ATC limit value identification method and system of multi-section power flow interaction influence

Abstract

The invention discloses an ATC limit value identification method and system for multi-section power flow interaction influence, and relates to the technical field of new energy grid connection, and the method comprises the following steps: based on historical new energy and novel load data, employing a probability distribution modeling method to respectively establish probability distribution models of new energy and novel load; inputting the probability distribution model into a mixed Copula function to obtain a joint probability density distribution model; based on the joint model, calculating the power adjustment range of the new energy and load of the section sending and receiving end; constructing a section power flow optimization model within the power adjustment range and solving to obtain a section extreme operation mode; based on the operation mode, obtaining the available transmission capability (ATC) of the section extreme operation mode in combination with a sub-channel power flow rated limit value and a preset adjustment coefficient; therefore, the section interval available power transmission capacity is formed, and the ATC limit value under the multi-section interaction influence is calculated through an ATC judgment 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, and 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 interaction of multiple sections and operating uncertainty at the same time.

[0006] To achieve the above object, the present application provides the following technical scheme: The method for identifying ATC limit value of multi-section power flow interaction includes 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 respectively 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 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.

[0007] In a preferred embodiment, the probability distribution modeling method is a Gaussian mixture model.

[0008] In a preferred embodiment, the input of 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 is 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 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; 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.

[0009] In a preferred embodiment, the calculation of the power adjustment range of the new energy and 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.

[0010] In a preferred embodiment, the section power flow optimization model includes 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.

[0011] In a preferred embodiment, the section power flow optimization model is numerically solved by using an interior point method.

[0012] 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.

[0013] In a preferred embodiment, the calculation formula of the ATC determination model is:

[0014] 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 , 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 .

[0015] 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.

[0016] The technical effects and advantages of the ATC limit identification method for multi-section power flow interaction of the present application are: The present application can accurately depict the power flow interaction relationship between multiple sections by modeling the probability of new energy and new type of load and modeling their correlation, combining power adjustment range calculation and section power flow optimization solution, and on this basis forming 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

[0017] Figure 1 The flowchart of the ATC limit identification method for multi-section power flow interaction provided by the embodiment of the present application is shown; Figure 2 The composition block diagram of the ATC limit identification system for multi-section power flow interaction provided by the embodiment of the present application is shown. DETAILED DESCRIPTION

[0018] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only 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 other embodiments obtained by a person of ordinary skill in the art without creative effort belong to the scope of protection of the present application.

[0019] Embodiment 1, Figure 1 The ATC limit identification method for multi-section power flow interaction of the present application is given, including the following steps: S1, based on historical new energy data and new load data, a new energy probability distribution model and a new load probability distribution model are respectively established by using a probability distribution modeling method; 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; S3, 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; S4, within the power adjustment range of the section, a section power flow optimization model is constructed and solved to obtain an extreme operating mode of the section; S5, based on the extreme operating mode of the section, the available transmission capacity of the extreme operating mode of the section is obtained according to the sub-channel power flow rating limit and a preset adjustment coefficient; S6, according to the available transmission capacity of the extreme operating mode of the section, the section interval available transmission capacity is formed, and the available transmission capacity limit under the multi-section interaction is calculated through an ATC determination model.

[0020] In this embodiment, the probability modeling is performed on the historical data of new energy and new load, which can effectively depict the randomness and uncertainty characteristics thereof; by establishing a joint probability density distribution model, the correlation relationship between the new energy and the new load is comprehensively described, 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 section power flow optimization model; finally, combined with the sub-channel limit constraint and the ATC determination model, the intervalized section available transmission capacity limit is formed, thereby realizing the quantitative identification of the multi-section power flow interaction, and providing a scientific basis for the safe and stable operation and dispatching decision of the power grid.

[0021] S1, based on historical new energy data and new load data, a new energy probability distribution model and a new load probability distribution model are respectively established by using a probability distribution modeling method;

[0022] 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 for modeling in this step is the grid-connected active power ; the historical new type 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 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 edge distribution , to avoid distribution aliasing caused by mixed seasonality.

