A probabilistic power flow calculation method and system, electronic equipment and storage medium

Abstract

The application discloses a kind of probabilistic power flow calculation method, system, electronic equipment and storage medium, it is related to the field of probabilistic power flow calculation in power system, the linearization of alternating current power flow equation is considered, the linear decoupling node power injection equation and branch power equation are obtained, based on each order moment and semi-invariant of injection power random variable, the semi-invariant of node voltage, node voltage amplitude and branch active power is deduced, the probability density function of system node voltage, node voltage amplitude and branch active power is obtained using Gram-Chalier series.The problem that calculation accuracy, effectiveness and state variable integrity are organically unified in probabilistic power flow calculation is solved, and a theoretical basis is provided for power grid planning and dispatch, power grid vulnerability analysis and power grid cascading failure prevention.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of probabilistic power flow calculation in power system, more particularly, it relates to a probabilistic power flow calculation method, system, electronic device and storage medium. BACKGROUND

[0002] Probabilistic power flow is an important basis for solving the uncertainty factors of power system. With the development of China's railway and power industry, large-scale access of uncertain sources and loads represented by traction load, solar and wind energy to the power grid has brought obvious intermittency and randomness to the power grid, which directly leads to significant increase in the uncertainty of the power system. Therefore, how to consider the uncertainty of sources and loads and how to perform probabilistic power flow calculation in power system power flow calculation have become key problems to be solved.

[0003] The existing technology mainly expands probabilistic power flow calculation from three aspects: the first type is a simulation method based on sampling of variable probability distribution, such as Monte Carlo simulation method, importance sampling method, etc. The advantage of this type of method is high calculation accuracy, but the disadvantage is large number of calculation samples, long calculation time, and difficult to realize online application analysis. The second type is a method based on digital characteristics of random variables to approximate the statistical characteristics of system state variables, such as point estimation method, first-order second-moment method. This type of method avoids large-scale repeated sampling, so the solving speed is faster. But its disadvantage is that the higher-order moments of random variables in the calculation result are not accurate enough, and the probability distribution function of the variable cannot be accurately obtained. The third type is an analytical method, such as fast Fourier transform, semi-invariant method, etc. The advantage of this type of method is that the probability density function of the state variable can be obtained, and the calculation accuracy is good, but it is difficult to balance the contradiction between calculation effectiveness and state variable integrity.

[0004] It can be seen that in the existing probabilistic power flow calculation method, the main problem is that it is difficult to organically unify the calculation accuracy, effectiveness and state variable integrity. Therefore, it is urgent to propose a probabilistic power flow calculation method with high calculation accuracy, short calculation time and complete calculation state variables. SUMMARY

[0005] The present application provides a probabilistic power flow calculation method, system, electronic device and storage medium, which considers the linearization of the AC power flow equation, obtains the node power injection equation and branch power equation after linear decoupling, based on the moments of the injection power random variable and the semi-invariant, the semi-invariant of the node voltage, the node voltage amplitude and the branch active power is derived, and the probability density function of the system node voltage, the node voltage amplitude and the branch active power is obtained by using Gram-Chalier series. The problem of organically unifying the calculation accuracy, effectiveness and state variable integrity in probabilistic power flow calculation is solved, which provides a theoretical basis for power grid planning and dispatching, power grid vulnerability analysis and power grid cascading failure prevention.

[0006] The above technical objective of the present application is achieved by the following technical solutions:

[0007] In a first aspect, the present application provides a probabilistic power flow calculation method, comprising:

[0008] linearly decoupling the node power injection equation to obtain a linearized node power injection equation;

[0009] linearly decoupling the branch power equation to obtain a linearized branch power equation;

[0010] calculating the moments of the node injection power distribution according to the expected value of the node injection power and the distribution of the node injection power fluctuation;

[0011] calculating the semi-invariants of the node injection power according to the moments;

[0012] calculating the semi-invariants of the node phase angle, the node voltage and the branch active power according to the semi-invariants of the node injection power, and the linearized node power injection equation and the branch power equation;

[0013] obtaining the probability distribution of the node voltage, the node phase angle and the branch active power by substituting the semi-invariants of the node phase angle, the node voltage and the branch active power into the Gram-Charlier series expansion formula.

