A static security constraint modeling method and terminal for power grid optimization calculation

Abstract

The present invention discloses a static safety constraint modeling method and terminal for power grid optimization calculation, which obtain a power grid model and its corresponding parameters, and perform AC method power flow calculation on the power grid model and its corresponding parameters to obtain calculation results; a DC method state estimation model is constructed based on the calculation results, and the DC method state estimation model is solved to obtain estimation results; the power grid model is corrected based on the estimation results; and static safety constraint modeling is performed on the corrected power grid model using the DC method, which can provide a branch power flow load rate with higher accuracy, so that the accuracy of the DC method static safety constraint model can meet the accuracy requirements of complex optimization decisions of large-scale power grids, thereby effectively improving the accuracy of complex optimization decisions of power grids.

Description

Technical Field

[0001] The present invention relates to the technical field of steady-state analysis of electric power systems, and in particular to a static safety constraint modeling method and a terminal for power grid optimization calculation. Background Art

[0002] The safe operation of the power system requires that the power flow of all equipment does not exceed the long-term current carrying capacity of the equipment during normal operation, and requires a certain ability to withstand the impact of expected faults, that is, after a credible expected fault, the power flow of each device does not exceed its short-term current carrying capacity. Complex optimization decisions of the power grid (such as transmission network reconstruction or power grid layering and partitioning optimization, etc.) need to consider the safety constraints of the power grid, including the safety constraints of the fault-free state and the safety constraints after N-1 expected faults. Since the scale of the expected fault set of large-scale power systems is also large, the AC power flow model is used to describe the safety constraints of the power grid, which has a large calculation scale and makes the optimization model a nonlinear mixed integer optimization problem. So far, there is still a lack of effective computing tools for solving large-scale nonlinear mixed integer optimization problems, the calculation time is long, and it is easy to have non-convergence, and the reliability of the algorithm solution is low.

[0003] Using the DC power flow model to describe static safety constraints can effectively reduce the computational scale of the optimization problem. More importantly, the safety constraints described are all linear constraints, which can greatly simplify the complexity of the optimization problem and improve the speed and reliability of the numerical solution. However, the DC power flow model ignores the active power loss of the power grid, so there is a certain error. For small-scale power grids, since the total amount of network active power loss is small, the branch power flow error caused by ignoring the active power loss is not large, which can meet the requirements of engineering applications. However, when applied to large-scale power systems, the total amount of network active power loss is large, far greater than the current carrying capacity of a single branch. Since the active power loss deviation between the two power flow models is completely borne by the balancing machine, the power flow near the balancing machine or the inter-provincial interconnection line has a large deviation. The static safety constraints described by the DC power flow cannot meet the actual power grid application requirements in terms of accuracy. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a static security constraint modeling method and terminal for power grid optimization calculation, which can effectively improve the accuracy of complex optimization decision-making of the power grid.

[0005] In order to solve the above technical problems, a technical solution adopted by the present invention is:

[0006] A static security constraint modeling method for power grid optimization calculation includes the following steps:

[0007] Acquire a power grid model and its corresponding parameters, and perform AC power flow calculation on the power grid model and its corresponding parameters to obtain calculation results;

[0008] Constructing a DC state estimation model based on the calculation results, and solving the DC state estimation model to obtain an estimation result;

[0009] Based on the estimation result, the power grid model is updated to generate a revised power grid model;

[0010] The DC method is used to perform static security constraint modeling on the modified power grid model.

[0011] In order to solve the above technical problems, another technical solution adopted by the present invention is:

[0012] A static security constraint modeling terminal for power grid optimization calculation includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the following steps are implemented:

[0013] Acquire a power grid model and its corresponding parameters, and perform AC power flow calculation on the power grid model and its corresponding parameters to obtain calculation results;

[0014] Constructing a DC state estimation model based on the calculation results, and solving the DC state estimation model to obtain an estimation result;

[0015] Based on the estimation result, the power grid model is updated to generate a revised power grid model;

[0016] The DC method is used to perform static security constraint modeling on the modified power grid model.