[0023] In this embodiment, the probability distribution modeling method is a Gaussian mixture model, and S1 specifically includes: For new energy samples , set the number of mixture components and the parameter set , then the probability density function of the new energy sample is: (1) where the mixture weight satisfies , the mean , and the variance , wherein is a preset positive threshold value, used to avoid singularity of the covariance matrix caused by too small variance, denotes the probability density function of a one-dimensional Gaussian distribution; Similarly, for new load samples , set the number of components and the parameter set , then the probability density function of the new load sample is: (2) Further, the expectation maximization (EM) algorithm is used to maximize the log-likelihood for parameter estimation, specifically: K-means++ is used to initialize , , and the cluster center, the proportion of samples within the cluster and the variance within the cluster are used as the initial values of , and respectively; In the E step, respectively calculate and Responsibility of each sample point to each Gaussian component and , the formula is: (3) (4) wherein and are the normalized new energy output sample and new load sample respectively.

[0024] On the basis of the responsibility calculated in the E step, the model parameters are updated in the M step to maximize the likelihood function, and the specific update formula is as follows: (5) (6) (7) wherein the superscript distinguishes new energy and new load, , , and the loop is iterated q times until or the maximum iteration number , is reached, and the convergence threshold is The number of components is selected by Bayesian information criterion (BIC), and the formula is: (8) wherein is the parameter set of maximum likelihood estimation, m is the total number of parameters, T is the sample size, and K is taken to make BIC minimum; Considering that the definition domain of the modeling variable is [0, 1], in order to avoid the extension of the Gaussian tail outside the interval, interval truncation normalization processing is adopted, and the formula is: (9) (10) wherein , is the normalized new energy output and new load active power; thus, the new energy edge probability distribution model and the new load edge probability distribution model for subsequent correlation modeling are obtained.

[0025] This step can approximate the real edge density with a limited number of Gaussian components by fitting the mixed distribution of new energy output with multi-peak and intermittency and new load affected by behavior events, to obtain the parameter set and the computable closed probability density To provide consistent probability description and repeatable estimation process for subsequent correlation modeling and power adjustment range derivation.

[0026] S2, input the new energy probability distribution model and the new load probability distribution model into the mixed Copula function to obtain a joint probability density distribution model, and the specific steps are as follows: S21, the new energy edge probability density obtained in step S1 is The corresponding distribution function is The new load edge probability density is The corresponding distribution function is ; define the probability integral transform , And introduce the boundary clipping constant in the numerical implementation Truncate to the interval to avoid divergence of inverse function evaluation.

[0027] S22, construct a mixed Copula function. The mixed Copula function is obtained by linear combination of Gaussian-Copula and t-Copula according to the weight, and the formula is: (11) 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.

[0028] S23, calculate the mixed Copula density function, and the specific formula is as follows: (12) Wherein and are the density functions of Gaussian-Copula and t-Copula respectively, and the definitions are as follows: (13) Wherein is the inverse function of the standard normal distribution; 2) t-Copula density function: (14) where is the inverse function of univariate t-distribution with degrees of freedom, is its density function, is the bivariate t-distribution density function with correlation matrix.

[0029] S24, combined with the marginal distribution and the mixed Copula density, the joint probability density distribution model of new energy and new load is obtained, and the formula is: (15) where, is the parameter set of the mixed Copula model.

[0030] This step inputs the marginal distribution of new energy and new load into the mixed Copula function to establish a joint probability density distribution model, which can describe the correlation and dependence structure of the two in the full range and extreme tail conditions, thereby providing a quantitative basis for the joint uncertainty of subsequent cross-section power adjustment range calculation.

[0031] S3, based on the joint probability density distribution model, the power adjustment range of new energy and load at the sending and receiving ends of the cross-section is calculated, specifically: The active power of the sending end new energy is expressed as: (16) where is the installed capacity of the sending end new energy unit; The power of the receiving end new load is expressed as: (17) where is the reference capacity of the receiving end new load.

[0032] The calculation of the power adjustment range needs to meet the following constraint conditions: 1) New energy unit output constraint: (18) where is the rated maximum active power of the unit; 2) New load power constraint: (19) where is the minimum adjustable power of the receiving end new load, is the maximum adjustable power thereof; 3) System reserve constraint: (20) in For the set of schedulable units within the system, For the unit Adjustable backup, For the system's required backup capacity, This represents the increase in the receiving-end load relative to the baseline value. This constraint ensures that the system reserve remains at or above the required level even with increased load.

[0033] Considering the above constraints, the feasible set of power at the sending and receiving ends of the cross-section is obtained as follows: (twenty one) Therefore, the power adjustment range of new energy sources and loads at the sending and receiving ends of the cross-section is obtained as follows: (twenty two) (twenty three) That is, each is taken from the feasible set. The minimum and maximum values ​​within this range constitute the adjustment range for new energy power output at the sending end. and the new load adjustment range at the receiving end. .

