Holomorphic Embedding Affine Power Flow Uncertainty Quantification Method for Spatiotemporal Correlation of New Energy Power Stations

Abstract

The present invention provides a holomorphic embedding affine power flow uncertainty quantification method for the spatio-temporal correlation of new energy power stations, comprising the following steps: obtaining the historical output data of multiple new energy power stations, and eliminating the zero-output data segments through sliding window analysis to obtain a standardized output sequence; based on the standardized output sequence, using the parallelogram model to convert the new energy output interval envelope into an affine number form through matrix multiplication to generate an affine output model representing spatio-temporal correlation; respectively constructing the classical holomorphic embedding equations for PQ nodes, PV nodes and balance nodes, and extracting the coefficients of each order of affine power series; constructing a constant recurrence relation matrix related to the network topology and node type according to the coefficients of the affine power series, and performing analytic continuation on the Maclaurin series through the Padé approximation technique to obtain the affine power flow solutions under different load levels; based on the noise element coefficients in the affine power flow solutions, quantifying the influence degree of the output uncertainty of each new energy power station on the system state variables.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system analysis and control, and in particular relates to a method for quantifying the uncertainty of a holomorphic embedded affine power flow based on the spatiotemporal correlation of a new energy station. Background Art

[0002] As the penetration rate of renewable energy continues to rise, its multi-temporal and spatial coupling uncertainty characteristics pose a severe challenge to the safe and stable operation of the power system. The traditional probabilistic power flow method realizes uncertainty analysis by constructing a probability distribution model of random variables, but it is highly dependent on accurate historical data and has significant difficulties in modeling probability density functions. The interval power flow algorithm uses the upper and lower limits of variables for boundary analysis instead. Although it reduces the dependence on probability distribution, it causes the "interval explosion" problem due to ignoring the correlation between variables, resulting in distorted calculation results. The affine power flow algorithm developed in this context is improved by introducing a variable correlation mechanism, but existing studies generally model the output of renewable energy stations as independent affine variables, which fails to effectively represent the objective spatiotemporal correlation between geographically adjacent stations, resulting in significant deviations between the model output and the actual operating conditions.

[0003] The current affine power flow method faces the following technical bottlenecks: the Ybus affine iteration method is not adaptable enough when dealing with PV node systems, while the affine power flow calculation using the Newton-Raphson method is limited by the sensitivity of initial values and the complexity of inverting high-dimensional affine matrices. Although the existing holomorphic embedded affine power flow method has non-iterative characteristics and strong PV node processing capabilities, it still has essential limitations: the initial value calculation mode that relies on the no-load assumption is difficult to adapt to the high penetration scenario of new energy; the physical meaning of the embedded factors is strict, and the recursive relationship matrix needs to be reconstructed when the load level is different; the independent affine variable modeling paradigm is continuously followed, and the difficulty of characterizing spatiotemporal correlation has not been broken through. Summary of the invention

[0004] In view of the defects and shortcomings of the prior art, the present invention provides a classical holomorphic embedding affine power flow uncertainty quantification method taking into account the spatiotemporal correlation of new energy stations, and its innovative design features include:

[0005] Improvement of spatiotemporal correlation modeling: Through the Parallelogram Model (PM), combined with the Pearson correlation coefficient matrix and matrix cascade multiplication method (Formula 6-7), the output range envelope of the new energy station is converted into an affine number form, correcting the defect of the traditional affine model that ignores spatiotemporal correlation and solving the "range explosion" problem.

[0006] Construction of classical holomorphic embedding equations: Holomorphic embedding affine equations for PQ nodes (Formula 8), PV nodes (Formula 10), and slack nodes (Formula 11) are established respectively. The coefficients of each order of affine power series are extracted through Maclaurin series expansion (Formula 9) to support the analytical solution of multi-load level scenarios.