[0014] In an embodiment, the calculation of the moments of the node injection power distribution according to the expected value of the node injection power and the distribution of the node injection power fluctuation comprises:

[0015] subtracting the expected value of the node injection power from the random variable of the node injection power to obtain the distribution of the node injection power fluctuation, wherein the node injection power includes the node injection active power and the node injection reactive power;

[0016] calculating the moments of the node injection power distribution according to the output of the generator and the distribution of the node injection power fluctuation.

[0017] In an embodiment, the formula for calculating the moments of the node injection power distribution is:

[0018] wherein μ ΔP is the expected value of the node active power fluctuation distribution ΔP, f ΔPi is the probability value of the i-th node active power fluctuation distribution ΔP, μ ΔQ is the expected value of the node reactive power fluctuation distribution ΔQ, f ΔQi is the probability value of the i-th node reactive power fluctuation distribution ΔQ, and v represents the order of the moment.

[0019] In one embodiment, the semi-invariants of each order of the distribution of the nodal injection power are calculated by using the relationship between the random variables and the semi-invariants, and the formula for calculating the semi-invariants of each order of the distribution of the nodal injection active power is:

[0020] wherein, represents the combination of any j numbers in v-1 numbers, K ΔP,v represents the v order semi-invariant of the nodal active power fluctuation distribution ΔP.

[0021] In one embodiment, the semi-invariants of the nodal phase angle, the nodal voltage and the branch active power are calculated according to the semi-invariants of each order of the nodal injection power and the linearized nodal power injection equation and the linearized branch power equation, including:

[0022] The linearized nodal power injection equation and the linearized branch power equation are converted into a matrix;

[0023] According to the linear additivity of the semi-invariants, the matrix is combined with the semi-invariants of each order of the distribution of the nodal injection active power to obtain the semi-invariants of the nodal phase angle, the nodal voltage and the branch active power.

[0024] In one embodiment, the matrix form of the linearized nodal power injection equation is: The matrix form of the linearized branch power injection equation is: wherein, k is the node correlation vector corresponding to the branch, P is the nodal injection active matrix, Q is the nodal injection reactive matrix, θ is the nodal voltage phase matrix, V is the nodal voltage amplitude matrix, G is the nodal admittance real part matrix, B is the nodal admittance imaginary part matrix, and B' is the nodal admittance imaginary part matrix without self-admittance;

[0025] The formula for calculating the semi-invariants of the nodal phase angle, the nodal voltage and the branch power is:

[0026] wherein, K ΔV,v is the v order semi-invariant of the nodal voltage, K Δθ,v is the v order semi-invariant of the nodal phase angle, and K ΔPL,v is the v order semi-invariant of the branch active power fluctuation.

[0027] In one embodiment, the probability distribution of the nodal voltage, the nodal phase angle and the branch active power is obtained by substituting the semi-invariants of the nodal phase angle, the nodal voltage and the branch active power into the Gram-Charlier series expansion formula, including:

[0028] When the nodal injection power takes the expected value, the branch power flow expected value vector, the nodal voltage amplitude vector and the nodal phase angle vector are calculated according to the linearized nodal power injection equation and the linearized branch power equation.

[0029] The semi-invariants of the node phase angle, the node voltage and the branch power are substituted into the Gram-Charlier series expansion formula to obtain the probability density functions of the node voltage amplitude fluctuation, the node phase angle fluctuation and the branch power fluctuation;

[0030] The probability distribution of the node voltage, the node phase angle and the branch active power is obtained by using the branch power expectation value vector, the node voltage amplitude vector, the node phase angle vector and the probability density functions of the corresponding random variable translation fluctuation.

