CN109687431B - Active power distribution network probability equivalence modeling method considering new energy randomness - Google Patents

Active power distribution network probability equivalence modeling method considering new energy randomness Download PDF

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CN109687431B
CN109687431B CN201811472690.5A CN201811472690A CN109687431B CN 109687431 B CN109687431 B CN 109687431B CN 201811472690 A CN201811472690 A CN 201811472690A CN 109687431 B CN109687431 B CN 109687431B
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active power
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CN109687431A (en
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李志刚
黄彬
吴青华
郑杰辉
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China Electric Power Research Institute Co Ltd CEPRI
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]

Abstract

The invention discloses an active power distribution network probability equivalence modeling method considering new energy randomness, which comprises the following steps of: 1) acquiring operation data of the active power distribution network; 2) calculating semi-invariant data of node load and node new energy power generation in the active power distribution network; 3) calculating semi-invariant data of node injection power in the active power distribution network; 4) calculating an approximate linear relation between node injection power of the active power distribution network and equivalent injection power of boundary nodes; 5) calculating semi-invariant data of boundary node equivalent injection power based on semi-invariant data of node injection power in the active power distribution network and an approximate linear relation; 6) and obtaining a probability equivalent model of the active power distribution network according to the semi-invariant data of the boundary node equivalent injection power. The method takes the semi-invariant as a link, and establishes a probability equivalent model of the active power distribution network based on a node voltage equation of the power system so as to improve the analysis efficiency of the power transmission network and adapt to the scene of independent operation of power transmission and distribution.

Description

Active power distribution network probability equivalence modeling method considering new energy randomness
Technical Field
The invention relates to the technical field of power system modeling, in particular to an active power distribution network probability equivalence modeling method considering randomness of new energy.
Background
As new energy sources such as solar energy and wind energy have an environment-friendly characteristic, large-scale new energy power generation is being connected to an electric power system as a main way of utilizing the new energy sources. Conventional power distribution networks are also gradually changing to active power distribution networks that deliver power to the transmission network with more abundant generation of new energy. Therefore, the analysis of the grid-grid coupling system must take into account the impact of new energy sources. The method has the advantages that the equivalent model of the power distribution network is established, so that the analysis efficiency of the power transmission network can be improved, and the method is also suitable for a scene that the power transmission network and the power distribution network are operated by independent operators. Therefore, how to consider the influence of new energy when constructing an equivalent model of the active power distribution network becomes a problem to be solved.
The active power distribution network is characterized in that a large-scale distributed new energy power source is embedded, so that modeling of the distributed new energy power source is an important step for building an equivalent model of the active power distribution network. Uncertainty is an inherent characteristic of new energy power sources. In the economic scheduling problem, the uncertainty is ignored to cause unreliable scheduling results, and the scheduler also deploys redundant rotating spare capacity, thereby sacrificing the economy of the scheduling scheme. The uncertainty is also considered to provide a good solution for long-term planning and congestion management of the power grid. The traditional equivalent modeling neglects the randomness of the new energy power supply, so that the method is not suitable for modeling the active power distribution network.
In addition to uncertainty, the relevance of renewable energy sources is an important property. Because the geographical range of the power distribution network is smaller than that of the power transmission network, the meteorological conditions are similar in the same power distribution network, and therefore certain correlations exist between wind speeds of different wind power plants and between solar radiation of different photovoltaic power stations. Ignoring the dependencies can bias the analysis of the power system, resulting in high operating costs and higher risk of instability. When the active power distribution network is subjected to equivalent modeling, the correlation between wind speeds and the correlation between solar radiation must be considered, and then the correlation between new energy power sources and power generation is considered.
The invention provides an active power distribution network probability equivalence modeling method considering randomness of new energy, which is characterized in that the randomness of the new energy is described by using semi-invariant based on operation data of an active power distribution network, semi-invariant data of boundary node equivalent injection power are obtained by using an approximate linear relation between active power distribution network node injection power and boundary node equivalent injection power derived from a power flow equation, and a probability equivalence model of the active power distribution network is finally established.
Disclosure of Invention
The invention aims to overcome the defects and shortcomings of the prior art, provides an active power distribution network probability equivalent modeling method considering randomness of new energy, solves the problem that the randomness and the correlation of the new energy are not considered in the traditional equivalent modeling method, and establishes a probability equivalent model of an active power distribution network by taking a semi-invariant as a link and based on a node voltage equation of an electric power system so as to improve the analysis efficiency of a power transmission network and adapt to a scene of independent operation of power transmission and distribution.