[0007] Constant recurrence relation matrix: Based on the network admittance matrix and node types (Formulas 14 - 21), a sparse recursive matrix is generated to avoid repeated calculation of the Jacobian matrix and improve the calculation efficiency.

[0008] Padé approximation analytic continuation: Perform fractional approximation on the coefficients of the affine power series (Formula 23), and independently process the coefficients of each noise element to achieve a fully linear operation mechanism for nonlinear problems.

[0009] Quantification of noise elements and data preprocessing: Eliminate the interference of zero output through sliding window detection (Formulas 1 - 2) and normalization processing (Formula 3), and locate the dominant uncertainty sources by comparing the absolute values of the noise element coefficients.

[0010] Through the above technical means, this solution significantly optimizes the calculation efficiency while ensuring the model accuracy, providing reliable support for the safety and stability analysis of high-proportion new energy power systems.

[0011] The technical solution specifically adopted by the present invention to solve its technical problems is:

[0012] A holomorphic embedding affine power flow uncertainty quantification method for the spatio-temporal correlation of new energy power stations, comprising the following steps:

[0013] Obtain the historical output data of multiple new energy power stations, eliminate the zero-output data segments through sliding window analysis, and perform normalization processing on the output data to obtain a standardized output sequence;

[0014] Based on the standardized output sequence, calculate the spatio-temporal correlation coefficient matrix between power stations, and use the parallelogram model to transform the envelope of the new energy output interval into an affine number form through matrix multiplication to generate an affine output model representing spatio-temporal correlation;

[0015] Based on the affine output model and the system network structure parameters, construct classical holomorphic embedding equations for PQ nodes, PV nodes, and slack nodes respectively, expand the equations into Maclaurin series form and extract the coefficients of each order of affine power series;

[0016] According to the coefficients of the affine power series, construct a constant recurrence relation matrix related to the network topology and node types, judge whether the power flow accuracy meets the convergence condition through iterative calculation, and then perform analytic continuation on the Maclaurin series through Padé approximation technology to obtain the affine power flow solutions under different load levels;

[0017] Quantify the influence degree of the output uncertainty of each new energy power station on the system state variables based on the noise element coefficients in the affine power flow solution.

[0018] Further, the method for removing the zero-output data segment in the sliding window analysis includes:

[0019] Set the window length and the sliding step size, and calculate the average output within the window;

[0020] If the average output of the window is lower than the preset threshold, determine that the window is a zero-output data segment and filter it out.

[0021] Further, the spatio-temporal correlation coefficient matrix is generated through the following steps:

[0022] Calculate the Pearson correlation coefficients of the output data of pairwise new energy power stations to form a correlation coefficient matrix;

[0023] The correlation coefficient is used to quantify the spatio-temporal correlation intensity of the output fluctuations of the power stations.

[0024] Further, the parallelogram model generates an affine number form in the following manner:

[0025] Decompose the new energy output interval envelope into an interval center value matrix, an interval width matrix, and a correlation coefficient matrix;

[0026] Combine the interval width matrix with a weight matrix whose diagonal elements are the reciprocals of the sum of the absolute values of the correlation coefficients through matrix multiplication, then cascade with the correlation coefficient matrix, and finally linearly combine with the noise element vector;

[0027] Each element in the noise element vector independently represents the output uncertainty of the power station itself or between power stations.

[0028] Further, the classical holomorphic embedding equation considering the spatio-temporal correlation of new energy satisfies the following conditions:

[0029] Rewrite the power flow equations of each node type into a holomorphic embedding affine power flow calculation model, and make the same-order affine membrane technology systems at both ends of the equal sign equal;

[0030] Extract the coefficients of each order of affine power series from the equation through Maclaurin expansion, and the coefficients include the central value term and the noise element coefficient term.

[0031] Further, the construction method of the constant recurrence relation matrix includes:

[0032] Decompose the real part and the imaginary part of the Maclaurin series into linear algebraic equations;

[0033] Generate a sparse recursive matrix based on the real and imaginary part elements of the network admittance matrix and the node type to avoid repeated calculation of the Jacobian matrix.

