Method and system for identifying atc limit of multi-section tidal current interaction

Abstract

This invention discloses an ATC limit identification method and system for multi-section power flow interaction, relating to the field of renewable energy grid connection technology. The method includes the following steps: based on historical renewable energy and new load data, probability distribution models for renewable energy and new loads are established using a probability distribution modeling method; the probability distribution models are input into a hybrid Copula function to obtain a joint probability density distribution model; based on this joint model, the power adjustment range of renewable energy and loads at the sending and receiving ends of the section is calculated; within the power adjustment range, a section power flow optimization model is constructed and solved to obtain the extreme operating mode of the section; based on this operating mode, combined with the sub-channel power flow rated limit and a preset adjustment coefficient, the available transmission capacity (ATC) of the section under the extreme operating mode is obtained; accordingly, the available transmission capacity of the section interval is formed, and the ATC limit under multi-section interaction is calculated using an ATC determination model.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of new energy grid-connection technology, and more particularly, to a multi-section power flow interaction ATC limit identification method and system. BACKGROUND

[0002] The safe and stable operation of a power system depends on the accurate assessment of the available transfer capability (ATC) of a section. The ATC limit determines not only the transmission capacity between different regions, but also is an important basis for power grid planning, dispatching and market transactions. However, there are still many deficiencies in the existing technology in the ATC calculation process. Traditional methods are mostly based on deterministic power flow calculation or single-section constraint analysis, usually ignoring the uncertainty characteristics of new energy output and new type of load, resulting in that the calculation results are difficult to reflect the volatility and risk under actual operating conditions. When new energy is massively connected to the power grid, or new type of load shows significant randomness, the ATC limit obtained by the existing method is easy to deviate from the true level, thereby affecting the reasonable assessment of the safe margin of the power grid operation.

[0003] At the same time, there is often a coupling effect of power flow between the sections of the power system, and the change of transmission power of one section may have a significant impact on the power flow distribution of the adjacent section. The existing methods generally use the way of analyzing a single section independently, lack of systematic modeling of the interaction of multiple sections, and are difficult to identify the transmission capacity bottleneck caused by the interaction coupling. In addition, some methods rely on empirical parameters or simplified assumptions to deal with complex conditions, lack of systematic integration of multi-source data, resulting in insufficient accuracy and adaptability of the results.

[0004] Especially under the background of high proportion of new energy access and frequent multi-regional power exchange, the coupling degree between sections is intensified, and the uncertainty factors are superimposed. The existing ATC identification means has been difficult to meet the fine management needs of power grid operation and dispatching. SUMMARY

[0005] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present application provide a multi-section power flow interaction ATC limit identification method, which probabilistically models new energy and new type of load and combines a hybrid Copula function, power adjustment range calculation and section power flow optimization solution, solving the problem that the existing available transmission capacity assessment is difficult to consider the multi-section interaction and operating uncertainty at the same time.

[0006] To achieve the above-mentioned purpose, the present application provides the following technical solutions:

[0007] The method comprises the following steps: based on historical new energy data and new load data, a new energy probability distribution model and a new load probability distribution model are established by using a probability distribution modeling method; the new energy probability distribution model and the new load probability distribution model are input into a mixed Copula function to obtain a joint probability density distribution model; based on the joint probability density distribution model, the power adjustment range of the new energy and the load at the sending end and the receiving end of the section is calculated; within the power adjustment range of the section, a section power flow optimization model is constructed and solved to obtain a section extreme operating mode; based on the section extreme operating mode, the available transmission capacity of the section extreme operating mode is obtained according to the sub-channel power flow rating limit and a preset adjustment coefficient; and the available transmission capacity limit under the interaction of multiple sections is calculated by an ATC determination model according to the available transmission capacity of the section extreme operating mode.

[0008] In a preferred embodiment, the probability distribution modeling method is a Gaussian mixture model.

