Inter-region available transmission capacity distributed calculation method considering wind speed correlation

Abstract

The invention relates to an inter-region available power transmission capacity distributed calculation method considering wind speed correlation. The method comprises the following steps: S1, acquiring a wind speed predicted value and acquiring a wind speed error matrix with time and space correlation; s2, a wind speed actual value with time and space correlation is obtained, and wind power generation power is obtained; s3, forming an available transmission capacity calculation model of the distributed system based on the direct current power flow; and S4, solving the available transmission capacity calculation model of the distributed system by using an alternating direction multiplier method. By designing the method for calculating the available power transmission capacity of the distributed system, the influence of wind speed correlation on the inter-region power transmission capacity of the power system is calculated, the power transmission capacity of the system is evaluated, and safe operation of the power system is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power systems, and more particularly to a distributed calculation method for inter-regional available power transmission capacity taking into account wind speed correlation. Background Art

[0002] In the power system, regional transfer capacity (Available Transfer Capability, ATC) refers to the remaining transfer capacity that can be utilized between specific regions under specific operating conditions. In mathematical terms, ATC is the difference between the total transfer capacity (TTC) and the sum of the existing transfer capacity (ETC), capacity benefit margin (CBM) and transmission reliability margin (TRM). Specifically, TTC represents the maximum power transfer capability between system regions under all necessary safety constraints. In contrast, ETC refers to the power transfer capability between system regions under baseline power flow conditions. Common deterministic ATC calculation modeling methods include power transfer distribution factor method, continuous power flow method, repeated power flow method and optimal power flow method.

[0003] With the continuous expansion of wind power integration, the uncertainty of wind power generation has also become an important factor affecting inter-regional ATC. With the large-scale integration of wind power generation, how to quantify the uncertainty of wind power generation output on inter-regional ATC by using probabilistic solutions is a technical problem that technicians in this field need to solve urgently. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a distributed calculation method for the available inter-regional transmission capacity taking into account the correlation of wind speed, by distributing the calculation of the available inter-regional transmission capacity taking into account the correlation of wind speed and multiple scenarios of wind power output, thereby quantifying the impact of wind power output on the inter-regional transmission capacity.

[0005] The technical solution adopted by the present invention to solve the technical problem is: constructing a distributed calculation method for the available power transmission capacity between regions considering the wind speed correlation, comprising the following steps:

[0006] S1, obtain the wind speed prediction value and obtain the wind speed error matrix with time and space correlation;

[0007] S2, obtaining the actual wind speed value with time and space correlation, and obtaining the wind power generation power;

[0008] S3, forming a calculation model for the available transmission capacity of the distributed system based on DC power flow;

[0009] S4. Use the alternating direction multiplier method to solve the calculation model of the available transmission capacity of the distributed system.

[0010] According to the above scheme, in step S1, the specific method for obtaining the wind speed prediction value and obtaining the wind speed error matrix with time and space correlation is: based on the statistical prediction method, the wind speed prediction value of each wind farm at each moment is obtained; based on the space and time correlation of the wind farm, the wind speed space and time correlation matrix is ​​obtained, and the matrix is ​​singular value decomposed to obtain the corresponding unitary matrix and characteristic matrix.

[0011] According to the above scheme, the method for obtaining the wind speed prediction value and obtaining the wind speed error matrix with time and space correlation includes the following steps:

[0012] S101, based on the historical wind speed data of the region, obtain a wind speed prediction matrix F by using a statistical prediction method;

[0013] S102, introduce spatial correlation between different wind farms in different regions, so construct N w 'N w The spatial correlation matrix R s,h , perform singular value decomposition on the matrix and obtain the decomposed unitary matrix and the diagonal matrix Among them, N w Indicates the number of wind farms;

[0014] S103, introducing time correlation in each wind farm in the region, constructing a time autocorrelation vector r T,w and variance vector s T,w , construct the time covariance matrix K of each wind field T,w , perform singular value decomposition on the matrix and obtain the decomposed unitary matrix and the diagonal matrix

[0015] According to the above scheme, in step S2, the actual value of wind speed with time and space correlation is obtained, and the specific method for obtaining wind power generation is: considering the wind speed prediction error, M scenarios are generated, and each scenario is based on the time and space correlation matrix obtained in step S1 to construct a time and space correlation wind speed prediction error matrix; then the time and space correlation wind speed prediction matrix is ​​derived, and finally the wind power output power under each scenario is determined.

