Full-pure embedded affine power flow uncertainty quantification method for new energy station space-time correlation

Abstract

The invention provides a new energy station spatio-temporal correlation full-pure embedded affine power flow uncertainty quantification method, which comprises the following steps: acquiring historical output data of a plurality of new energy stations, and eliminating zero output data segments through sliding window analysis to obtain a standardized output sequence; based on the standardized output sequence, the parallelogram model is utilized to convert the new energy output interval envelope into an affine number form through matrix multiplication, and an affine output model representing spatial-temporal correlation is generated; constructing ancient typical full-pure embedding equations of PQ nodes, PV nodes and balance nodes respectively, and extracting affine power series coefficients of all orders; constructing a constant recursion relation matrix related to network topology and node types according to the affine power series coefficient, and performing analysis and continuation on the Maclaurin series through Pade approximation technology to obtain affine power flow solutions under different load levels; and quantifying the influence degree of the output uncertainty of each new energy station on the system state quantity based on the noise element coefficient in the affine power flow solution.

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: 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.

[0005] Construction of classical holomorphic embedded equations: Holomorphic embedded affine equations for PQ nodes (Formula 8), PV nodes (Formula 10) and balance nodes (Formula 11) are established respectively, and the coefficients of affine power series of various orders are extracted through Maclaurin series expansion (Formula 9), which supports analytical solution of multi-load level scenarios.

[0006] Constant recursive relationship matrix: Based on the network admittance matrix and node type (Formula 14-21), a sparse recursive matrix is ​​generated to avoid repeated calculation of the Jacobian matrix and improve calculation efficiency.

[0007] Padé approximate analytical extension: The coefficients of the affine power series are approximated as fractions (Formula 23), and the coefficients of each noise element are processed independently to achieve a fully linear operation mechanism for nonlinear problems.

[0008] Noise element quantification and data preprocessing: Zero output interference is eliminated through sliding window detection (Formula 1-2) and normalization processing (Formula 3), and the dominant uncertainty source is located by comparing the absolute values ​​of the noise element coefficients.

[0009] Through the above-mentioned technical means, this solution significantly optimizes the calculation efficiency while ensuring the accuracy of the model, providing reliable support for the safe and stable analysis of high-proportion new energy power systems.

[0010] The technical solution specifically adopted by the present invention to solve the technical problem is: A method for quantifying uncertainty of holomorphic embedded affine power flow based on spatiotemporal correlation of new energy stations includes the following steps: Obtain historical output data of multiple new energy stations, remove 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, the spatiotemporal correlation coefficient matrix between stations is calculated, and the envelope of the new energy output interval is converted into an affine number form by matrix multiplication using a parallelogram model to generate an affine output model that represents the spatiotemporal correlation; Based on the affine output model and system network structure parameters, classical holomorphic embedding equations of PQ nodes, PV nodes and balance nodes are constructed respectively, the equations are expanded into Maclaurin series form and the coefficients of affine power series of each order are extracted; According to the affine power series coefficients, a constant recursive relationship matrix related to the network topology and node type is constructed, and iterative calculation is performed to determine whether the power flow accuracy meets the convergence condition, and then the Maclaurin series is analytically extended by the Padé approximation technology to obtain the affine power flow solution under different load levels; Based on the noise element coefficient in the affine power flow solution, the influence of the output uncertainty of each renewable energy station on the system state quantity is quantified.

[0011] Furthermore, the method for eliminating zero-output data segments in the sliding window analysis includes: Set the window length and sliding step size, and calculate the output mean within the window; If the window output mean is lower than the preset threshold, the window is determined to be a zero output data segment and filtered out.

[0012] Further, 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 output fluctuations of the power stations.

[0013] Further, the parallelogram model generates an affine number form in the following manner: 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 a noise element vector; Each element in the noise element vector independently represents the output uncertainty of the power station itself or between power stations.

[0014] Further, 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 same-order affine membrane technology systems at both ends of the equal sign equal; Extract the coefficients of each order of affine power series from the equation through Maclaurin expansion, and the coefficients include a central value term and a noise element coefficient term.

[0015] Further, the construction method of the constant recurrence relation matrix includes: Decompose the real and imaginary parts of the Maclaurin series into a system of linear algebraic equations; Generate a sparse recurrence 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.