In order to achieve the purpose, the technical scheme provided by the invention is as follows: an active power distribution network probability equivalence modeling method considering new energy randomness comprises the following steps:
1) acquiring operation data of the active power distribution network;
2) calculating semi-invariant data of node load and node new energy power generation in the active power distribution network;
3) calculating semi-invariant data of node injection power in the active power distribution network;
4) calculating an approximate linear relation between node injection power of the active power distribution network and equivalent injection power of boundary nodes;
5) calculating semi-invariant data of boundary node equivalent injection power based on semi-invariant data of node injection power in the active power distribution network and an approximate linear relation;
6) and obtaining a probability equivalent model of the active power distribution network according to the semi-invariant data of the boundary node equivalent injection power.
In step 1), the operation data of the active power distribution network comprises line impedance, line-to-ground capacitance susceptance, bus-to-ground capacitance susceptance, a power distribution network topological relation and transformer impedance.
In the step 2), acquiring semi-invariant data of node loads in the active power distribution network, comprising the following steps:
2.1.1) establishing a normal distribution model of the node load;
2.1.2) calculating a semi-invariant according to a normal distribution model of node loads:
Figure GDA0002467419860000031
Figure GDA0002467419860000032
in the formula (I), the compound is shown in the specification,
Figure GDA0002467419860000033
is a v-order semi-invariant vector, mu, of the active load of a nodePlIs an expected value vector of the normal distribution of the node active load,
Figure GDA0002467419860000034
is a variance vector of the normal distribution of the active load of the node,
Figure GDA0002467419860000035
is a v-order semi-invariant vector, mu, of the reactive load of the nodeQlIs an expected value vector of the node reactive load normal distribution,
Figure GDA0002467419860000036
the variance vector of the node reactive load normal distribution is shown, and 0 is a vector taking 0 as an element;
the method for acquiring semi-invariant data of node new energy power generation in the active power distribution network comprises the following steps:
2.2.1) respectively establishing a beta distribution model of illumination radiation and a Weibull distribution model of wind speed according to the characteristics of new energy power generation;
2.2.2) respectively obtaining an accumulated distribution function of the Gaussian Copula function corresponding to the wind speed and an accumulated distribution function of the Gaussian Copula function corresponding to the illumination radiation according to the wind speed correlation and the illumination radiation correlation;
2.2.3) sampling the cumulative distribution function obtained in the step 2.2.2) to obtain a sample with correlation;
2.2.4) based on the samples with the correlation in the step 2.2.3), the beta distribution model of the illumination radiation and the Weibull distribution model of the wind speed, adopting inverse transformation of the cumulative distribution function to respectively obtain correlation samples of the illumination radiation and the wind speed;
2.2.5) obtaining an output sample of the photovoltaic power station according to a function conversion relation between illumination radiation and the output of the photovoltaic power station, and obtaining an output sample of the wind power station according to a function conversion relation between wind speed and the output of the wind power station;
2.2.6) calculating the origin moments of all orders of the output of the photovoltaic power station and the output of the wind power plant:
Figure GDA0002467419860000041
in the formula, xjiIs a photovoltaic output sample or a wind power output sample of a node i, N is the number of samples, j is the index of the samples,
Figure GDA0002467419860000042
the output is node i photovoltaic output or wind power output v-order origin moment;
2.2.7) obtaining semi-invariant data of node new energy power generation in the active power distribution network according to the conversion relation between the origin moment and the semi-invariant:
γ(1)＝a1
Figure GDA0002467419860000043
in the formula, gamma(v)Is a semi-invariant of order v, avIs the origin moment of the v-order,
Figure GDA0002467419860000044
the number of combinations of j elements is taken out for v different elements.
In the step 3), adding the semi-invariant of the node new energy power generation and the semi-invariant of the node load according to the additivity of the semi-invariant to obtain semi-invariant data of the node injection power in the active power distribution network.
