CN116345463A - Main and distribution network integrated system random power flow calculation method - Google Patents

Abstract

The invention discloses a random power flow calculation method for a main distribution network integrated system, which includes the following steps: Step 1: collecting network parameters of the main distribution network; step 2: dividing the main distribution network according to the line voltage level and network topology structure and determining the boundary Node; Step 3: Establish wind power, photovoltaic output power and load probability density functions from data such as wind speed, light intensity and load; Step 4: Consider the randomness of wind power, photovoltaic and load, and use the point estimation method to calculate the random power flow of the distribution network; Step 5: Obtain the expected voltage amplitude and expected phase angle of the boundary nodes from the power flow results in Step 4; Step 6: Calculate the sum of the absolute values of the corresponding voltage amplitude differences of n boundary nodes in two adjacent iterations; Step 7: Use Gram ‑Charlier series expansion to find the probability density function of the output power flow variable. The present invention accurately evaluates load randomness and the power flow probability distribution of the main-distribution integrated power grid after a large number of new energy sources such as wind power and photovoltaics are connected, and performs static security analysis more effectively.

Description

A stochastic power flow calculation method for the integrated system of main distribution network

technical field

The invention belongs to the field of steady-state analysis of electric power systems, and relates to a random power flow calculation method of an integrated main distribution network system.

Background technique

The power flow calculation in the traditional sense is to calculate parameters such as network topology, line impedance parameters, transformer ratio, active power and reactive power output of generator nodes, active power and reactive power of load nodes as known quantities, generally known as Deterministic power flow calculations. However, in recent years, a large number of clean energy sources such as wind power and photovoltaics have been connected to the power grid, which has changed the power flow distribution of the system, and the output of wind power and photovoltaics is easily affected by environmental factors such as wind speed, light intensity, and weather. Wind power and photovoltaic output are no longer constants, but random variables that obey a certain type of probability distribution. Moreover, in the actual operation of the power grid, the load demand is also changing all the time. The above-mentioned changes lead to deterministic power flow calculations that cannot reflect the power flow distribution of the power grid in real time and accurately. The stochastic power flow can effectively consider various uncertain factors in the system, obtain the probability characteristics of the system power flow, and more truly reflect the power flow distribution of the power grid.

For the power flow calculation of the traditional power grid, a unified model is generally established, and the power flow calculation results can be obtained by using a certain method. For example, the Newton-Raphson method and PQ decomposition method can be used for the main network; Z-Bus method, etc., can obtain results that meet the convergence accuracy. However, since my country's power grid adopts a hierarchical and partitioned management system, dispatching agencies at all levels carry out detailed modeling of the power grid within their jurisdiction, and decentralized management leads to the phenomenon of information islands. Only limited information can be exchanged between systems at different levels. Fundamentally, it is difficult to establish a unified model for power flow calculation of the main distribution network. Even if sufficient information can be obtained and the integrated model of master and distribution is established at the expense of accuracy, the huge number of nodes and branches in the entire system will lead to an extremely large calculation scale, which requires a large amount of computing resources and is difficult to meet the needs of online computing. And there are obvious differences in the data of the main distribution network, such as the reactance-resistance ratio in the main network is much larger than that in the distribution network; there are also orders of magnitude differences in the parameter values of the main distribution network and branch power, etc., resulting in the nonlinear equation of power flow calculation Problems such as poor group convergence and easy singularity of the Jacobian matrix greatly increase the difficulty of power flow solution. Master-slave split method, as the most common master-distribution integrated power flow calculation method, conducts separate modeling and separate power flow calculations for the main distribution network, which fundamentally solves the above problems. When calculating the main network power flow, the distribution network is equivalent to For constant power loads, the main grid is equivalent to a constant voltage source in the power flow calculation of the distribution network, and the main distribution network is connected through boundary nodes, and finally converges through alternate iterative operations, which has been widely used. However, with a large number of new energy sources such as wind power and photovoltaics connected and considering the randomness of loads, the coupling between the main network and the distribution network is more closely coupled, and two-way energy flow may occur. The two continue to be simply equivalent to constant voltage source and The constant power load will increase the error between the calculation result and the actual value, so the traditional master-slave split method also has corresponding problems when calculating the random power flow of the master-distribution integrated system.

