Probabilistic power flow method and system based on maximum entropy method and semi-invariant

Abstract

The invention discloses a probabilistic power flow method and system based on a maximum entropy method and a semi-invariant, and relates to the field of power system operation and analysis, and the method comprises the steps: obtaining the illumination intensity and temperature information of a new energy station, and the basic data of a power grid system; load power distribution in the line is described by adopting normal distribution, and probability distribution of new energy station output is fitted by using a Beta distribution model and a two-parameter Weibull function; and a semi-invariant method is adopted to solve each order of semi-invariant of the output variable, and each order of semi-invariant is fitted to a power flow probability density function by using a maximum entropy method. According to the method, the power grid power flow probability density function can be effectively calculated, and a basis is provided for optimal scheduling and active support of a new energy access system.

Description

Technical Field

[0001] The present invention relates to the field of power system operation and analysis, and in particular to a probability power flow method and system based on maximum entropy method and semi-invariant. Background Art

[0002] With the continuous development of new energy technology, the penetration rate of new energy in active distribution networks is getting higher and higher. However, the access of new energy makes the uncertainty and volatility in the power system more serious due to strong random factors such as weather, and safety and stability problems such as voltage over-limit in the power system frequently occur. Therefore, based on this, studying the safety issues caused by the uncertainty of the distribution network caused by the access of new energy to the distribution network is of great significance to promoting the safe and rapid development of the power grid.

[0003] Due to the uncertainty brought by new energy, probabilistic power flow calculation is used to solve these problems. In probabilistic power flow calculation, the first-order to high-order moments of random variables (such as mean, variance, skewness, and kurtosis) can be used to establish a probability model, but moment calculation usually involves complex calculations such as convolution calculation, which increases the amount of calculation. Therefore, the additivity and homogeneity of semi-invariants are used to reduce the amount of calculation, and then the maximum entropy principle is used to fit the probability density function of the system power flow without assuming the distribution type. The semi-invariant and maximum entropy methods directly estimate the probability distribution of power grid power flow variables through low-order moments without the need for a large number of sampling calculations. The combination of skewness and kurtosis to describe the asymmetry and tail characteristics of random variables is more accurate than the traditional normal distribution, and is suitable for complex distributions (asymmetric, heavy tail, etc.) in new energy scenarios. It can effectively deal with the uncertainty of new energy and provide data basis for the safety and stability issues after the access of new energy. Summary of the invention

[0004] In view of the above-mentioned existing problems, the present invention provides a probabilistic power flow method for a power grid containing new energy stations based on the maximum entropy method and semi-invariants. Under the existing technology, this method reduces the amount of calculation through the semi-invariant method while improving the accuracy of probabilistic power flow calculation, providing support and basis for the safe and stable access of large-scale new energy to the distribution network.

[0005] In order to solve the above technical problems, a probabilistic power flow method based on maximum entropy method and semi-invariant is proposed, including:

[0006] Obtain the light intensity, temperature information and basic data of the power grid system under the renewable energy station; describe the load power distribution in the line with normal distribution, and use the Beta distribution model and two-parameter Weibull function to fit the probability distribution of the output of the renewable energy station; use the semi-invariant method to obtain the semi-invariants of each order of the output variable, and use the maximum entropy method to fit the power flow probability density function of each order of the semi-invariant.

[0007] As a preferred scheme of the probabilistic power flow method based on maximum entropy method and semi-invariant described in the present invention, the basic data includes stratifying each node of the distribution network line, determining the type of each node, obtaining the active power, reactive power and voltage data of each node from the power grid system, and obtaining the light intensity information within a set time range, the total area of ​​photovoltaic modules, the average photoelectric conversion efficiency, and the new energy station data of wind speed data.

[0008] As a preferred solution of the probability power flow method based on maximum entropy method and semi-invariant described in the present invention, wherein: the normal distribution description includes:

[0009] Establish the output probability distribution model of the load in the line;

[0010] The output probability distribution model of wind farms within the line is established using a two-parameter Weibull function model.