[0009] In a preferred embodiment, the new energy probability distribution model and the new load probability distribution model are input into a mixed Copula function to obtain a joint probability density distribution model, which specifically comprises: based on the new energy probability distribution model and the new load probability distribution model, probability integral transformation is performed on the new energy output and the new load unit value to obtain uniform scale variables; a mixed Copula function is constructed according to the uniform scale variables and the mixed Copula density function is calculated; and the mixed Copula density function is multiplied by the edge probability density function of the new energy and the new load to obtain the joint probability density distribution model.

[0010] In a preferred embodiment, the calculation of the power adjustment range of the new energy and the load at the sending end and the receiving end of the section needs to consider the new energy unit output constraint and the system reserve constraint.

[0011] In a preferred embodiment, the section power flow optimization model comprises a section power flow maximum optimization model and a section power flow minimum optimization model, the objective functions are respectively the maximization of the section power flow and the minimization of the section power flow, and the constraint conditions include grid safety constraints, power balance constraints and system reserve constraints.

[0012] In a preferred embodiment, the section power flow optimization model is numerically solved by using an interior point method.

[0013] In a preferred embodiment, in the process of iterative solution by using the interior point method, a penalty function term is introduced into the objective function to correct the solution that violates the grid safety constraints, the power balance constraints and the system reserve constraints, and the corrected feasible solution is output as the iterative result.

[0014] In a preferred embodiment, the calculation formula of the ATC determination model is:

[0015]

[0016] wherein, is the available transmission capacity of the section, is the number of sub-channels constituting the transmission section, , , are the rated limit, adjusted active power and initial active power of the sub-channel respectively, is the active adjustment amount of the unit , is the active sensitivity of the unit to the sub-channel , is the active power adjustment coefficient of the sub-channel .

[0017] The present application provides an ATC limit identification system for multi-section power flow interaction, comprising: a power boundary calculation module for calculating the power adjustment range of new energy and load at the sending end and receiving end of the section; an extreme scenario generation module for constructing a section power flow optimization model and solving it within the section power adjustment range to obtain a section extreme operating mode; a static ATC calculation module for obtaining the available transmission capacity of the section extreme operating mode based on the sub-channel power flow rated limit and the preset adjustment coefficient; a dynamic ATC aggregation module for forming the available transmission capacity of the section interval according to the available transmission capacity of the section extreme operating mode, and calculating the available transmission capacity limit under the influence of multi-section interaction through an ATC determination model.

[0018] The technical effects and advantages of the ATC limit identification method for multi-section power flow interaction of the present application are:

[0019] The present application can accurately depict the power flow interaction relationship between multiple sections by probabilistic modeling of new energy and new type of load and correlation modeling thereof, combined with power adjustment range calculation and section power flow optimization solving, and on this basis, form intervalized available transmission capacity limit, thereby effectively improving the reflection ability of the ATC identification result to operating uncertainty and section coupling effect, and ensuring the rationality and reliability of the calculation result. BRIEF DESCRIPTION OF DRAWINGS

[0020] Figure 1 is a flowchart of the ATC limit identification method for multi-section power flow interaction provided by the embodiment of the present application;

[0021] Figure 2The composition block diagram of the ATC limit value identification system for multi-section power flow interaction is provided for the embodiments of the present application. DETAILED DESCRIPTION

[0022] The technical solutions in the embodiments of the present application will be clearly and completely described with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all the other embodiments obtained by those skilled in the art without creative work are within the protection scope of the present application.

[0023] Embodiment 1, Figure 1 The ATC limit value identification method for multi-section power flow interaction is given, including the following steps:

[0024] S1, based on historical new energy data and new load data, a probability distribution modeling method is used to establish a new energy probability distribution model and a new load probability distribution model;

[0025] S2, the new energy probability distribution model and the new load probability distribution model are input into a mixed Copula function to obtain a joint probability density distribution model;

[0026] S3, based on the joint probability density distribution model, the power adjustment range of the new energy and load at the sending end and the receiving end of the section is calculated;

[0027] S4, within the power adjustment range of the section, a section power flow optimization model is constructed and solved to obtain a section extreme operating mode;

[0028] S5, based on the section extreme operating mode, according to the sub-channel power flow rated limit value and the preset adjustment coefficient, the available transmission capacity of the section extreme operating mode is obtained;

[0029] S6, according to the available transmission capacity of the section extreme operating mode, the section interval available transmission capacity is formed, and the available transmission capacity limit value under the multi-section interaction is calculated through an ATC determination model.