In step 4), based on a node voltage equation of the power system, obtaining an approximate linear relation between node injection power of the active power distribution network and equivalent injection power of a boundary node:
ΔPB＝EPPE+EQQE
ΔQB＝-EQPE+EPQE
in the formula,. DELTA.PBInjecting an active power vector, Δ Q, for boundary node equivalenceBReactive power vector, P, is injected for boundary node equivalenceEActive power vector, Q, is injected into active power distribution network nodesEReactive power vector, E, is injected into active distribution network nodesPAnd EPAre constant coefficient matrices, which can be calculated by:
EP＝CB1diag[|VE|]-2VE,Re+CB2diag[|VE|]-2VE,Im
EQ＝CB1diag[|VE|]-2VE,Im-CB2diag[|VE|]-2VE,Re
CB1＝diag[VB,Re](YBEYEE -1)Re-diag[VB,Im](YBEYEE -1)Im
CB2＝diag[VB,Im](YBEYEE -1)Re+diag[VB,Re](YBEYEE -1)Im
in the formula (x)ReAnd ()ImRespectively the real and imaginary parts of the complex, | is the magnitude of the complex, YBEFor the partial matrix of the power grid node admittance matrix, which is formed by the row boundary nodes and the column of the active power distribution network nodes, YEEFor partial matrixes V, which are formed by rows of active distribution network nodes and columns of active distribution network nodes in the grid node admittance matrixBIs a boundary node voltage vector, VEAnd the voltage vector is the node voltage vector of the active power distribution network.
In step 5), calculating to obtain semi-invariant data of the equivalent injection power of the boundary node based on the semi-invariant data of the node injection power in the active power distribution network and an approximate linear relationship, wherein a calculation formula is as follows:
Figure GDA0002467419860000051
Figure GDA0002467419860000052
wherein [. X [ ]]°vIs a Hadamard product of order v, E of a matrixPAnd EQIs a matrix of constant coefficients and is,
Figure GDA0002467419860000053
and
Figure GDA0002467419860000054
boundary node equivalent injection active power and reactive powerIs a vector of a semi-invariant quantity of,
Figure GDA0002467419860000055
and
Figure GDA0002467419860000056
the active power distribution network node injects semi-invariant vectors of active power and reactive power.
In step 6), obtaining a probability equivalent model of the active power distribution network according to the semi-invariant data of the boundary node equivalent injection power: the first-order semi-invariant is an expectation of a random variable, the second-order semi-invariant is a variance of the random variable, and the third-order and above semi-invariant also contain probability information of the random variable, so that an active power distribution network probability equivalent model can be built in a form of internal network boundary injection based on semi-invariant data of boundary node equivalent injection power, and consistency of power flow and probability characteristics is maintained.
Compared with the prior art, the invention has the following advantages and beneficial effects:
1. the method realizes the modeling of the active power distribution network considering the randomness of new energy for the first time, and breaks through the defect that the randomness is neglected in equivalent modeling of the traditional power network.
2. The active power distribution network modeling method based on the new energy source correlation is capable of realizing active power distribution network modeling considering the new energy source correlation for the first time, and breaking through the defect that the correlation is neglected in equivalent modeling of the traditional power distribution network.
3. The equivalent modeling work provided by the invention can be independently completed by an operator of the active power distribution network, so that the independence of power transmission and distribution operation is ensured, and the privacy of data is ensured.
4. The method uses the semi-invariant as a link to realize the modeling of the active power distribution network considering the randomness of new energy, thereby not only ensuring the consistency before and after the equivalence of the power transmission network tide, but also ensuring the consistency before and after the equivalence of the probability characteristic of the active power distribution network.
5. The method has wide application space in static safety analysis, load flow calculation, optimal load flow calculation and other applications of the power transmission network, has simple model and strong adaptability, and has wide prospect in improving the analysis efficiency of the power transmission network.
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FIG. 1 is a logic flow diagram of the present invention.
Detailed Description
The present invention will be further described with reference to the following specific examples.
As shown in fig. 1, the active power distribution network probability equivalence modeling method considering randomness of new energy provided by this embodiment includes the following steps:
1) and acquiring operation data of the active power distribution network, wherein the operation data comprises line impedance, line-to-ground capacitance susceptance, bus-to-ground capacitance susceptance, a power distribution network topological relation and transformer impedance.