[0034] Furthermore, the Padé approximation analytic continuation satisfies the following conditions:

[0035] Construct the numerator and denominator polynomial coefficients by independently processing the affine power series coefficients of each noise element;

[0036] The selection of the numerator order and the denominator order satisfies the constraint relationship with the Maclaurin expansion order;

[0037] The analytic continuation process cooperates with the constant recurrence relation matrix to achieve a full linear operation mechanism, so as to reduce the computational complexity.

[0038] Furthermore, the noise element coefficient quantization method includes: comparing the absolute values of the coefficients of each noise element to identify the dominant uncertainty source.

[0039] And an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the steps of the above method when executing the program.

[0040] A non-transitory computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0041] Compared with the prior art, the present invention and its preferred solutions at least include the following beneficial effects:

[0042] Correct the defects in spatio-temporal correlation modeling: By combining the parallelogram model (PM) with the matrix concatenation operation of the correlation coefficient matrix, break through the assumption of the independence of the new energy power station output in the traditional affine power flow, effectively suppress the "interval explosion" problem, and improve the accuracy of the model in depicting the actual operating conditions.

[0043] Improve the calculation efficiency and adaptability: Based on the classical holomorphic embedding equation, construct a constant recurrence relation matrix, avoid the computational burden of repeatedly updating the Jacobian matrix due to the change of the load level in the traditional method, significantly reduce the high-dimensional affine operation complexity, and enhance the adaptability of multi-scenario analysis.

[0044] Efficiently analyze non-linear problems: Adopt the Padé approximation technology with independent processing of noise elements, transform the non-linear power flow equation into a full linear operation mechanism, realize the fast analytic continuation of the affine solution under different load levels, and at the same time support the accurate separation and quantization of the uncertainty propagation path.

[0045] Optimize data preprocessing and noise element quantization: Eliminate the zero-output interference through sliding window detection and normalization processing, and combine the noise element coefficient absolute value comparison mechanism to accurately locate the dominant uncertainty source, providing a reliable data basis for the stability analysis of the new energy power system.

[0046] Through the above technical means, this solution realizes collaborative optimization in three dimensions: model accuracy, calculation efficiency, and engineering adaptability, providing a systematic solution for the uncertainty analysis of complex power systems with a high proportion of new energy grid connection. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The present invention will be further described in detail below in conjunction with the drawings and specific embodiments:

[0048] Figure 1 It is a flowchart of the holomorphic embedding affine power flow analysis and uncertainty quantification method for the spatio-temporal correlation of new energy power stations in the embodiments of the present invention. SPECIFIC EMBODIMENTS

[0049] To make the features and advantages of the present invention more obvious and understandable, specific embodiments are given below for detailed description as follows:

[0050] It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used in this specification have the same meaning as commonly understood by those of ordinary skill in the technical field to which this application belongs.

[0051] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular forms are also intended to include the plural forms. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0052] In order to realize the quantification of uncertainty of holomorphic embedded affine power flow of spatiotemporal correlation of new energy stations, the embodiment of the present invention constructs an affine model that takes into account the spatiotemporal correlation of new energy stations, introduces holomorphic embedded factors with practical physical significance into the uncertainty power flow calculation, and forms an embedded affine power flow calculation framework. In the preprocessing stage of historical output data of new energy stations, the sliding window detection technology is used to eliminate the zero output data segment, and the dimension difference is eliminated by maximum and minimum normalization to provide a standardized data basis for modeling; based on the preprocessed data, the Pearson correlation coefficient is used to quantify the output correlation strength between stations, and the parallelogram model (PM) is introduced to convert the statistically related output interval envelope into an uncertainty characterization model that can be affine-operated, effectively correcting the defect of the traditional affine model deviating from the actual operating conditions. Then, a classical holomorphic embedded affine power flow calculation model considering the spatiotemporal correlation of new energy output is established, and a recursive relationship matrix that remains constant during the calculation process is derived, avoiding the computational burden of the traditional method that requires repeated updates of the Jacobian matrix. Finally, the recursive relationship is solved through linear operation to obtain the system uncertainty state quantity, and the relative influence of each uncertainty factor on the system state quantity is quantitatively analyzed. This process significantly reduces the computational complexity with a full linear operation mechanism, achieves a breakthrough optimization of computational efficiency while improving model accuracy, and provides reliable support for the safe and stable analysis of high-proportion new energy systems.