[0034] This step introduces new energy unit output constraints, new load power upper and lower limit constraints, and system reserve constraints into the support set of the joint probability density distribution model to obtain a feasible set of power at the sending and receiving ends. The power adjustment range is determined by the maximum and minimum values ​​of the set, thereby providing the input boundary for the cross-sectional power flow optimization model.

[0035] S4. Within the range of cross-sectional power adjustment, construct and solve the cross-sectional power flow optimization model to obtain the extreme operating mode of the cross-section.

[0036] Specifically, the cross-sectional power flow is defined within the cross-sectional power adjustment range obtained by S3. for: (twenty four) in, , , For the unit Active sensitivity to cross-sectional power flow. For load Active power sensitivity to cross-sectional power flow. Based on this, an optimization model for cross-sectional power flow is established.

[0037] 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.

[0038] Specifically: 1) the maximum cross-section power flow optimization model, the objective function is shown as formula (25): (25) The constraint conditions are power grid safety constraint, power balance constraint and system reserve constraint respectively; The power grid safety constraint is shown as formula (26): (26) Wherein is the power flow of branch , and is the rated capacity thereof; The power balance constraint is shown as formula (27): (27) Wherein is the system network loss; The system reserve constraint is shown as formula (28): (28) It should be noted that in S3, the system reserve constraint is introduced in the form of formula (20), which is used to reflect the dynamic influence of the increase of the load power at the receiving end on the reserve; and in the cross-section power flow optimization model, for the purpose of simplifying the optimization expression, the power after adjustment of the load at the receiving end is regarded as a known input, therefore, the standard form is adopted, that is, formula (28) is used to express the reserve constraint, and the two are consistent in logic.

[0039] 1) the minimum cross-section power flow optimization model, the objective function is shown as formula (29), and the constraint conditions are the same as those of the maximum optimization model: (29) The above two types of models jointly constitute the cross-section power flow optimization problem, and the interior point method is used for numerical solution, specifically: Let the Lagrange function of the optimization problem be: (30) Wherein, indicates the objective function (maximizing or minimizing the cross-section power flow), indicates the equality constraint, indicates the inequality constraint, and

[0040] In the iteration process, a penalty function term is introduced in the objective function, as shown in formula (31): (31) Wherein As a penalty coefficient, it is used to correct the infeasible solution that violates the constraints, to ensure that when the solution violates the grid security 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.

[0041] Through the above method iteration, two types of results are obtained: 1) The maximum optimization model outputs the maximum value of the section power flow and the corresponding unit and load distribution, forming the section maximum operating mode. 2) The minimum optimization model outputs the minimum value of the section power flow and the corresponding unit and load distribution, forming the section minimum operating mode.

[0042] This step can identify the extreme operating mode that meets the grid security, power balance and reserve constraints by establishing section power flow maximization and minimization optimization models within the section power adjustment range and implementing numerical solution by interior point method combined with penalty function, and can provide boundary conditions for interval calculation of available transmission capacity.

[0043] S5, based on the section extreme operating mode, according to the sub-channel power rating limit and the preset adjustment coefficient, the available transmission capacity of the section extreme operating mode is obtained.

[0044] 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 rating limit is .

[0045] 1) In the maximum operating mode: If there is a sub-channel power flow that satisfies equation (32): (32) record the sub-channel as a constraint channel; If all sub-channels do not reach the rating limit, increase the sub-channel power flow by the preset adjustment coefficient in the same proportion, as shown in equation (33): (33) until a sub-channel first reaches , and it is recorded as a constraint channel.

[0046] 2) In the minimum operating mode: Using the same method, another group of constraint channels and corresponding adjusted sub-channel power flows are obtained .

[0047] The following parameter sets are recorded to obtain the sub-channel parameter sets under extreme operating modes: (35) wherein is the initial sub-channel power flow; is the adjusted sub-channel power flow; is the sub-channel rated limit; is the active power adjustment of unit j; is the power flow sensitivity of unit j to sub-channel i.

[0048] This step can identify the system parameters affecting 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 under the section extreme operating mode, form the interval type available transfer capability, and provide quantitative basis for the final ATC limit value determination under the multi-section interaction.

[0049] S6, according to the available transfer capability of the section extreme operating mode, forming the section interval available transfer capability, and calculating the available transfer capability limit value under the multi-section interaction through the ATC determination model.