Contents of the invention

与相角的期望值/>

接着以边界节点为联系计算主网随机潮流，得到边界节点电压幅值的期望/>

与相角期望方差/>

计算相邻迭代n个边界节点相应电压幅值差的绝对值之和与相应相位差的绝对值之和是否均满足收敛精度。若均满足，则达到全局随机潮流收敛；否则步骤4至步骤6交替迭代直至收敛；判定潮流收敛后，当前主、配网潮流变量各阶矩即认为是主配网一体化系统潮流变量的各阶矩，利用Gram-Charlier级数展开求输出潮流变量的概率密度函数，最终可得到主配一体化系统潮流的概率分布，可用于主配网一体化系统的静态安全分析。In order to overcome the disadvantages of the above-mentioned prior art, the present invention provides a random power flow calculation method of the main distribution network integration system. Based on the principle of the master-slave split method, the stochastic power flow of the main distribution network is calculated separately. Firstly, the voltage of the boundary node is given, and the randomness of wind power, photovoltaic output and load is considered to calculate the random power flow of the distribution network, and the voltage amplitude of the boundary node is obtained.

Expected value with phase angle />

Then calculate the random power flow of the main network with the boundary node as the connection, and get the expectation of the voltage amplitude of the boundary node />

vs Phase Angle Expected Variance />

Calculate whether the sum of the absolute value of the corresponding voltage amplitude difference and the sum of the absolute value of the corresponding phase difference of n boundary nodes in adjacent iterations satisfy the convergence accuracy. If they are all satisfied, the convergence of the global random power flow is achieved; otherwise, steps 4 to 6 are iterated alternately until convergence; after the power flow is determined to be convergent, the moments of the current main and distribution network power flow variables are considered to be the respective moments of the main and distribution network integrated system power flow variables. Moments, using the Gram-Charlier series expansion to obtain the probability density function of the output power flow variable, and finally the probability distribution of the power flow of the integrated main distribution system can be obtained, which can be used for static security analysis of the integrated main distribution network system.

The present invention takes following technical scheme for solving technical problem:

A random power flow calculation method for an integrated main distribution network system includes the following steps:

Step 1: Collect network parameters of the main distribution network, including network topology, line parameters, generator node active power, reactive power and other parameters; historical data such as wind speed, light intensity and load;

Step 2: Divide the main distribution network according to the line voltage level, network topology, etc. and determine the boundary nodes. The boundary nodes form a set B, and the number of nodes is n. Set the boundary node voltage amplitude convergence criterion ε1 and phase angle convergence criterion ε ₂ ;

Step 3: Establish the wind power, photovoltaic output power and load probability density functions from the wind speed, light intensity and load data, and obtain their digital characteristics such as expectation and variance;

Step 4: Considering the randomness of wind power, photovoltaics and loads, use the point estimation method to calculate the random power flow of the distribution network, and obtain the moments of each order of the power flow variables of the distribution network;

与相角期望

以边界节点为联系计算主网随机潮流,得到边界节点电压的幅值期望

与相角期望/>

Step 5: Obtain the boundary node voltage amplitude expectation from the power flow results in step 4

with phase angle expectation

Calculate the random power flow of the main network with the boundary node as the connection, and obtain the amplitude expectation of the boundary node voltage

with phase angle expectation />

Step 6: Calculate whether the sum of the absolute value of the corresponding voltage amplitude difference and the sum of the absolute value of the corresponding phase difference of n boundary nodes in two adjacent iterations all meet the convergence accuracy, and convergence is achieved after all meet the accuracy, otherwise step 4 to step 6 alternate iterations until convergence;

Step 7: After judging the convergence of the power flow, the moments of the current flow variables of the main and distribution networks are the moments of the flow variables of the integrated system of the main distribution network, and the probability density function of the output flow variables is obtained by using the Gram-Charlier series expansion;

A random power flow calculation method for a main distribution network integration system, characterized in that the step 3 is carried out as follows:

For wind power generation, it is considered that the wind speed obeys the Weibull distribution. When the wind speed changes, the relationship between the output power of the wind turbine and the wind speed and the probability density function of the active power of wind power generation are as follows:

Where k ₁ =P _r /(v _r -v _ci ), k ₂ =-k ₁ v _ci is a constant coefficient; v is wind speed; v _ci is cut-in wind speed; v _co cut-out wind speed; v _r is rated wind speed; k and c are the shape parameters and scale parameters of the Weibull distribution respectively; they can be obtained from the average and standard deviation of the collected historical wind speed data; P _r is the rated output power of the wind turbine.