[0053] The technical points of the solution implementation include:

[0054] (1) Introducing the Parallelogram Model (PM) to establish an affine model that takes into account the temporal and spatial correlation of the output of renewable energy stations;

[0055] (2) Establishing a classical holomorphic embedded affine power flow calculation model for each node type;

[0056] (3) Obtain the initial value of the affine power flow solution based on the actual physical meaning corresponding to the initial state of the classical holomorphic embedded affine power flow;

[0057] (4) The problem of solving uncertain affine power flow is converted into solving the coefficients of various orders of affine power series of Maclaurin expansion and summarized into a recursive relationship matrix;

[0058] (5) Using the Padé approximation technique, an analytical expression of the power flow state in affine form is established to transform the complex nonlinear problem into a form that can be analytically solved;

[0059] (6) By comparing the sizes of different noise element coefficients, the degree to which the state quantity is affected by uncertainty factors can be accurately quantified.

[0060] like Figure 1 As shown, the typical workflow of its calculation process is:

[0061] Preprocess the historical output data of the new energy power station.

[0062] Use PM to construct an affine model to characterize spatio-temporal correlation.

[0063] Input the network structure and parameters of the system, and set the uncertain quantities.

[0064] Obtain the k-th order affine power series coefficients through formula (21).

[0065] Does it meet the power flow calculation accuracy of formula (22)?

[0066] No (N): If not, let k = k + 1, and return for continued iteration.

[0067] Yes (Y): If it meets, enter the next step.

[0068] Solve the Padé coefficients required for the affine power flow analytical expression.

[0069] Substitute the given load level s into the affine power flow analytical expression of formula (23) to calculate the affine solution of the state quantity.

[0070] The following is a specific introduction to the solution of the embodiment of the present invention:

[0071] 1 Affine model considering spatio-temporal correlation of new energy power stations

[0072] First, obtain the historical output data of new energy power stations with adjacent geographical locations;

[0073] Secondly, set the sliding window size and step length, slide on the historical output data of the new energy power station, and calculate the mean value within the window. The calculation formula is as follows:

[0074] (1)

[0075] In the formula: K is the data length within the window; P j,i is the j -th i new energy output data corresponding to the -th window; j is the mean value of the new energy output of the i -th window; here j is used to represent the new energy output data number within the window,

[0076] Judge whether the mean value within the window is lower than the threshold. If the mean value within the window is lower than the threshold, then judge that this window is a zero output data segment. The calculation formula is as follows:

[0077] (2)

[0078] Wherein: P mean,j represents the average new - energy output of the j th window; ε threshold is the threshold for judging zero output; here j is used to represent the window number;

[0079] At the same time, filter out the zero - output data segments of the new - energy power stations participating in the spatio - temporal correlation calculation, and ensure that each output data strictly matches the measured time stamp;

[0080] Perform maximum - minimum normalization on the output data of the new - energy power stations after the above processing to eliminate the influence of different dimensions. The calculation formula is as follows:

[0081] (3)

[0082] Wherein: P i,norm is the output data of the i th new - energy power station after normalization; P max and P min are the maximum and minimum values of the output of the new - energy power station; P i is the output data of the i th new - energy power station after filtering out the zero - output data segment but before normalization; here i is used to represent the new - energy output data number;

[0083] Use the Pearson correlation coefficient to quantify the correlation degree of the output of the new - energy power station. The calculation formula is as follows:

