A method, device and medium for analyzing uncertainty of distributed power supply acceptance capacity

Abstract

This application provides a method, apparatus, and medium for analyzing the uncertainty of distributed generation (DG) capacity. It employs interval mathematics and affine mathematics to quantify the mapping relationship between multiple uncertainties and voltage sensitivity in DG distribution networks. This approach combines the advantages of interval methods (easy access to upper and lower bounds for uncertain variables, simple model, strong applicability, and fast solution speed) with the advantages of affine methods (advanced ability to track the correlation and coupling of multiple uncertain variables). By deriving an affine three-phase voltage sensitivity equation based on the Jacobian matrix and combining it with a Monte Carlo-based stochastic scenario simulation method, it achieves both accuracy and speed in analyzing DG capacity in distribution networks. It also provides the possibility for assisting distribution network planners in conducting online rapid analysis and comprehensive, accurate decision-making regarding DG capacity. Compared with existing DG capacity analysis methods, it exhibits better performance and has significant theoretical and practical value.

Description

Technical Field

[0001] The present application relates to the field of distribution network planning, and in particular to a method, device and medium for analyzing uncertainty in the acceptance capacity of distributed power sources. Background Art

[0002] Today, increasingly serious environmental pollution and energy crisis are constantly promoting the development and utilization of renewable energy such as solar energy and wind energy in countries around the world. In this context, a variety of renewable energy sources will be connected to the distribution network on a large scale in the form of distributed power sources. The power supply of distributed power sources has a certain degree of intermittency, which will cause many problems in the safe operation of the distribution network, posing a great challenge to the efficient acceptance and utilization of distributed power sources.

[0003] At present, domestic and foreign scholars have conducted many studies on the analysis methods of distributed acceptance capacity of distribution networks. The mathematical analysis methods mainly include analytical methods, intelligent optimization methods and random scenario simulation methods. The analysis of analytical methods and intelligent optimization methods is optimistic and has low accuracy, while the random scenario simulation method has the problem of too long calculation time. Finding a balance between the two to achieve both analysis accuracy and speed is a key issue to be studied.

[0004] Voltage sensitivity analysis provides an effective way to solve this problem because it can effectively quantify the analytical relationship between node voltage and injected power. As the uncertainty of intermittent sources and loads in the distribution network becomes more prominent, the applicable scenarios of voltage sensitivity deterministic analysis will be subject to certain restrictions.

[0005] At present, the commonly used uncertainty analysis methods mainly include probability method, robust method and interval method. For probability method, probability opportunity constrained programming is one of its typical applications. Some scholars have established a probability opportunity constrained programming model for photovoltaic limit access capacity with the qualified rate of operation indicators as the opportunity constraint, but only consider the single-point access situation of photovoltaic, and the scenario is relatively simple; some scholars have adopted two-stage linear transformation and double-layer probability optimization to improve the calculation efficiency. However, the disadvantage of probability method is that the inaccurate data samples cannot fully fit the probability density function (PDF) of distributed power output and load demand, which can easily lead to deviations in the analysis results. For this reason, some scholars have introduced robust method into the uncertainty analysis method of distributed power acceptance capacity. This method does not depend on the probability density function of random variables, and the calculation conservatism can be flexibly controlled by parameter adjustment, so it has better solution performance. However, the conservatism of robust method is usually only for the uncertainty of distributed power output and load demand, and it is difficult to quantify the potential uncertainty of distributed power configuration mode of distribution network. Therefore, the analysis results obtained by robust method are often optimistic and there is a certain deviation from the actual acceptance capacity. Summary of the invention

[0006] In order to solve the above technical problems or at least partially solve the above technical problems, the present application provides a method, device and medium for analyzing uncertainty in acceptance capacity of distributed power sources.