[0017] The beneficial effects of the present invention are: performing AC power flow calculation on the power grid model and its corresponding parameters to obtain calculation results. Based on the calculation results, a DC state estimation model is constructed and solved, and based on the solution results, the power grid model is corrected. The corrected power grid model can obtain higher calculation accuracy by using the DC power flow calculation. In view of this, the corrected power grid model is used for power grid verification and optimization calculation of DC static safety constraints, which can meet the calculation accuracy requirements. At the same time, the solution difficulty and convergence are significantly better than the AC power flow calculation method, thereby effectively improving the accuracy of complex optimization decisions of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A flowchart of a static security constraint modeling method for power grid optimization calculation according to an embodiment of the present invention;

[0019] Figure 2 The present invention is a schematic diagram of the structure of a static security constraint modeling terminal for power grid optimization calculation according to an embodiment of the present invention. DETAILED DESCRIPTION

[0020] In order to explain the technical content, achieved objectives and effects of the present invention in detail, the following is an explanation in combination with the implementation modes and the accompanying drawings.

[0021] Please refer to Figure 1 The embodiment of the present invention provides a static security constraint modeling method for power grid optimization calculation, comprising the steps of:

[0022] Acquire a power grid model and its corresponding parameters, and perform AC power flow calculation on the power grid model and its corresponding parameters to obtain calculation results;

[0023] Constructing a DC state estimation model based on the calculation results, and solving the DC state estimation model to obtain an estimation result;

[0024] Based on the estimation result, the power grid model is updated to generate a revised power grid model;

[0025] The DC method is used to perform static security constraint modeling on the modified power grid model.

[0026] From the above description, it can be seen that the beneficial effects of the present invention are: performing AC power flow calculation on the power grid model and its corresponding parameters to obtain calculation results. Based on the calculation results, a DC state estimation model is constructed and solved, and the power grid model is corrected based on the solution results. The corrected power grid model can obtain higher calculation accuracy by using the DC power flow calculation. In view of this, the corrected power grid model is used for power grid verification and optimization calculation of DC static safety constraints, which can meet the calculation accuracy requirements. At the same time, the solution difficulty and convergence are significantly better than the AC power flow calculation method, thereby effectively improving the accuracy of complex optimization decisions of the power grid.

[0027] Furthermore, constructing a DC method state estimation model based on the calculation result includes:

[0028] According to the calculation results, the active power flow value of each branch sending end of the power grid model, the long-term current carrying capacity of each branch and each zero injection node are obtained;

[0029] Determine the active power flow value of each branch sending end as the branch active power measurement value;

[0030] Determining a preset ratio of the long-term current carrying capacity of each branch as a measurement standard deviation;

[0031] determining an equality constraint based on the zero injection nodes;

[0032] A DC method state estimation model is constructed according to the branch active power measurement value, the measurement standard deviation and the equality constraint.

[0033] From the above description, it can be seen that the active power flow value at the sending end of each branch is determined as the branch active power measurement value, with the purpose of making the branch load rate calculated according to the branch active power measurement value consistent with the branch load rate calculated by the AC power flow, and the preset ratio of the long-term current carrying capacity of each branch is determined as the measurement standard deviation, with the purpose of minimizing the branch power flow percentage error of the DC method state estimation result, and the equation constraint is determined according to each zero injection node, with the purpose of not changing the grid structure, that is, the zero injection node after correction still maintains a state of no power injection, so there is no need to add new power elements, thereby constructing a DC method state estimation model based on the current carrying capacity of the grid branch, which can effectively correct the power output and load power, and realize the error correction of the DC power flow model.