[0011] Use the Beta function model to establish the output probability distribution model of the photovoltaic stations within the line;

[0012] The output probability distribution model of the load in the line includes: for the power grid line, there is uncertainty in the load, and the load fluctuates with the region and time. The output model f(P) of the load is fitted by the normal distribution function model:

[0013]

[0014] Where μ and σ are the expected and standard deviation of the load output, respectively.

[0015] As a preferred solution of the probability power flow method based on maximum entropy method and semi-invariant described in the present invention, the output probability distribution model of the wind farm station in the line includes using a two-parameter Weibull function model to establish the output probability distribution model of the wind farm station in the line:

[0016] According to wind power output and wind speed, wind speed obeys the two-parameter Weibull distribution, and the probability density expression f(v) is:

[0017]

[0018] Among them, v is the actual wind speed, k is the Weibull shape parameter, which reflects the function shape; c is the Weibull scale coefficient, which reflects the average level of wind speed in the period;

[0019] According to the wind speed model, the wind power active output probability model is obtained as follows:

[0020]

[0021] Among them, p ω is the probability density function of wind power output, vi is the wind turbine cut-in wind speed, v o is the fan cut-out wind speed, v t is the rated wind speed of the fan, k 1 is the Weibull shape parameter;

[0022] According to the probability distribution function of wind speed, the probability density distribution model of wind power output is obtained.

[0023] As a preferred solution of the probability power flow method based on maximum entropy method and semi-invariant described in the present invention, wherein: the output probability distribution model of the photovoltaic field station in the line includes using the Beta function model to establish the output probability distribution model of the photovoltaic field station in the line;

[0024] Photovoltaic output is related to light intensity, and light intensity follows Beta distribution, that is:

[0025]

[0026] Among them, α and β are shape parameters, r max is the maximum light intensity, r is the actual light intensity, Γ(.) is the Gamma function;

[0027] The probability function of photovoltaic output is obtained according to the light intensity model:

[0028]

[0029] Among them, P i is the maximum output active power of the distributed grid-connected photovoltaic system, P max is the maximum output active power of the distributed grid-connected photovoltaic system, P max =A·η·μ·r max .

[0030] As a preferred solution of the probability power flow method based on maximum entropy method and semi-invariant described in the present invention, the semi-invariant method includes: the semi-invariant of the input data is obtained by the origin moments of each order of the data:

[0031]

[0032] Among them, γ (r) represents the r-order semi-invariant, α (i) represents the i-th order origin moment.

[0033] As a preferred solution of the probability power flow method based on maximum entropy method and semi-invariant described in the present invention, the maximum entropy method fitting includes converting the obtained semi-invariant of each order into the origin moment of each order, and solving the power flow probability density function by using the maximum entropy principle:

[0034]

[0035] Among them, H(x) is the entropy of the random variable x, f(x) is the probability density function of the random variable, and u i is the weight function, is the center distance of each order of measured data, and Ω is the integration interval.

[0036] Construct the Lagrangian function to obtain the optimal solution that conforms to the maximum entropy principle:

[0037]

[0038] Among them, L(x) is the constructed Lagrangian function, λ 0 ,λ i ,i=1,2,…m are Lagrange coefficients; u i (x) is the weight function of the random variable; Ω is the integration interval.

[0039] Find the Lagrange multiplier λ for solving the constructed Lagrangian function problem i , and obtain the power flow probability density fitting function.

[0040] Another object of the present invention is to provide a probabilistic power flow system based on the maximum entropy method and semi-invariants. The present invention solves the power flow calculation problem of the power grid containing new energy stations under the influence of uncertainty and volatility factors, and provides a scientific basis for the stable operation, risk assessment and optimal scheduling of the power grid by accurately simulating and predicting the probability distribution of the output and load power of the new energy stations, thereby improving the overall performance and reliability of the power grid.