[0031] In a second aspect, the present application provides a probability flow calculation system, comprising:

[0032] The first decoupling module is configured to linearly decouple the node power injection equation to obtain a linearized node power injection equation;

[0033] The second decoupling module is configured to linearly decouple the branch power equation to obtain a linearized branch power equation;

[0034] The moment calculation module is configured to calculate the moments of the node injection power distribution according to the expectation value of the node injection power and the distribution of the node injection power fluctuation;

[0035] The semi-invariant calculation module is configured to calculate the semi-invariants of the node injection power according to the moments;

[0036] The semi-invariant calculation module is configured to calculate the semi-invariants of the node injection power according to the moments;

[0037] The probability distribution calculation module is configured to substitute the semi-invariants of the node phase angle, the node voltage and the branch active power into the Gram-Charlier series expansion formula to obtain the probability distribution of the node voltage, the node phase angle and the branch active power.

[0038] In a third aspect, the present application provides an electronic device, comprising:

[0039] One or more processors;

[0040] A memory coupled to the processor, configured to store one or more programs;

[0041] When the one or more programs are executed by the one or more processors, the one or more processors implement the steps of the probability flow calculation method of any one of the first aspect.

[0042] In a fourth aspect, the present application provides a computer readable storage medium, having stored thereon a computer program, which, when executed by a processor, implements the probabilistic power flow calculation method according to any one of the first aspect.

[0043] Compared with the prior art, the present application has the following beneficial effects:

[0044] The present application considers linearization of the AC power flow equation, obtains linear decoupled node power injection equation and branch power equation, deduces the semi-invariant of the node voltage, the node voltage amplitude and the branch active power based on the moments of the injection power random variable and the semi-invariant, and obtains the probability density function of the system node voltage, the node voltage amplitude and the branch active power by using the Gram-Chalier series. The present application solves the problem of organically unifying the calculation accuracy, the effectiveness and the integrity of the state variable in the probabilistic power flow calculation, and provides a theoretical basis for power grid planning and dispatching, power grid vulnerability analysis and power grid cascading failure prevention. BRIEF DESCRIPTION OF DRAWINGS

[0045] The accompanying drawings, which are included to provide a further understanding of the embodiments of the present application and are incorporated in and constitute a part of this application, illustrate embodiments of the present application and, together with the description, serve to explain the principles of the present application. In the drawings:

[0046] Figure 1 A flowchart of a probabilistic power flow calculation method provided by the embodiments of the present application is shown in FIG. 1;

[0047] Figure 2 A schematic diagram of a high-altitude mountainous area power grid structure provided by the embodiments of the present application is shown in FIG. 2;

[0048] Figure 3 A comparison chart of the node 30 voltage probability density provided by the embodiments of the present application is shown in FIG. 3;

[0049] Figure 4 A comparison chart of the node 30 voltage cumulative probability density provided by the embodiments of the present application is shown in FIG. 4;

[0050] Figure 5 A comparison chart of the branch 13-30 power flow probability density provided by the embodiments of the present application is shown in FIG. 5;

[0051] Figure 6 A comparison chart of the branch 13-30 power flow cumulative probability density provided by the embodiments of the present application is shown in FIG. 6;

[0052] Figure 7 A principle block diagram of a probabilistic power flow calculation system provided by the embodiments of the present application is shown in FIG. 7. DETAILED DESCRIPTION

[0053] In order to make the objects, technical solutions and advantages of the present application clearer, further, the present application will be described in detail below with embodiments and drawings. The schematic embodiments of the present application and the descriptions thereof are only used to explain the present application, and do not limit the present application.

[0054] In a first aspect, the present application provides a probability power flow calculation method, comprising:

[0055] S100, linear decoupling is performed on a node power injection equation to obtain a linearized node power injection equation.

[0056] It should be understood that, according to the access nodes of all distributed power sources in the power system, the node injection power equation of the access nodes can be obtained.

[0057] The node power injection equation is shown in formula (1):

[0058] In formula (1), P i and Q i are the injected active power and reactive power of node i respectively; g ii and b ii are the self conductance and self susceptance of node i respectively; g ij and b ij are the conductance and susceptance of line ij; V i and V j are the voltage amplitudes of nodes i and j respectively, and θ ij is the voltage phase angle difference between nodes i and j.