[0030] The embodiment can effectively depict the randomness and uncertainty characteristics of new energy and new load by probabilistic modeling of historical data of new energy and new load; the correlation between new energy and new load is comprehensively described by establishing a joint probability density distribution model, thereby providing a statistical basis for subsequent analysis; on this basis, the power adjustment range of the sending end and the receiving end is determined, so that the construction of the extreme operating mode has clear boundary conditions; further, the extreme operating mode reflecting the most unfavorable operating condition is obtained by solving the cross-section power flow optimization model; finally, the intervalized cross-section available transfer capability limit is formed by combining the sub-channel limit constraint and the ATC determination model, thereby realizing the quantitative identification of the interaction of multi-section power flow, and providing a scientific basis for the safe and stable operation and dispatching decision of the power grid.

[0031] S1, based on historical new energy data and new load data, a probability distribution modeling method is used to establish a new energy probability distribution model and a new load probability distribution model.

[0032] It should be noted that the historical new energy data includes wind power and photovoltaic grid-connected active power, unit available state, planned maintenance mark and meteorological elements (wind speed, irradiance, temperature) aligned according to a unified time reference, etc., wherein the main variable used for modeling in this step is the grid-connected active power ; the historical new load data includes electric vehicle charging load, data center adjustable load, source-load interaction record and load management event mark, etc., wherein the main variable used for modeling in this step is the grid-connected active load , both of which are recorded at a unified sampling interval and labeled with a timestamp t, and the time span covers at least multiple seasonal cycles to ensure sample representativeness, specifically: when there are different time layers (such as peak / flat / valley, weekdays / weekends), the above layer labels are used for layered modeling to obtain layered marginal distribution , to avoid distribution aliasing caused by mixed seasonality.

[0033] In the embodiment, the probability distribution modeling method is a Gaussian mixture model, and S1 specifically includes:

[0034] For new energy samples , set the number of mixed components and the parameter set , then the probability density function of the new energy sample is:

[0035] (1)

[0036] wherein the mixed weight satisfies , the mean is , and the variance is , wherein A preset positive threshold is used to prevent excessively small variance from causing a singular covariance matrix. The probability density function representing a one-dimensional Gaussian distribution;

[0037] Similarly, for novel load samples Set the number of components With parameter set Then the probability density function of the new load sample is:

[0038] (2)

[0039] Furthermore, the Expectation-Maximization (EM) algorithm is used to maximize the log-likelihood. Parameter estimation is performed, specifically by using K-means++ to estimate the parameters. , Initialize them separately, using cluster centers, the proportion of samples within a cluster, and the variance within a cluster as... , and Initial value;

[0040] Calculate separately in step E and The responsibility of each sample point to each Gaussian component and The formula is:

[0041] (3)

[0042] (4)

[0043] in and These are normalized samples of new energy power output and new load samples, respectively.

[0044] Based on the responsibility calculated in the E-step, the model parameters are updated in the M-step to maximize the likelihood function. The specific update formula is as follows:

[0045] (5)

[0046] (6)

[0047] (7)

[0048] superscript Distinguishing between new energy sources and new types of loads, , Iterate q times until... Or reach the maximum number of iterations , convergence threshold;

[0049] number of components The Bayesian information criterion (BIC) is selected, and the formula is:

[0050] (8)

[0051] wherein is the parameter set of maximum likelihood estimation, m is the total number of parameters, T is the sample size, and K is the minimum BIC;

[0052] Considering that the definition domain of the modeling variable is [0, 1], in order to avoid the extension of the Gaussian tail to the outside of the interval, interval truncation normalization processing is adopted, and the formula is:

[0053] (9)

[0054] (10)

[0055] wherein is the normalized new energy output and new load active power; thus, the new energy edge probability distribution model for subsequent correlation modeling is obtained and the new load edge probability distribution model .

