CN106548410B - Method for evaluating voltage unbalance probability of power distribution network containing distributed power supply - Google Patents
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Abstract
The invention provides a method for evaluating the probability of voltage unbalance of a power distribution network with a distributed power supply, which comprises the steps of firstly analyzing the relevant characteristics among various DGs, putting forward the output data of different Copula model simulation DGs, and finally evaluating the voltage unbalance of the power distribution network by a Monte Carlo simulation method (LHS-MCS) based on a Latin hypercube sampling technology, thereby obtaining the influence of the different relevant characteristics of various DGs on the power distribution network; the method can accurately describe different nonlinear characteristics among DGs, and avoids the occurrence of larger calculation errors; the influence of DG on the voltage unbalance of the power distribution network can be accurately evaluated, and the method has positive significance for distributed power supply planning and power distribution network operation.
Description
Technical Field
The invention relates to a method for evaluating the power quality of a power distribution network containing distributed power supplies in a power system, in particular to a method for evaluating the voltage unbalance probability of the power distribution network containing the distributed power supplies.
Background
A large number of single phase loads, two phase loads, and three phase unbalanced loads exist in the power distribution network. Although operators can try to uniformly arrange a large number of single-phase loads on a three-phase line according to actual conditions so as to keep the overall three-phase balance, due to the diversity, non-simultaneity and asymmetry of the loads, the power distribution network is often in an unbalanced multi-phase operation state, at the moment, a large number of negative sequence components contained in the system can generate a plurality of adverse effects on electrical equipment, such as additional heating and vibration of a rotating motor, increased transformer leakage flux and local overheating, accelerated insulation aging, reduced utilization rate, increased power grid line loss, reduced economy, even misoperation of relay protection and automatic devices is caused, and the safe operation of the power distribution network is influenced. Therefore, corresponding standards are provided at home and abroad to ensure the three-phase balance of the system, such as the regulations in the national standard GB/T15543-2008 'three-phase voltage unbalance of electric energy quality' and the European electric energy quality standard EN 50160: the 95% probability value of the negative sequence voltage imbalance (VUF) of the utility grid should be no greater than 2% for the duration of the measurement (one week), with a maximum value of no greater than 4% being required for all measurements.
With the Distributed Generation (DG) of a large-scale Distributed power supply being connected to a power distribution network in a decentralized manner, the power distribution network changes in power flow distribution and operation mode, and the problem of three-phase imbalance is more prominent. The DG can adopt a single-phase grid-connected operation mode, and wind power generation, photovoltaic power generation and the like in the DG have the characteristics of volatility, randomness and intermittence, so that uncertain factors in the operation of the power distribution network are greatly increased. Therefore, the three-phase load at each moment is more difficult to maintain a balanced state, the voltage three-phase imbalance of the public connection point is further aggravated, and the analysis and calculation of the three-phase imbalance of the power distribution network become more complicated.
Probabilistic methods, which are algorithms that can account for randomness and uncertainty factors, are finding increasing application in voltage imbalance assessment. However, in the operation of a distribution network containing a plurality of DGs, various uncertain factors are not changed independently, and a certain correlation exists between the uncertain factors. In a certain area, because of being under basically the same meteorological condition, there is certain correlation between the fans of different wind power stations and between the photovoltaic power supply of different photovoltaic power stations that are relatively close apart, output has certain synchronism. The wind-solar hybrid system can make up for random fluctuation of independent wind power generation and photovoltaic power generation to a certain extent, and the relevance between uncertain factors is reflected. Usually, when dealing with the relationship between random variables, a matrix of correlation coefficients is used, and the correlation coefficients are only linear in terms of relationship, not independent. When the relationship between the random variables is non-linear, the correlation coefficient is used to represent the independence between the random variables, which brings about a large calculation error and even leads to an erroneous conclusion.
Disclosure of Invention
In view of the above, the method for evaluating the voltage unbalance probability of the power distribution network with the distributed power supply can accurately describe different nonlinear characteristics among DGs, and avoids the occurrence of larger calculation errors; the influence of DG on the voltage unbalance of the power distribution network can be accurately evaluated, and the method has positive significance for distributed power supply planning and power distribution network operation.
The purpose of the invention is realized by the following technical scheme:
a method for evaluating the voltage unbalance probability of a power distribution network with distributed power sources is provided, wherein the power distribution network comprises a distributed power generation system; the distributed power generation system is a wind power generation system, a photovoltaic power generation system or a wind-solar combined power generation system; the method comprises the following steps:
step 1, determining the type and the relevance of the distributed power generation system;
step 2, constructing an edge distribution function model of the distributed power generation system;
step 3, constructing a joint probability distribution function of the distributed power generation systems by using a Copula function according to the correlation among the distributed power generation systems and an edge distribution function model;
and 4, carrying out unbalance probability evaluation on the distribution network voltage comprising the distributed power generation system according to the joint probability distribution model.