[0059] The nonlinear term in formula (1) can be approximately expressed as:

[0060] In formula (2), θ i represents the phase of node i.

[0061] After formula (2) is brought into formula (1), the linearized node power injection equation can be finally obtained as:

[0062] In formula (3), G ij and B ij are the real part and imaginary part of the node admittance matrix element respectively, and B ij ' is the imaginary part of the node admittance matrix element without self susceptance. S110, linear decoupling is performed on a branch power equation to obtain a linearized branch power equation.

[0063] Similarly, the branch power equation of the power system can also be obtained, and the original branch power equation of the alternating current power flow is formula (4) as follows:

[0064] P ij = gij V i (V i -V j cosθ ij )-b ij V i V j sinθ ij (3)

[0065] After bringing the formula (2) in the step S100 into the formula (4), the linearized branch power equation can be finally obtained as the following formula (5):

[0066] P ij =g ij (V i -V j )-b ij (θ i -θ j ) (4)。

[0067] S120, calculating each order moment of the node injection power distribution according to the expected value of the node injection power and the distribution of the node injection power fluctuation.

[0068] Specifically, the distribution of the node injection power fluctuation is a distribution of fluctuation fluctuation, and the difference between the current measured node injection power Pt and the next period measured node injection power Pt+1 in a period can be obtained, that is, the probability of the node injection power fluctuation distribution can be obtained, and each order moment of the node injection power distribution can be calculated according to the expected value and the probability of the fluctuation distribution. It should be understood that the node injection power includes the node injection active power and the node injection reactive power, and in the power flow calculation of the power system, only the active power flow probability distribution is generally considered.

[0069] S130, calculating each order semi-invariant of the node injection power according to each order moment.

[0070] Since the distribution of the node injection power fluctuation in the step S120 is a random variable, the relationship between the random variable and the semi-invariant is used, and each order semi-invariant of the node injection power can be calculated according to each order moment of the node injection power distribution.

[0071] S140, calculating the semi-invariant of the node phase angle, the node voltage and the branch active power according to each order semi-invariant of the node injection power, and the linearized node power injection equation and the branch power equation.

[0072] Specifically, in the embodiment, since the phase angle of the node, the voltage, and the semi-invariant of the branch active power need to be calculated, which are related to the formula (3) and the formula (5) obtained in the steps S100 and S110, therefore, according to the linear additivity of the semi-invariant, the semi-invariant of the node phase angle, the node voltage, and the branch active power can be obtained by combining the semi-invariant of each order of the node power injection and the linearized node power injection equation and the branch power equation.

[0073] S150, the semi-invariant of the node phase angle, the node voltage, and the branch active power is substituted into the Gram-Charlier series expansion formula to obtain the probability distribution of the node voltage, the node phase angle, and the branch active power.

[0074] Specifically, substituting the parameters corresponding to the semi-invariant into the Gram-Charlier series expansion formula to obtain the probability distribution, or the probability density function, of the node voltage, the node phase angle, and the branch active power is a conventional technical means, and therefore will not be described here.

[0075] In summary of the above embodiments, the probability power flow calculation method provided by the application is applied to the probability calculation of the power flow of the power system, considers the linearization of the alternating current power flow equation, obtains the linearly decoupled node power injection equation and the branch power equation, derives the semi-invariant of the node voltage, the node voltage amplitude, and the branch active power based on the moments of each order and the semi-invariant of the injection power random variable, and obtains the probability density function of the system node voltage, the node voltage amplitude, and the branch active power by using the Gram-Chalier series. The problem of organically unifying the calculation accuracy, the effectiveness, and the completeness of the state variables in the probability power flow calculation is solved, and a theoretical basis is provided for the power grid planning and dispatching, the power grid vulnerability analysis, and the power grid cascading failure prevention.

[0076] In one embodiment, the moments of each order of the node injection power distribution are calculated according to the expected value of the node injection power and the distribution of the node injection power fluctuation, including:

[0077] The distribution of the node injection power fluctuation is obtained by subtracting the expected value of the node injection power from the random variable of the node injection power, wherein the node injection power includes the node injection active power and the node injection reactive power;

[0078] The moments of each order of the node injection power distribution are calculated according to the output of the generator and the distribution of the node injection power fluctuation.

