond to vertices in the feasible region where the optimal solution lies. When parameters change, the vertex corresponding to the optimal value, i.e., the equality inequality constraint, may change. Critical regions are used to represent the parameter space. The parameters within the same critical region are partitioned, and all parameters in the same critical region share the same equality and inequality constraints. When a parameter moves within the critical region, the equality inequality constraints remain unchanged. That is, for linear programming, parameters... Critical region This can be further expressed as:

[0157] ,

[0158] Therefore, for the above multi-period economic scheduling model, the critical region corresponding to its parameter samples can be constructed using the following formula:

[0159] ,

[0160] in, This represents the coefficient matrix corresponding to the inequality constraints that are not equal.

[0161] In the application scenario of this application, due to the line capacity parameter S FM It is fixed and does not exhibit uncertainty; therefore, this parameter does not change during the sampling process. Thus, the term containing line capacity can be included in the constant vector. middle, This represents the constant term corresponding to the equality constraint. Wherein, The expression is as follows:

[0162] .

[0163] Within each critical region, the equality inequality constraints remain unchanged, i.e., the matrix... , The marginal carbon emission factor remains unchanged. Therefore, based on the above calculation expression for the marginal carbon emission factor, it can be determined that the marginal carbon emission factor within each critical region is a constant. For a given critical region, the marginal carbon emission factor corresponding to that critical region can be obtained by solving only one economic scheduling problem.

[0164] Therefore, the server can generate the target critical region using the above calculation formula; add the target critical region corresponding to the parameter sample to the critical region list, and record the marginal carbon emission factor corresponding to the target critical region.

[0165] In this embodiment, by updating the critical domain list using the target critical domain corresponding to the parameter sample, the data richness of the critical domain list can be improved, thereby increasing the matching success rate between the parameter sample generated by subsequent sampling and the critical domain.

[0166] In an exemplary embodiment, the method provided in this application may further include, in response to the result of matching the parameter sample with the critical domain, using the marginal carbon emission factor corresponding to the critical domain as the marginal carbon emission factor corresponding to the parameter sample.

[0167] For example, if a parameter sample matches a critical region, the server can read the marginal carbon emission factor corresponding to the currently matched critical region as the marginal carbon emission factor corresponding to the currently matched parameter sample.

[0168] In this embodiment, by directly using the marginal carbon emission factor corresponding to the successfully matched critical domain as the marginal carbon emission factor of the parameter sample, the time required for optimization and solution of the multi-period economic scheduling model can be significantly reduced, the computational efficiency of the marginal carbon emission factor can be improved, and the computational cost of the marginal carbon emission factor can be reduced.

[0169] In one exemplary embodiment, such as Figure 3 As shown, the method provided in this application may further include steps S302 to S306. Wherein:

[0170] Step S302: Record the number of parameter samples that match each critical region.

[0171] Step S304: Sort each critical region in the critical region list according to the number of parameter samples to obtain the sorted critical region list.

[0172] Step S306: Match each critical region with the parameter sample in turn according to the order of the critical regions in the sorted critical region list.

[0173] For example, after performing a preset number of sampling operations, the server can count the number of parameter samples that match each critical region in the current critical region list. The critical regions in the critical region list are then sorted in descending order based on the number of parameter samples, resulting in a sorted critical region list. In subsequent sampling operations, each critical region is matched with a parameter sample sequentially according to the order of the critical regions in the sorted critical region list.

[0174] Optionally, in some implementations, when a critical region corresponding to a parameter sample has been matched, the marginal carbon emission factor corresponding to the matched critical region can be directly used as the carbon emission factor corresponding to the parameter sample. That is, there is no need to match the remaining critical regions in the critical region list that are ranked after the currently matched critical region with the current parameter sample.

[0175] Alternatively, in some other implementations, if no critical domain corresponding to the parameter sample can be matched after traversing all critical domains in the critical domain list, then the marginal carbon emission factor needs to be solved by the operations in steps S202 to S208.

[0176] In this embodiment, by dynamically sorting the critical regions with a large number of matching parameter samples that appear frequently, the critical regions are placed at the front of the critical region list, which can help reduce the critical region matching query time.

[0177] In an exemplary embodiment, step S304 may further include: sorting each critical domain in the critical domain list in descending order according to the number of parameter samples to obtain the sorting result of each critical domain; and constructing a sorted critical domain list using several critical domains before a preset threshold based on the sorting result.