[0034] Furthermore, the DC method state estimation model is:

[0035]

[0036] in,

[0037]

[0038]

[0039] In the formula, represents the branch (i, j) active power measurement value flowing from node i to node j, θ i represents the voltage phase of node i, θ j represents the voltage phase of node j, x ij represents the reactance of branch (i, j), ΔP ij represents the measurement error corresponding to the branch active power measurement value, S B represents the branch set, PDF(ΔP ij ) represents the probability density function of the measurement error, σ ij represents the measurement standard deviation, represents the long-term current carrying capacity of branch (i, j), N i represents the adjacent branch set of node i, S Z Denotes the zero injection nodes.

[0040] Further, solving the DC method state estimation model to obtain an estimation result includes:

[0041] The DC state estimation model is solved using the weighted least squares method to obtain an estimation result.

[0042] The weighted least squares method is:

[0043]

[0044]

[0045] In the formula, represents the branch (i, j) active power measurement value flowing from node i to node j, θ i represents the voltage phase of node i, θ j represents the voltage phase of node j, x ij represents the reactance of branch (i, j), S B represents the branch set, σ ij represents the measurement standard deviation, N i represents the adjacent branch set of node i, S Z Denotes the zero injection nodes.

[0046] From the above description, we can see that by solving θ i ,θ j , the correction amount for node load and power output can be calculated, and the DC power flow model can be corrected, so that the deviation between the calculation result of the active power flow load rate of the branch of the DC method and the accurate AC power flow result is smaller, so that the static safety constraint of the DC method can be used in the complex optimization decision-making of large-scale power grids, which greatly reduces the difficulty of solving the optimization decision-making problem and improves the efficiency of optimization decision-making.

[0047] Further, updating the power grid model based on the estimation result to generate a revised power grid model includes:

[0048] Acquire the voltage phase of each node in the power grid model according to the estimation result;

[0049] Determining a DC power flow model corresponding to the power grid model;

[0050] Calculating the active power flow value of each branch based on the DC power flow model according to the voltage phase of each node;

[0051] The active output of each unit and the active power of each load in the power grid model are updated according to the active power flow value of each branch and the active power balance constraint of each node to generate a revised power grid model.

[0052] From the above description, it can be seen that compared with the original power grid model, the revised power grid model only changes the active power of the original power supply nodes and load nodes, but does not change the active power balance of the zero injection node, avoiding the addition of equivalent power sources or equivalent loads at the zero injection node, that is, the power grid structure is not changed, thereby avoiding the modification of the subsequent power grid optimization decision algorithm and reducing the complexity of the power grid optimization decision algorithm.

[0053] Please refer to Figure 2, a static security constraint modeling terminal for power grid optimization calculation, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program:

[0054] Acquire a power grid model and its corresponding parameters, and perform AC power flow calculation on the power grid model and its corresponding parameters to obtain calculation results;

[0055] Constructing a DC state estimation model based on the calculation results, and solving the DC state estimation model to obtain an estimation result;

[0056] Based on the estimation result, the power grid model is updated to generate a revised power grid model;

[0057] The DC method is used to perform static security constraint modeling on the modified power grid model.

[0058] From the above description, it can be seen that the beneficial effects of the present invention are: performing AC power flow calculation on the power grid model and its corresponding parameters to obtain calculation results. Based on the calculation results, a DC state estimation model is constructed and solved, and the power grid model is corrected based on the solution results. The corrected power grid model can obtain higher calculation accuracy by using the DC power flow calculation. In view of this, the corrected power grid model is used for power grid verification and optimization calculation of DC static safety constraints, which can meet the calculation accuracy requirements. At the same time, the solution difficulty and convergence are significantly better than the AC power flow calculation method, thereby effectively improving the accuracy of complex optimization decisions of the power grid.

[0059] Furthermore, constructing a DC method state estimation model based on the calculation result includes:

[0060] According to the calculation results, the active power flow value of each branch sending end of the power grid model, the long-term current carrying capacity of each branch and each zero injection node are obtained;

[0061] Determine the active power flow value of each branch sending end as the branch active power measurement value;

[0062] Determining a preset ratio of the long-term current carrying capacity of each branch as a measurement standard deviation;

[0063] determining an equality constraint based on the zero injection nodes;

[0064] A DC method state estimation model is constructed according to the branch active power measurement value, the measurement standard deviation and the equality constraint.