2) The method for acquiring semi-invariant data of node loads in the active power distribution network comprises the following steps:
2.1.1) establishing a normal distribution model of the node load;
2.1.2) calculating a semi-invariant according to a normal distribution model of node loads:
Figure GDA0002467419860000061
Figure GDA0002467419860000071
in the formula (I), the compound is shown in the specification,
Figure GDA0002467419860000072
is a v-order semi-invariant vector, mu, of the active load of a nodePlIs an expected value vector of the normal distribution of the node active load,
Figure GDA0002467419860000073
is a variance vector of the normal distribution of the active load of the node,
Figure GDA0002467419860000074
is a v-order semi-invariant vector, mu, of the reactive load of the nodeQlIs an expected value vector of the node reactive load normal distribution,
Figure GDA0002467419860000075
the variance vector of the node reactive load normal distribution is shown, and 0 is a vector taking 0 as an element.
The method for acquiring semi-invariant data of node new energy power generation in the active power distribution network comprises the following steps:
2.2.1) respectively establishing a beta distribution model of illumination radiation and a Weibull distribution model of wind speed according to the characteristics of new energy power generation;
2.2.2) respectively obtaining an accumulated distribution function of the Gaussian Copula function corresponding to the wind speed and an accumulated distribution function of the Gaussian Copula function corresponding to the illumination radiation according to the wind speed correlation and the illumination radiation correlation;
2.2.3) sampling the cumulative distribution function obtained in the step 2.2.2 to obtain a sample with correlation;
2.2.4) based on the sample with the correlation in the 2.2.3, the beta distribution model of the illumination radiation and the Weibull distribution model of the wind speed, adopting inverse transformation of the cumulative distribution function to respectively obtain correlation samples of the illumination radiation and the wind speed;
2.2.5) obtaining an output sample of the photovoltaic power station according to a function conversion relation between illumination radiation and the output of the photovoltaic power station, and obtaining an output sample of the wind power station according to a function conversion relation between wind speed and the output of the wind power station;
2.2.6) calculating the origin moments of all orders of the output of the photovoltaic power station and the output of the wind power plant:
Figure GDA0002467419860000076
in the formula, xjiIs a photovoltaic output sample or a wind power output sample of a node i, N is the number of samples, j is the index of the samples,
Figure GDA0002467419860000081
the output is node i photovoltaic output or wind power output v-order origin moment;
2.2.7) obtaining semi-invariant data of node new energy power generation in the active power distribution network according to the conversion relation between the origin moment and the semi-invariant:
γ(1)＝a1
Figure GDA0002467419860000082
in the formula, gamma(v)Is a semi-invariant of order v, avIs the origin moment of the v-order,
Figure GDA0002467419860000083
the number of combinations of j elements is taken out for v different elements.
3) Semi-invariant data for calculating node injection power of active power distribution network
And adding the semi-invariant generated by the new energy of the node and the semi-invariant of the node load according to the additivity of the semi-invariant to obtain semi-invariant data of the node injection power of the active power distribution network.
4) Based on a node voltage equation of a power system, obtaining an approximate linear relation between node injection power of an active power distribution network and equivalent injection power of a boundary node:
ΔPB＝EPPE+EQQE
ΔQB＝-EQPE+EPQE
in the formula,. DELTA.PBInjecting an active power vector, Δ Q, for boundary node equivalenceBReactive power vector, P, is injected for boundary node equivalenceEActive power vector, Q, is injected into active power distribution network nodesEReactive power vector, E, is injected into active distribution network nodesPAnd EQAre constant coefficient matrices, which can be calculated by:
EP＝CB1diag[|VE|]-2VE,Re+CB2diag[|VE|]-2VE,Im
EQ＝CB1diag[|VE|]-2VE,Im-CB2diag[|VE|]-2VE,Re
CB1＝diag[VB,Re](YBEYEE -1)Re-diag[VB,Im](YBEYEE -1)Im
CB2＝diag[VB,Im](YBEYEE -1)Re+diag[VB,Re](YBEYEE -1)Im
in the formula (x)ReAnd ()ImRespectively the real and imaginary parts of the complex, | is the magnitude of the complex, YBEFor the partial matrix of the power grid node admittance matrix, which is formed by the row boundary nodes and the column of the active power distribution network nodes, YEEFor partial matrixes V, which are formed by rows of active distribution network nodes and columns of active distribution network nodes in the grid node admittance matrixBIs a boundary node voltage vector, VEAnd the voltage vector is the node voltage vector of the active power distribution network.