[0079] Specifically, the random variables of the node injection power, the branch active power flow, the node voltage, and the node phase angle are respectively represented as:

[0080] In the formula (6), P0, Q0, V0, θ0, P L0are the expected values of the node injected active power, the node injected reactive power, the node voltage, the node phase angle and the branch power, respectively; ΔP, ΔQ, ΔV, Δθ, ΔP L are the corresponding fluctuation powers.

[0081] According to the distribution of the generator output and the node active power fluctuation ΔP and the node reactive power fluctuation ΔQ, the moments M ΔP,v and M ΔQ,v of the active power fluctuation ΔP and the reactive power fluctuation ΔQ are calculated.

[0082] In formula (7), μ ΔP is the expected value of the node active power fluctuation distribution ΔP, f ΔPi is the probability value of the i-th node active power fluctuation distribution ΔP, μ ΔQ is the expected value of the node reactive power fluctuation distribution ΔQ, f ΔQi is the probability value of the i-th node reactive power fluctuation distribution ΔQ, and v represents the order of the moment.

[0083] In an embodiment, the corresponding moments K ΔP,v and K ΔQ,v are obtained by using the relationship between the random variable and the semi-invariant, according to M ΔP,v and M ΔQ,v The calculation formula of the moments of the node injected active power distribution is:

[0084] wherein, represents the combination of any j numbers in v-1 numbers, K ΔP,v represents the v-order semi-invariant of the node active power fluctuation distribution ΔP.

[0085] In an embodiment, the semi-invariants of the node phase angle, the node voltage and the branch active power are calculated according to the moments of the node injected power and the linearized node power injection equation and the linearized branch power equation, including:

[0086] The linearized node power injection equation and the linearized branch power equation are converted into matrices.

[0087] Specifically, the matrix form of the linearized node power injection equation is: The matrix form of the linearized branch power injection equation is: In formula (9) and (10), k is the node correlation vector corresponding to the branch, P is the node injected active power matrix, Q is the node injected reactive power matrix, θ is the node voltage phase matrix, V is the node voltage amplitude matrix, G is the node admittance real part matrix, B is the node admittance imaginary part matrix, and B' is the node admittance imaginary part matrix without self-admittance.

[0088] According to the linear additivity of semi-invariants, the semi-invariants of the node phase angle, the node voltage and the branch active power are combined with the matrix to obtain the semi-invariants of the node phase angle, the node voltage and the branch active power.

[0089] Specifically, the semi-invariants of the node phase angle, the node voltage and the branch power are calculated by the formula (11) and the formula (12) respectively.

[0090] In the formula (11) and (12), K ΔV,v is the v-order semi-invariant of the node voltage, K Δθ,v is the v-order semi-invariant of the node phase angle, and K ΔPL,v is the v-order semi-invariant of the branch active power fluctuation.

[0091] In an embodiment, the semi-invariants of the node phase angle, the node voltage and the branch active power are substituted into the Gram-Charlier series expansion formula to obtain the probability distribution of the node voltage, the node phase angle and the branch active power, including:

[0092] When the node injection power takes the expected value, the branch power flow expected value vector, the node voltage amplitude vector and the node phase angle vector are calculated according to the linearized node power injection equation and the linearized branch power equation;

[0093] The semi-invariants of the node phase angle, the node voltage and the branch power are substituted into the Gram-Charlier series expansion formula to obtain the probability density function of the node voltage amplitude fluctuation, the node phase angle fluctuation and the branch power fluctuation;

[0094] The probability distribution of the node voltage, the node phase angle and the branch active power is obtained by the branch power flow expected value vector, the node voltage amplitude vector, the node phase angle vector and the corresponding probability density function of the random variable translation fluctuation.

[0095] Specifically, since the node injection power is determined by the access nodes of all distributed power sources in the power system, the branch power flow expected value vector P L0 , the node voltage amplitude vector V0and the node phase angle vector θ0can be calculated by the formula (3) and the formula (5) of the above steps S100 and S110 when the node injection power takes the expected value.