[0041] As a preferred solution of the probability power flow system based on the maximum entropy method and semi-invariant described in the present invention, it is characterized by comprising a data acquisition module, a probability distribution fitting module, and a power flow probability density calculation module;

[0042] The data acquisition module is responsible for collecting and integrating the light intensity and temperature information of the new energy station and the basic data of the power grid system, including the hierarchical information of the distribution network line nodes, node type, active power, reactive power, voltage data, total area of ​​PV modules, photoelectric conversion efficiency, and wind speed data;

[0043] The probability distribution fitting module receives the data provided by the data acquisition module, and respectively establishes a normal distribution model of load power, a two-parameter Weibull function model of wind power station output, and a Beta function model of photovoltaic station output to describe the uncertainty of different types of new energy stations and loads;

[0044] The power flow probability density calculation module extracts semi-invariants of each order from the model obtained by the probability distribution fitting module using the semi-invariant method, and uses the maximum entropy method to transform these semi-invariants into a power flow probability density function, and calculates the power flow probability distribution of the power grid.

[0045] A computer device includes a memory and a processor. The memory stores a computer program. The processor, when executing the computer program, implements the steps of a probability power flow method based on the maximum entropy method and semi-invariants.

[0046] A computer-readable storage medium stores a computer program. The computer program, when executed by a processor, implements the steps of a probability power flow method based on the maximum entropy method and semi-invariants.

[0047] The beneficial effects of the present invention: The present invention does not need to assume the distribution form of random variables. Through the maximum entropy principle, it constructs a probability distribution using limited known information (such as low-order moments), and adapts to new energy power distributions with skewness, heavy tails or asymmetric characteristics. Compared with traditional probability distribution assumptions, the present invention can capture the randomness characteristics of new energy power stations more accurately.

[0048] Compared with traditional sampling methods such as Monte Carlo simulation, the probability power flow method based on the maximum entropy and semi-invariants uses analytical calculations instead of a large number of random samplings, greatly reducing the calculation time and improving the calculation efficiency. At the same time, by characterizing the distribution characteristics with semi-invariant information, without too many input parameters, it can perform rapid probability analysis on large-scale power grids.

[0049] The present invention is applicable to the power flow analysis of distribution networks containing new energy power stations, and shows good generalization ability under various new energy distribution types such as wind power and photovoltaic power. At the same time, it provides strong support for the randomness analysis, optimal scheduling and risk assessment of power grids under different penetration scenarios, and has broad practical application value. Description of the Drawings

[0050] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained according to these drawings.

[0051] Figure 1 It is the overall flowchart of a probability power flow method based on the maximum entropy method and semi-invariants provided by an embodiment of the present invention.

[0052] Figure 2A 9-node topology diagram of a distribution network based on a maximum entropy method and a semi-invariant probabilistic power flow method is provided as an embodiment of the present invention.

[0053] Figure 3 A voltage probability function diagram of node 8 calculated based on a maximum entropy method and a semi-invariant probability power flow method is provided in one embodiment of the present invention.

[0054] Figure 4 A system solution module diagram of a probability power flow system based on maximum entropy method and semi-invariant provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0055] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0056] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0057] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it an embodiment that is mutually exclusive with other embodiments, either individually or selectively.

[0058] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0059] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0060] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0061] Example 1, reference Figure 1-Figure 2 , which is the first embodiment of the present invention, provides a probability power flow method based on maximum entropy method and semi-invariant, including:

[0062] S1: Obtain the light intensity, temperature information and basic data of the power grid system at the new energy station.

[0063] Furthermore, each node of the distribution network line is layered, the type of each node is determined, and the active power, reactive power, voltage and other data of each node are obtained from the power grid system. In addition, the light intensity information over a period of time, the total area of ​​PV panels, the average photoelectric conversion efficiency, wind speed data and other new energy station-related data are obtained.

