Probability optimal power flow calculation method based on second-order cone and unscented transformation

Abstract

The invention discloses a probabilistic optimal power flow calculation method based on a second-order cone and unscented transformation, belongs to the field of power system optimization, and efficiently processes the uncertainty of new energy output and load by coupling the probabilistic sampling capability of unscented transformation (UT) and a convex optimization framework of second-order cone programming (SOCP). According to the method, unscented transformation efficient sampling is utilized, a covariance matrix is directly embedded to simplify correlation processing, and accurate probability distribution of network loss, voltage and line power can be output. Test results show that the calculation efficiency is improved by dozens of times compared with Monte Carlo simulation, the error is lower than 3%, and the method is suitable for risk assessment and operation optimization of the power system.

Description

Technical Field

[0001] This invention belongs to the field of power system optimization, specifically involving a probabilistic optimal power flow calculation method based on second-order cone and unscented transformation. Background Technology

[0002] With the large-scale integration of new energy sources into the power system, the randomness of wind and solar power output and the uncertainty of loads have increased significantly, making it difficult for traditional deterministic optimal power flow models to accurately assess system operational risks. Existing probabilistic optimal power flow methods suffer from the following problems: Monte Carlo simulation (MCS) requires extensive sampling, resulting in low computational efficiency; point estimation methods are insufficient for handling higher-order moments; analytical methods have limited accuracy; and semi-invariant methods require complex matrix operations. Furthermore, existing methods rely on orthogonal transformations to handle the correlation of random variables, leading to high computational complexity.

[0003] Second-order cone relaxation (SOCP) can transform non-convex power flow equations into convex optimization problems, but the traditional SOCP framework does not fully integrate uncertainty modeling and correlation handling. Summary of the Invention

[0004] In order to solve the technical problems existing in the background art, the present invention aims to provide a probabilistic optimal power flow calculation method based on second-order cone and unscented transformation, which can be used to efficiently handle the uncertainty of new energy output and load fluctuation, and accurately evaluate the probabilistic statistical information of power system operation characteristics.

[0005] To solve the technical problem, the technical solution of the present invention is as follows:

[0006] A probabilistic optimal power flow calculation method based on second-order cone and unscented transformation, the method comprising:

[0007] To obtain the statistical characteristics of wind power output, load, power system topology, component parameters, and power flow optimization objectives, a Weibull distribution model of wind power output and a normal distribution model of load are established. The dimension, mean vector, and covariance matrix of all input random variables are calculated, and the mean u of the input variables is obtained. x The covariance matrix C xx and the dimension n of the input random variable;

[0008] Based on the mean vector, covariance matrix, and dimension, Sigma sample points χ are generated using unscented transformation. i The covariance matrix was processed using Cholesky decomposition to preserve the correlation of the input variables, and the mean weight W for each sample point was calculated. i,n Covariance weights W i,c Sigma sample points χ i ;

[0009] Using the input sample points χ of each groupi The power grid topology, component parameters, and power flow optimization objectives are considered. The nonlinear power flow equations are transformed into a second-order cone constraint form. A convex optimization model is constructed and solved for each Sigma point. The objective function is to minimize generation cost or network loss, resulting in the corresponding χ for each input sample. i Trend interpretation i ;

[0010] Based on all obtained output power flow solution samples y i By combining mean weight and covariance weight, and applying the statistical back-substitution method of unscented transformation, the mean and covariance of the output variables are estimated in a weighted manner. Then, the statistical characteristics of the operating features are evaluated, and the statistical characteristics of each operating indicator are output, thereby obtaining the optimal operating scheme that can be used for scheduling or control reference.

[0011] Furthermore, the Weibull distribution model G(v) of wind power output and the normal distribution model G(x) of load are established as follows:

[0012]

[0013] Where K is the shape parameter of the Weibull distribution, D is the scale parameter of the Weibull distribution, μ and σ are the mean and standard deviation of the normal distribution, respectively, v is the wind speed, x is the load power, which is the independent variable of the normal distribution, and t is the integral variable.