[0096] The probability density function of the random variable can be expressed in the form of the Gram-Charlier series expansion formula as follows:

[0097] In the formula (13), N(x) is a standard normal distribution function, x is a random variable, K x,v represents the v-order semi-invariant of the random variable x, and H v(x) is a v-th order Hermite polynomial.

[0098] The Kth order semi-invariant of the node voltage amplitude, the node phase, and the branch power flow is substituted into formula (13) to obtain the probability density functions f(ΔV), f(Δθ) and f(ΔP ΔV,v ) of the node voltage amplitude fluctuation, the node phase fluctuation, and the branch power fluctuation. Δθ,v and K ΔPL,v ) of the node voltage amplitude fluctuation, the node phase fluctuation, and the branch power fluctuation. L ) of the node voltage amplitude fluctuation, the node phase fluctuation, and the branch power fluctuation. L0 ) of the node voltage amplitude fluctuation, the node phase fluctuation, and the branch power fluctuation. L ) of the node voltage amplitude fluctuation, the node phase fluctuation, and the branch power fluctuation.

[0099]

[0100] To further analyze the accuracy of the probabilistic power flow calculation method of the embodiment, a high-altitude mountainous power grid is taken as an analysis object, as shown in FIG. 3. Figure 2 The branch 13-30 power flow and the node 30 voltage are taken as examples and compared with the semi-invariant alternating current probabilistic power flow and the Monte Carlo method, and the probability distributions are shown in FIGS. 4, Figure 3 , 4 , 5 and 6. To quantitatively describe the accuracy, the Monte Carlo method calculation result is taken as a standard, and the root mean square error (RMSE) and the Hill inequality coefficient (TIC) are used to analyze the calculation error of the probabilistic power flow, as shown in Table 1. Figures 3-6 From FIG. 3 and Table 1, it can be seen that the probabilistic power flow calculation method proposed in the embodiment has little difference from the alternating current probabilistic power flow calculation result, can achieve high calculation accuracy, and does not need to be iteratively calculated, while the alternating current probabilistic power flow needs to be iteratively calculated 4 times, so the method greatly reduces the calculation time.

[0101] Table 1 Error statistical results and iteration times of different models

[0102]

[0103] Based on the same inventive concept, the embodiment provides a power flow calculation system. Since the principles of the systems for solving problems are similar to those of the power flow calculation method shown in FIG. 1, the implementation of the systems can refer to the embodiments of the method shown in FIG. 2, and the repeated parts will not be described herein again. As shown in FIG. 3, the power flow calculation system includes: Figure 1 Figure 1 Figure 7

[0104] ​​​The first decoupling module is configured to linearly decouple the node power injection equation to obtain a linearized node power injection equation.

[0105] The second decoupling module is configured to linearly decouple the branch power equation to obtain a linearized branch power equation.

[0106] The each-order moment calculation module is configured to calculate each-order moments of the node injection power distribution according to an expected value of the node injection power and a distribution of the node injection power fluctuation.

[0107] The each-order semi-invariant calculation module is configured to calculate each-order semi-invariants of the node injection power according to the each-order moments.

[0108] The semi-invariant calculation module is configured to calculate semi-invariants of the node phase angle, the node voltage and the branch active power according to the each-order semi-invariants of the node injection power and the linearized node power injection equation and the branch power equation.

[0109] The probability distribution calculation module is configured to obtain probability distributions of the node voltage, the node phase angle and the branch active power by substituting the semi-invariants of the node phase angle, the node voltage and the branch active power into a Gram-Charlier series expansion.

[0110] The power flow calculation system provided in the embodiment has the following advantages: the power flow probability calculation system provided in the application considers linearization of the AC power flow equation, obtains linearly decoupled node power injection equation and branch power equation, deduces semi-invariants of the node voltage, the node voltage amplitude and the branch active power based on each-order moments and semi-invariants of the injection power random variable, and obtains probability density functions of the system node voltage, the node voltage amplitude and the branch active power by using the Gram-Chalier series. The power flow probability calculation system solves the problem of organically unifying calculation accuracy, effectiveness and state variable integrity in the probability power flow calculation, and provides a theoretical basis for power grid planning and dispatching, power grid vulnerability analysis and power grid cascading failure prevention.