[0007] In a first aspect, the present application provides a method for analyzing uncertainty in acceptance capacity of distributed power sources, comprising:

[0008] According to the three-phase load Jacobian matrix, the three-phase voltage sensitivity equation of the node voltage amplitude change vector of each phase to the node active power injection power change vector and the node reactive power injection power change vector of the corresponding phase is obtained;

[0009] Affine mathematics is used to quantify the uncertainty of node injection power, and the node affine voltage amplitude change vector, node affine active injection power change vector, and node affine reactive injection power change vector of each phase are obtained. The central term and noise term of the affine polynomial of the node voltage amplitude change are further calculated and sorted out, that is, the affine three-phase voltage sensitivity equation is obtained;

[0010] A three-phase power flow calculation is performed once at the initial moment, and a three-phase voltage sensitivity coefficient matrix in an affine three-phase voltage sensitivity equation is obtained according to the result of the three-phase power flow calculation;

[0011] Based on Monte Carlo simulation, random scenarios of distributed power sources are simulated to construct the node affine active injection power change vector and node affine reactive injection power change vector corresponding to each scenario, and then the node affine voltage amplitude change vector of each scenario is calculated;

[0012] Based on the affine-interval mathematical transformation, the node affine voltage amplitude change vector is converted into interval form, and the maximum voltage amplitude interval value of the distributed power configuration scenario is calculated Then, the distributed power installation capacity P DG The horizontal axis is the maximum voltage amplitude interval value. Formed as a vertical axis Interval distribution relationship;

[0013] Will The overvoltage distributed power configuration scenarios in the interval distribution relationship are divided into the first and second scenarios. The width of the distributed power installation capacity interval is selected to calculate the overvoltage severity correction coefficients of the first and second scenarios.

[0014] The range of distribution network overvoltage risk is calculated by using the overvoltage severity correction coefficients of the first and second scenarios, the distribution network overvoltage risk threshold is selected considering planning requirements, and the range value of distributed generation acceptance capacity is calculated based on the distribution network overvoltage risk range and the distribution network overvoltage risk threshold.

[0015] Furthermore, according to the three-phase load Jacobian matrix shown in formula (1), the node voltage phase angle change vector of the corresponding phase is eliminated to obtain the three-phase voltage sensitivity equation of the node voltage amplitude change vector to the node active injection power change vector and the node reactive injection power change vector of the corresponding phase, as shown in formula (2):

[0016]

[0017] Where: for Phase Jacobian matrix; express Phase node voltage phase angle Sensitivity of phase active injection power, express Phase node voltage phase angle Sensitivity of phase reactive injection power; express Phase node voltage amplitude Sensitivity of phase active injection power, express Phase node voltage amplitude Sensitivity of phase reactive injection power; Representation Node Phase active injection power change vector, Representation Node Phase reactive injection power change vector; express Phase node voltage phase angle change vector, express Phase node voltage amplitude change vector; for right Influence Phase sensitivity coefficient matrix, for right Influence Phase sensitivity coefficient matrix, and That is, the three-phase voltage sensitivity coefficient matrix is ​​further calculated based on the results of the three-phase power flow calculation.

[0018] Furthermore, affine mathematics is used to quantify the uncertainty of node injection power, and the node affine voltage amplitude change vector, node affine active injection power change vector, and node affine reactive injection power change vector of each phase are obtained as shown in equations (3)-(5):

[0019]

[0020] Where: is the time t Phase node affine voltage amplitude change vector; is the time t Phase node affine active injection power change vector; is the time t Phase node affine reactive injection power change vector; is the node n at time t Phase-affine voltage amplitude change; and is the node n at time t Phase node affine active injection power variation; is the node n at time t Phase node affine reactive injection power variation; for Central terms of affine polynomials; for The mth noise coefficient of the affine polynomial; for The mth noise element of the affine polynomial, is the value injected by node n at time t The upper bound of the interval of affine active power variation, is the value injected by node n at time t The lower bound of the interval of phase-affinity active power variation; is the value injected by node n at time t The upper bound of the interval of affine reactive power variation, are respectively the injected The lower bound of the interval of phase-affine reactive power variation; and They are Affine polynomials and The noise element of the affine polynomial,

[0021] The central term and noise term of the node voltage amplitude variation affine polynomial shown in equations (6) and (7) are further calculated and the central term and noise term are combined to obtain the affine three-phase voltage sensitivity equation.

[0022]

[0023] Where: and The sensitivity coefficient matrices are and The i-th row submatrix of and They are and The element in the i-th row and j-th column of .

[0024] Furthermore, the random scenario simulation of distributed power supply is carried out based on Monte Carlo simulation, and the node affine active injection power change vector corresponding to each scenario is constructed. and node affine reactive injection power change vector Then, the node affine voltage amplitude change vector of each scene is calculated by equations (3)-(7):

[0025] Furthermore, based on the affine-interval mathematical transformation, the node affine voltage amplitude change vector is converted into Convert to the corresponding interval form The maximum voltage amplitude interval value of each distributed power configuration scenario is calculated by formula (9): Then, the distributed power installation capacity P DG The horizontal axis is the maximum voltage amplitude interval value. Formed as a vertical axis Interval distribution relationship;

[0026]

[0027] Where: is the node i at the initial time t0 Phase voltage amplitude.