[0065] From the above description, it can be seen that the active power flow value at the sending end of each branch is determined as the branch active power measurement value, with the purpose of making the branch load rate calculated according to the branch active power measurement value consistent with the branch load rate calculated by the AC power flow, and the preset ratio of the long-term current carrying capacity of each branch is determined as the measurement standard deviation, with the purpose of minimizing the branch power flow percentage error of the DC method state estimation result, and the equation constraint is determined according to each zero injection node, with the purpose of not changing the grid structure, that is, the zero injection node after correction still maintains a state of no power injection, so there is no need to add new power elements, thereby constructing a DC method state estimation model based on the current carrying capacity of the grid branch, which can effectively correct the power output and load power, and realize the error correction of the DC power flow model.

[0066] Furthermore, the DC method state estimation model is:

[0067]

[0068] in,

[0069]

[0070]

[0071] In the formula, represents the branch (i, j) active power measurement value flowing from node i to node j, θ i represents the voltage phase of node i, θ j represents the voltage phase of node j, x ij represents the reactance of branch (i, j), ΔP ij represents the measurement error corresponding to the branch active power measurement value, S B represents the branch set, PDF(ΔP ij ) represents the probability density function of the measurement error, σ ij represents the measurement standard deviation, represents the long-term current carrying capacity of branch (i, j), N i represents the adjacent branch set of node i, S Z Denotes the zero injection nodes.

[0072] Further, solving the DC method state estimation model to obtain an estimation result includes:

[0073] The DC state estimation model is solved using the weighted least squares method to obtain an estimation result.

[0074] The weighted least squares method is:

[0075]

[0076]

[0077] In the formula, represents the branch (i, j) active power measurement value flowing from node i to node j, θ i represents the voltage phase of node i, θ j represents the voltage phase of node j, x ij represents the reactance of branch (i, j), S B represents the branch set, σ ij represents the measurement standard deviation, N i represents the adjacent branch set of node i, S Z Denotes the zero injection nodes.

[0078] From the above description, we can see that by solving θ i ,θ j , the correction amount for node load and power output can be calculated, and the DC power flow model can be corrected, so that the deviation between the calculation result of the active power flow load rate of the branch of the DC method and the accurate AC power flow result is smaller, so that the static safety constraint of the DC method can be used in the complex optimization decision-making of large-scale power grids, which greatly reduces the difficulty of solving the optimization decision-making problem and improves the efficiency of optimization decision-making.

[0079] Further, updating the power grid model based on the estimation result to generate a revised power grid model includes:

[0080] Acquire the voltage phase of each node in the power grid model according to the estimation result;

[0081] Determining a DC power flow model corresponding to the power grid model;

[0082] Calculating the active power flow value of each branch based on the DC power flow model according to the voltage phase of each node;

[0083] The active output of each unit and the active power of each load in the power grid model are updated according to the active power flow value of each branch and the active power balance constraint of each node to generate a revised power grid model.

[0084] From the above description, it can be seen that compared with the original power grid model, the revised power grid model only changes the active power of the original power supply nodes and load nodes, but does not change the active power balance of the zero injection node, avoiding the addition of equivalent power sources or equivalent loads at the zero injection node, that is, the power grid structure is not changed, thereby avoiding the modification of the subsequent power grid optimization decision algorithm and reducing the complexity of the power grid optimization decision algorithm.