[0056] This step can approximate the real edge density with a limited number of Gaussian components by fitting the mixed distribution of the new energy output with multi-peak and intermittence and the new load affected by behavior events, to obtain the parameter set and the computable closed probability density , which provides consistent probability description and repeatable estimation process for subsequent correlation modeling and power adjustment range derivation.

[0057] S2, input the new energy probability distribution model and the new load probability distribution model into the mixed Copula function to obtain the joint probability density distribution model, and the specific steps are as follows:

[0058] S21, the new energy edge probability density obtained in step S1 is , and the corresponding distribution function is , the new load edge probability density is , and the corresponding distribution function is ; define the probability integral transform , , and introduce the boundary clipping constant in numerical implementation. truncate to the interval to avoid divergence of inverse function evaluation.

[0059] S22, construct the mixed Copula function. The mixed Copula function is obtained by linear combination of Gaussian-Copula and t-Copula with weights, and the formula is:

[0060] (11)

[0061] wherein, is the parameter set of the mixed Copula model; ; is the distribution function of Gaussian-Copula; is the correlation coefficient of Gaussian-Copula; is the distribution function of t-Copula; is the correlation coefficient of t-Copula; is the degree of freedom of t-Copula.

[0062] S23, calculate the mixed Copula density function, and the specific formula is as follows:

[0063] (12)

[0064] wherein and are the density functions of Gaussian-Copula and t-Copula respectively, and the definitions are as follows:

[0065] 1) Gaussian-Copula density function:

[0066] (13)

[0067] wherein is the inverse function of the standard normal distribution;

[0068] 2) t-Copula density function:

[0069] (14)

[0070] wherein is the inverse function of the univariate t distribution with the degree of freedom , is the density function thereof, and is the two-dimensional t distribution density function with the correlation matrix . S24, combine the marginal distribution and the mixed Copula density to obtain the joint probability density distribution model of new energy and new load, and the formula is:

[0071]

[0072] ​​(15)

[0073] wherein, is the parameter set of the mixed Copula model.

[0074] This step inputs the marginal distribution of new energy and new load into the mixed Copula function, establishes a joint probability density distribution model, and can depict the correlation in the full range and the dependence structure in the tail extreme case, thereby providing a quantitative basis for the joint uncertainty of subsequent cross-section power adjustment range calculation.

[0075] S3, based on the joint probability density distribution model, calculate the power adjustment range of new energy and load at the sending end and the receiving end of the cross-section, specifically:

[0076] The active power of the sending end new energy is expressed as:

[0077] (16)

[0078] wherein is the installed capacity of the sending end new energy unit;

[0079] The power of the receiving end new load is expressed as:

[0080] (17)

[0081] wherein is the reference capacity of the receiving end new load.

[0082] The calculation of the power adjustment range needs to meet the following constraint conditions:

[0083] 1) New energy unit output constraint:

[0084] (18)

[0085] wherein is the rated maximum active power of the unit;

[0086] 2) New load power constraint:

[0087] (19)

[0088] wherein is the minimum adjustable power of the receiving end new load, is the maximum adjustable power thereof;

[0089] 3) System reserve constraint:

[0090] (20)

[0091] wherein for the dispatchable unit set in the system, for the unit for the standby, for the standby capacity required by the system, for the increase of the load at the receiving end relative to the reference value. This constraint ensures that the system standby is still not lower than the requirement in the case of load increase.

[0092] In consideration of the above constraints, the feasible set of the sending end and receiving end power is obtained as:

[0093] (21)

[0094] Thus, the power adjustment range of the sending end and receiving end new energy and load is obtained as:

[0095] (22)

[0096] (23)

[0097] That is, the minimum value and the maximum value in the feasible set are taken respectively to constitute the sending end new energy output adjustment interval and the receiving end new type load adjustment interval .