[0028] Furthermore, the overvoltage severity correction coefficient of the first scenario is calculated by equations (10)-(17) and (18)-(21) respectively: and the overvoltage severity correction factor for the second scenario

[0029]

[0030]

[0031] Where: m max The total number of installed capacity intervals for distributed generation; is the total number of the first type of scenarios in the mth distributed generation installation capacity interval; is the severity of overvoltage of the first scenario k in the mth distributed generation installation capacity interval; and are decision variables with values ​​of 0 and 1, respectively indicating whether the first scenario k belongs to category A or category B in time period t; is the upper bound of the maximum voltage amplitude interval of the first type of scenario k in time period t, is the lower bound of the maximum voltage amplitude interval of the first type of scenario k in time period t; CΔV is the voltage change proportional coefficient; is the upper bound of the maximum voltage amplitude interval of the first type of scenario k, is the lower bound of the maximum voltage amplitude interval for the first type of scenario k, is the center value of the maximum voltage amplitude interval of the first type of scenario k;

[0032]

[0033] Where: is the overvoltage severity of overvoltage scenario k in the second type of scenario in the mth distributed generation installation capacity interval; is a 0-1 decision variable, indicating whether the second type of scenario k belongs to the B type in time period t; is the total number of the second type of scenarios in the mth distributed generation installation capacity interval; is the upper bound of the maximum voltage amplitude range of the second scenario k in time period t, It is the lower bound of the maximum voltage amplitude range of the second type scenario k in time period t.

[0034] Furthermore, based on the overvoltage severity correction coefficient of the first scenario and the overvoltage severity correction coefficient of the second scenario, the distribution network overvoltage risk interval value is calculated by formula (22), and the distribution network overvoltage risk threshold λ′ is selected considering the actual distributed power planning requirements. max , and then the distributed generation capacity interval is calculated by formula (23):

[0035]

[0036] Where: N m,total is the total number of distributed generation configuration scenarios in the mth distributed generation installation capacity interval; P DG,m is the lower bound of the distributed generation installation capacity interval; λ′ max is the overvoltage risk threshold of the distribution network; is the upper bound of the overvoltage risk interval of the distribution network, l m ' is the lower bound of the overvoltage risk interval of the distribution network; is the upper bound of the distributed generation capacity range, HC' It is the lower bound of the distributed generation capacity range.

[0037] In a second aspect, the present application provides a device for analyzing the uncertainty of distributed power supply acceptance capacity, comprising: a processing unit, a storage unit, an input unit, a display unit and a bus unit, wherein the bus unit connects the processing unit, the storage unit, the input unit and the display unit, the storage unit stores instructions and operands, and the processing unit executes instructions to process operands to implement the distributed power supply acceptance capacity uncertainty analysis method.

[0038] In a third aspect, the present application provides a storage medium for implementing a distributed power supply acceptance capacity uncertainty analysis method, wherein the storage medium for implementing a distributed power supply acceptance capacity uncertainty analysis method stores at least one instruction, and reads and executes the instruction to implement the distributed power supply acceptance capacity uncertainty analysis method.

[0039] The above technical solution provided by the embodiment of the present application has the following advantages compared with the prior art:

[0040] This application uses interval mathematics and affine mathematics to quantify the mapping relationship between multivariate uncertainty factors and voltage sensitivity in the distribution network of distributed power sources. It has the advantages of easy acquisition of upper and lower bounds of uncertainty variables of the interval method, simple model, strong applicability, and fast solution speed, and the advantages of the affine method in tracking the correlation and coupling of multivariate uncertain variables. By deriving the affine three-phase voltage sensitivity equation based on the Jacobian matrix and combining it with the random scenario simulation method based on Monte Carlo, the accuracy and speed of the distribution network distributed power acceptance capacity analysis are achieved. At the same time, it also provides the possibility for auxiliary distribution network planners to conduct online rapid analysis of distributed power acceptance capacity and comprehensive and accurate decision-making. Compared with the existing distributed power acceptance capacity analysis methods, it has better performance and significant theoretical value and practical significance. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0042] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0043] Figure 1 A flow chart of a method for analyzing uncertainty in the acceptance capacity of distributed power sources provided in an embodiment of the present application;