[0014] Furthermore, based on the mean u of the input variable x With covariance matrix C xx Generate N = 2n + 1 Sigma sample points, and directly handle variable correlations through Cholesky decomposition to avoid orthogonal transformations. Sigma sample points χ² i The weight W satisfies:

[0015]

[0016] Where T is the transpose, α is the scaling parameter controlling the scattering range of Sigma points, β is the prior knowledge parameter used to adjust the covariance, W0 is the basic weight of the 0th sample point, and W... 0,m The mean weight of the 0th sample.

[0017] Furthermore, the power flow equations are transformed into second-order cone constraints, and a convex optimization model is constructed with the objective function of minimizing generation cost or grid loss; the optimization model is as follows:

[0018]

[0019] The objective function is network loss, P j P is the injected active power at node j. jkP represents the active power flowing out of node j. ij Let r be the active power flowing into node j. ij For branch resistance, l ij The square of the branch current amplitude. This represents the maximum value of the variable. (·) Q represents the minimum value of the variable, where N is the set of network nodes. j The injected reactive power Q is the reactive power at node j. jk Q represents the reactive power flowing out of node j. ij Let x be the reactive power flowing into node j. ij For branch reactance, v j Let (i,j) represent the square of the voltage magnitude at node j, (i,j) represent the branch, and B represent the set of branches.

[0020] Furthermore, all Sigma sample points y i Substituting into the power flow model and performing weighted processing, we obtain the mean u of the output random variable. y With covariance matrix C yy :

[0021]

[0022] N is the total number of Sigma sample points, and T represents transpose. The transpose operation is used to construct a matrix for summation.

[0023] A probabilistic optimal power flow calculation system based on second-order cone and unscented transformation, comprising:

[0024] Statistical Characteristics and Model Building Module: This module acquires the statistical characteristics of wind power output, load, power system topology, component parameters, and power flow optimization objectives. It establishes a Weibull distribution model for wind power output and a normal distribution model for load. It also calculates the dimension, mean vector, and covariance matrix of all input random variables, obtaining the mean u of the input variables. x The covariance matrix C xx and the dimension n of the input random variable;

[0025] Sigma Sample Generation and Weight Calculation Module: Based on the mean vector, covariance matrix, and dimension, it generates Sigma sample points χ using unscented transformation. i The covariance matrix was processed using Cholesky decomposition to preserve the correlation of the input variables, and the mean weight W for each sample point was calculated. i,m Covariance weights W i,c Sigma sample points χ i ;

[0026] Second-order cone constraint optimization module: Utilizing the χ of each set of input sample points iThe power grid topology, component parameters, and power flow optimization objectives are considered. The nonlinear power flow equations are transformed into a second-order cone constraint form. A convex optimization model is constructed and solved for each Sigma point. The objective function is to minimize generation cost or network loss, resulting in the corresponding χ for each input sample. i Trend interpretation i ;

[0027] Based on all obtained output power flow solution samples y i By combining mean weight and covariance weight, and applying the statistical back-substitution method of unscented transformation, the mean and covariance of the output variables are estimated in a weighted manner. Then, the statistical characteristics of the operating features are evaluated, and the statistical characteristics of each operating indicator are output, thereby obtaining the optimal operating scheme that can be used for scheduling or control reference.

[0028] Furthermore, the Weibull distribution model G(v) of wind power output and the normal distribution model G(x) of load are established as follows:

[0029]

[0030] Where K is the shape parameter of the Weibull distribution, D is the scale parameter of the Weibull distribution, μ and σ are the mean and standard deviation of the normal distribution, respectively, v is the wind speed, x is the load power, which is the independent variable of the normal distribution, and t is the integral variable.