[0084] (4)

[0085] Wherein: n is the number of data for calculating the correlation of the output of the new - energy power station; P 1,i and P 2,i are the i th output data of power station 1 and power station 2 after normalization by formula (3); P 1,mean and P 2,mean are the average values of the outputs of power station 1 and power station 2 after normalization by formula (3); r is the Pearson correlation coefficient; here i is used to represent the new - energy output data number;

[0086] Set the time period for evaluating the spatio-temporal correlation, and obtain the output range of the station to be evaluated within this time period ;

[0087] In the formula: and are respectively the lower and upper bounds of the output of the i th station after normalization by Equation (3); i represents the new energy station number. For example, when i = 1, 2, it represents Station 1 and Station 2.

[0088] Construct the correlation coefficient matrix to describe the above interval variables ρ , and the calculation formula is as follows:

[0089] (5)

[0090] In the formula: and are respectively the output correlation coefficients of Station 1 and Station 2, and there is ;

[0091] Then, introduce PM to transform the output interval envelope with statistical correlation into an uncertainty characterization model that can perform affine operations. The calculation formula is as follows:

[0092] (6)

[0093] In the formula: P 1 w , P 2 w are respectively the interval widths of the outputs of Station 1 and Station 2 after normalization by Equation (3), P w is an example of the calculation of the interval width of the station output; P 1 c , P 2 c are respectively the interval center values of the outputs of Station 1 and Station 2 after normalization by Equation (3), P c is an example of the calculation of the interval center value of the station output; w 1 , w 2 are respectively the sum of the absolute values of the elements in the row corresponding to Station 1 and the sum of the absolute values of the elements in the row corresponding to Station 2 in the correlation coefficient matrix ρ , w i refers to when iWhen taking 1 or 2, it represents that of substation 1 w 1 or that of substation 2 w 2 ; R 、 D c 、 T respectively from P 1 w ( P 2 w )、 P 1 c ( P 2 c )、 w 1 ( w 2 )constitute the diagonal matrix; e is the unit vector; here i , j both represent the new energy substation numbers;

[0094] Finally, combine the interval variable with the affine number to form an affine number form representing the spatio-temporal correlation of substation output. The calculation formula is as follows:

[0095] (7)

[0096] In the formula: ε 1 and ε 2 are noise elements, and ; here i , j both represent the new energy substation numbers.

[0097] 2 Classical holomorphic embedding affine power flow calculation model considering spatio-temporal correlation of new energy substations

[0098] (1)PQ node classical holomorphic embedding affine model:

[0099] Regard the new energy substation as the PQ node of the system, and establish the classical holomorphic embedding affine model of the PQ node. The calculation formula is as follows:

[0100] (8)

[0101] In the formula: is the affine variable identifier; Y ik is the element in the i th row and k th column of the node admittance matrix; s is the holomorphic embedding factor, representing the system load level; is the holomorphic embedding affine form of the voltage of the node k ; is the affine form of the complex power injected into the node i ; " " represents the conjugate operation N represents the total number of system nodes; here i and k both represent the node numbers

[0102] In equation (8), can be expanded into an affine Maclaurin series form with respect to the embedding factor s and is analytic with respect to s as shown in the following equation

[0103] (9)

[0104] In the equation n is the order of the Maclaurin expansion is the i -th n order affine power series coefficient of the voltage of the node is the central value of the i -th n order affine power series coefficient of the voltage of the node is the j -th noise element is the i -th n order affine power series coefficient of the voltage of the node j -th noise element coefficient M is the total number of noise elements s n is the s -th power of the embedding factor n ; here i represents the node number j represents the noise element number

[0105] (2) Classical holomorphic embedding affine model for PV nodes

[0106] Establish the classical holomorphic embedding affine model for PV nodes, and the calculation formula is as follows

[0107] (10)

[0108] In the equation is the affine form of the active power injected into the node i ; is the holomorphic embedding affine form of the reactive power injected into the node i ; is the iSetting value of voltage amplitude; here i and k both represent node numbers;

[0109] (3) Slack node classical holomorphic embedding affine model:

[0110] Establish the classical holomorphic embedding affine model of the slack node, and the calculation formula is as follows:

[0111] (11)

[0112] In the formula: is the setting value of the slack node voltage; slack is the slack node identifier; i represents the node number.