[0016] Further, the Padé approximation 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 full linear operation mechanism to reduce the calculation complexity.

[0017] Further, the method for quantifying the noise element coefficients includes: comparing the absolute values of the coefficients of each noise element to identify the dominant uncertainty source.

[0018] In addition, an electronic device includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the steps of the above-mentioned method are implemented.

[0019] A non-transitory computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned method are implemented.

[0020] Compared with the prior art, the present invention and its preferred solutions at least include the following beneficial effects: Correcting the defects in spatio-temporal correlation modeling: By combining the parallelogram model (PM) with matrix concatenation operations of the correlation coefficient matrix, the assumption of the independence of new energy power station output in traditional affine power flow is broken through, the "interval explosion" problem is effectively suppressed, and the accuracy of the model in depicting actual operating conditions is improved.

[0021] Improving calculation efficiency and adaptability: Based on the classical holomorphic embedding equation, a constant recurrence relation matrix is constructed to avoid the calculation burden of repeatedly updating the Jacobian matrix in traditional methods due to changes in load levels, significantly reducing the complexity of high-dimensional affine operations and enhancing the adaptability of multi-scenario analysis.

[0022] Efficient analysis of non-linear problems: Using the Padé approximation technique for independent processing of noise elements, the non-linear power flow equation is transformed into a fully linear operation mechanism, realizing fast analytical continuation of affine solutions under different load levels, and at the same time supporting accurate separation and quantification of uncertainty propagation paths.

[0023] Data preprocessing and noise element quantization optimization: By using sliding window detection and normalization processing to eliminate zero-output interference, combined with the mechanism of comparing the absolute values of noise element coefficients, the dominant uncertainty sources are accurately located, providing a reliable data basis for the stability analysis of new energy power systems.

[0024] 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 high proportion of new energy grid connection. Description of the Drawings

[0025] The following further details the present invention in conjunction with the drawings and specific embodiments: Figure 1 It is a flow chart of the holomorphic embedding affine power flow analysis and uncertainty quantification method for spatio-temporal correlation of new energy power stations in an embodiment of the present invention. Specific Embodiments

[0026] To make the features and advantages of the present invention more obvious and understandable, specific embodiments are hereinafter given and described in detail as follows: It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used in this specification have the same meanings as those commonly understood by those skilled in the art to which the present application belongs.

[0027] 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 form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0028] 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.

[0029] The technical points of the solution implementation include: (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; (2) Establishing a classical holomorphic embedded affine power flow calculation model for each node type; (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; (4) Convert the uncertain affine power flow solution problem into solving the coefficients of each order of the affine power series of the Maclaurin expansion, and summarize it into a recurrence relation matrix; (5) Use the Padé approximation technique to establish an analytical expression of the power flow state quantity in affine form, and transform the complex nonlinear problem into a form that can be solved analytically; (6) By comparing the magnitudes of the coefficients of different noise elements, accurately quantify the degree of influence of the state quantity by uncertain factors.

[0030] As Figure 1 shown, the typical workflow implemented in its calculation process is as follows: Preprocess the historical output data of the new energy power station.

[0031] Use PM to construct an affine model to characterize the spatio-temporal correlation.

[0032] Input the network structure and parameters of the system, and set the uncertain quantities.

[0033] Obtain the k-th order affine power series coefficients through formula (21).

[0034] Does it meet the power flow calculation accuracy of formula (22)? No (N): If not satisfied, let k = k + 1, and return for continued iteration.

[0035] Yes (Y): If satisfied, proceed to the next step.

[0036] Solve the Padé coefficients required for the analytical expression of the affine power flow.

[0037] Substitute the given load level s into the analytical expression of the affine power flow in formula (23) to calculate the affine solution of the state quantity.

[0038] The following is a specific introduction to the solution of the embodiment of the present invention: 1 Affine model considering spatio-temporal correlation of new energy power stations First, obtain the historical output data of new energy power stations with adjacent geographical locations; Second, 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: (1) Where: 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 iNumbers used to represent new energy output data within a window, j Numbers used to represent the window number.