Preferably, if the distributed power generation system is a wind power generation system, the step 1 includes:
and drawing a wind speed scatter diagram between two adjacent wind power generation systems according to the measurement data of the wind power generation systems, and obtaining that the correlation of the wind speeds between the wind power generation systems is a tail-up positive correlation.
Preferably, if the distributed power generation system is a photovoltaic power generation system, the step 1 includes:
and drawing a photovoltaic power output scatter diagram between two adjacent photovoltaic power generation systems according to the measurement data of the photovoltaic power generation systems, and obtaining the output correlation of the photovoltaic power between the photovoltaic power generation systems as the positive symmetry.
Preferably, if the distributed power generation system is a wind-solar hybrid power generation system, the step 1 includes:
and drawing a sequence scatter diagram of the wind-solar hybrid power generation system according to the measurement data of the wind-solar hybrid power generation system to obtain that the correlation between the wind speed of the wind power generation system in the wind-solar hybrid power generation system and the output of the photovoltaic power generation system is negative correlation.
Preferably, if the distributed power generation system is a wind power generation system, the step 2 includes:
and constructing an edge distribution function model of the output of the wind power generation system, which is composed of the wind speed measurement data and the power curve of the wind turbine generator set, by adopting an indirect method according to the wind speed measurement data of the wind power generation system and the power curve of the wind turbine generator set.
Preferably, the indirect method comprises:
a. establishing a Weibull parameter model of the wind speed, and estimating parameters lambda and k by using a maximum likelihood method;
the Weibull probability density function of the wind speed v is as follows:
wherein k > 0 is a shape parameter and λ > 0 is a scale parameter;
wherein, the likelihood function of the maximum likelihood method is constructed as follows:
in the formula, vi(i 1, 2.., n) are n measured wind speeds of the wind power generation system;
the likelihood estimation values of k and lambda are obtained by solving the following formula:
b. combining the Weibull parameter model of the wind speed with the power curve of the wind turbine generator set to obtain an edge distribution function model of the output of the wind power generation system:
the power curve of the wind turbine is represented as:
in the formula, PrRated power v for wind turbineciFor cutting into the wind speed, vcoTo cut out wind speed, vrFor rated wind speed, P (v) for output power, A, B, C for model parameters, the calculation is as follows:
preferably, if the distributed power generation system is a photovoltaic power generation system, the step 2 includes:
according to the measurement data of the photovoltaic power generation system output, an edge distribution function model of the photovoltaic power generation system output is constructed by adopting a direct method:
the direct method is an nonparametric nuclear density estimation method; the non-parametric nuclear density estimation method comprises the following steps:
in the formula, pi(i 1, 2.., n) are n measurement samples of the photovoltaic power generation system output p, the probability density function of the output p is f (p),
for its estimate, h is the bandwidth, n is the number of samples, and K () is the kernel function.
Preferably, if the distributed power generation system is a wind-solar hybrid power generation system, the step 2 includes:
and determining an edge distribution function model of the output of the wind-solar combined power generation system by adopting the Weibull parameter model or the nonparametric kernel density estimation method according to the wind speed measurement data, the power curve of the wind turbine generator and the photovoltaic output measurement data of the wind-solar combined power generation system.
Preferably, the step 3 comprises:
3-1, selecting a common N-element Archimedes Copula function, and obtaining an estimated value of a corresponding correlation parameter alpha by using a maximum likelihood method;
the N-ary Archimedes Copula function is:
wherein, C () is an N-membered Archimedes Copula function; u. ofi＝Fi(xi) (i 1, 2.. N) is an edge distribution function of the N distributed power generation system outputs,
generating different Archimedes Copula functions for the generating elements of the Archimedes functions according to different generating elements: gauss Copula, Frank Copula, Gumbel Copula and Clayton Copula；
The maximum likelihood method is as follows:
in the formula (I), the compound is shown in the specification,
is an estimate of the correlation parameter α, ui＝Fi(vi,t) (i 1, 2.., N; t ═ 1, 2., N) is an edge distribution function of the distributed power supply, N is the element number of the Copula function, N is the measurement data sample number of the distributed power supply, c is the density function of the Copula function, and argmax represents the value of α when the maximum value is reached in the formula;
3-2, obtaining an optimal Copula function based on a shortest Euclidean distance method between an empirical Copula function and a theoretical Copula function, wherein the optimal Copula function is the joint probability distribution model:
the empirical Copula function is:
wherein I (·) is an indicative function, and if the condition in parentheses is satisfied, I ═ 1, whereas I ═ 0; v { (v)1,j,...,vN,j) J 1,2,. N } is an N-dimensional observation sample with the capacity of N; n is the element number of the Copula function;
is an order statistic and 1 is less than or equal to i1,...,iNN is less than or equal to n; said C iseIs an empirical Copula function;
the Euclidean distance is as follows:
preferably, the step 4 includes:
and according to the combined probability distribution model, performing probability evaluation on the voltage unbalance degree of the power distribution network comprising the wind-solar combined power generation system by adopting a Monte Carlo simulation method based on a Latin hypercube sampling technology.