[0016] According to the above scheme, in step S2, the actual wind speed value with time and space correlation is obtained, and the method for obtaining wind power generation power includes the following steps:

[0017] S201, using independent and identically distributed Gaussian random variable matrix W h represents the wind speed prediction error, generating M scenarios;

[0018] S202: In each scenario, considering spatial correlation, generating the unitary matrix and diagonal matrix decomposed according to steps S102 and S103, and calculating the wind speed prediction error matrix W considering spatiotemporal correlation ST :

[0019]

[0020] S203, based on the prediction error matrix W ST And the wind speed prediction matrix F, generate a wind speed prediction value matrix v with spatiotemporal correlation in a scenario:

[0021] v=F+W ST

[0022] S204. According to the relationship between wind speed and wind turbine output power, calculate the wind power output P according to the following formula: w :

[0023]

[0024] Among them, P r is the rated power of the wind turbine; v0, v r and v i They represent cut-in wind speed, cut-out wind speed and rated wind speed respectively; v represents actual wind speed.

[0025] According to the above scheme, in step S3, the specific method for forming a calculation model for the available transmission capacity of a distributed system based on DC power flow is: establishing a mathematical optimization model based on DC power flow, the objective function is the sum of all powers passing through the regional transmission lines, the constraints are DC power flow constraints, transmission line active power flow constraints, etc., and global variables are used to connect the same variables of a node and its replicated nodes.

[0026] According to the above scheme, the method for forming a calculation model of the available transmission capacity of a distributed system based on DC power flow includes the following steps:

[0027] A mathematical model based on DC power flow is established to calculate the available transmission capacity between regions. The objective function is the maximum transmission capacity transmitted on the transmission line between regions. The objective function is constructed. For each scenario:

[0028]

[0029] represents the sum of all the power passing through the regional transmission lines;

[0030] For each node, the active power conservation constraint is:

[0031]

[0032] Among them, P Gi, P wi , P Di Respectively represent the active power of thermal power unit, wind power unit and load on node i, P L,l represents the power of the line connected to node i, s l and e l They represent the starting nodes of line l respectively;

[0033] The active power flow constraint of the transmission line is:

[0034]

[0035] Among them, x l represents the reactance of line l;

[0036] The output constraint of the generator set is:

[0037]

[0038] The capacity constraint of the transmission line is:

[0039]

[0040] To ensure that the phase angle of the same node in different areas is consistent, the following constraints are imposed:

[0041]

[0042] Among them, S and R represent the power transmission and receiving areas respectively;

[0043] When calculating the inter-regional transmission capacity, the boundary problem is handled by replicating the information of the boundary nodes; the boundary nodes of the power supply area, the phase angle of the node in its own area is expressed as q i,S , when copied to the power receiving area, this node is represented as q i,R To ensure that the phase angle of the same node in different regions is the same, a global variable z is used i To connect a node and the same variable of the node it copies:

[0044] z i =(θ i，S +θ i，R ) / 2.