[0039] Judge whether the average value within the window is lower than the threshold. If the average 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: (2) In the formula: 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; At the same time, filter out the zero-output data segments of new energy power stations participating in the spatio-temporal correlation calculation, and ensure that each output data strictly matches the measured timestamp; Perform maximum-minimum normalization on the output data of the new energy power station after the above processing to eliminate the influence of different dimensions. The calculation formula is as follows: (3) In the formula: 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 number of new energy output data; Use the Pearson correlation coefficient to quantify the correlation degree of the output of new energy power stations. The calculation formula is as follows: (4) In the formula: n Is the number of data used to calculate the correlation of the output of new energy power stations; P 1,i And P 2,i Are the output data of the i th 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 iUsed to represent the new energy output data number; Set the time period for evaluating the spatio-temporal correlation, and take the output interval of the station to be evaluated within this time period ; In the formula: and are respectively the lower and upper bounds of the output after normalization of the i th station by formula (3); i Represents the new energy station number. For example, i = 1, 2 means station 1 and station 2.

[0040] Construct the correlation coefficient matrix describing the above interval variables ρ , and the calculation formula is as follows: (5) In the formula: and are respectively the output correlation coefficients of station 1 and station 2, and there is ; 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: (6) 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 formula (3), P w is an example of calculating 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 formula (3), P c is an example of calculating 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 i takes 1 or 2 to represent w 1 of station 1 or w2 ; R 、 D c 、 T are respectively diagonal matrices composed of P 1 w ( P 2 w ), P 1 c ( P 2 c ), w 1 ( w 2 ); e is a unit vector; here, i , j both represent the new - energy power - station numbers; Finally, combine the interval variable with the affine number to form an affine - number form representing the spatio - temporal correlation of the power - station output. The calculation formula is as follows: (7) In the formula: ε 1 and ε 2 are noise elements, and ; here, i , j both represent the new - energy power - station numbers.

[0041] 2 Holomorphic Embedding Affine Power - Flow Calculation Model Considering the Spatio - temporal Correlation of New - energy Power Stations (1) PQ - node holomorphic embedding affine model: Regard the new - energy power station as a PQ - node of the system, and establish a holomorphic embedding affine model for the PQ - node. The calculation formula is as follows: (8) In the formula: is the affine - variable identifier; Y ik is the element in the i th row and k th column of the nodal admittance matrix; s is the holomorphic - embedding factor, representing the system load level; is the holomorphic - embedding affine form of the voltage at node k ; is the affine form of the complex power injected at node i ; " " represents the conjugate operation; NIndicates the total number of system nodes; herein i and k both indicate the node number; In formula (8), can be expanded into an affine-type Maclaurin series form with respect to the embedding factor s and is analytic for s as shown in the following formula: (9) In the formula: n is the Maclaurin expansion order; is the i th n order affine power series coefficient of the node voltage; i is the n th order affine power series coefficient central value of the node voltage; j is the th i noise element; n is the j th noise element coefficient of the M th order affine power series coefficient of the node s n is the embedding factor s to the n power; here i represents the node number, j represents the noise element number; (2) PV node classical holomorphic embedding affine model: Establish the classical holomorphic embedding affine model of the PV node, and the calculation formula is as follows: (10) In the formula: is the affine form of the active power injected by the node i ; is the holomorphic embedding affine form of the reactive power injected by the node i ; is the set value of the voltage amplitude of the node i ; herein i and k both represent the node number; (3) Slack node classical holomorphic embedding affine model: Establish the classical holomorphic embedding affine model of the slack node, and the calculation formula is as follows: (11) In the formula: is the set value of the slack node voltage; slack is the slack node identifier; iIndicates the node number.

[0042] 3 Solving the classical holomorphic embedded affine power flow model

[0043] (1) Solving the initial solution of the classical holomorphic embedded affine power flow 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 at PV nodes to meet the given voltage magnitude. 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: (12) In the formula: is the voltage of the PV node when there is no load and no generation at the remaining nodes except the slack node; at the same time, the initial value of the holomorphic embedding of the voltage of the PQ node 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 , have different meanings. The former represents the 0th-order affine power series coefficient corresponding to when the power series order n takes 0, while the latter is in the form of a holomorphic embedded affine power series expansion as shown in Equation (9); here i , k both represent the node number.