[0064] S2: The load power distribution within the line is described by normal distribution, and the Beta distribution model and two-parameter Weibull function are used to fit the probability distribution of the output of the new energy station.

[0065] S21: Establish a probability distribution model of the load output within the line.

[0066] For power grid lines, there is a great deal of uncertainty in the load, which will fluctuate with region and time, and its probability distribution generally conforms to the normal distribution. Therefore, the output model of the load is fitted using the normal distribution function model as follows:

[0067]

[0068] Where μ and σ are the expected and standard deviation of the load output, respectively.

[0069] S22: Use the two-parameter Weibull function model to establish the output probability distribution model of the wind farm stations within the line.

[0070] Since wind power output is related to randomly changing wind speed, the wind speed in most areas obeys the two-parameter Weibull distribution, and the probability density expression is as follows:

[0071]

[0072] Among them, v is the actual wind speed, k is the Weibull shape parameter, which reflects the shape of the function; c is the Weibull scale coefficient, which reflects the average level of wind speed in a certain period of time; k and c can be obtained by the following formula:

[0073]

[0074] According to the probability distribution function of wind speed, the probability density distribution model of wind power output is obtained.

[0075] Assuming that wind power output is only related to wind speed, the probability density model is:

[0076]

[0077] Among them, P N is the rated power of the wind turbine, rate v i is the wind turbine cut-in wind speed, v a is the cut-out wind speed, v r is the rated wind speed of the fan.

[0078] From the above model, the wind power active output probability model can be deduced as:

[0079]

[0080] S23: Use the Beta function model to establish the output probability distribution model of the photovoltaic stations within the line.

[0081] Photovoltaic output is related to light intensity, and light intensity follows Beta distribution, that is:

[0082]

[0083] Among them, α and β are shape parameters; r max is the maximum light intensity; r is the actual light intensity; Γ(.) is the Gamma function.

[0084] The photovoltaic output power is approximately linearly related to the light intensity, and considering the power loss, that is, P i =μP N , μ is the grid-connected inverter output efficiency, then the distributed grid-connected photovoltaic system output active power is:

[0085] P i =μ·r·A·η

[0086] Among them, A is the total area of ​​the photovoltaic cell array, η is the total photoelectric conversion efficiency of the photovoltaic module, and r is the light intensity.

[0087] Therefore, according to the model of light intensity, the probability function of photovoltaic output can be obtained:

[0088]

[0089] Among them, P max is the maximum output active power of the distributed grid-connected photovoltaic system, P max =A·η·μ·r max .

[0090] S3: The semi-invariant method is used to obtain the semi-invariants of each order of the output variable, and the semi-invariants of each order are fitted to the power flow probability density function using the maximum entropy method.

[0091] S31: The semi-invariant of the input data can be obtained from the origin moments of the data, and the relationship between them is:

[0092]

[0093] Among them, γ (r) It represents the r-order semi-invariant. By utilizing the additivity and homogeneity of the semi-invariant, the linear addition and convolution operations between the semi-invariants are equivalent to obtain the results of each order of moments, which avoids the complex convolution calculation involved in directly using the origin moments of each order to solve the probability power flow calculation, thereby greatly simplifying the amount of calculation.

[0094] S32: The semi-invariants of each order obtained in step S31 are converted into the origin moments of each order, and the maximum entropy principle is used to solve the power flow probability density function. The mathematical model of the maximum entropy function is:

[0095]

[0096] Where H(x) is the entropy of the random variable x; f(x) is the probability density function of the random variable; u i is the weight function; is the center distance of each order of measured data; Ω is the integration interval.

[0097] In order to obtain the optimal solution that conforms to the maximum entropy principle, the following Lagrangian function is constructed:

[0098]

[0099] Among them, L(x) is the constructed Lagrangian function, λ 0 ,λ i , i=1,2,…m are Lagrange coefficients, u i (x) is the weight function of the random variable; Ω is the integration interval.