5) The semi-invariant data of the boundary node equivalent injection power is obtained by calculation based on the semi-invariant data of the node injection power in the active power distribution network and an approximate linear relation, and the calculation formula is as follows:
Figure GDA0002467419860000091
Figure GDA0002467419860000092
wherein [. X [ ]]°vIs a Hadamard product of order v, E of a matrixPAnd EPIs a matrix of constant coefficients and is,
Figure GDA0002467419860000093
and
Figure GDA0002467419860000094
respectively injecting semi-invariant vectors of active power and reactive power with the same value of boundary nodes,
Figure GDA0002467419860000095
and
Figure GDA0002467419860000096
the active power distribution network node injects semi-invariant vectors of active power and reactive power.
6) Obtaining a probability equivalent model of the active power distribution network according to the semi-invariant data of the boundary node equivalent injection power: the first-order semi-invariant is an expectation of a random variable, the second-order semi-invariant is a variance of the random variable, and the third-order and above semi-invariant also contain probability information of the random variable, so that an active power distribution network probability equivalent model can be constructed in an internal network boundary injection mode based on semi-invariant data of boundary node equivalent injection power, and consistency of power flow and probability characteristics is maintained.
In conclusion, after the scheme is adopted, the method provides a new method for the probability equivalent modeling of the active power distribution network considering the randomness of the new energy, and breaks through the problem that the randomness and the correlation of the new energy are not considered in the traditional equivalent modeling method. The method takes the semi-invariant as a link, establishes a probability equivalent model of the active power distribution network based on a node voltage equation of the power system, effectively improves the analysis efficiency of the power transmission network, can also be applied to a scene of independent operation of power transmission and distribution, has practical popularization value, and is worthy of popularization.
The above-mentioned embodiments are merely preferred embodiments of the present invention, and the scope of the present invention is not limited thereto, so that the changes in the shape and principle of the present invention should be covered within the protection scope of the present invention.

Claims (6)

1. An active power distribution network probability equivalence modeling method considering new energy randomness is characterized by comprising the following steps:
1) acquiring operation data of the active power distribution network;
2) calculating semi-invariant data of node load and node new energy power generation in the active power distribution network;
3) calculating semi-invariant data of node injection power in the active power distribution network;
4) based on a node voltage equation of a power system, obtaining an approximate linear relation between node injection power of an active power distribution network and equivalent injection power of a boundary node:
ΔPB＝EPPE+EQQE
ΔQB＝-EQPE+EPQE
in the formula,. DELTA.PBInjecting an active power vector, Δ Q, for boundary node equivalenceBReactive power vector, P, is injected for boundary node equivalenceEActive power vector, Q, is injected into active power distribution network nodesEReactive power vector, E, is injected into active distribution network nodesPAnd EPAre constant coefficient matrices, which can be calculated by:
EP＝CB1diag[|VE|]-2VE,Re+CB2diag[|VE|]-2VE,Im
EQ＝CB1diag[|VE|]-2VE,Im-CB2diag[|VE|]-2VE,Re
CB1＝diag[VB,Re](YBEYEE -1)Re-diag[VB,Im](YBEYEE -1)Im
CB2＝diag[VB,Im](YBEYEE -1)Re+diag[VB,Re](YBEYEE -1)Im
in the formula (x)ReAnd ()ImRespectively the real and imaginary parts of the complex, | is the magnitude of the complex, YBEFor the partial matrix of the power grid node admittance matrix, which is formed by the row boundary nodes and the column of the active power distribution network nodes, YEEFor partial matrixes V, which are formed by rows of active distribution network nodes and columns of active distribution network nodes in the grid node admittance matrixBIs a boundary node voltage vector, VEThe node voltage vector of the active power distribution network is obtained;
5) calculating semi-invariant data of boundary node equivalent injection power based on semi-invariant data of node injection power in the active power distribution network and an approximate linear relation;
6) and obtaining a probability equivalent model of the active power distribution network according to the semi-invariant data of the boundary node equivalent injection power.
2. The active power distribution network probability equivalence modeling method considering the randomness of new energy sources according to claim 1, characterized in that: in step 1), the operation data of the active power distribution network comprises line impedance, line-to-ground capacitance susceptance, bus-to-ground capacitance susceptance, a power distribution network topological relation and transformer impedance.