[0031] Furthermore, based on the mean u of the input variable x With covariance matrix C xx Generate N = 2n + 1 Sigma sample points, and directly handle variable correlations through Cholesky decomposition to avoid orthogonal transformations. Sigma sample points χ² i The weight W satisfies:

[0032]

[0033]

[0034] Where T is the transpose, α is the scaling parameter controlling the scattering range of Sigma points, β is the prior knowledge parameter used to adjust the covariance, W0 is the basic weight of the 0th sample point, and W... 0,m The mean weight of the 0th sample.

[0035] Furthermore, the power flow equations are transformed into second-order cone constraints, and a convex optimization model is constructed with the objective function of minimizing generation cost or grid loss; the optimization model is as follows:

[0036]

[0037] The objective function is network loss, P jP is the injected active power at node j. jk P represents the active power flowing out of node j. ij Let r be the active power flowing into node j. ij For branch resistance, l ij The square of the branch current amplitude. This represents the maximum value of the variable. (·) Q represents the minimum value of the variable, where N is the set of network nodes. j The injected reactive power Q is the reactive power at node j. jk Q represents the reactive power flowing out of node j. ij Let x be the reactive power flowing into node j. ij For branch reactance, v j Let (i,j) represent the square of the voltage magnitude at node j, (i,j) represent the branch, and B represent the set of branches.

[0038] Furthermore, all Sigma sample points y i Substituting into the power flow model and performing weighted processing, we obtain the mean u of the output random variable. y With covariance matrix C yy :

[0039]

[0040] N is the total number of Sigma sample points, and T represents transpose. The transpose operation is used to construct a matrix for summation.

[0041] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the probabilistic optimal power flow calculation method based on second-order cone and unscented transformation as described above.

[0042] A computer-readable storage medium storing a computer program that, when executed by a processor, implements a probabilistic optimal power flow calculation method based on a second-order cone and unscented transformation as described above.

[0043] Compared with the prior art, the advantages of the present invention are as follows:

[0044] Efficiently handle uncertainty:

[0045] This method effectively models uncertainties such as wind power output and load through unscented transformation (UT), accurately captures nonlinear relationships, and considers the correlation between input variables, thus introducing high-precision probabilistic assessment in power system optimization.

[0046] Solving complex nonlinear constraints:

[0047] Using second-order cone programming (SOCP) to transform the nonlinear power flow equations into a convex optimization problem not only ensures the solvability of the problem, but also enables rapid solution in large-scale power grids, ensuring high efficiency and reliability.

[0048] Comprehensive risk assessment:

[0049] The method can not only calculate the optimal operating scheme of the system, but also provide the probability distribution, mean and standard deviation of various operating indicators (such as voltage, power flow, network loss, etc.), providing decision-makers with a comprehensive risk assessment and helping to carry out robust power system dispatching and planning.

[0050] Highly adaptable and suitable for complex power systems:

[0051] This method can be adapted to various types of power systems, including large power grids containing renewable energy sources such as wind power. It can handle a large number of random disturbances and uncertainties, and is an effective supplement to traditional power flow optimization methods.

[0052] It combines computational efficiency and accuracy:

[0053] Unscented transformation significantly reduces computational complexity while maintaining accuracy by generating a finite number of Sigma sample points. Combined with the SOCP optimization method, it can efficiently solve power flow problems in large-scale power systems while ensuring optimization accuracy.

[0054] Enhancing system reliability and economy:

[0055] By taking into account the impact of uncertainty, optimized calculations not only ensure the safe operation of the system, but also minimize operating costs within a controllable risk range, thereby improving the economic efficiency of the power grid. Attached Figure Description

[0056] Figure 1 shows the comparison results of this invention with other algorithms;

[0057] Figure 2 This is a schematic diagram of the process of the present invention. Detailed Implementation

[0058] The specific implementation of the present invention is described below with reference to embodiments:

[0059] It should be noted that the structures, proportions, sizes, etc. shown in this specification are only used to complement the content disclosed in the specification for those skilled in the art to understand and read, and are not intended to limit the conditions under which the present invention can be implemented. Any modifications to the structure, changes in the proportions, or adjustments to the size, without affecting the effects and objectives that the present invention can produce, should still fall within the scope of the technical content disclosed in the present invention.