[0050] In this embodiment, the calculation formula of the ATC determination model is: (36) wherein, is the section available transfer capability, is the number of sub-channels constituting the power transmission section, is the sub-channel active power adjustment coefficient, and K is the minimum value in the adjustment coefficient of each sub-channel.

[0051] In the calculation: substitute the maximum operating mode parameter set into the above formula to obtain the maximum available transfer capability of the section ; substitute the minimum operating mode parameter set into the above formula to obtain the minimum available transfer capability of the section .

[0052] Therefore, the final section interval available transfer capability is:

[0053] This step realizes the quantitative identification of the ATC limit value under the multi-section power flow interaction by using the ATC determination model to uniformly map the rated limit, power adjustment and sensitivity characteristics of each sub-channel into the available transfer capability of the section.

[0054] Embodiment 2, Figure 2An ATC limit identification system of multi-section power flow interaction is given, including: A power boundary calculation module is configured to calculate power adjustment ranges of new energy and loads at a sending end and a receiving end of a section; An extreme scenario generation module is configured to construct a section power flow optimization model and solve the model to obtain an extreme operation mode of the section within the power adjustment range of the section; A static ATC calculation module is configured to obtain available transmission capacity of the extreme operation mode of the section according to a sub-channel power flow rating limit and a preset adjustment coefficient; A dynamic ATC aggregation module is configured to form available transmission capacity of a section interval according to the available transmission capacity of the extreme operation mode of the section, and calculate an available transmission capacity limit under the interaction of multiple sections through an ATC determination model.

[0055] Since the ATC limit identification device of the multi-section power flow interaction is a device used to implement the method in Embodiment 1 of the present application, the specific implementation of the electronic device and various changes thereof can be understood by those skilled in the art based on the method described in Embodiment 1 of the present application. Therefore, how the electronic device implements the method in the present application will not be described in detail. As long as the device used to implement the method in the present application is implemented by those skilled in the art, it belongs to the scope of the present application.

[0056] The above formulas are dimensionless values, and the formulas are obtained by collecting a large amount of data to simulate a formula of the most real situation. The preset parameters in the formula are set by those skilled in the art according to the actual situation.

[0057] 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 in the form of a computer program product.

[0058] 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 solutions. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0059] In addition, the functional modules in each embodiment of the present 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.

[0060] The above merely provides the specific implementation of the present application, but the protection scope of the present application is not limited to this. Any person skilled in the art can easily think of the changes or replacements within the technical range disclosed by the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0061] Finally, the above merely provides the preferred embodiments of the present application, and is not used to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. A method for identifying ATC limits of multi-contingency power flow interaction, characterized in that, The method comprises the following steps: Calculate the power adjustment range of the new energy and load at the sending end and the receiving end of the section; 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 limit under the interaction of multiple sections is calculated through an ATC determination model.

2. The method of claim 1, wherein, The power adjustment range of the new energy and load at the sending end and the receiving end of the section is calculated through a joint probability density distribution model, and 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 new energy probability distribution model and a new load probability distribution model are respectively established by using a probability distribution modeling method; 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.

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

4. The method of claim 2, wherein, 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, and the specific construction steps are as follows: Based on the new energy probability distribution model and the new load probability distribution model, a probability integral transformation is performed on the new energy output and the new load unit 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 marginal probability density functions of the new energy and the new load to obtain a joint probability density distribution model.

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

6. 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 a power grid safety constraint, a power balance constraint and a system reserve constraint.

7. The method of claim 6, wherein, The section power flow optimization model is numerically solved by using an interior point method.

8. The method of claim 1, wherein, 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, wherein, in the solving process, an infeasible solution is modified by introducing a penalty function term into the objective function, and a feasible solution is output as an iteration result.

9. 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.

10. A system for using the method for identifying ATC limits of multi-profile power flow interaction according to any one of claims 1-9, characterized in that, The method comprises the following steps: A power boundary calculation module is configured to calculate the power adjustment range of the new energy and load at the sending end and the receiving end of the section; An extreme scenario generation module is configured to, within the power adjustment range of the section, construct a section power flow optimization model and solve the model to obtain a section extreme operating mode; A static ATC calculation module is configured to, based on the section extreme operating mode, determine the available transmission capacity of the section extreme operating mode according to the sub-channel power flow rating limit and a preset adjustment coefficient; and A dynamic ATC aggregation module is used to form the available transmission capacity of the section interval according to the available transmission capacity of the section extreme operation mode, and to calculate the available transmission capacity limit under the multi-section interaction by an ATC judgment model.

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