3. The active power distribution network probability equivalence modeling method considering the randomness of new energy sources according to claim 1, characterized in that: in the step 2), acquiring semi-invariant data of node loads in the active power distribution network, comprising the following steps:
2.1.1) establishing a normal distribution model of the node load;
2.1.2) calculating a semi-invariant according to a normal distribution model of node loads:
Figure FDA0002467419850000021
Figure FDA0002467419850000022
in the formula (I), the compound is shown in the specification,
Figure FDA0002467419850000023
is a v-order semi-invariant vector, mu, of the active load of a nodePlIs an expected value vector of the normal distribution of the node active load,
Figure FDA0002467419850000024
is a variance vector of the normal distribution of the active load of the node,
Figure FDA0002467419850000025
is a v-order semi-invariant vector, mu, of the reactive load of the nodeQlIs an expected value vector of the node reactive load normal distribution,
Figure FDA0002467419850000026
the variance vector of the node reactive load normal distribution is shown, and 0 is a vector taking 0 as an element;
the method for acquiring semi-invariant data of node new energy power generation in the active power distribution network comprises the following steps:
2.2.1) respectively establishing a beta distribution model of illumination radiation and a Weibull distribution model of wind speed according to the characteristics of new energy power generation;
2.2.2) respectively obtaining an accumulated distribution function of the Gaussian Copula function corresponding to the wind speed and an accumulated distribution function of the Gaussian Copula function corresponding to the illumination radiation according to the wind speed correlation and the illumination radiation correlation;
2.2.3) sampling the cumulative distribution function obtained in the step 2.2.2) to obtain a sample with correlation;
2.2.4) based on the samples with the correlation in the step 2.2.3), the beta distribution model of the illumination radiation and the Weibull distribution model of the wind speed, adopting inverse transformation of the cumulative distribution function to respectively obtain correlation samples of the illumination radiation and the wind speed;
2.2.5) obtaining an output sample of the photovoltaic power station according to a function conversion relation between illumination radiation and the output of the photovoltaic power station, and obtaining an output sample of the wind power station according to a function conversion relation between wind speed and the output of the wind power station;
2.2.6) calculating the origin moments of all orders of the output of the photovoltaic power station and the output of the wind power plant:
Figure FDA0002467419850000031
in the formula, xjiIs a photovoltaic output sample or a wind power output sample of a node i, N is the number of samples, j is the index of the samples, av,x·iThe output is node i photovoltaic output or wind power output v-order origin moment;
2.2.7) obtaining semi-invariant data of node new energy power generation in the active power distribution network according to the conversion relation between the origin moment and the semi-invariant:
γ(1)＝a1
Figure FDA0002467419850000032
in the formula, gamma(v)Is a semi-invariant of order v, avIs the origin moment of the v-order,
Figure FDA0002467419850000033
the number of combinations of j elements is taken out for v different elements.
4. The active power distribution network probability equivalence modeling method considering the randomness of new energy sources according to claim 1, characterized in that: in the step 3), adding the semi-invariant of the node new energy power generation and the semi-invariant of the node load according to the additivity of the semi-invariant to obtain semi-invariant data of the node injection power in the active power distribution network.
5. The active power distribution network probability equivalence modeling method considering the randomness of new energy sources according to claim 1, characterized in that: in step 5), calculating to obtain semi-invariant data of the equivalent injection power of the boundary node based on the semi-invariant data of the node injection power in the active power distribution network and an approximate linear relationship, wherein a calculation formula is as follows:
Figure FDA0002467419850000041
Figure FDA0002467419850000042
wherein [. X [ ]]°vIs a Hadamard product of order v, E of a matrixPAnd EQIs a matrix of constant coefficients and is,
Figure FDA0002467419850000043
and
Figure FDA0002467419850000044
respectively injecting semi-invariant vectors of active power and reactive power with the same value of boundary nodes,
Figure FDA0002467419850000045
and
Figure FDA0002467419850000046
the active power distribution network node injects semi-invariant vectors of active power and reactive power.
6. The active power distribution network probability equivalence modeling method considering the randomness of new energy sources according to claim 1, characterized in that: in step 6), obtaining a probability equivalent model of the active power distribution network according to the semi-invariant data of the boundary node equivalent injection power: the first-order semi-invariant is an expectation of a random variable, the second-order semi-invariant is a variance of the random variable, and the third-order and above semi-invariant also contain probability information of the random variable, so that an active power distribution network probability equivalent model can be built in a form of internal network boundary injection based on semi-invariant data of boundary node equivalent injection power, and consistency of power flow and probability characteristics is maintained.
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