[0060] Furthermore, the terms such as "upper," "lower," "left," "right," "middle," and "one" used in this specification are merely for clarity of description and are not intended to limit the scope of the invention. Any changes or adjustments to their relative relationships, without substantially altering the technical content, should also be considered within the scope of the invention.

[0061] Example 1:

[0062] like Figure 2 As shown, this embodiment proposes a probabilistic optimal power flow calculation method based on second-order cone and unscented transformation, including the following steps:

[0063] S1. Random signal modeling: Establish an uncertainty model for wind power output and load, and statistically analyze the dimension of input random variables;

[0064] S2. Sample random input signal: Perform UT sampling on random signal and process correlation;

[0065] S3. Second-order cone relaxation modeling: The power flow equation is transformed into a second-order cone constraint to construct a convex optimization model. The objective function is to minimize the power generation cost or grid loss.

[0066] S4. Statistical output: Weighted calculation of the mean, standard deviation, and probability distribution of system operating characteristic quantities (network loss, voltage, line power, etc.).

[0067] Specifically, the following steps are included:

[0068] Stochastic signal modeling: Establishing uncertainty models for wind power output (Weibull distribution) and load (normal distribution). The cumulative distribution models for wind power output and load are as follows:

[0069]

[0070] Where K and D are the shape and scale parameters of the Weibull distribution, respectively, and μ and σ are the mean and standard deviation of the normal distribution, respectively.

[0071] Sampling random input signals: based on the mean u of the input variables x With covariance matrix C xx N = 2n + 1 Sigma sample points are generated, and variable correlations are directly handled through Cholesky decomposition, avoiding orthogonal transformations. Sigma sample points χ² i The weight W satisfies:

[0072]

[0073] Second-order cone relaxation modeling: The power flow equations are transformed into second-order cone constraints, and a convex optimization model is constructed. The objective function is to minimize generation cost or grid loss. The optimization model is as follows:

[0074]

[0075] The objective function is network loss, P j P is the injected active power at node j. jk P represents the active power flowing out of node j. ij Let r be the active power flowing into node j. ij For branch resistance, l ij The square of the branch current amplitude. This represents the maximum value of the variable. (·) This represents the minimum value of the variable. N is the set of network nodes. Q j The injected reactive power Q is the reactive power at node j. jk Q represents the reactive power flowing out of node j. ij Let x be the reactive power flowing into node j. ij For branch circuit reactance. j Let (i,j) represent the square of the voltage magnitude at node j, (i,j) represent the branch, and B represent the set of branches.

[0076] Statistical output: Substituting all Sigma sample points into the optimal power flow model and performing weighted processing, the mean u of the output random variable is obtained. y With covariance matrix C yy :

[0077]

[0078] To highlight the algorithm's performance, this embodiment selects the Semidefinite Programming-Monte Carlo Simulation (SDP-MCS) method and the Semidefinite Programming-Unscented Transformation (SDP-UT) method as comparison algorithms. The IEEE 30 and IEEE 118-node systems are used as research objects, with 27 and 115 load nodes respectively, and 2 nodes connected to wind power generation equipment. The wind speeds of both wind farms follow a Weibull distribution with a shape parameter of 2.5 and a scale parameter of 9. The load nodes all follow a normal distribution, with the mean being the predicted value and the standard deviation being 5% of the mean; the final test results are shown in Figure 1.