[0113] 3 Solving the classical holomorphic embedding affine power flow model

[0114] (1) Solving the initial solution of the classical holomorphic embedding affine power flow

[0115] The initial solution is obtained by assuming zero injection at PQ nodes, no load and no active power generation at PV nodes, and adjusting the reactive power injection of PV nodes to meet the given voltage amplitude. The initial value solution can also be calculated by performing a holomorphic embedding calculation on the initial value, and its calculation formula is as follows:

[0116] (12)

[0117] In the formula: is the voltage of the PV node when there is no load and no power generation at the other nodes except the slack node; at the same time, the holomorphic embedding initial value of the PQ node voltage can be calculated ; the initial reactive power value of the holomorphic embedding of the PV node is set to 0. It should be noted that here and and and have different meanings. The former represents the coefficient of the 0th-order affine power series corresponding to the power series order n taking 0, while the latter is the holomorphic embedding affine power series expansion form in the form of formula (9); here i and k both represent node numbers.

[0118] (2) Constructing the recursive relation of the power series coefficients of each node type

[0119] Define the reciprocal of the affine node voltage

[0120] ; combined with and being reciprocal to each other, let The coefficients of the affine power series on both sides of the equation are equal, and the following can be deduced The expression of

[0121] is as follows: (13)

[0122] In the formula: Introduce l aiming to satisfy The result of multiplying by n is that the order of the affine power series is

[0123] For the PQ bus, expand Equation (8) into the form of Maclaurin series, and make the coefficients of the affine power series of the same order on both sides of the equation equal, then the recursive relationship for solving Equation (8) can be obtained:

[0124] (14)

[0125] For the PV bus, expand Equation (10) into the form of Maclaurin series, and make the coefficients of the affine power series of the same order on both sides of the equation equal, then the recursive relationship for solving Equation (10) can be obtained:

[0126] (15)

[0127] In the formula: δ n0 is the step function. When the power series is of order 0, δ n0 = 1; otherwise, δ n0 = 0; The subscripts of Equations (14)-(15) i , k both represent the bus numbers;

[0128] For the slack bus, expand Equation (11) into the form of Maclaurin series, and make the coefficients of the affine power series of the same order on both sides of the equation equal, then the recursive relationship for solving Equation (11) can be obtained:

[0129] (16)

[0130] In the formula: i represents the bus number.

[0131] (3) Construct the recursive relationship matrix of the affine power series coefficients

[0132] Let , Decompose Equations (14)-(16) into real and imaginary parts, and the following can be obtained:

[0133] (17)

[0134] (18)

[0135] (19)

[0136] (20)

[0137] In the formula: the subscripts im and re respectively represent the imaginary part and the real part of the variable; the subscripts of formulas (17)-(20) i , k both represent the node numbers.

[0138] Further, formulas (17)-(20) can be arranged into the form of a system of linear algebraic equations:

[0139] (21)

[0140] In the formula: H is a highly sparse recursive relation matrix, which only depends on the network parameters and node types and is a constant; is the n order affine power series coefficient vector to be solved, is the affine power series coefficient whose order is not greater than n order and can be obtained through the previous recursion n order - 1 affine power series coefficient.

[0141] (4) Judge whether the classical holomorphic embedding affine power flow calculation satisfies the convergence condition

[0142] (22)

[0143] In the formula: is the i th order affine power series coefficient of the k th state variable; represents taking the maximum value for all quantities to be solved; sup and inf respectively represent taking the upper bound and the lower bound after converting the affine number into an interval number; ε tol represents the convergence accuracy of the power flow calculation;

[0144] If formula (22) is not satisfied, then let k = k + 1, and repeat the calculation of formula (21) until the convergence accuracy of the power flow calculation is satisfied.