[0044] (2) Constructing the recursive relation of the power series coefficients of each node type Define the reciprocal of the affine node voltage ; combined with , being reciprocal to each other, let the affine power series coefficients on both sides of the equation be equal, and the expression of can be deduced as follows: (13) In the formula: Introduce l to satisfy multiplied by results in an affine power series order of n .

[0045] For PQ nodes, expand Equation (8) into the Maclaurin series form and make the affine power series coefficients of the same order on both sides of the equation equal to obtain the recursive relation for solving Equation (8): (14) For PV nodes, 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: (15) where: 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 node numbers; For swing nodes, 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: (16) where: i represents the node number.

[0046] (3) Construct the recursive relationship matrix of the affine power series coefficients Let , Decompose Equations (14)-(16) into real and imaginary parts, and the following can be obtained: (17) (18) (19) (20) where: the subscripts im and re represent the imaginary and real parts of the variable respectively; the subscripts of Equations (17)-(20) i , k both represent the node numbers.

[0047] Furthermore, Equations (17)-(20) can be arranged into the form of a system of linear algebraic equations: (21) where: H is a highly sparse recursive relationship matrix, which only depends on the network parameters and node types and is a constant; is the vector of the n order affine power series coefficients to be solved, is the affine power series coefficient whose order is not greater than n order, which can be obtained through the previous recursion n the affine power series coefficient of order -1.

[0048] (4) Determine whether the classical holomorphic embedding affine power flow calculation satisfies the convergence condition (22) where: is the i -th order affine power series coefficient of the k -th state variable; represents taking the maximum value over all quantities to be solved; sup and inf respectively represent taking the upper bound and lower bound after converting the affine number into an interval number; ε tol represents the convergence accuracy of the power flow calculation; If the equation (22) is not satisfied, then let k = k + 1, and repeat the calculation of equation (21) until the convergence accuracy of the power flow calculation is satisfied.

[0049] (5) Padé approximation analytic continuation Perform Padé approximation analytic continuation on the above calculated affine power series coefficients to obtain the classical holomorphic embedding affine power flow solution. The Padé approximation analytic continuation calculation formula is as follows: Let L + H = k (23) where: L is interpreted as the order of the numerator polynomial, H is interpreted as the order of the denominator polynomial, usually taking the diagonal ( L = H ) or approximately diagonal (| L - H | = 1); in the case where the affine power series coefficients of each order have been obtained, the matrix equations for obtaining , coefficients can be constructed from equation (23), where the matrix equation can be constructed successively from the respective noise element coefficients of the affine power series; the subscripts L and H respectively represent the power series coefficients corresponding to the L -th order power series and the H -th order power series.

[0050] Equation (23) can be expressed as an affine power flow analytic expression considering the correlation of new energy power stations. By substituting the holomorphic embedding factor s representing different load levels, the power flow state variables under different operating scenarios can be quickly calculated.

[0051] (6) Evaluate the impact of uncertainty factors on the state variables 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, an accurate assessment of the influence degree of uncertainty factors on the power flow state variables can be achieved. Thus, the calculation of the classical holomorphic embedding affine power flow considering the spatio-temporal correlation of new energy power stations is completed.

[0052] 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 used 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.

[0053] 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, when run by a processor, executes the above method. 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 may be used by or in combination with an instruction execution system, apparatus, or device.

[0054] 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 belongs. The "first", "second" and similar words used in the present invention do not indicate 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 the word cover the elements or objects listed after the 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. "Upper", "lower", "left", "right", etc. are only used to represent relative position relationships. When the absolute position of the object being described changes, the relative position relationship may also change accordingly.

[0055] As described above, it is only the preferred embodiment of the present invention, and it is not a limitation of the present invention in other forms. 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 modification, equivalent change and modification 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 belong to the protection scope of the technical solution of the present invention.

[0056] The present invention is not limited to the above best implementation 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 coverage scope of the present invention.