[0098] This step introduces the new energy unit output constraint, the upper and lower limit constraint of the new type load power and the system standby constraint on the support set of the joint probability density distribution model to obtain the feasible set of the sending end and receiving end power, and determines the power adjustment range through the maximum value and the minimum value of the set, thereby providing the input boundary for the sectional power flow optimization model.

[0099] S4, within the sectional power adjustment range, a sectional power flow optimization model is constructed and solved to obtain the sectional extreme operating mode.

[0100] Specifically, within the sectional power adjustment range obtained in S3, the sectional power flow is defined as:

[0101] (24)

[0102] wherein, , , is the unit active sensitivity of the sectional power flow, is the load active sensitivity of the sectional power flow. On this basis, the sectional power flow optimization model is established.

[0103] In this embodiment, the cross-sectional power flow optimization model includes a cross-sectional power flow maximization optimization model and a cross-sectional power flow minimization optimization model, with objective functions of maximizing and minimizing cross-sectional power flow, respectively, and constraints including grid security constraints, power balance constraints, and system reserve constraints.

[0104] Specifically:

[0105] 1) The cross-sectional power flow maximization optimization model has an objective function as shown in equation (25):

[0106] (25)

[0107] The constraints are power grid security constraints, power balance constraints, and system reserve constraints.

[0108] The power grid security constraints are shown in equation (26):

[0109] (26)

[0110] in branch road The trend Its rated capacity;

[0111] The power balance constraint is shown in equation (27):

[0112] (27)

[0113] in This is due to network loss in the system.

[0114] The system's backup constraints are shown in equation (28):

[0115] (28)

[0116] It should be noted that in S3, the system reserve constraint is introduced in the form of Equation (20) to reflect the dynamic impact of the increase in the power of the receiving end load on the reserve; while in the cross-sectional power flow optimization model, in order to simplify the optimization expression, the power after the receiving end load adjustment is regarded as a known input, so the standard form, namely Equation (28), is used to express the reserve constraint. The two are logically consistent.

[0117] 1) The cross-sectional power flow minimum optimization model has the objective function shown in equation (29), and the constraints are the same as those of the maximum optimization model:

[0118] (29)

[0119] The two types of models mentioned above together constitute the cross-sectional power flow optimization problem, and the interior point method is used for numerical solution, as follows:

[0120] Let the Lagrangian function of the optimization problem be:

[0121] (30)

[0122] where, represents the objective function (maximize or minimize the section power flow), represents the equality constraints, represents the inequality constraints, and are the Lagrange multipliers, respectively.

[0123] In the iteration process, a penalty function term is introduced into the objective function, as shown in equation (31):

[0124] (31)

[0125] where is a penalty coefficient, used to correct the infeasible solution that violates the constraints, to ensure that when the solution violates the power grid safety constraints, power balance constraints or system reserve constraints, its target value is significantly penalized, so that the iteration gradually converges to the feasible region, and the feasible solution is output in the interior point method iteration process.

[0126] Through the above method iteration, two types of results are obtained:

[0127] 1) The maximum optimization model outputs the maximum value of the section power flow and its corresponding unit and load distribution, forming the section maximum operating mode.

[0128] 2) The minimum optimization model outputs the minimum value of the section power flow and its corresponding unit and load distribution, forming the section minimum operating mode.

[0129] This step establishes a section power flow maximization and minimization optimization model within the section power adjustment range, and realizes numerical solution through the interior point method combined with the penalty function, which can identify the extreme operating mode that meets the power grid safety, power balance and reserve constraint conditions, and provide boundary conditions for the interval calculation of available transfer capability.

[0130] S5, based on the section extreme operating mode, the available transfer capability of the section extreme operating mode is obtained according to the sub-channel power flow rated limit and the preset adjustment coefficient.

[0131] Specifically, the section extreme operating mode obtained in step S4 is is the result of the section power flow maximum optimization model, is the result of the section power flow minimum optimization model. The sub-channel set that constitutes the section is , the active power flow of the sub-channel is , and the rated limit is .