[0079] Example 2:

[0080] A probabilistic optimal power flow calculation system based on second-order cone and unscented transformation, the system being used to execute a probabilistic optimal power flow calculation method based on second-order cone and unscented transformation, the system comprising:

[0081] Statistical Characteristics and Model Building Module: This module acquires the statistical characteristics of wind power output, load, power system topology, component parameters, and power flow optimization objectives. It establishes a Weibull distribution model for wind power output and a normal distribution model for load. It also calculates the dimension, mean vector, and covariance matrix of all input random variables, obtaining the mean u of the input variables. x The covariance matrix C xx and the dimension n of the input random variable;

[0082] Sigma Sample Generation and Weight Calculation Module: Based on the mean vector, covariance matrix, and dimension, it generates Sigma sample points χ using unscented transformation. i The covariance matrix was processed using Cholesky decomposition to preserve the correlation of the input variables, and the mean weight W for each sample point was calculated. i,m Covariance weights W i,c Sigma sample points χ i ;

[0083] Second-order cone constraint optimization module: Utilizing the χ of each set of input sample points i The power grid topology, component parameters, and power flow optimization objectives are considered. The nonlinear power flow equations are transformed into a second-order cone constraint form. A convex optimization model is constructed and solved for each Sigma point. The objective function is to minimize generation cost or network loss, resulting in the corresponding χ for each input sample. i Trend interpretation i That is, each Sigma sample point y i ;

[0084] Output statistical characteristic calculation module: Based on all output sample points, mean weight and covariance weight, the mean and covariance of the output variables are calculated in a weighted manner, and the statistical characteristics of the operating characteristics are evaluated. The statistical characteristics of each operating indicator are output, thus obtaining the optimal operating plan for scheduling or control reference.

[0085] Example 3:

[0086] This embodiment provides a terminal device, which includes a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to achieve a corresponding method flow or corresponding function. The processor described in this embodiment can be used to operate a probabilistic optimal power flow calculation method based on a second-order cone and unscented transformation, including the following steps:

[0087] S1: Obtain the statistical characteristics of wind power output, load, power system topology, component parameters, and power flow optimization objectives. Establish the Weibull distribution model of wind power output and the normal distribution model of load. Calculate the dimension, mean vector, and covariance matrix of all input random variables to obtain the mean u of the input variables. x The covariance matrix C xx and the dimension n of the input random variable;

[0088] S2: Based on the mean vector, covariance matrix, and dimension, generate Sigma sample points χ using unscented transformation. i The covariance matrix was processed using Cholesky decomposition to preserve the correlation of the input variables, and the mean weight W for each sample point was calculated. i,m Covariance weights W i,c Sigma sample points χ i ;

[0089] S3: Using the input sample points χ of each group i The power grid topology, component parameters, and power flow optimization objectives are considered. The nonlinear power flow equations are transformed into a second-order cone constraint form. A convex optimization model is constructed and solved for each Sigma point. The objective function is to minimize generation cost or network loss, resulting in the corresponding χ for each input sample. i Trend interpretation i That is, each Sigma sample point y i ;

[0090] S4: Based on all obtained output power flow solution samples y i By combining mean weight and covariance weight, and applying the statistical back-substitution method of unscented transformation, the mean and covariance of the output variables are estimated in a weighted manner. Then, the statistical characteristics of the operating features are evaluated, and the statistical characteristics of each operating indicator are output, thereby obtaining the optimal operating scheme that can be used for scheduling or control reference.

[0091] Example 4:

[0092] This embodiment provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a terminal device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and extended storage media supported by the terminal device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device.

[0093] One or more instructions stored in a computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the probabilistic optimal power flow calculation method based on second-order cone and unscented transformation in the above embodiments; one or more instructions in the computer-readable storage medium are loaded and executed by the processor to perform the following steps:

[0094] S1: Obtain the statistical characteristics of wind power output, load, power system topology, component parameters, and power flow optimization objectives. Establish the Weibull distribution model of wind power output and the normal distribution model of load. Calculate the dimension, mean vector, and covariance matrix of all input random variables to obtain the mean u of the input variables. x The covariance matrix C xx and the dimension n of the input random variable;

[0095] S2: Based on the mean vector, covariance matrix, and dimension, generate Sigma sample points χ using unscented transformation. i The covariance matrix was processed using Cholesky decomposition to preserve the correlation of the input variables, and the mean weight W for each sample point was calculated. i,m Covariance weights W i,c Sigma sample points χ i ;