According to the technical scheme, the invention provides a power distribution network voltage unbalance probability evaluation method comprising a distributed power supply, the method comprises the steps of firstly analyzing relevant characteristics among various DGs, proposing to establish different Copula models to simulate output data of the DGs, and finally evaluating the voltage unbalance degree of the power distribution network by a Monte Carlo simulation method (LHS-MCS) based on a Latin hypercube sampling technology, so that the influence of the different relevant characteristics of the various DGs on the power distribution network is obtained; the method can accurately describe different nonlinear characteristics among DGs, and avoids the occurrence of larger calculation errors; the influence of DG on the voltage unbalance of the power distribution network can be accurately evaluated, and the method has positive significance for distributed power supply planning and power distribution network operation.
Compared with the closest prior art, the technical scheme provided by the invention has the following excellent effects:
1. according to the technical scheme provided by the invention, the DG output modeling of various types of distribution can be met, and the method can be applied to a non-normal distribution parameter model and a model obtained by a non-parameter nuclear density estimation method.
2. The technical scheme provided by the invention can keep the nonlinear relation among different DGs unchanged when nonlinear transformation with the same monotonicity is carried out, thereby improving the accuracy of describing different nonlinear characteristics among the DGs.
3. The technical scheme provided by the invention can embody different nonlinear relations presented among random variables in the DG and can well simulate various DG output forces.
4. The technical scheme provided by the invention can accurately evaluate the influence of the relevant characteristics of different DGs on the voltage unbalance of the power distribution network and accurately analyze the running state of the power distribution network.
5. The technical scheme provided by the invention has important significance for reasonably planning the DGs in the power distribution network so as to reduce the fluctuation of the power distribution network.
Drawings
FIG. 1 is a flow chart of a method for estimating the probability of voltage imbalance in a power distribution network including distributed power sources according to the present invention;
fig. 2 is a flowchart of an application example of the method for estimating the voltage imbalance probability of the power distribution network including the distributed power source according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, the present invention provides a method for evaluating a voltage imbalance probability of a power distribution network including a distributed power source, where the power distribution network includes a distributed power generation system; the distributed power generation system is a wind power generation system, a photovoltaic power generation system or a wind-solar combined power generation system; the method comprises the following steps:
step 1, determining the type and the relevance of a distributed power generation system;
step 2, constructing an edge distribution function model of the distributed power generation system;
step 3, constructing a joint probability distribution function of the distributed power generation system by using a Copula function according to the correlation among the distributed power generation systems and the edge distribution function model;
and 4, carrying out unbalance probability evaluation on the voltage of the power distribution network comprising the distributed power generation system according to the joint probability distribution model.
Wherein, if the distributed power generation system is a wind power generation system, the step 1 comprises:
and drawing a wind speed scatter diagram between two adjacent wind power generation systems according to the measurement data of the wind power generation systems, and obtaining that the correlation of the wind speeds between the wind power generation systems is the tail-up positive correlation.
Wherein, if the distributed power generation system is a photovoltaic power generation system, the step 1 includes:
and drawing a photovoltaic power output scatter diagram between two adjacent photovoltaic power generation systems according to the measurement data of the photovoltaic power generation systems, and obtaining the output correlation of the photovoltaic power between the photovoltaic power generation systems as the positive symmetry.
Wherein, if the distributed power generation system is a wind-solar combined power generation system, the step 1 comprises:
and drawing a sequence scatter diagram of the wind-solar hybrid power generation system according to the measurement data of the wind-solar hybrid power generation system to obtain the negative correlation between the wind speed of the wind power generation system in the wind-solar hybrid power generation system and the output power of the photovoltaic power generation system.
Wherein, if the distributed power generation system is a wind power generation system, the step 2 includes:
and constructing an edge distribution function model of the output of the wind power generation system, which is composed of the wind speed measurement data and the power curve of the wind turbine generator set, by adopting an indirect method according to the wind speed measurement data of the wind power generation system and the power curve of the wind turbine generator set.