[0132] 1) In the maximum operating mode:

[0133] If there is a sub-channel current that satisfies equation (32):

[0134] (32)

[0135] Record the sub-channel as a constraint channel;

[0136] If all sub-channels do not reach the rated limit, increase the current of each sub-channel by a predetermined adjustment coefficient in the same proportion, as shown in equation (33):

[0137] (33)

[0138] Until there is a sub-channel first reaches and is recorded as a constraint channel.

[0139] 2) In the minimum operating mode:

[0140] The same method is used to obtain another set of constraint channels and corresponding adjusted sub-channel currents .

[0141] The following parameter sets are recorded to obtain the sub-channel parameter sets in the extreme operating mode:

[0142] (35)

[0143] Where is the initial sub-channel current; is the adjusted sub-channel current; is the rated limit of the sub-channel; is the active power adjustment of unit j; is the current sensitivity of unit j to sub-channel i.

[0144] This step can identify the system parameters that affect the maximum and minimum transferable power of the section under the constraint condition by introducing the sub-channel rated limit constraint and adjustment coefficient mechanism in the section extreme operating mode, forming the interval type available transfer capability, and providing quantitative basis for the final ATC limit determination under the influence of multiple section interactions.

[0145] S6, according to the available transfer capability of the section extreme operating mode, form the section interval available transfer capability, and calculate the available transfer capability limit under the influence of multiple section interactions through the ATC determination model.

[0146] In this embodiment, the calculation formula of the ATC determination model is:

[0147] (36)

[0148] wherein, is the available transmission capability of the section, is the number of sub-channels constituting the transmission section, is the sub-channel is the active power adjustment coefficient, K is the minimum value among the adjustment coefficients of each sub-channel.

[0149] In the calculation:

[0150] Substitute the maximum operating mode parameter set into the above formula to obtain the maximum available transmission capability of the section .

[0151] Substitute the minimum operating mode parameter set into the above formula to obtain the minimum available transmission capability of the section .

[0152] Therefore, the final available transmission capability of the section is:

[0153]

[0154] This step maps the rated limit value, power adjustment and sensitivity characteristics of each sub-channel into the available transmission capability of the section by using the ATC determination model, so as to realize quantitative identification of the ATC limit value under the interaction of multi-section power flow.

[0155] Embodiment 2, Figure 2 An ATC limit value identification system for multi-section power flow interaction is given, which includes:

[0156] A power boundary calculation module is used to calculate the power adjustment range of new energy and load at the sending end and the receiving end of the section;

[0157] An extreme scenario generation module is used to construct a section power flow optimization model and solve it within the power adjustment range of the section, so as to obtain the extreme operating mode of the section;

[0158] A static ATC calculation module is used to obtain the available transmission capability of the extreme operating mode of the section according to the sub-channel power flow rated limit value and the preset adjustment coefficient based on the extreme operating mode of the section;

[0159] A dynamic ATC aggregation module is used to form the available transmission capability of the section interval according to the available transmission capability of the extreme operating mode of the section, and calculate the available transmission capability limit value under the interaction of multi-sections through the ATC determination model.

[0160] Since the multi-section tide flow interaction ATC limit value identification device introduced in the embodiment is the device used for implementing the method in Embodiment 1 of the application, based on the method introduced in Embodiment 1 of the application, those skilled in the art can understand the specific implementation of the electronic device of the embodiment and various changes thereof, so the method of the electronic device how to implement the method in the embodiment of the application will not be introduced in detail. As long as the device used for implementing the method in the embodiment of the application is implemented by those skilled in the art, it belongs to the scope of protection of the application.

[0161] The above formulas are all dimensionless numerical calculations, the formulas are obtained by collecting a large amount of data to simulate a formula of the most recent real situation, and the preset parameters in the formula are set by those skilled in the art according to the actual situation.

[0162] The above embodiments can be realized wholly or partially by software, hardware, firmware or any combination thereof. When realized by software, the above embodiments can be realized wholly or partially in the form of a computer program product.

[0163] Those skilled in the art can realize that the modules and algorithm steps of the examples described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized 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 realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the application.