[0096] S3: Using the input sample points χ of each group i The power grid topology, component parameters, and power flow optimization objectives are considered. The nonlinear power flow equations are transformed into a second-order cone constraint form. A convex optimization model is constructed and solved for each Sigma point. The objective function is to minimize generation cost or network loss, resulting in the corresponding χ for each input sample. i Trend interpretation i That is, each Sigma sample point y i ;

[0097] S4: Based on all obtained output power flow solution samples y i By combining mean weight and covariance weight, and applying the statistical back-substitution method of unscented transformation, the mean and covariance of the output variables are estimated in a weighted manner. Then, the statistical characteristics of the operating features are evaluated, and the statistical characteristics of each operating indicator are output, thereby obtaining the optimal operating scheme that can be used for scheduling or control reference.

[0098] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0099] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in one or more blocks of the flowchart illustrations and / or one or more blocks of the block diagrams.

[0100] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means that implement the functions specified in one or more flowcharts and / or one or more block diagrams.

[0101] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, such that the instructions, which execute on the computer or other programmable apparatus, provide steps for implementing the functions specified in one or more flowcharts and / or one or more block diagrams.

[0102] The preferred embodiments of the present invention have been described in detail above. However, the present invention is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of the present invention.

[0103] Many other changes and modifications can be made without departing from the concept and scope of this invention. It should be understood that this invention is not limited to the specific embodiments, and the scope of this invention is defined by the appended claims.

Claims

1. A method for probabilistic optimal power flow calculation based on second order cone and unscented transformation, characterized in that, The method comprises: Obtain the statistical characteristics of wind power output, the statistical characteristics of load, the topology of power system, the parameters of elements and the optimization target of power flow, establish the Weibull distribution model of wind power output and the normal distribution model of load, count the dimension, mean vector and covariance matrix of all input random variables, obtain the mean u x and covariance matrix C xx of input variables and the dimension n of input random variables ; Based on the mean vector, covariance matrix and dimension, Sigma sample points χ are generated using unscented transformation i , the covariance matrix is processed using Cholesky decomposition, the correlation of input variables is retained, the mean weight W and covariance weight W of each sample point are calculated i,m , and Sigma sample points χ are obtained i,c i ;​ Using the input sample points χ i , power grid topology, element parameters and power flow optimization objectives, the nonlinear power flow equation is converted into a second-order cone constraint form, a convex optimization model is constructed and solved for each Sigma point, the objective function is to minimize the power generation cost or network loss, and the power flow solution y i corresponding to each input sample χ i is obtained. Based on the obtained all output power flow solution samples y i , combined with the mean weight and the covariance weight, the statistical back substitution method of unscented transformation is applied to weight estimate the mean and covariance of the output variable, then the statistical characteristics of the operation characteristics are evaluated, the statistical characteristics of each operation index are output, and thus the optimal operation scheme available for scheduling or control reference is obtained.

2. The method of claim 1, wherein, Based on the mean value u of the input variable x With the covariance matrix C xx Generate N = 2n+1 Sigma sample points, directly handle variable correlation through Cholesky decomposition, avoid orthogonal transformation, Sigma sample points χ i And the weight W satisfies: where T is a transpose, a is a scaling parameter to control the spread of Sigma points, β is a priori knowledge parameter to adjust the covariance, W0is a base weight of the 0th sample point, W 0,m is a mean weight of the 0th sample.

3. The method of claim 1, wherein, The power flow equation is converted into a second-order cone constraint, a convex optimization model is constructed, and a target function is a minimum generation cost or network loss; the optimization model is as follows: where the objective function is the network loss, P j is the injected real power at node j, P jk is the real power flowing out of node j, P ij is the real power flowing into node j, r ij is the branch resistance, l ij is the square of the branch current magnitude, denotes the maximum value of a variable, (·) denotes the minimum value of a variable, N is the set of network nodes, Q j is the injected reactive power at node j, Q jk is the reactive power flowing out of node j, Q ij is the reactive power flowing into node j, x ij is the branch reactance, v j denotes the square of the voltage magnitude at node j, (i,j) denotes a branch, B denotes the set of branches.