[0100] When the Lagrange extreme value condition is met, the probability density function is:

[0101]

[0102] Find the Lagrangian multiplier λ to solve the above Lagrangian problemi :

[0103]

[0104] Among them, g i (x) is the weight function set to x i , α (i) is the i-th order origin moment, and substituting it into the probability density function, we get the fitting function.

[0105] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0106] Example 2, reference Figure 2-Figure 3 , which is the first embodiment of the present invention, provides a probability power flow method based on maximum entropy method and semi-invariant. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through experiments.

[0107] according to Figure 2 The IEEE 9-node topology with PV shown in the figure is used to build a corresponding simulation model on the Matlab simulation platform. The specific parameters are as follows: distributed photovoltaic is connected to node 5, distributed wind power is connected to node 3, and node 1 is a balancing node. The photovoltaic output is described according to the Beta distribution at node 5, the wind power output is described according to the Weibull function at node 3, and the output of other loads except the balancing node is described according to the normal distribution function.

[0108] According to the semi-invariant and maximum entropy probability power flow method mentioned in this paper, the probability density function of node 8 is obtained as follows: Figure 3 As shown in the figure, the results of this method converge, and the obtained probability fitting size is basically consistent with the theoretical value.

[0109] Embodiment 3, the second embodiment of the present invention, is different from the first two embodiments in that:

[0110] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program codes.

[0111] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0112] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0113] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0114] Example 3, reference Figure 4 , which is the third embodiment of the present invention, and which provides a probability power flow system based on maximum entropy method and semi-invariant, including a data acquisition module 10, a probability distribution fitting module 20, and a power flow probability density calculation module 30;

[0115] The data acquisition module 10 is responsible for collecting and integrating the light intensity and temperature information of the new energy station and the basic data of the power grid system, including the hierarchical information of the distribution network line nodes, node types, active power, reactive power, voltage data, total area of ​​PV modules, photoelectric conversion efficiency, and wind speed data;

[0116] The probability distribution fitting module 20 receives the data provided by the data acquisition module, and respectively establishes a normal distribution model of load power, a two-parameter Weibull function model of wind power station output, and a Beta function model of photovoltaic station output to describe the uncertainty of different types of new energy stations and loads;

[0117] The power flow probability density calculation module 30 uses the semi-invariant method to extract semi-invariants of various orders from the model obtained by the probability distribution fitting module, and uses the maximum entropy method to convert these semi-invariants into power flow probability density functions to calculate the power flow probability distribution of the power grid.

[0118] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A probability power flow method based on maximum entropy method and semi-invariant, characterized by: include, Obtain the light intensity, temperature information and basic data of the power grid system at the new energy station; The load power distribution in the line is described by normal distribution, and the Beta distribution model and two-parameter Weibull function are used to fit the probability distribution of the output of the new energy station; The semi-invariant method is used to obtain the semi-invariants of each order of the output variable, and the maximum entropy method is used to fit the power flow probability density function of each order of the semi-invariant.

2. A probability power flow method based on maximum entropy method and semi-invariant as claimed in claim 1, characterized in that: The basic data includes stratifying each node of the distribution network line, determining the type of each node, obtaining the active power, reactive power, and voltage data of each node from the power grid system, and obtaining the light intensity information within a set time range, the total area of ​​​​photovoltaic modules, the average photoelectric conversion efficiency, and the new energy station data of wind speed data.

3. A probability power flow method based on maximum entropy method and semi-invariant as claimed in claim 2, characterized in that: The normal distribution description includes, Establish the output probability distribution model of the load in the line; The output probability distribution model of wind farms within the line is established using a two-parameter Weibull function model. Use the Beta function model to establish the output probability distribution model of the photovoltaic stations within the line; The output probability distribution model of the load in the line includes: for the power grid line, there is uncertainty in the load, and the load fluctuates with the region and time. The output model f(P) of the load is fitted by the normal distribution function model: Where μ and σ are the expected and standard deviation of the load output, respectively.