[0085] The static security constraint modeling method and terminal for power grid optimization calculation described above in the present invention can be applied to complex optimization decision-making problems of large-scale power grids, and are described below through specific implementation methods:

[0086] Embodiment 1

[0087] Referring to the figure, a static security constraint modeling method for power grid optimization calculation in this embodiment includes the following steps:

[0088] S1. Obtain a power grid model and its corresponding parameters, and perform AC power flow calculation on the power grid model and its corresponding parameters to obtain calculation results;

[0089] The parameters include a list of power grid nodes, node loads, power supply parameters, lines and their impedance parameters, transformers and their impedance and ratio parameters, etc.

[0090] S2. constructing a DC state estimation model based on the calculation result, and solving the DC state estimation model to obtain an estimation result, specifically including:

[0091] S21, obtaining the active power flow value of each branch sending end of the power grid model, the long-term current carrying capacity of each branch, and each zero injection node according to the calculation result;

[0092] S22, determining the active power flow value of each branch sending end as the branch active power measurement value;

[0093] S23, determining the preset ratio of the long-term current carrying capacity of each branch as the measurement standard deviation;

[0094] The preset ratio is set according to actual conditions. In this embodiment, the preset ratio is 1%.

[0095] S24, determining an equality constraint according to each zero injection node;

[0096] Specifically, the active power balance of each zero injection node is used as an equality constraint.

[0097] S25. Construct a DC method state estimation model according to the branch active power measurement value, the measurement standard deviation and the equality constraint.

[0098] Wherein, the DC method state estimation model is:

[0099]

[0100] in,

[0101]

[0102]

[0103] In the formula, represents the branch active power measurement value of branch (i, j) flowing from node i to node j, that is, the calculation result, θ i represents the voltage phase of node i, θ j represents the voltage phase of node j, x ij represents the reactance of branch (i, j), ΔP ij represents the measurement error corresponding to the branch active power measurement value, that is, the error between the DC estimated power flow and the AC estimated power flow, S B represents the branch set, PDF(ΔP ij ) represents the probability density function of the measurement error, σ ij represents the measurement standard deviation, represents the long-term current carrying capacity of branch (i, j), N i represents the adjacent branch set of node i, S Z Denotes the zero injection nodes.

[0104] S26. Use the weighted least square method to solve the DC state estimation model to obtain an estimation result.

[0105] The weighted least squares method is:

[0106]

[0107]

[0108] In the formula, represents the branch (i, j) active power measurement value flowing from node i to node j, θ i represents the voltage phase of node i, θ j represents the voltage phase of node j, x ij represents the reactance of branch (i, j), S B represents the branch set, σ ij represents the measurement standard deviation, N i represents the adjacent branch set of node i, S Z Denotes the zero injection nodes.

[0109] The above formula is a quadratic programming problem with equality constraints, which can be solved by the Lagrange multiplier method.

[0110] S3, updating the power grid model based on the estimation result to generate a modified power grid model, specifically including:

[0111] S31, obtaining the voltage phase of each node in the power grid model according to the estimation result;

[0112] S32, determining a DC power flow model corresponding to the power grid model, where the DC power flow model is a general DC power flow model. After the voltage phase of each node is obtained, the general DC power flow model can be used to obtain a corrected DC power flow calculation result;

[0113] S33, calculating the active power flow value of each branch according to the voltage phase of each node based on the DC power flow model;

[0114] S34. Update the active output of each unit and the active power of each load in the power grid model according to the active power flow value of each branch and the active power balance constraint of each node to generate a revised power grid model; each node is connected to several branches, and the active injection of the corresponding node can be calculated as long as the active power of each branch is known.

[0115] S4. Use the DC method to perform static safety constraint modeling on the modified power grid model.

[0116] According to the estimated results obtained by the solution, the voltage phase of each node in the system can be obtained, and the active power flow value of each branch can be calculated by the DC method. Then, the active output of each unit and the active power of each load can be determined according to the active power balance constraint of each node. The result is equivalent to allocating the network active loss to the power supply nodes and load nodes in the system, so that the load rate deviation of each branch in the DC method power flow calculation result of the corrected power grid is small, and the active zero injection state of other non-load and power supply nodes in the system is not changed. Therefore, after the load and power generation active power are corrected by this method, the DC power flow calculation result is closer to the initial AC power flow calculation result (i.e., the true value), and the branch load rate calculation error is smaller. The DC method static safety constraint can be used in complex optimization decision-making of large-scale power grids, which greatly reduces the difficulty of solving optimization decision-making problems and improves the efficiency of optimization decision-making, thereby effectively improving the accuracy of complex optimization decision-making of power grids.