[0045] According to the above scheme, in step S4, the specific method of using the alternating direction multiplier method to solve the calculation model of the available transmission capacity of the distributed system is as follows: based on the standard alternating direction multiplier method, the objective function of each sub-region's available transmission capacity calculation problem is composed of multiple independent functions, global variables and Lagrange multipliers are initialized, the sub-region available transmission capacity calculation problem is iteratively solved, regional operators exchange adjacent region update information, update global variables and Lagrange multipliers, calculate the original residual and the dual residual until they are less than the set iteration stop benchmark, and obtain the available transmission capacity between regions in a scenario and a time period; calculate the system's inter-regional available transmission capacity under all scenarios, and obtain the mean of the system's inter-regional available transmission capacity probability distribution; calculate the system's available probability transmission capacity at all times, and finally obtain the change curve of the inter-regional probabilistic available transmission capacity.

[0046] According to the above scheme, the method for solving the calculation model of the available transmission capacity of the distributed system using the alternating direction multiplier method includes the following steps:

[0047] Based on the standard alternating direction multiplier method, which contains multiple interrelated functions, the objective function of the distributed computing available transmission capacity problem consists of multiple independent functions. The objective function proposed by the power supply area is as follows:

[0048]

[0049] Among them, l i,S represents the Lagrange multiplier, r i,S Indicates the set iteration step size;

[0050] S401, Initialize global variables and Lagrange multipliers Set the number of calculations k = 0;

[0051] S402, solve the optimal problem for each partition, each regional operator exchanges the update information of adjacent regions, and then updates the global variable z between sub-regions i and Lagrange multipliers The formula is as follows:

[0052]

[0053]

[0054] S403, calculate the original residual r k and the dual residual s k , when the original residual and the dual residual satisfy the following formula, the algorithm ends:

[0055]

[0056] Calculate the available power transmission capacity between regions in a single scenario; otherwise, set k=k+1 and repeat S401-S402;

[0057] S404, repeat S401-S403 M times, calculate the available power transmission capacity between the system regions under all scenarios, and calculate the mean value of the probability distribution of the available power transmission capacity between the system regions according to the following formula:

[0058]

[0059] S405. Repeat step S404 T times to calculate the system's available probabilistic transmission capacity at all times, and finally obtain a curve of the variation of the inter-regional probabilistic available transmission capacity to calculate the inter-regional available transmission capacity.

[0060] The distributed calculation method of available inter-regional transmission capacity considering wind speed correlation of the present invention has the following beneficial effects:

[0061] The present invention calculates the wind power correlation matrix to more accurately predict the output power of wind power generation; by designing a distributed computing system available transmission capacity method, under the premise of ensuring the independence of operation of each area of ​​the power system, the influence of wind speed correlation on the inter-regional transmission capacity is calculated, thereby improving the stability and reliability of the power grid and ensuring the safe operation of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] The present invention will be further described below with reference to the accompanying drawings and embodiments, in which:

[0063] Figure 1 is a flow chart of a distributed calculation method of available inter-regional transmission capacity considering wind speed correlation according to the present invention;

[0064] Figure 2 is a system reliability-demand side response curve provided by an embodiment of the present invention;

[0065] Figure 3 is a system incremental reliability-demand side response curve provided by an embodiment of the present invention;

[0066] Figure 4 It is the demand side response price-demand side response curve of the system provided by the embodiment of the present invention. DETAILED DESCRIPTION

[0067] In order to have a clearer understanding of the technical features, purposes and effects of the present invention, specific embodiments of the present invention are now described in detail with reference to the accompanying drawings.

[0068] like Figure 1-4 As shown, the distributed calculation method of the available inter-regional transmission capacity considering wind speed correlation of the present invention comprises the following steps:

[0069] S1. Obtain wind speed prediction values ​​and obtain a wind speed error matrix with time and space correlation.

[0070] Based on the statistical prediction method, the wind speed forecast value of each wind farm at each moment is obtained; based on the spatial and temporal correlation of the wind farm, the wind speed spatial and temporal correlation matrix is ​​obtained, and the matrix is ​​decomposed by singular values ​​to obtain the corresponding unitary matrix and characteristic matrix. Specifically:

[0071] S101. Based on the historical wind speed data of the region, a wind speed prediction matrix F is obtained by a statistical prediction method.