[0111] In still another embodiment of the present application, a computer device is provided, which comprises a processor and a memory, the memory is configured to store a computer program, the computer program comprises program instructions, and the processor is configured to execute the program instructions stored in the computer storage medium. The processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc., which are the computing core and control core of the terminal, and are suitable for implementing one or more instructions, and are particularly suitable for loading and executing one or more instructions in the computer storage medium to implement a corresponding method flow or a corresponding function; the processor in the embodiment of the present application can be used for the operation of the power system probabilistic power flow calculation method.

[0112] In still another embodiment of the present application, the present application further provides a storage medium, specifically a computer readable storage medium (Memory), which is a memory device in the computer device, and is configured to store programs and data. It can be understood that the computer readable storage medium herein can include the built-in storage medium in the computer device, and of course can also include the expansion storage medium supported by the computer device. The computer readable storage medium provides a storage space, and the storage space stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and the instructions can be one or more computer programs (including program codes). It should be noted that the computer readable storage medium herein can be a high-speed RAM memory, or a non-volatile memory such as at least one disk memory. One or more instructions stored in the computer readable storage medium can be loaded and executed by the processor to implement the corresponding steps of the probabilistic power flow calculation method in the above embodiment.

[0113] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can adopt a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer usable program codes.

[0114] The above detailed description of the specific embodiments of the present application is provided for the purpose of further explaining the purpose, technical solutions and beneficial effects of the present application. It should be understood that the above description is only a specific embodiment of the present application and is not used to limit the protection scope of the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A probabilistic power flow calculation method, characterized in that, include: Linear decoupling of the node power injection equation yields a linearized node power injection equation. Linear decoupling of the branch power equations yields linearized branch power equations; The moments of the node injected power distribution are calculated based on the expected value of the node injected power and the distribution of node injected power fluctuations. This calculation includes: subtracting the expected value of the node injected power from the random variable of the node injected power to obtain the distribution of node injected power fluctuations, where the node injected power includes node injected active power and node injected reactive power; and calculating the moments of the node injected power distribution based on the generator output and the distribution of node injected power fluctuations. The formulas for calculating the moments of the node injected power distribution are as follows: ,in, ΔP f is the expected value of the active power fluctuation distribution ΔP at the node. ΔPi Let be the probability value of the active power fluctuation distribution ΔP at the i-th node. ΔQ Let f be the expected value of the reactive power fluctuation distribution ΔQ at the node. ΔQi Let ΔQ be the probability value of the reactive power fluctuation distribution at the i-th node, and v represent the order of the moment. The semi-invariants of nodal injected power are calculated based on each order of moments. Specifically, the semi-invariants of the nodal injected power distribution are calculated using the relationship between random variables and semi-invariants. The formulas for calculating the semi-invariants of the nodal injected active power distribution are as follows: ;in, K represents the combination of choosing j numbers from v-1 numbers. ΔP,v The v-th order semi-invariant represents the distribution of active power fluctuations ΔP at the nodes; Based on the semi-invariants of the node injected power and the linearized node power injection equation and branch power equation, calculate the semi-invariants of node phase angle, node voltage and branch active power. Substituting the semi-invariants of node phase angle, node voltage, and branch active power into the Gram-Charlier series expansion yields the probability distributions of node voltage, node phase angle, and branch active power.

2. The probabilistic power flow calculation method according to claim 1, characterized in that, Based on the semi-invariants of nodal injected power and the linearized nodal power injection equation and branch power equation, calculate the semi-invariants of nodal phase angle, nodal voltage, and branch active power, including: The linearized nodal power injection equations and linearized branch power equations are converted into matrices; Based on the linear additivity of semi-invariants, the matrix is ​​combined with the semi-invariants of each order of the node injected active power distribution to obtain the semi-invariants for calculating the node phase angle, node voltage, and branch active power.