Claims

1. A method for quantifying uncertainty of holomorphic embedding affine power flow based on spatiotemporal correlation of new energy stations, characterized by: The following steps are involved: Obtain historical output data of multiple new energy stations, remove 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, the spatiotemporal correlation coefficient matrix between stations is calculated, and the envelope of the new energy output interval is converted into an affine number form by matrix multiplication using a parallelogram model to generate an affine output model that represents the spatiotemporal correlation; Based on the affine output model and system network structure parameters, classical holomorphic embedding equations of PQ nodes, PV nodes and balance nodes are constructed respectively, the equations are expanded into Maclaurin series form and the coefficients of affine power series of each order are extracted; According to the affine power series coefficients, a constant recursive relationship matrix related to the network topology and node type is constructed, and iterative calculation is performed to determine whether the power flow accuracy meets the convergence condition, and then the Maclaurin series is analytically extended by the Padé approximation technology to obtain the affine power flow solution under different load levels; Based on the noise element coefficient in the affine power flow solution, the influence of the output uncertainty of each renewable energy station on the system state quantity is quantified.

2. The method for quantifying uncertainty of a holomorphic embedded affine power flow based on spatiotemporal correlation of a new energy station according to claim 1 is characterized by: The method for eliminating zero-output data segments in the sliding window analysis includes: Set the window length and sliding step size, and calculate the output mean within the window; If the window output mean is lower than the preset threshold, the window is determined to be a zero output data segment and filtered out.

3. The method for quantifying uncertainty of a holomorphic embedded affine power flow based on spatiotemporal correlation of a new energy station according to claim 1 is characterized by: The spatiotemporal correlation coefficient matrix is ​​generated by the following steps: Calculate the Pearson correlation coefficient of the output data of each pair of new energy stations to form a correlation coefficient matrix; The correlation coefficient is used to quantify the temporal and spatial correlation strength of station output fluctuations.

4. The method for quantifying uncertainty of a holomorphic embedded affine power flow based on spatiotemporal correlation of a new energy station according to claim 1 is characterized by: The parallelogram model is generated in the following way: Decompose the envelope of the new energy output interval into an interval center value matrix, an interval width matrix and a correlation coefficient matrix; The interval width matrix is ​​combined with a weight matrix whose diagonal elements are the inverse of the sum of the absolute values ​​of the correlation coefficients by matrix multiplication, and then cascaded with the correlation coefficient matrix, and finally linearly combined with the noise element vector; Each element in the noise element vector independently represents the output uncertainty of the station itself or between stations.

5. The method for quantifying uncertainty of a holomorphic embedded affine power flow based on spatiotemporal correlation of a new energy station according to claim 1 is characterized by: The classical holomorphic embedding equation considering the spatiotemporal correlation of new energy satisfies the following conditions: The power flow equation of each node type is rewritten into a holomorphic embedded affine power flow calculation model, and the coefficients of the affine power series of the same order on both sides of the equal sign are made equal; The equation is expanded by Maclaurin to extract coefficients of affine power series of various orders, wherein the coefficients include central value terms and noise element coefficient terms.

6. The method for quantifying uncertainty of holomorphic embedding affine power flow based on spatiotemporal correlation of new energy stations according to claim 1 is characterized by: The method for constructing the constant recursive relationship matrix includes: Decompose the real and imaginary parts of the Maclaurin series into a system of linear algebraic equations; Generate a sparse recursive matrix based on the real and imaginary elements of the network admittance matrix and the node type.

7. The method for quantifying uncertainty of holomorphic embedding affine power flow based on spatiotemporal correlation of new energy stations according to claim 1 is characterized by: The Padé approximate analytical continuation satisfies the following conditions: The numerator and denominator polynomial coefficients are constructed by processing the affine power series coefficients of each noise element independently; The selection of the numerator and denominator orders satisfies the constraint relationship with the Maclaurin expansion order; The analytical extension process cooperates with the constant recursive relationship matrix to realize a full linear operation mechanism.

8. The method for quantifying uncertainty of holomorphic embedding affine power flow based on spatiotemporal correlation of new energy stations according to claim 1 is characterized by: The noise element coefficient quantification method includes: comparing the absolute values ​​of each noise element coefficient and identifying the dominant uncertainty source.

9. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of any one of the methods of claims 1-8 when executing the program.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method according to any one of claims 1 to 8.

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