[0072]

[0073] Among them, N w Represents the number of wind farms, N H represents the predicted time in hours, f W,it It represents the predicted wind speed value of the i-th wind farm at the t-th hour.

[0074] S102, introduce spatial correlation between different wind farms in different regions, so construct N w 'N w The spatial correlation matrix R s,h , where r S,ab represents the spatial correlation of the wind speed forecast error at location (a, b) and is constructed as follows:

[0075]

[0076] Perform singular value decomposition on a matrix:

[0077]

[0078] in and are the unitary matrix and diagonal matrix after decomposition respectively.

[0079] S103, introducing time correlation in each wind farm in the region, constructing a time autocorrelation vector r T,w and variance vector s T,w :

[0080]

[0081] The spatiotemporal autocorrelation matrix R is constructed as follows T,w , where R T,w The pth row and fth column of T,w The (|pf|+1)th element in:

[0082]

[0083] Construct the time covariance matrix K of the w-th wind field T,w :

[0084] K T，ω =diag{σ T，ω} T，ω diag{σ T，ω}

[0085] For K T,w Perform singular value decomposition:

[0086]

[0087] in and are the unitary matrix and diagonal matrix after decomposition respectively.

[0088] S2. Obtain the actual wind speed value with time and space correlation, and obtain the wind power generation power.

[0089] Considering the wind speed prediction error, M scenarios are generated. For each scenario, based on the spatiotemporal correlation matrix obtained in step S1, a spatiotemporal correlation wind speed prediction error matrix is ​​constructed; then the spatiotemporal correlation wind speed prediction matrix is ​​derived, and finally the wind power output power under each scenario is determined.

[0090] S201, using independent and identically distributed Gaussian random variable matrix W h Represents the wind speed prediction error, generates M scenarios, each W h It is expressed as follows:

[0091]

[0092] Among them, w h,it It represents the wind speed prediction error of the i-th wind farm at the t-th hour.

[0093] S202: In each scenario, considering spatial correlation, generating the unitary matrix and diagonal matrix decomposed according to steps S12 and S13, and calculating the wind speed prediction error matrix W considering spatiotemporal correlation ST :

[0094]

[0095] S203, based on the prediction error matrix W ST And the wind speed prediction matrix F, generate a wind speed prediction value matrix v with spatiotemporal correlation in a scenario:

[0096] v=F+W ST

[0097] S204. According to the relationship between wind speed and wind turbine output power, calculate the wind power output P according to the following formula: w :

[0098]

[0099] Among them, P r is the rated power of the wind turbine; v0, v r and v i They represent cut-in wind speed, cut-out wind speed and rated wind speed respectively; v represents actual wind speed.

[0100] S3. Form a calculation model for the available transmission capacity of a distributed system based on DC power flow.

[0101] A mathematical optimization model based on DC power flow is established. The objective function is the sum of all powers passing through the regional transmission lines. The constraints are DC power flow constraints, transmission line active power flow constraints, etc. Global variables are used to connect the same variables of nodes and their replicated nodes.

[0102] In a normal operating actual power system, the node voltage is stable near the rated voltage, the node phase angle difference is very small, and the line resistance is negligible relative to the line reactance. By simplifying as above, the DC power flow equation is obtained.

[0103] A mathematical model based on DC power flow is established to calculate the available transmission capacity between regions. The objective function is the maximum transmission capacity of the transmission line between regions. The objective function is constructed. For each scenario:

[0104]

[0105] Represents the sum of all powers passing through the area's transmission lines.

[0106] For each node, the active power conservation constraint is:

[0107]

[0108] Among them, P Gi , P wi , P Di Respectively represent the active power of thermal power unit, wind power unit and load on node i, P L,l represents the power of the line connected to node i, s l and e l They represent the starting nodes of line l respectively.

[0109] The active power flow constraint of the transmission line is:

[0110]

[0111] Among them, xl Represents the reactance of line l.