[0044] Figure 2 A schematic diagram of a device for analyzing uncertainty in the acceptance capacity of distributed power sources provided in an embodiment of the present application. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0046] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0047] Example 1

[0048] The embodiment of the present application provides a method for analyzing uncertainty in acceptance capacity of a distributed power supply, including:

[0049] According to the three-phase load Jacobian matrix, the three-phase voltage sensitivity equation of the node voltage amplitude change vector of each phase to the node active power injection power change vector and the node reactive power injection power change vector of the corresponding phase is obtained;

[0050] In the specific implementation process, according to the three-phase load Jacobian matrix shown in formula (1), the node voltage phase angle change vector of the corresponding phase is eliminated to obtain the three-phase voltage sensitivity equation of the node voltage amplitude change vector to the node active injection power change vector and the node reactive injection power change vector of the corresponding phase, as shown in formula (2):

[0051]

[0052] Where: for Phase Jacobian matrix; express Phase node voltage phase angle Sensitivity of phase active injection power, express Phase node voltage phase angle Sensitivity of phase reactive injection power; express Phase node voltage amplitude Sensitivity of phase active injection power, express Phase node voltage amplitude Sensitivity of phase reactive injection power; Representation Node Phase active injection power change vector, Representation Node Phase reactive injection power change vector; express Phase node voltage phase angle change vector, express Phase node voltage amplitude change vector; for right Influence Phase sensitivity coefficient matrix, for right Influence Phase sensitivity coefficient matrix.

[0053] Affine mathematics is used to quantify the uncertainty of node injection power, and the node affine voltage amplitude change vector, node affine active injection power change vector, and node affine reactive injection power change vector of each phase are obtained. The central term and noise term of the affine polynomial of the node voltage amplitude change are further calculated and sorted out, that is, the affine three-phase voltage sensitivity equation is obtained;

[0054] In the specific implementation process, affine mathematics is used to quantify the uncertainty of node injection power, and the node affine voltage amplitude change vector, node affine active injection power change vector, and node affine reactive injection power change vector of each phase are obtained as shown in equations (3)-(5):

[0055]

[0056]

[0057] Where: is the time t Phase node affine voltage amplitude change vector; is the time t Phase node affine active injection power change vector; is the time t Phase node affine reactive injection power change vector; is the node n at time t Phase-affine voltage amplitude change; is the node n at time t Phase node affine active injection power variation; is the node n at time t Phase node affine reactive injection power variation; for Central terms of affine polynomials; for The mth noise coefficient of the affine polynomial; for The mth noise element of the affine polynomial, is the value injected by node n at time t The upper bound of the interval of affine active power variation, is the value injected by node n at time t The lower bound of the interval of phase-affinity active power variation; is the value injected by node n at time t The upper bound of the interval of affine reactive power variation, are respectively the injected The lower bound of the interval of phase-affine reactive power variation; and They are Affine polynomials and The noise element of the affine polynomial,

[0058] The central term and noise term of the node voltage amplitude variation affine polynomial shown in equations (6) and (7) are further calculated and the central term and noise term are combined to obtain the affine three-phase voltage sensitivity equation.

[0059]

[0060]

[0061] Where: and The sensitivity coefficient matrices are and The i-th row submatrix of and They are and The element in the i-th row and j-th column of .

[0062] A three-phase power flow calculation is performed at the initial moment, and the three-phase voltage sensitivity coefficient matrix in the affine three-phase voltage sensitivity equation is obtained according to the result of the three-phase power flow calculation. and

[0063]

[0064] Based on Monte Carlo simulation, random scenarios of distributed power sources are simulated to construct the node affine active injection power change vector and node affine reactive injection power change vector corresponding to each scenario, and then the node affine voltage amplitude change vector of each scenario is calculated;

[0065] In the specific implementation process, the random scenario simulation of distributed power supply is carried out based on Monte Carlo simulation, and the node affine active injection power change vector corresponding to each scenario is constructed. and node affine reactive injection power change vector Then, the node affine voltage amplitude change vector of each scene is calculated by equations (3)-(7):