4. A probability power flow method based on maximum entropy method and semi-invariant as claimed in claim 3, characterized in that: The output probability distribution model of the wind farm station within the line includes establishing the output probability distribution model of the wind farm station within the line using a two-parameter Weibull function model: According to wind power output and wind speed, wind speed obeys the two-parameter Weibull distribution, and the probability density expression f(v) is: Among them, v is the actual wind speed, k is the Weibull shape parameter, which reflects the function shape; c is the Weibull scale coefficient, which reflects the average level of wind speed in the period; According to the wind speed model, the wind power active output probability model is obtained as follows: Among them, p ω is the probability density function of wind power output, v i is the wind turbine cut-in wind speed, v o is the fan cut-out wind speed, v t is the rated wind speed of the fan, k1 is the Weibull shape parameter; According to the probability distribution function of wind speed, the probability density distribution model of wind power output is obtained.

5. A probability power flow method based on maximum entropy method and semi-invariant as claimed in claim 4, characterized in that: The output probability distribution model of the photovoltaic station within the line includes using a Beta function model to establish the output probability distribution model of the photovoltaic station within the line; Photovoltaic output is related to light intensity, and light intensity follows Beta distribution, that is: Among them, α and β are shape parameters, r max is the maximum light intensity, r is the actual light intensity, Γ(.) is the Gamma function; The probability function of photovoltaic output is obtained according to the light intensity model: Among them, P i is the maximum output active power of the distributed grid-connected photovoltaic system, P max is the maximum output active power of the distributed grid-connected photovoltaic system, P max =A·η·μ·r max .

6. A probability power flow method based on maximum entropy method and semi-invariant as claimed in claim 5, characterized in that: The semi-invariant method includes that the semi-invariant of the input data is obtained by the origin moments of each order of the data: Among them, γ (r) represents the r-order semi-invariant, α (i) represents the i-th order origin moment.

7. A probability power flow method based on maximum entropy method and semi-invariant as claimed in claim 6, characterized in that: The maximum entropy method fitting includes converting the obtained semi-invariants of each order into the origin moments of each order, and solving the power flow probability density function using the maximum entropy principle: Among them, H(x) is the entropy of the random variable x, f(x) is the probability density function of the random variable, and u i is the weight function, is the center distance of each order of measured data, Ω is the integration interval; Construct the Lagrangian function to obtain the optimal solution that conforms to the maximum entropy principle: Among them, L(x) is the constructed Lagrangian function, λ0,λ i ,i=1,2,...m are Lagrange coefficients, u i (x) is the weight function of the random variable; Ω is the integration interval. Find the Lagrange multiplier λ for solving the constructed Lagrangian function problem i , and obtain the power flow probability density fitting function.

8. A system using a probability power flow method based on maximum entropy method and semi-invariant as claimed in any one of claims 1 to 7, characterized in that: Including data acquisition module, probability distribution fitting module, and power flow probability density calculation module; The data acquisition module is responsible for collecting and integrating the light intensity and temperature information of the new energy station and the basic data of the power grid system, including the hierarchical information of the distribution network line nodes, node types, active power, reactive power, voltage data, total area of ​​PV modules, photoelectric conversion efficiency, and wind speed data; The probability distribution fitting module receives the data provided by the data acquisition module, and respectively establishes a normal distribution model of load power, a two-parameter Weibull function model of wind power station output, and a Beta function model of photovoltaic station output to describe the uncertainty of different types of new energy stations and loads; The power flow probability density calculation module uses the semi-invariant method to extract semi-invariants of various orders from the model obtained by the probability distribution fitting module, and uses the maximum entropy method to convert these semi-invariants into power flow probability density functions to calculate the power flow probability distribution of the power grid.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the processor implements the steps of a probability power flow method based on maximum entropy method and semi-invariant according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a probability power flow method based on maximum entropy method and semi-invariant are implemented as described in any one of claims 1 to 7.

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