[0164] In addition, the functional modules in each embodiment of the application can be integrated in one processing module, or each module can exist physically, or two or more modules can be integrated in one module.

[0165] The above is only a specific implementation of the application, but the protection scope of the application is not limited thereto, any skilled person in the art can easily think of changes or replacements within the technical range disclosed in the application, which should be included in the protection scope of the application. Therefore, the protection scope of the application should be subject to the protection scope of the claims.

[0166] Finally: the above is only the preferred embodiment of the application, and is not used to limit the application, any modification, equivalent replacement, improvement, etc. within the spirit and principles of the application, should be included in the protection scope of the application.

Claims

1. A method for identifying ATC limits of multi-contingency power flow interaction, characterized in that, The method comprises the following steps: The power adjustment range of the sending end and the receiving end of the section new energy and load is calculated by a joint probability density distribution model; Within the power adjustment range of the section, a section power flow optimization model is constructed and solved to obtain a section extreme operating mode; Based on the section extreme operating mode, the available transmission capacity of the section extreme operating mode is determined according to the sub-channel power flow rating limit and a preset adjustment coefficient; Based on the available transmission capacity, the available transmission capacity of the section interval is formed, and the available transmission capacity limit under the influence of multi-section interaction is calculated through an ATC determination model; The specific construction steps of the joint probability density distribution model are as follows: Based on historical new energy data and new load data, a probability distribution modeling method is used to establish a new energy probability distribution model and a new load probability distribution model; The new energy probability distribution model and the new load probability distribution model are combined through a mixed Copula function to obtain a joint probability density distribution model, specifically: based on the new energy probability distribution model and the new load probability distribution model, probability integral transformation is performed on the new energy output and the new load standard value to obtain a unified scale variable; a mixed Copula function is constructed according to the unified scale variable and the mixed Copula density function is calculated; the mixed Copula density function is multiplied by the edge probability density function of the new energy and the new load to obtain the joint probability density distribution model.

2. The method of claim 1, wherein, The probability distribution modeling method is a Gaussian mixture model.

3. The method of claim 1, wherein, The calculation of the power adjustment range of the sending end and the receiving end of the section new energy and load comprises considering the new energy unit output constraint and the system reserve constraint.

4. The method of claim 1, wherein, The section power flow optimization model comprises a section power flow maximum optimization model and a section power flow minimum optimization model, and the objective functions thereof are to maximize and minimize the section power flow, respectively; the constraint conditions comprise power grid safety constraints, power balance constraints and system reserve constraints.

5. The method of claim 4, wherein, The section power flow optimization model is numerically solved by using an interior point method.

6. The method of claim 1, wherein, Within the power adjustment range of the section, the section power flow optimization model is constructed and solved to obtain the section extreme operating mode, wherein, in the solving process, the infeasible solution is modified by introducing a penalty function term into the objective function, and the feasible solution is output as the iteration result.

7. The method of claim 1, wherein, The calculation formula of the ATC determination model is: in, The available power transmission capacity of the cross section, The number of sub-channels that make up the transmission section, , , Sub-channels The rated limits, adjusted active power, and initial active power. For the unit Active power adjustment For the unit Pair channels Active sensitivity, Sub-channel The active power adjustment coefficient, K, is the minimum value among the adjustment coefficients of each sub-channel.

8. A system for using the method for identifying ATC limits of multi-profile power flow interaction according to any one of claims 1-7, characterized in that, It comprises: A power boundary calculation module for calculating the power adjustment range of the sending end and the receiving end of the section new energy and load; An extreme scenario generation module for constructing a section power flow optimization model within the power adjustment range of the section and solving to obtain a section extreme operating mode; A static ATC calculation module for obtaining the available transmission capacity of the section extreme operating mode based on the section extreme operating mode and according to the sub-channel power flow rating limit and a preset adjustment coefficient; A dynamic ATC aggregation module for forming the available transmission capacity of the section interval according to the available transmission capacity of the section extreme operating mode, and calculating the available transmission capacity limit under the influence of multi-section interaction through an ATC determination model.

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