For photovoltaic power generation, it is considered that the light intensity approximately obeys the Beta distribution, and the probability density function of the output power of the photovoltaic array is:

In the formula: P _m and P _m,max are the actual value and maximum value of the output active power respectively; a and b are the shape parameters of the Beta distribution, which are determined by the average value and standard deviation of the collected historical light intensity data; Г is the gamma function. In the distribution simulation of the actual historical data of the load, it is considered that the load approximately obeys the normal distribution, then the load active power P _load ,

式中，，μ_P、μ_Q分别为负荷吸收有功、无功功率的期望；σ_P、σ_Q分别为负荷吸收有功、无功功率的方差。The probability density function of reactive power Q _load is:

In the formula, μ _P , μ _Q are the expectations of active and reactive power absorbed by the load, respectively; σ _P , σ _Q are the variances of active and reactive power absorbed by the load, respectively.

A method for calculating a random power flow in an integrated main distribution network, characterized in that the point estimation method in step 4 is performed as follows:

The numerical characteristics such as expected μ _k and variance _{σ k} are obtained from the probability density function of wind power, photovoltaic output and load, and the position coefficient ξ _k,i and probability coefficient p _k,i of each input variable are calculated.

Determine three value points x _k,i according to the mean and variance of random variables, i=1,2,3

x _k,i =μ _k +ξ _k,i σ _k ,i=1,2,3 (7)

Each value point of the random variable is x _k,i , and the mean value of the remaining random variables is used to calculate the deterministic power flow using the Newton-Raphson method to obtain the power flow distribution X(i, k).

For each random variable, there are three values, and three deterministic power flow calculations are required until all random variables are calculated. Use the following formulas to calculate the moments of each order of the distribution network power flow X.

A method for calculating the random power flow of the main distribution network integration system, characterized in that the step 6 is carried out as follows:

和/>

其中i＝1,2,…，n，n为边界节点个数；计算相邻两次迭代n个边界节点相应电压幅值差的绝对值之和与相应相位差的绝对值之和是否均满足收敛精度，满足精度后达到收敛，否则步骤4至步骤6交替迭代直至收敛，收敛判据如下：The stochastic power flow calculations for the distribution network and the main network respectively as described in steps 4 and 5 can obtain the expected value of the voltage amplitude and phase angle of the boundary nodes for two adjacent iterative calculations

and />

Wherein i=1,2,...,n, n is the number of boundary nodes; calculate whether the sum of the absolute value of the corresponding voltage amplitude difference and the sum of the absolute value of the corresponding phase difference of n boundary nodes in two adjacent iterations satisfy Convergence accuracy. Convergence is achieved after the accuracy is satisfied. Otherwise, step 4 to step 6 are iterated alternately until convergence. The convergence criterion is as follows:

Further, a device comprising:

one or more processors;

memory for storing one or more programs;

When one or more of the programs are executed by one or more of the processors, one or more of the processors implements the above-mentioned random power flow calculation method for the main distribution network integration system.

Further, a storage medium containing computer-executable instructions, the computer-executable instructions, when executed by a computer processor, are used to execute the above-mentioned random power flow calculation method for an integrated main distribution network system.

The beneficial effects of the present invention are:

Based on the principle of the master-slave split method and the point estimation random power flow algorithm, the present invention proposes a random power flow calculation method for the integrated system of the main distribution network, using the boundary nodes as links to calculate the random power flow for the distribution network and the main power grid respectively, and obtain the corresponding Adjacent to the expectation of the voltage amplitude and phase angle of the boundary node for two iterations, a new criterion is proposed to judge whether the random power flow of the main-distribution integrated system is convergent, taking into account the differences in the structure and parameters of the main-distribution network and the The influence of massive new energy access and load randomness in the integrated system on the power flow of the system is obtained, and the probabilistic characteristics of the power flow distribution of the integrated system of main and distribution systems are obtained, which can be used for static security analysis of the power system and is conducive to the safe and stable operation of the power system.

Description of drawings

Fig. 1 is a schematic flow sheet of the present invention;

Detailed ways

The following will clearly and completely describe the technical solutions in the embodiments of the present disclosure with reference to the accompanying drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only some of the embodiments of the present disclosure, not all of them. Based on the embodiments in the present disclosure, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present disclosure.