4. The method of claim 1, wherein, Substitute all Sigma sample points y i into the power flow model, and get the output random variable mean u y and covariance matrix C yy : N is the total number of Sigma sample points, T represents transposition, and the transposition operation is used for constructing a matrix to facilitate summation.

5. A system for probabilistic optimal power flow calculation based on second order cone and unscented transformation, characterized in that, The method comprises: Statistical characteristics and model establishing module: acquire statistical characteristics of wind power output, statistical characteristics of load, power system topology structure, element parameters and power flow optimization target, establish Weibull distribution model of wind power output and normal distribution model of load, and statistically acquire dimension, mean vector and covariance matrix of all input random variables to obtain mean vector u x and covariance matrix C xx of input variables and dimension n of input random variables; Sigma sample generation and weight calculation module: based on the mean vector, covariance matrix and dimension, sigma sample points χ are generated by using unscented transformation i , the covariance matrix is processed using Cholesky decomposition, the correlation of the input variables is retained, the mean weight W i,m and the covariance weight W i,c of each sample point are calculated, and sigma sample points x i are obtained; Second-order cone constrained optimization module: using the each set of input sample points χ i , power grid topology, element parameters and power flow optimization objectives, the nonlinear power flow equation is converted into a second-order cone constraint form, a convex optimization model is constructed and solved for each Sigma point, the objective function is to minimize the power generation cost or network loss, and the power flow solution y i corresponding to each input sample χ i is obtained; based on all the obtained output power flow solution samples y i , combined with the mean weight and the covariance weight, the statistical back substitution method of unscented transformation is applied, the mean and covariance of the output variables are weighted estimated, then the statistical characteristics of the operating characteristics are evaluated, and the statistical characteristics of each operating index are output, so as to obtain the optimal operation scheme for reference of scheduling or control.

6. The system for probabilistic optimal power flow calculation based on second order cone and unscented transformation of claim 5, wherein, Based on the mean value u of the input variable x With the covariance matrix C xx Generate N = 2n+1 Sigma sample points, directly handle variable correlation through Cholesky decomposition, avoid orthogonal transformation, Sigma sample points x i And the weight W satisfies: where T is a transpose, a is a scaling parameter to control the spread of Sigma points, β is a priori knowledge parameter to adjust the covariance, W0is a base weight of the 0th sample point, W 0,m is a mean weight of the 0th sample.

7. The system for probabilistic optimal power flow calculation based on second order cone and unscented transformation of claim 5, wherein, The power flow equation is converted into a second-order cone constraint, a convex optimization model is constructed, and a target function is a minimum generation cost or network loss; the optimization model is as follows: where the objective function is the network loss, P j is the injected real power at node j, P jk is the real power flowing out of node j, P ij is the real power flowing into node j, r ij is the branch resistance, l ij is the square of the branch current magnitude, l denotes the maximum value of a variable, (·) denotes the minimum value of a variable, N is the set of network nodes, Q j is the injected reactive power at node j, Q jk is the reactive power flowing out of node j, Q ij is the reactive power flowing into node j, x ij is the branch reactance, v j denotes the square of the voltage magnitude at node j, (i,j) denotes a branch, and B denotes the set of branches.

8. The system for probabilistic optimal power flow calculation based on second order cone and unscented transformation of claim 5, wherein, Substitute all Sigma sample points y i into the power flow model, and get the output random variable mean u y and covariance matrix C yy : N is the total number of Sigma sample points, T represents transposition, and the transposition operation is used for constructing a matrix to facilitate summation.

9. A computer device, comprising: The computer readable storage medium stores a computer program, and the program is executed by the processor to implement the method of claim 1 to 4.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the program is executed by the processor to implement the method of claim 1 to 4.

Images