[0117] The method of the present invention is tested by taking an actual power grid as an example. The overview of the power grid is shown in Table 1, and the test result branch load rate calculation error statistics are shown in Table 2. It can be seen from Table 2 that when the DC power flow model is directly used, although the average deviation of the branch load rate is not too large, the error of the maximum branch load rate exceeds 96%, which cannot meet the actual engineering application requirements; when the method proposed in the present invention is used, the error of the maximum branch load rate is 3.27%, and the average load rate error is reduced to 0.12%, which can better meet the application requirements of the actual power grid.

[0118] Table 1 Overview of a real power grid

[0119] Number of nodes Number of branches Number of generators Load number Total load(MW) Total network loss (MW) 10386 15207 1582 4330 361477.7 6375.5

[0120] Table 2 Statistics of calculation errors of load factor of a certain actual power grid branch

[0121]

[0122] Embodiment 2

[0123] Please refer to Figure 2 , a static safety constraint modeling terminal for power grid optimization calculation in this embodiment includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, each step of the static safety constraint modeling method for power grid optimization calculation in Example 1 is implemented.

[0124] In summary, the present invention provides a static safety constraint modeling method and terminal for power grid optimization calculation, which obtains a power grid model and its corresponding parameters, and performs AC method power flow calculation on the power grid model and its corresponding parameters to obtain calculation results; constructs a DC method state estimation model based on the calculation results, and solves the DC method state estimation model to obtain estimation results; updates the power grid model based on the estimation results to generate a revised power grid model; uses the DC method to perform static safety constraint modeling on the revised power grid model, specifically, constructs a DC method state estimation model based on the branch active measurement value, the measurement standard deviation and the equality constraint Estimation model, compared with the original power grid model, the revised power grid model only changes the active power of the original power supply nodes and load nodes, but does not change the active power balance of the zero injection node, avoiding the addition of equivalent power or equivalent load at the zero injection node, that is, the power grid structure is not changed, avoiding the modification of the subsequent power grid optimization decision algorithm, and reducing the complexity of the power grid optimization decision algorithm; at the same time, the error correction of the DC power flow model is realized, and a higher precision branch power flow load rate can be given, so that the accuracy of the DC method static safety constraint model can meet the accuracy requirements of complex optimization decisions of large-scale power grids, thereby effectively improving the accuracy of complex optimization decisions of power grids.

[0125] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent transformations made using the contents of the present invention's specification and drawings, or directly or indirectly applied in related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A static security constraint modeling method for power grid optimization calculation, It is characterized in that Includes steps: Acquire a power grid model and its corresponding parameters, and perform AC power flow calculation on the power grid model and its corresponding parameters to obtain calculation results; Constructing a DC state estimation model based on the calculation results, and solving the DC state estimation model to obtain an estimation result; Based on the estimation result, the power grid model is updated to generate a revised power grid model; Using a DC method to perform static security constraint modeling on the modified power grid model; The constructing of a DC method state estimation model based on the calculation result comprises: According to the calculation results, the active power flow value of each branch sending end of the power grid model, the long-term current carrying capacity of each branch and each zero injection node are obtained; Determine the active power flow value of each branch sending end as the branch active power measurement value; Determining a preset ratio of the long-term current carrying capacity of each branch as a measurement standard deviation; determining an equality constraint based on the zero injection nodes; Constructing a DC method state estimation model according to the branch active power measurement value, the measurement standard deviation and the equality constraint; The DC state estimation model is: ； in, ; ； ； In the formula, represents the measured value of the active power of branch (i, j) flowing from node i to node j, represents the voltage phase of node i, represents the voltage phase of node j, represents the reactance of branch (i, j), represents the measurement error corresponding to the branch active power measurement value, represents a branch set, represents the probability density function of the measurement error, represents the measurement standard deviation, represents the long-term current carrying capacity of branch (i, j), represents the adjacent branch set of node i, represents each zero injection node; The updating of the power grid model based on the estimation result to generate a modified power grid model comprises: Acquire the voltage phase of each node in the power grid model according to the estimation result; Determining a DC power flow model corresponding to the power grid model; Calculating the active power flow value of each branch based on the DC power flow model according to the voltage phase of each node; The active output of each unit and the active power of each load in the power grid model are updated according to the active power flow value of each branch and the active power balance constraint of each node to generate a revised power grid model.