[0145] (5) Padé approximation analytic continuation

[0146] Perform Padé approximation analytic continuation on the affine power series coefficients calculated above to obtain the classical holomorphic embedding affine power flow solution. The calculation formula for Padé approximation analytic continuation is as follows:

[0147] LetL + H = k

[0148] (23)

[0149] Wherein: L is defined as the order of the molecular polynomial, H is defined as the order of the denominator polynomial, usually taking the diagonal ( L = H ) or approximately diagonal (| L - H | = 1); when the coefficients of each order of affine power series have been obtained in the case, the matrix equation for obtaining the coefficients of , can be constructed from Equation (23), and the matrix equation can be constructed by successively using the coefficients of each noise element of the affine power series; the subscripts L and H respectively represent the power series coefficients corresponding to the L-order power series and the H-order power series.

[0150] Equation (23) can be expressed as an analytical expression of affine power flow considering the correlation of new energy power stations. By substituting the holomorphic embedding factor s characterizing different load levels, the power flow state variables under different operating scenarios can be quickly calculated.

[0151] (6) Evaluate the impact of uncertainty factors on state variables

[0152] According to the calculation results of the classical holomorphic embedding affine power flow, by comparing the magnitudes of the noise element coefficients corresponding to different noise elements, the accurate assessment of the impact degree of uncertainty factors on the power flow state variables can be realized. Thus, the calculation of the classical holomorphic embedding affine power flow considering the spatio-temporal correlation of new energy power stations is completed.

[0153] Based on the same inventive concept, the present invention further provides a computer device, which includes: one or more processors, and a memory for storing one or more computer programs; the program includes program instructions, and the processor is configured to execute the program instructions stored in the memory. The processor may be a Central Processing Unit (CPU), or may also be 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 core and control core of the terminal, and is used to implement one or more instructions. Specifically, it is used to load and execute one or more instructions in the computer storage medium to implement the above method.

[0154] It should be further noted that, based on the same inventive concept, the present invention further provides a computer storage medium, on which a computer program is stored, and the computer program executes the above method when run by a processor. The storage medium may adopt any combination of one or more computer-readable media. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electrical, magnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (non-exhaustive list) of the computer-readable storage medium include: an electrical connection having one or more wires, a portable computer disk, a hard disk, a Random Access Memory (RAM), a Read-Only Memory (ROM), an Erasable Programmable Read-Only Memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, the computer-readable storage medium may be any tangible medium that contains or stores a program, and the program can be used by or combined with an instruction execution system, apparatus, or device.

[0155] It should be noted that unless otherwise defined, the technical terms or scientific terms used in the present invention should have the ordinary meanings understood by those with ordinary skills in the field to which the present invention pertains. The "first", "second" and similar terms used in the present invention do not denote any order, quantity or importance, but are only used to distinguish different components. Words such as "including" or "comprising" mean that the elements or objects appearing before this word cover the elements or objects listed after this word and their equivalents, without excluding other elements or objects. Words such as "connected" or "linked" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right", etc. are only used to indicate relative position relationships. When the absolute position of the object being described changes, the relative position relationship may also change accordingly.

[0156] The above are only the preferred embodiments of the present invention, and are not intended to limit the present invention in any other form. Any person skilled in the art may use the technical content disclosed above to make changes or modifications into equivalent embodiments with equivalent changes. However, any simple modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the technical solution content of the present invention still fall within the protection scope of the technical solution of the present invention.

[0157] The present invention is not limited to the above best mode. Anyone inspired by the present invention can obtain various other forms of the holomorphic embedding affine current uncertainty quantification method for the spatio-temporal correlation of new energy power stations. All equal changes and modifications made according to the scope of the patent application of the present invention shall fall within the scope covered by the present invention.