3. The probabilistic power flow calculation method according to claim 2, characterized in that, The matrix form of the linearized nodal power injection equation is: The matrix form of the linearized branch power injection equation is: In the formula, k is the node association vector of the corresponding branch, P is the node injected active power matrix, Q is the node injected reactive power matrix, θ is the node voltage phase matrix, V is the node voltage magnitude matrix, G is the node admittance real part matrix, B is the node admittance imaginary part matrix, and B' is the node admittance imaginary part matrix without self-admittance. The formulas for calculating the semi-invariants of node phase angle, node voltage, and branch power are as follows: , In the formula, K ΔV,v K is a v-order semi-invariant of the node voltage. Δθ,v K is a semi-invariant of order v for the phase angle of the nodes. ΔPL,v Let v be the v-order semi-invariant of the active power fluctuation of the branch.

4. The probabilistic power flow calculation method according to claim 1, characterized in that, Substituting the semi-invariants of node phase angle, node voltage, and branch active power into the Gram-Charlier series expansion yields the probability distributions of node voltage, node phase angle, and branch active power, including: When the node injected power is taken as the expected value, the branch power flow expected value vector, node voltage magnitude vector and node phase angle vector are calculated according to the linearized node power injection equation and the linearized branch power equation. Substituting the semi-invariants of node phase angle, node voltage, and branch power into the Gram-Charlier series expansion, we obtain the probability density functions of node voltage amplitude fluctuation, node phase angle fluctuation, and branch power flow fluctuation. The probability distributions of node voltage, node phase angle, and branch active power are obtained by using the branch power flow expectation vector, node voltage amplitude vector, node phase angle vector, and the probability density function of the corresponding random variable translation fluctuation.

5. A probabilistic power flow calculation system, characterized in that, include: The first decoupling module is configured to linearly decouple the node power injection equation to obtain a linearized node power injection equation. The second decoupling module is configured to linearly decouple the branch power equations to obtain linearized branch power equations. Each moment calculation module is configured to calculate the moments of the node injected power distribution based on the expected value of the node injected power and the distribution of node injected power fluctuations. Specifically, calculating the moments of the node injected power distribution includes: subtracting the expected value of the node injected power from the random variable of the node injected power to obtain the distribution of node injected power fluctuations, where the node injected power includes node injected active power and node injected reactive power; and calculating the moments of the node injected power distribution based on the generator output and the distribution of node injected power fluctuations. The formulas for calculating the moments of the node injected power distribution are as follows: ,in, ΔP f is the expected value of the active power fluctuation distribution ΔP at the node. ΔPi Let be the probability value of the active power fluctuation distribution ΔP at the i-th node. ΔQ Let f be the expected value of the reactive power fluctuation distribution ΔQ at the node. ΔQi Let ΔQ be the probability value of the reactive power fluctuation distribution at the i-th node, and v represent the order of the moment. The module for calculating semi-invariants of each moment is configured to calculate the semi-invariants of each order of node injected power based on each moment; wherein, by utilizing the relationship between random variables and semi-invariants, the semi-invariants of each order of node injected power distribution are calculated from each moment, and the formula for calculating the semi-invariants of each order of node injected active power distribution is as follows: ;in, K represents the combination of choosing j numbers from v-1 numbers. ΔP,v The v-th order semi-invariant represents the distribution of active power fluctuations ΔP at the nodes; The semi-invariant calculation module is configured to calculate the semi-invariants of node phase angle, node voltage and branch active power based on the semi-invariants of each order of node injected power and the linearized node power injection equation and branch power equation. The probability distribution calculation module is configured to substitute the semi-invariants of node phase angle, node voltage, and branch active power into the Gram-Charlier series expansion to obtain the probability distribution of node voltage, node phase angle, and branch active power.

6. An electronic device, characterized in that, include: One or more processors; A memory, coupled to the processor, for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the steps of the probabilistic power flow calculation method according to any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements a probabilistic power flow calculation method as described in any one of claims 1 to 4.

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