2. A static security constraint modeling method for power grid optimization calculation according to claim 1, It is characterized in that Solving the DC method state estimation model to obtain an estimation result includes: Using weighted least squares method to solve the DC state estimation model to obtain an estimation result; The weighted least squares method is: ； In the formula, represents the measured value of the active power of branch (i, j) flowing from node i to node j, represents the voltage phase of node i, represents the voltage phase of node j, represents the reactance of branch (i, j), represents a branch set, represents the measurement standard deviation, represents the adjacent branch set of node i, Denotes the zero injection nodes.

3. A static security constraint modeling terminal for power grid optimization calculation, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, It is characterized in that When the processor executes the computer program, the following steps are implemented: Acquire a power grid model and its corresponding parameters, and perform AC power flow calculation on the power grid model and its corresponding parameters to obtain calculation results; Constructing a DC state estimation model based on the calculation results, and solving the DC state estimation model to obtain an estimation result; Based on the estimation result, the power grid model is updated to generate a revised power grid model; Using a DC method to perform static security constraint modeling on the modified power grid model; The constructing of a DC method state estimation model based on the calculation result comprises: According to the calculation results, the active power flow value of each branch sending end of the power grid model, the long-term current carrying capacity of each branch and each zero injection node are obtained; Determine the active power flow value of each branch sending end as the branch active power measurement value; Determining a preset ratio of the long-term current carrying capacity of each branch as a measurement standard deviation; determining an equality constraint based on the zero injection nodes; Constructing a DC method state estimation model according to the branch active power measurement value, the measurement standard deviation and the equality constraint; The DC state estimation model is: ； in, ; ； ； In the formula, represents the measured value of the active power of branch (i, j) flowing from node i to node j, represents the voltage phase of node i, represents the voltage phase of node j, represents the reactance of branch (i, j), represents the measurement error corresponding to the branch active power measurement value, represents a branch set, represents the probability density function of the measurement error, represents the measurement standard deviation, represents the long-term current carrying capacity of branch (i, j), represents the adjacent branch set of node i, represents each zero injection node; The updating of the power grid model based on the estimation result to generate a modified power grid model comprises: Acquire the voltage phase of each node in the power grid model according to the estimation result; Determining a DC power flow model corresponding to the power grid model; Calculating the active power flow value of each branch based on the DC power flow model according to the voltage phase of each node; The active output of each unit and the active power of each load in the power grid model are updated according to the active power flow value of each branch and the active power balance constraint of each node to generate a revised power grid model.

4. A static security constraint modeling terminal for power grid optimization calculation according to claim 3, It is characterized in that Solving the DC method state estimation model to obtain an estimation result includes: Using weighted least squares method to solve the DC state estimation model to obtain an estimation result; The weighted least squares method is: ； In the formula, represents the measured value of the active power of branch (i, j) flowing from node i to node j, represents the voltage phase of node i, represents the voltage phase of node j, represents the reactance of branch (i, j), represents a branch set, represents the measurement standard deviation, represents the adjacent branch set of node i, Denotes the zero injection nodes.

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