[0066] Based on the affine-interval mathematical transformation, the node affine voltage amplitude change vector is converted into interval form, and the maximum voltage amplitude interval value of the distributed power configuration scenario is calculated Then, the distributed power installation capacity P DG The horizontal axis is the maximum voltage amplitude interval value. Formed as a vertical axis Interval distribution relationship;

[0067] In the specific implementation process, based on the affine-interval mathematical transformation, the node affine voltage amplitude change vector is converted into Convert to the corresponding interval form The maximum voltage amplitude interval value of each distributed power configuration scenario is calculated by formula (9): Then, the distributed power installation capacity P DG The horizontal axis is the maximum voltage amplitude interval value. Formed as a vertical axis Interval distribution relationship;

[0068]

[0069] Where: is the node i at the initial time t0 Phase voltage amplitude.

[0070] Will The overvoltage distributed power configuration scenarios in the interval distribution relationship are divided into the first scenario and the second scenario. The width of the distributed power installation capacity interval is selected. A feasible way to select the capacity interval width is to select it according to 5% of the total load. The overvoltage severity correction coefficient of the first scenario and the second scenario is calculated;

[0071] In the specific implementation process, the overvoltage severity correction coefficient of the first type of scenario is calculated by equations (10)-(17) and (18)-(21) respectively: and the overvoltage severity correction factor for the second scenario

[0072]

[0073]

[0074] Where: m max The total number of installed capacity intervals for distributed generation; is the total number of the first type of scenarios in the mth distributed generation installation capacity interval; is the severity of overvoltage of the first scenario k in the mth distributed generation installation capacity interval; and are decision variables with values ​​of 0 and 1, respectively indicating whether the first scenario k belongs to category A or category B in time period t; is the upper bound of the maximum voltage amplitude interval of the first type of scenario k in time period t, is the lower bound of the maximum voltage amplitude interval of the first type of scenario k in time period t; C ΔV is the voltage change proportional coefficient; is the upper bound of the maximum voltage amplitude interval of the first type of scenario k, is the lower bound of the maximum voltage amplitude interval for the first type of scenario k, is the center value of the maximum voltage amplitude interval of the first type of scenario k;

[0075]

[0076] Where: is the overvoltage severity of overvoltage scenario k in the second type of scenario in the mth distributed generation installation capacity interval; is a 0-1 decision variable, indicating whether the second type of scenario k belongs to the B type in time period t; is the total number of the second type of scenarios in the mth distributed generation installation capacity interval; is the upper bound of the maximum voltage amplitude range of the second scenario k in time period t, It is the lower bound of the maximum voltage amplitude range of the second type scenario k in time period t.

[0077] The range of distribution network overvoltage risk is calculated by using the overvoltage severity correction coefficients of the first and second scenarios, the distribution network overvoltage risk threshold is selected considering planning requirements, and the range value of distributed generation acceptance capacity is calculated based on the distribution network overvoltage risk range and the distribution network overvoltage risk threshold.

[0078] In the specific implementation process, based on the overvoltage severity correction coefficient of the first scenario and the overvoltage severity correction coefficient of the second scenario, the distribution network overvoltage risk interval value is calculated by formula (22), and the distribution network overvoltage risk threshold λ′ is selected considering the actual distributed power planning requirements. max , and then the distributed generation capacity interval is calculated by formula (23):

[0079]

[0080] Where: N m,total is the total number of distributed generation configuration scenarios in the mth distributed generation installation capacity interval; P DG,m is the lower bound of the distributed generation installation capacity interval; λ′ max is the overvoltage risk threshold of the distribution network; is the upper bound of the overvoltage risk interval of the distribution network, l m ' is the lower bound of the overvoltage risk interval of the distribution network; is the upper bound of the distributed generation capacity range, HC' It is the lower bound of the distributed generation capacity range.

[0081] Example 2

[0082] An embodiment of the present application provides a device for analyzing the uncertainty of distributed power supply acceptance capacity, including: a processing unit, a storage unit, an input unit, a display unit and a bus unit, the bus unit connecting the processing unit, the storage unit, the input unit and the display unit, the storage unit storing instructions and operands, the processing unit executing instructions to process operands to implement the distributed power supply acceptance capacity uncertainty analysis method; the input unit function includes inputting operands of the distributed power supply acceptance capacity uncertainty analysis method, and the display unit function includes displaying the analysis results of the distributed power supply acceptance capacity uncertainty analysis method.