The present invention will be further described in detail with reference to the accompanying drawings.

As shown in Figure 1, a random power flow calculation method for the main distribution network integration system, the specific steps are as follows:

Step 1: Collect network parameters of the main distribution network, including deterministic parameters such as network topology, line parameters, active power and reactive power of generators and load nodes; historical data such as wind speed, light intensity and load;

Step 2: Divide the main distribution network and determine the boundary nodes according to the line voltage level, line topology, etc., and the number of boundary nodes is n;

As a bridge connecting the main distribution network, the boundary nodes can be understood as both load nodes in the main network and generator nodes in the distribution network. Set up the border node set B, the main network nodes remove the border nodes and the remaining nodes form a set T, and the remaining distribution network nodes form a set D.

Step 3: Establish the wind power, photovoltaic output power and load probability density functions from the wind speed, light intensity and load data, and obtain their digital characteristics such as expectation and variance;

For wind power generation, it is considered that the wind speed obeys the Weibull distribution. When the wind speed changes, the relationship between the output power of the wind turbine and the wind speed and the probability density function of the active power of wind power generation are as follows:

Where k ₁ =P _r /(v _r -v _ci ), k ₂ =-k ₁ v _ci is a constant coefficient; v is wind speed; v _ci is cut-in wind speed; v _co cut-out wind speed; v _r is rated wind speed; k and c are the shape parameters and scale parameters of the Weibull distribution respectively; they can be obtained from the average and standard deviation of the collected historical wind speed data; P _r is the rated output power of the wind turbine.

For photovoltaic power generation, it is considered that the light intensity approximately obeys the Beta distribution, and the probability density function of the output power of the photovoltaic array is:

In the formula: P _m and P _m,max are the actual value and maximum value of the output active power respectively; a and b are the shape parameters of the Beta distribution, which are determined by the average value and standard deviation of the collected historical light intensity data; Г is the gamma function.

In the distribution simulation of the actual historical data of the load, it is considered that the load approximately obeys the normal distribution, then the probability model of the load absorbing active power P _load and reactive power Q _load can be described as:

Among them, μ _P , σ _P , μ _Q , and σ _Q are the expectation and variance of the collected historical load active power and reactive power, respectively.

Step 4: Given the voltage amplitude and phase angle of the boundary nodes, considering the randomness of wind power, photovoltaics and loads, the point estimation method is used to calculate the random power flow of the distribution network, and obtain the moments of each order of the power flow variables of the distribution network;

The power flow equation of the distribution network is: S _D -S _DB -S _DD ＝0

Among them: S _D is the complex power vector injected by the nodes in the set D, S _DT is the vector composed of the branch complex power of the nodes in the node set D flowing into the node set T, S _DD is the support of the nodes in the node set D flowing into its own node set Replex power vector. Due to the randomness of wind power, photovoltaic output and load, the number of random variables contained in the distribution network power flow calculation is set to m, and one of the random variables is determined according to its expectation and variance. Three value points x _k,i , i =1,2,3, k=1,2,...m.

x _{k, i} = μ _k + ξ _{k, i} σ _k , i = 1, 2, 3 (5)

in:

For each value point, the remaining variables are averaged, and a deterministic power flow calculation is performed to obtain a definite power flow result f(x _k,i ). Three deterministic power flow calculations are required for a random variable. Repeat the above calculation process until all the random variables are calculated, and then the moments E(X ^j ) of distribution network power flow distribution can be obtained:

与相角期望/>

其中i＝1,2,…，n。n为边界节点个数。k为配网与主网相邻迭代的区分。B表示边界节点集合。The expected voltage amplitudes of the n boundary nodes can be obtained from the moments of the power flow distribution of the distribution network

with phase angle expectation />

where i=1, 2, . . . , n. n is the number of border nodes. k is the distinction between the adjacent iterations of the distribution network and the main network. B represents the set of boundary nodes.

与相角/>

具体过程如下所示：主网潮流方程为：Step 5: Calculate the random power flow of the main network with the boundary node as the connection, and get the expectation of the voltage amplitude of the boundary node

and phase angle />

The specific process is as follows: the power flow equation of the main network is:

Among them, S _T and S _B are the complex power vectors injected by _the nodes corresponding to the node set; S _XY is the complex power vector of each node on the node set X flowing into the node set Y; complex power vector.