[0112] The output constraint of the generator set is:

[0113]

[0114] The capacity constraint of the transmission line is:

[0115]

[0116] To ensure that the phase angle of the same node in different areas is consistent, the following constraints are imposed:

[0117]

[0118] Among them, S and R represent the power transmission and receiving areas respectively.

[0119] When calculating the inter-regional transmission capacity, the boundary problem is handled by replicating the information of the boundary nodes. Taking the boundary nodes of the power supply area as an example, the phase angle of the node in its own area is expressed as q i,S , when copied to the power receiving area, this node is represented as q i,R To ensure that the phase angles of each node and its replicas are the same, a global variable z is used i To connect the phase angle of a node and its copies:

[0120] z i =(θ i，S +θ i，R ) / 2

[0121] S4. Use the alternating direction multiplier method to solve the calculation model of the available transmission capacity of the distributed system.

[0122] Based on the standard alternating direction multiplier method, the objective function of each sub-region's available transmission capacity calculation problem is composed of multiple independent functions. The global variables and Lagrange multipliers are initialized, and the sub-region available transmission capacity calculation problem is solved iteratively. Regional operators exchange adjacent region update information, update global variables and Lagrange multipliers, and calculate the primal residual and the dual residual until they are less than the set iteration stop benchmark. The available transmission capacity between regions in a scenario and a time period is obtained; the system's available transmission capacity between regions in all scenarios is calculated, and the mean of the probability distribution of the system's available transmission capacity between regions is obtained; the system's available probabilistic transmission capacity at all times is calculated, and finally the change curve of the probabilistic available transmission capacity between regions is obtained.

[0123] The objective function of the available transmission capacity problem of distributed computing is composed of multiple independent functions. For this problem, taking the power supply area as an example, the proposed objective function is as follows:

[0124]

[0125] Among them, l i,S represents the Lagrange multiplier, r i,S Indicates the set iteration step size.

[0126] S401, initialize global variable z i and Lagrange multipliers Set the number of calculations k=0.

[0127] S402, solve the optimal problem for each partition, each regional operator exchanges the update information of adjacent regions, and then updates the global variable z between sub-regions i and Lagrange multipliers The formula is as follows:

[0128] z i =(θ i，S +θ i，R ) / 2

[0129]

[0130] S403, calculate the original residual r k and the dual residual sk , when the primal residual and the dual residual satisfy the following formula, the algorithm ends.

[0131]

[0132] The available inter-regional transmission capacity in a single scenario is calculated; otherwise, k=k+1 is set and S401-S402 are repeated.

[0133] S404, repeat S401-S403 M times, calculate the available power transmission capacity between the system regions under all scenarios, and calculate the mean value of the probability distribution of the available power transmission capacity between the system regions according to the following formula:

[0134]

[0135] S405. Repeat step S404 T times to calculate the system's available probabilistic transmission capacity at all times, and finally obtain a curve of the variation of the inter-regional probabilistic available transmission capacity to calculate the inter-regional available transmission capacity.

[0136] The specific implementation steps of the present invention are further described below in conjunction with specific test examples:

[0137] Applying this calculation method to a 2-zone 12-node system, such as Figure 2As shown in Figure 1, there is one wind farm in each region, and each wind farm contains 5 wind turbines. Assuming that the spatial correlation of wind speed prediction errors between wind farms is 0.5 and the spatial correlation of the same wind farm is 0.9, a temporal autocorrelation vector and variance vector are randomly generated in each wind farm. Steps S1 and S2 are used to calculate a scenario where the wind speed prediction considering spatiotemporal correlation is as follows: Figure 3 As shown, the left figure shows the wind speed between wind farms, and the right figure shows the wind speed of different wind turbines in the same wind farm.

[0138] Based on the calculated wind speed forecast, the output of each wind farm is obtained. Based on steps S3 and S4, the available transmission capacity of the system under all scenarios and all time periods is calculated as follows: Figure 4 shown.