[0178] For example, the server can store a preset threshold to limit the length of the critical region list. The critical regions are sorted in descending order of the number of parameter samples, and only the critical regions that are below the preset threshold are retained to construct the sorted critical region list.

[0179] In this embodiment, by retaining several critical regions before the preset threshold to construct a sorted critical region list, the problem of excessive time required for querying and sorting at the critical regions at the end of the critical region list due to excessively large storage space in the critical region list can be avoided.

[0180] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0181] Based on the same inventive concept, this application also provides a device for determining the probability distribution of marginal carbon emission factors to implement the above-described method for determining the probability distribution of marginal carbon emission factors. The solution provided by this device is similar to the solution described in the above-described method. Therefore, the specific limitations of one or more embodiments of the device for determining the probability distribution of marginal carbon emission factors provided below can be found in the limitations of the method for determining the probability distribution of marginal carbon emission factors described above, and will not be repeated here.

[0182] In one exemplary embodiment, such as Figure 4 As shown, a probability distribution determination device 400 for marginal carbon emission factors is provided, comprising: a data acquisition module 402, a parameter sampling module 404, a critical domain matching module 406, an economic scheduling module 408, and a distribution generation module 410, wherein:

[0183] The data acquisition module 402 is used to acquire network topology information, generator configuration information, the first probability distribution of renewable energy output, and the second probability distribution of power system load.

[0184] The parameter sampling module 404 is used to repeatedly perform the following sampling operation multiple times until the preset number of samplings is reached: sampling is performed according to the first probability distribution and the second probability distribution to obtain the parameter sample of this sampling.

[0185] The critical region matching module 406 is used to match parameter samples with each critical region in the critical region list.

[0186] The economic dispatch module 408 is used to respond to the result of the mismatch between the parameter sample and the critical domain. It uses a multi-period economic dispatch model to solve for the marginal carbon emission factor corresponding to the parameter sample. The constraints of the multi-period economic dispatch model include power balance constraints for each period and inequality constraints consisting of line power flow constraints, unit output constraints and unit ramping constraints. The constraints are generated using network topology information and unit configuration information.

[0187] The distribution generation module 410 is used to generate a probability distribution of the marginal carbon emission factor using the marginal carbon emission factor obtained from each sampling.

[0188] In an exemplary embodiment, the economic dispatch module 408 is further configured to input parameter samples into a multi-period economic dispatch model to obtain the optimal solution for unit output; to equalize the inequality constraints in the multi-period economic dispatch model to obtain the correlation between the optimal solution for unit output and the node load parameters, the line transmission capacity parameters, and the renewable energy unit output parameters; to differentiate the optimal solution for unit output with respect to the node load parameters based on the correlation to obtain the marginal carbon emission factors corresponding to each node in the multi-period; and to perform component decomposition on the marginal carbon emission factors corresponding to each node in the multi-period to obtain the marginal carbon emission factors of the balanced nodes and the marginal carbon emission factors of the congested lines in each period as the marginal carbon emission factors corresponding to the parameter samples.

[0189] In an exemplary embodiment, the probability distribution determination device 400 for marginal carbon emission factors further includes: a list update module, used to determine the target critical region corresponding to the parameter sample based on the inequality constraints that are not equal in the multi-period economic scheduling model; add the target critical region corresponding to the parameter sample to the critical region list, and record the marginal carbon emission factor corresponding to the target critical region.

[0190] In an exemplary embodiment, the economic scheduling module 408 is further configured to, in response to the result of matching the parameter sample with the critical domain, use the marginal carbon emission factor corresponding to the critical domain as the marginal carbon emission factor corresponding to the parameter sample.

[0191] In an exemplary embodiment, the probability distribution determination device 400 for marginal carbon emission factors further includes: a list sorting module, used to record the number of parameter samples matching each critical region; sort each critical region in the critical region list according to the number of parameter samples to obtain a sorted critical region list; and match each critical region with the parameter samples in turn according to the order of the critical regions in the sorted critical region list.

[0192] In an exemplary embodiment, the list sorting module is further configured to sort each critical region in the critical region list in descending order according to the number of parameter samples, thereby obtaining the sorting result of each critical region; and to construct a sorted critical region list using several critical regions before a preset threshold based on the sorting result.

[0193] Each module in the aforementioned probability distribution determination device 400 for marginal carbon emission factors can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the operations corresponding to each module.