与相角期望方差/>

其中i＝1,2,…，n。n为边界节点个数。k+1为配网与主网相邻迭代的区分。B表示边界节点集合。The point estimation method is still used to calculate the random power flow of the main network. The process is the same as that described in step 4, and will not be repeated here. Among them, the boundary nodes are equivalent to load nodes, which are treated as random variables. After the calculation is completed, the moment of each order of the power flow variable of the main network is obtained, that is, the expected value of the voltage amplitude of the boundary node is obtained

vs Phase Angle Expected Variance />

where i=1, 2, . . . , n. n is the number of border nodes. k+1 is the distinction between adjacent iterations of the distribution network and the main network. B represents the set of boundary nodes.

Step 6: Calculate whether the sum of the absolute value of the corresponding voltage amplitude difference and the sum of the absolute value of the corresponding phase difference of n boundary nodes in adjacent iterations satisfy the convergence accuracy. If all are satisfied, the convergence of global stochastic power flow is achieved; otherwise, step 4 to step 6 are iterated alternately until convergence, and the convergence criterion is as follows:

与/>

认为达到全局随机潮流收敛，退出循环，进行步骤7；否则回到步骤4，步骤4与步骤6交替迭代直至收敛。That is, if both satisfies

with />

It is considered that the global stochastic power flow has converged, exit the cycle, and proceed to step 7; otherwise, return to step 4, and step 4 and step 6 are iterated alternately until convergence.

Step 7: After judging the convergence of the power flow, the moment of each order of the current main and distribution network power flow variables is considered as each order moment of the power flow variable of the main distribution network integration system, and the probability density function of the output power flow variable is obtained by using the Gram-Charlier series expansion. The specific process is as follows:

其中μ和σ分别为X的期望及方差。则/>

的概率密度函数为：First, standardize the trend X of the main distribution network integration:

Among them, μ and σ are the expectation and variance of X, respectively. Then />

The probability density function of is:

为服从标准正态分布的概率密度函数。in

is a probability density function that follows a standard normal distribution.

Then the probability density function of the integrated power flow X of the main distribution network is:

Based on the same inventive concept, the present invention also provides a computer device, which includes: one or more processors, and a memory for storing one or more computer programs; the program includes program instructions, and the processor is used to execute Program instructions stored in memory. The processor may be a central processing unit (Central Processing Unit, CPU), or other general-purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable GateArray, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc., which are the computing core and control core of the terminal, which are used to implement one or more instructions, specifically for Load and execute one or more instructions in the computer storage medium to realize the above method.

It should be further explained that, based on the same inventive concept, the present invention also provides a computer storage medium, on which a computer program is stored, and when the computer program is run by a processor, the above method is executed. The storage medium may be any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electrical, magnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (non-exhaustive list) of computer readable storage media include: electrical connections with one or more leads, portable computer disks, hard disks, random access memory (RAM), read only memory (ROM), Erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

In the description of this specification, descriptions with reference to the terms "one embodiment", "example", "specific example" and the like mean that specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one of the present disclosure. In an embodiment or example. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

The basic principles, main features and advantages of the present disclosure have been shown and described above. Those skilled in the industry should understand that the present disclosure is not limited by the above-mentioned embodiments. The above-mentioned embodiments and descriptions only illustrate the principle of the present disclosure. Variations and improvements all fall within the scope of the claimed disclosure.

Claims (6)

1. A random power flow calculation method of a main distribution network integrated system comprises the following steps:

step 1: collecting network parameters of a main distribution network, including network topology, line parameters and active and reactive power parameters of generator nodes; wind speed, illumination intensity and load history data;

step 2: dividing a main distribution network according to line voltage levels and a network topological structure, determining boundary nodes, wherein the boundary nodes form a set B, the number of the boundary nodes is n, and setting boundary node voltage amplitude convergence criteria epsilon 1 and phase angle convergence criteria epsilon ₂ ；

Step 3: building wind power, photovoltaic output power and load probability density functions according to wind speed, illumination intensity and load data to obtain expected variance digital characteristics;

step 4: taking wind power, photovoltaic and load randomness into consideration, carrying out random power flow calculation on the distribution network by using a point estimation method to obtain each moment of a power flow variable of the distribution network;

step 5: obtaining the expected voltage amplitude of the boundary node from the tide result in the step 4