Claims

1. A holomorphic embedding affine power flow uncertainty quantification method for the spatio-temporal correlation of new energy power stations, characterized in that It includes the following steps: Obtain the historical output data of multiple new energy power stations, eliminate the zero-output data segments through sliding window analysis, and normalize the output data to obtain a standardized output sequence; Based on the standardized output sequence, calculate the spatio-temporal correlation coefficient matrix between power stations, and use the parallelogram model to transform the new energy output interval envelope into an affine number form through matrix multiplication to generate an affine output model representing spatio-temporal correlation; Based on the affine output model and the system network structure parameters, construct the classical holomorphic embedding equations for PQ nodes, PV nodes, and balance nodes respectively, expand the equations into the form of Maclaurin series, and extract the affine power series coefficients of each order; According to the affine power series coefficients, construct a constant recurrence relation matrix related to the network topology and node types, judge whether the power flow accuracy meets the convergence condition through iterative calculation, and then perform analytic continuation on the Maclaurin series through Padé approximation technology to obtain the affine power flow solutions under different load levels; Based on the noise element coefficients in the affine power flow solutions, quantify the influence degree of the output uncertainty of each new energy power station on the system state variables; The parallelogram model generates the affine number form in the following way: Decompose the new energy output interval envelope into an interval center value matrix, an interval width matrix, and a correlation coefficient matrix; Combine the interval width matrix with a weight matrix whose diagonal elements are the reciprocals of the sum of the absolute values of the correlation coefficients through matrix multiplication, then cascade with the correlation coefficient matrix, and finally linearly combine with the noise element vector; Each element in the noise element vector independently represents the output uncertainty of the power station itself or between power stations; The classical holomorphic embedding equation considering the spatio-temporal correlation of new energy satisfies the following conditions: Rewrite the power flow equations of each node type into a holomorphic embedding affine power flow calculation model, and make the affine power series coefficients of the same order on both sides of the equal sign equal; The equation extracts the affine power series coefficients of each order through Maclaurin expansion, and the coefficients include the center value term and the noise element coefficient term; The construction method of the constant recurrence relation matrix includes: Decompose the real and imaginary parts of the Maclaurin series into linear algebraic equations; Generate a sparse recursive matrix based on the real and imaginary part elements of the network admittance matrix and the node types; The analytic continuation satisfies the following conditions: Construct the numerator and denominator polynomial coefficients by independently processing the affine power series coefficients of each noise element; The selection of the numerator order and the denominator order satisfies the constraint relationship with the Maclaurin expansion order; The analytic continuation process cooperates with the constant recurrence relation matrix to realize a fully linear operation mechanism.

2. The holomorphic embedding affine power flow uncertainty quantification method for the spatio-temporal correlation of new energy power stations according to claim 1, wherein: The method for eliminating zero-output data segments in the sliding window analysis includes: Set the window length and the sliding step size, and calculate the output mean value within the window; If the window output mean value is lower than the preset threshold, determine that the window is a zero-output data segment and filter it out.

3. The holomorphic embedding affine power flow uncertainty quantification method for the spatio-temporal correlation of new energy power stations according to claim 1, wherein: The spatio-temporal correlation coefficient matrix is generated through the following steps: Calculate the Pearson correlation coefficient of the output data of pairwise new energy power stations to form a correlation coefficient matrix; The correlation coefficient is used to quantify the spatio-temporal correlation intensity of the power station output fluctuations.

4. The holomorphic embedding affine power flow uncertainty quantification method for spatio-temporal correlation of new energy power stations according to claim 1, characterized in that: The method for quantifying the noise element coefficient includes: comparing the absolute values of each noise element coefficient to identify the dominant uncertainty source.

5. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein when the processor executes the program, the steps of the method according to any one of claims 1-4 are implemented.

6. A non-transitory computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the method according to any one of claims 1-4 are implemented.

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