[0194] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows:Figure 5 As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores network topology information, generator configuration information, a first probability distribution of renewable energy output, a second probability distribution of power system load, parameter samples, marginal carbon emission factors, and other data. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When executed by the processor, the computer program implements a method for determining the probability distribution of marginal carbon emission factors.

[0195] Those skilled in the art will understand that Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0196] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0197] In one exemplary embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in the above-described method embodiments.

[0198] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0199] 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 non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0200] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0201] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for determining the probability distribution of marginal carbon emission factors, characterized in that, The method includes: Obtain network topology information, generator configuration information, the first probability distribution of renewable energy output, and the second probability distribution of power system load; Repeat the following sampling operation multiple times until the preset number of samples is reached: The parameter samples for this sampling are obtained by sampling based on the first probability distribution and the second probability distribution; The parameter samples are matched with each critical region in the critical region list; In response to the result of the parameter sample not matching the critical domain, the marginal carbon emission factor corresponding to the parameter sample is obtained by using a multi-period economic dispatch model. The constraints of the multi-period economic dispatch model include power balance constraints for each period and inequality constraints consisting of line power flow constraints, unit output constraints and unit ramping constraints. The constraints are generated using the network topology information and the unit configuration information. The probability distribution of the marginal carbon emission factor is generated using the marginal carbon emission factor obtained from each sampling.

2. The method according to claim 1, characterized in that, In response to the mismatch between the parameter sample and the critical domain, the marginal carbon emission factor corresponding to the parameter sample is obtained by using a multi-period economic scheduling model, including: The parameter samples are input into the multi-period economic dispatch model to obtain the optimal solution for unit output. By applying equality to the inequality constraints in the multi-period economic dispatch model, the correlation between the optimal solution of the unit output and the node load parameters, line transmission capacity parameters, and renewable energy unit output parameters are obtained. Based on the aforementioned correlation, the optimal solution of the unit output is differentiated with respect to the node load parameters to obtain the marginal carbon emission factor corresponding to each node in multiple time periods. The marginal carbon emission factors corresponding to each node in the multi-time period are decomposed to obtain the marginal carbon emission factors of the equilibrium node and the marginal carbon emission factors of the congested line in each time period, which are used as the marginal carbon emission factors corresponding to the parameter sample.

3. The method according to claim 2, characterized in that, The method further includes: Based on the inequality constraints that are not equal in the multi-period economic scheduling model, the target critical domain corresponding to the parameter sample is determined; Add the target critical region corresponding to the parameter sample to the critical region list, and record the marginal carbon emission factor corresponding to the target critical region.

4. The method according to claim 3, characterized in that, The method further includes: In response to the result of matching the parameter sample with the critical domain, the marginal carbon emission factor corresponding to the critical domain is used as the marginal carbon emission factor corresponding to the parameter sample.

5. The method according to claim 1, characterized in that, The method further includes: Record the number of parameter samples that match each of the aforementioned critical regions; The critical regions in the critical region list are sorted according to the number of parameter samples to obtain a sorted critical region list. According to the order of the critical regions in the sorted critical region list, each critical region is matched with the parameter sample in turn.

6. The method according to claim 5, characterized in that, The step of sorting each critical region in the critical region list according to the number of parameter samples to obtain the sorted critical region list includes: The critical regions in the critical region list are sorted in descending order according to the number of parameter samples to obtain the sorting result of each critical region. The sorted critical region list is constructed using several critical regions before a preset threshold based on the sorting results.

7. A device for determining the probability distribution of marginal carbon emission factors, characterized in that, The device includes: The data acquisition module is used to acquire network topology information, generator configuration information, the first probability distribution of renewable energy output, and the second probability distribution of power system load. The parameter sampling module is used to repeatedly perform the following sampling operation multiple times until a preset number of samplings is reached: sampling is performed according to the first probability distribution and the second probability distribution to obtain the parameter sample of this sampling. The critical region matching module is used to match the parameter sample with each critical region in the critical region list; The economic dispatch module is used to respond to the result of the mismatch between the parameter sample and the critical domain by using a multi-period economic dispatch model to obtain the marginal carbon emission factor corresponding to the parameter sample. The constraints of the multi-period economic dispatch model include power balance constraints for each period and inequality constraints composed of line power flow constraints, unit output constraints and unit ramping constraints. The constraints are generated using the network topology information and the unit configuration information. The distribution generation module is used to generate a probability distribution of the marginal carbon emission factor using the marginal carbon emission factor obtained from each sampling.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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