And phase angle expectation

Calculating the random power flow of the main network by taking the boundary nodes as relations to obtain the amplitude expectation of the boundary node voltage

Is about to phase angle expectation>

Step 6: calculating whether the sum of absolute values of corresponding voltage amplitude differences and the sum of absolute values of corresponding phase differences of n boundary nodes of two adjacent iterations meet convergence precision or not, and reaching convergence after the sum of absolute values meets the precision, otherwise, alternately iterating the steps 4 to 6 until convergence;

step 7: after the convergence of the power flow is judged, the moment of each step of the current main power flow variable and the moment of each step of the current power flow variable of the distribution network are the moment of each step of the power flow variable of the main power flow and distribution network integrated system, and the probability density function of the output power flow variable is obtained by means of Gram-Charlier series expansion.

2. The method for calculating the random power flow of the integrated system of the main distribution network according to claim 1, wherein the step 3 comprises:

for wind power generation, the wind speed is considered to follow Weibull distribution, and when the wind speed changes, the relation between the output power of the wind power generator and the wind speed and the probability density function of the active power of wind power generation are as follows:

k in ₁ ＝P _r /(v _r -v _ci )，k ₂ ＝-k ₁ v _ci Is a constant coefficient; v is wind speed; v _ci Is the cut-in wind speed; v _co Cutting out wind speed; v _r Is the rated wind speed; k. c is the shape parameter and the scale parameter of Weibull distribution respectively; the method can be obtained by the average value and standard deviation of the collected historical wind speed data; p (P) _r Rated output power of the wind driven generator;

for photovoltaic power generation, the illumination intensity is considered to obey beta distribution, and the probability density function of the output power of the photovoltaic array is as follows:

wherein: p (P) _m And P _m,max Respectively outputting an actual value and a maximum value of active power; a and b are shape parameters of Beta distribution, and are determined by the average value and standard deviation of collected historical illumination intensity data; f is a gamma function; in the distribution simulation of the actual historical data of the load, the load is considered to be approximately subjected to normal distribution, and then the load active power P _load Reactive power Q _load The probability density function of (2) is:

in the middle, mu _P 、μ _Q The method is characterized by respectively absorbing the expectations of active power and reactive power for the load; sigma (sigma) _P 、σ _Q The variances of the active power and the reactive power are absorbed by the load respectively.

3. The method for calculating the random power flow of the integrated system of the main distribution network according to claim 1, wherein the point estimation method of the step 4 is performed according to the following steps:

the expected mu is obtained by wind power, photovoltaic output and load probability density functions _k Variance sigma _k Equal digital characteristic, calculating position coefficient xi of each input variable _k,i Probability coefficient p _k,i ；

Determining three value points x according to the mean value and variance of the random variable _k,i ，i＝1,2,3

x _k,i ＝μ _k +ξ _k,i σ _k ,i＝1,2,3 (7)

Each value point value of the random variable is x _k,i Taking the average value of the rest random variables, and carrying out deterministic power flow calculation by utilizing a Newton-Lapherson method to obtain power flow distribution X (i, k) of the main-distribution integrated system;

three values are needed for each random variable, and 3 deterministic power flow calculations are needed until calculation is completed for all random variables; calculating each moment of the distribution network power flow X by using the following formula;

4. the method for calculating the random power flow of the integrated system of the main distribution network according to claim 1, wherein the step 6 is performed as follows:

the random power flow calculation is carried out on the distribution network and the main network respectively in the step 4 and the step 5 to obtain the expectation of calculating the voltage amplitude and the phase angle of the boundary node in two adjacent iterations

And->

Wherein i=1, 2, …, n, n is the number of boundary nodes; calculating whether the sum of absolute values of corresponding voltage amplitude differences and the sum of absolute values of corresponding phase differences of n boundary nodes of two adjacent iterations meet convergence precision, and reaching convergence after the convergence precision is met, otherwise, alternately iterating the steps 4 to 6 until convergence, wherein the convergence criterion is as follows:

5. an apparatus, the apparatus comprising:

one or more processors;

a memory for storing one or more programs,

the one or more programs, when executed by the one or more processors, cause the one or more processors to perform a method of stochastic load flow calculation of a primary distribution network integration system according to any of claims 1-4.

6. A computer readable storage medium storing a computer program, wherein the program when executed by a processor implements a method for calculating a random power flow of a main distribution network integrated system according to any one of claims 1 to 4.

Images