[0139] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation modes, which are merely illustrative rather than restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the present invention and the claims, all of which are within the protection of the present invention.

Claims

1. A distributed calculation method for available inter-regional transmission capacity considering wind speed correlation, characterized in that: The following steps are involved: S1, obtain the wind speed prediction value and obtain the wind speed error matrix with time and space correlation; S2, obtaining the actual wind speed value with time and space correlation, and obtaining the wind power generation power; S3, forming a calculation model for the available transmission capacity of the distributed system based on DC power flow; S4. Use the alternating direction multiplier method to solve the calculation model of the available transmission capacity of the distributed system.

2. The distributed calculation method of available inter-regional transmission capacity considering wind speed correlation according to claim 1 is characterized in that: In step S1, the specific method of obtaining the wind speed prediction value and obtaining the wind speed error matrix with time and space correlation is: obtaining the wind speed prediction value of each wind farm at each moment based on the statistical prediction method; Based on the spatial and temporal correlation of the wind farm, the spatial and temporal correlation matrix of wind speed is obtained, and the matrix is ​​subjected to singular value decomposition to obtain the corresponding unitary matrix and characteristic matrix.

3. The distributed calculation method of available inter-regional transmission capacity considering wind speed correlation according to claim 2 is characterized in that: The method for obtaining a wind speed prediction value and obtaining a wind speed error matrix with time and space correlation comprises the following steps: S101, based on the historical wind speed data of the region, obtain a wind speed prediction matrix F by using a statistical prediction method; S102, introduce spatial correlation between different wind farms in different regions, so construct N w 'N w The spatial correlation matrix R s,h , perform singular value decomposition on the matrix and obtain the decomposed unitary matrix and the diagonal matrix Among them, N w Indicates the number of wind farms; S103, introducing time correlation in each wind farm in the region, constructing a time autocorrelation vector r T,w and variance vector s T,w , construct the time covariance matrix K of each wind field T,w , perform singular value decomposition on the matrix and obtain the decomposed unitary matrix and the diagonal matrix 4. The distributed calculation method of available inter-regional transmission capacity considering wind speed correlation according to claim 1, characterized in that: In step S2, the actual wind speed value with time and space correlation is obtained, and the specific method for obtaining the wind power generation power is: considering the wind speed prediction error, M scenarios are generated, and each scenario is based on the time and space correlation matrix obtained in step S1, and the time and space correlation wind speed prediction error matrix is ​​constructed; then the time and space correlation wind speed prediction matrix is ​​derived, and finally the wind power output power under each scenario is determined.

5. The distributed calculation method of available inter-regional transmission capacity considering wind speed correlation according to claim 4 is characterized in that: In step S2, the actual wind speed value with time and space correlation is obtained, and the method for obtaining wind power generation power includes the following steps: S201, using independent and identically distributed Gaussian random variable matrix W h represents the wind speed prediction error, generating M scenarios; S202: In each scenario, considering spatial correlation, generating the unitary matrix and diagonal matrix decomposed according to steps S102 and S103, and calculating the wind speed prediction error matrix W considering spatiotemporal correlation ST : S203, based on the prediction error matrix W ST And the wind speed prediction matrix F, generate a wind speed prediction value matrix v with spatiotemporal correlation in a scenario: v=F+W ST S204. According to the relationship between wind speed and wind turbine output power, calculate the wind power output P according to the following formula: w : Among them, P r is the rated power of the wind turbine; v0, v r and v i They represent cut-in wind speed, cut-out wind speed and rated wind speed respectively; v represents actual wind speed.

6. The distributed calculation method of available inter-regional transmission capacity considering wind speed correlation according to claim 1, characterized in that: In step S3, the specific method for forming a calculation model for the available transmission capacity of a distributed system based on DC power flow is: establishing a mathematical optimization model based on DC power flow, the objective function is the sum of all powers passing through the regional transmission lines, the constraints are DC power flow constraints, transmission line active power flow constraints, etc., and global variables are used to connect the same variables of a node and its replicated nodes.