[0083] Example 3

[0084] An embodiment of the present application provides a storage medium for implementing a distributed power supply acceptance capacity uncertainty analysis method. The storage medium for implementing a distributed power supply acceptance capacity uncertainty analysis method stores at least one instruction, and the instruction is read and executed to implement the distributed power supply acceptance capacity uncertainty analysis method.

[0085] In the embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0086] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0087] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0088] The foregoing is merely a specific embodiment of the present invention, which enables those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A distributed power supply acceptance capacity uncertainty analysis method, characterized in that: include: According to the three-phase load Jacobian matrix, the three-phase voltage sensitivity equation of the node voltage amplitude change vector of each phase to the node active power injection power change vector and the node reactive power injection power change vector of the corresponding phase is obtained; Affine mathematics is used to quantify the uncertainty of node injection power, and the node affine voltage amplitude change vector, node affine active injection power change vector, and node affine reactive injection power change vector of each phase are obtained. The central term and noise term of the affine polynomial of the node voltage amplitude change are further calculated and sorted out, that is, the affine three-phase voltage sensitivity equation is obtained; A three-phase power flow calculation is performed once at the initial moment, and a three-phase voltage sensitivity coefficient matrix in an affine three-phase voltage sensitivity equation is obtained according to the result of the three-phase power flow calculation; Based on Monte Carlo simulation, random scenarios of distributed power sources are simulated to construct the node affine active injection power change vector and node affine reactive injection power change vector corresponding to each scenario, and then the node affine voltage amplitude change vector of each scenario is calculated; Based on the affine-interval mathematical transformation, the node affine voltage amplitude change vector is converted into interval form, and the maximum voltage amplitude interval value of the distributed power configuration scenario is calculated Then, the distributed power installation capacity P DG The horizontal axis is the maximum voltage amplitude interval value. Formed as a vertical axis Interval distribution relationship; Will The overvoltage distributed power configuration scenarios in the interval distribution relationship are divided into the first scenario and the second scenario. The width of the distributed power installation capacity interval is selected to calculate the overvoltage severity correction coefficient of the first scenario and the second scenario. Among them, the overvoltage severity correction coefficient of the first scenario is calculated by equations (10)-(17) and (18)-(21) respectively. and the overvoltage severity correction factor for the second scenario Where: m max The total number of installed capacity intervals for distributed generation; is the total number of the first type of scenarios in the mth distributed generation installation capacity interval; is the severity of overvoltage of the first scenario k in the mth distributed generation installation capacity interval; and are decision variables with values ​​of 0 and 1, respectively indicating whether the first scenario k belongs to category A or category B in time period t; is the upper bound of the maximum voltage amplitude interval of the first type of scenario k in time period t, is the lower bound of the maximum voltage amplitude interval of the first type of scenario k in time period t; C ΔV is the voltage change proportional coefficient; is the upper bound of the maximum voltage amplitude interval of the first type of scenario k, is the lower bound of the maximum voltage amplitude interval for the first type of scenario k, is the center value of the maximum voltage amplitude interval of the first type of scenario k; Where: is the overvoltage severity of overvoltage scenario k in the second type of scenario in the mth distributed generation installation capacity interval; is a 0-1 decision variable, indicating whether the second type of scenario k belongs to the B type in time period t; is the total number of the second type of scenarios in the mth distributed generation installation capacity interval; is the upper bound of the maximum voltage amplitude range of the second scenario k in time period t, is the lower bound of the maximum voltage amplitude interval of the second type of scenario k in time period t; The interval of distribution network overvoltage risk is calculated by the overvoltage severity correction coefficient of the first and second scenarios, and the distribution network overvoltage risk threshold is selected considering the planning requirements. The distributed generation capacity acceptance capacity interval is calculated based on the distribution network overvoltage risk interval and the distribution network overvoltage risk threshold. Among them, the distribution network overvoltage risk interval is calculated by formula (22), and the distribution network overvoltage risk threshold λ′ is selected considering the actual distributed generation planning requirements. max , and then the distributed generation capacity interval is calculated by formula (23): Where: N m,total is the total number of distributed generation configuration scenarios in the mth distributed generation installation capacity interval; P DG,m is the lower bound of the distributed generation installation capacity interval; λ′ max is the overvoltage risk threshold of the distribution network; is the upper bound of the overvoltage risk interval of the distribution network, l m ' is the lower bound of the overvoltage risk interval of the distribution network; is the upper bound of the distributed generation capacity range, HC' It is the lower bound of the distributed generation capacity range.