7. The distributed calculation method of available inter-regional transmission capacity considering wind speed correlation according to claim 6, characterized in that: The method for forming a calculation model of the available transmission capacity of a distributed system based on DC power flow comprises the following steps: A mathematical model based on DC power flow is established to calculate the available transmission capacity between regions. The objective function is the maximum transmission capacity transmitted on the transmission line between regions. The objective function is constructed. For each scenario: represents the sum of all the power passing through the regional transmission lines; For each node, the active power conservation constraint is: Among them, P Gi , P wi , P Di Respectively represent the active power of thermal power unit, wind power unit and load on node i, P L,l represents the power of the line connected to node i, s l and e l They represent the starting nodes of line l respectively; The active power flow constraint of the transmission line is: Among them, x l represents the reactance of line l; The output constraint of the generator set is: The capacity constraint of the transmission line is: To ensure that the phase angle of the same node in different areas is consistent, the following constraints are imposed: Among them, S and R represent the power transmission and receiving areas respectively; When calculating the inter-regional transmission capacity, the boundary problem is handled by replicating the information of the boundary nodes; the boundary nodes of the power supply area, the phase angle of the node in its own area is expressed as q i，S , when copied to the power receiving area, this node is represented as q i，R To ensure that the phase angle of the same node in different regions is the same, a global variable z is used i To connect a node and the same variable of the node it copies: z i =(θ i，S +θ i，R ) / 2。 8. The distributed calculation method of available inter-regional transmission capacity considering wind speed correlation according to claim 1, characterized in that: In step S4, the specific method of using the alternating direction multiplier method to solve the calculation model of the available transmission capacity of the distributed system is as follows: based on the standard alternating direction multiplier method, the objective function of each sub-region to calculate the available transmission capacity problem is composed of multiple independent functions, global variables and Lagrange multipliers are initialized, the sub-region available transmission capacity calculation problem is iteratively solved, regional operators exchange adjacent regional update information, update global variables and Lagrange multipliers, calculate the original residual and the dual residual until they are less than the set iteration stop benchmark, and obtain the available transmission capacity between regions in a scenario and a time period; The available inter-regional transmission capacity of the system under all scenarios is calculated, and the mean of the probability distribution of the available inter-regional transmission capacity of the system is obtained; the available probabilistic transmission capacity of the system at all times is calculated, and finally the change curve of the probabilistic available transmission capacity between regions is obtained.

9. The distributed calculation method of available inter-regional transmission capacity considering wind speed correlation according to claim 8, characterized in that: The method for solving the calculation model of the available transmission capacity of the distributed system using the alternating direction multiplier method includes the following steps: Based on the standard alternating direction multiplier method, which contains multiple interrelated functions, the objective function of the distributed computing available transmission capacity problem consists of multiple independent functions. The objective function proposed by the power supply area is as follows: Among them, l i,S represents the Lagrange multiplier, r i,S Indicates the set iteration step size; S401, Initialize global variables and Lagrange multipliers Set the number of calculations k = 0; S402, solve the optimal problem for each partition, each regional operator exchanges the update information of adjacent regions, and then updates the global variable z between sub-regions i and Lagrange multipliers The formula is as follows: S403, calculate the original residual r k and the dual residual sk , when the original residual and the dual residual satisfy the following formula, the algorithm ends: Calculate the available power transmission capacity between regions in a single scenario; otherwise, set k=k+1 and repeat S401-S402; S404, repeat S401-S403 M times, calculate the available power transmission capacity between the system regions under all scenarios, and calculate the mean value of the probability distribution of the available power transmission capacity between the system regions according to the following formula: S405. Repeat step S404 T times to calculate the system's available probabilistic transmission capacity at all times, and finally obtain a curve of the variation of the inter-regional probabilistic available transmission capacity to calculate the inter-regional available transmission capacity.

Images