2. The method for analyzing uncertainty in distributed generation capacity according to claim 1 is characterized in that: According to the three-phase load Jacobian matrix shown in formula (1), the node voltage phase angle change vector of the corresponding phase is eliminated to obtain the three-phase voltage sensitivity equation of the node voltage amplitude change vector to the node active injection power change vector and the node reactive injection power change vector of the corresponding phase, as shown in formula (2): Where: for Phase Jacobian matrix; express Phase node voltage phase angle Sensitivity of phase active injection power, express Phase node voltage phase angle Sensitivity of phase reactive injection power; express Phase node voltage amplitude Sensitivity of phase active injection power, express Phase node voltage amplitude Sensitivity of phase reactive injection power; Representation Node Phase active injection power change vector, Representation Node Phase reactive injection power change vector; express Phase node voltage phase angle change vector, express Phase node voltage amplitude change vector; for right Influence Phase sensitivity coefficient matrix, for right Influence Phase sensitivity coefficient matrix, and That is, the three-phase voltage sensitivity coefficient matrix is ​​further calculated based on the results of the three-phase power flow calculation.

3. The method for analyzing uncertainty in distributed generation capacity according to claim 1 is characterized in that: Affine mathematics is used to quantify the uncertainty of node injection power, and the node affine voltage amplitude change vector, node affine active injection power change vector, and node affine reactive injection power change vector of each phase are obtained as shown in equations (3)-(5): Where: is the time t Phase node affine voltage amplitude change vector; is the time t Phase node affine active injection power change vector; is the time t Phase node affine reactive injection power change vector; is the node n at time t Phase-affine voltage amplitude change; is the node n at time t Phase node affine active injection power variation; is the node n at time t Phase node affine reactive injection power variation; for Central terms of affine polynomials; for The mth noise coefficient of the affine polynomial; for The mth noise element of the affine polynomial, is the value injected by node n at time t The upper bound of the interval of affine active power variation, is the value injected by node n at time t The lower bound of the interval of phase-affinity active power variation; is the value injected by node n at time t The upper bound of the interval of affine reactive power variation, are respectively the injected The lower bound of the interval of phase-affine reactive power variation; and They are Affine polynomials and The noise element of the affine polynomial, The central term and noise term of the node voltage amplitude variation affine polynomial shown in equations (6) and (7) are further calculated and the central term and noise term are combined to obtain the affine three-phase voltage sensitivity equation. Where: and The sensitivity coefficient matrices are and The i-th row submatrix of and They are and The element in the i-th row and j-th column of .

4. The method for analyzing uncertainty in distributed generation capacity according to claim 3 is characterized in that: Based on Monte Carlo simulation, random scenarios of distributed power sources are simulated to construct the node affine active injection power change vector corresponding to each scenario. and node affine reactive injection power change vector Then, the node affine voltage amplitude change vector of each scene is calculated by equations (3)-(7):

5. The method for analyzing uncertainty in distributed generation capacity according to claim 1 is characterized in that: Based on the affine-interval mathematical transformation, the node affine voltage amplitude change vector is converted into Convert to the corresponding interval form The maximum voltage amplitude interval value of each distributed power configuration scenario is calculated by formula (9): Then, the distributed power installation capacity P DG The horizontal axis is the maximum voltage amplitude interval value. Formed as a vertical axis Interval distribution relationship; Where: is the node i at the initial time t0 Phase voltage amplitude.

6. A device for analyzing uncertainty in the acceptance capacity of distributed power sources, characterized in that: include: A processing unit, a storage unit, an input unit, a display unit and a bus unit, wherein the bus unit connects the processing unit, the storage unit, the input unit and the display unit, the storage unit stores instructions and operands, and the processing unit executes instructions to process operands to implement the distributed power supply acceptance capacity uncertainty analysis method as described in any one of claims 1-5.

7. A storage medium for implementing a method for analyzing uncertainty in distributed power supply acceptance capacity, characterized in that: The storage medium for implementing the distributed power supply acceptance capacity uncertainty analysis method stores at least one instruction, and reading and executing the instruction implements the distributed power supply acceptance capacity uncertainty analysis method as described in any one of claims 1-5.

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