Wherein, the indirect method comprises the following steps:
a. establishing a Weibull parameter model of the wind speed, and estimating parameters lambda and k by using a maximum likelihood method;
the Weibull probability density function for wind speed v is:
wherein k > 0 is a shape parameter and λ > 0 is a scale parameter;
the likelihood function of the maximum likelihood method is constructed as follows:
in the formula, vi(i 1, 2.., n) are n measured wind speeds of the wind power generation system;
the likelihood estimation values of k and lambda are obtained by solving the following formula:
b. combining the Weibull parameter model of the wind speed with the power curve of the wind turbine generator set to obtain an edge distribution function model of the output of the wind power generation system:
the power curve of the wind turbine is represented as:
in the formula, PrRated power v for wind turbineciFor cutting into the wind speed, vcoTo cut out wind speed, vrFor rated wind speed, P (v) for output power, A, B, C for model parameters, the calculation is as follows:
wherein, if the distributed power generation system is a photovoltaic power generation system, step 2 includes:
according to the measurement data of the photovoltaic power generation system output, an edge distribution function model of the photovoltaic power generation system output is constructed by adopting a direct method:
the direct method is an nonparametric nuclear density estimation method; the non-parametric nuclear density estimation method comprises the following steps:
in the formula, pi(i 1, 2.., n) are n measurement samples of photovoltaic power generation system output p, the probability density function of the output p is f (p),
for its estimate, h is the bandwidth, n is the number of samples, and K () is the kernel function.
Wherein, if the distributed power generation system is a wind-solar combined power generation system, the step 2 comprises:
and determining an edge distribution function model of the output of the wind-solar combined power generation system by adopting a Weibull parameter model or a nonparametric kernel density estimation method according to wind speed measurement data, a power curve of a wind turbine generator and photovoltaic output measurement data of the wind-solar combined power generation system.
Wherein, step 3 includes:
3-1, selecting a common N-element Archimedes Copula function, and obtaining an estimated value of a corresponding correlation parameter alpha by using a maximum likelihood method;
the N-gram Archimedes Copula function is:
wherein, C () is an N-membered Archimedes Copula function; u. ofi＝Fi(xi) (i 1, 2.. N) is an edge distribution function of the N distributed power generation system outputs,
generating different Archimedes Copula functions for the generating elements of the Archimedes functions according to different generating elements: gauss Copula, Frank Copula, Gumbel Copula and Clayton Copula;
the maximum likelihood method is:
in the formula (I), the compound is shown in the specification,
is an estimate of the correlation parameter α, ui＝Fi(vi,t) (i 1, 2.., N; t ═ 1, 2., N) is an edge distribution function of the distributed power supply, N is the element number of the Copula function, N is the measurement data sample number of the distributed power supply, c is the density function of the Copula function, and argmax represents the value of α when the maximum value is reached in the formula;
3-2, obtaining an optimal Copula function based on a shortest Euclidean distance method between an empirical Copula function and a theoretical Copula function, wherein the optimal Copula function is a joint probability distribution model:
the empirical Copula function is:
wherein I (·) is an indicative function, and if the condition in parentheses is satisfied, I ═ 1, whereas I ═ 0; v { (v)1,j,...,vN,j) J 1,2,. N } is an N-dimensional observation sample with the capacity of N; n is the element number of the Copula function;
is an order statistic and 1 is less than or equal to i1,...,iN≤n；CeIs an empirical Copula function;
the Euclidean distance is:
wherein, step 4, include:
and according to the joint probability distribution model, performing probability evaluation on the voltage unbalance degree of the power distribution network comprising the wind-solar combined power generation system by adopting a Monte Carlo simulation method based on a Latin hypercube sampling technology.
As shown in fig. 2, the present invention provides a specific application example of a method for estimating a voltage imbalance probability of a power distribution network including a distributed power source, which first analyzes correlation characteristics among various DGs, proposes to establish different Copula models to simulate output data of the DGs, and finally estimates a voltage imbalance of the power distribution network based on a monte carlo simulation method (LHS-MCS) of a latin hypercube sampling technique, so as to obtain influences of different correlation characteristics of various DGs on the power distribution network. The method consists of four parts, namely DG correlation characteristic analysis, DG output modeling, correlation modeling and distribution network voltage unbalance probability assessment, and comprises the following steps:
(1) analysis of DG-related characteristics:
with the large-scale utilization of renewable energy sources, a plurality of wind power generation systems and photovoltaic power generation systems are connected to a power distribution network in the same area, and due to the fact that geographical positions are close, wind speed and illumination intensity have different regularity in development and change, and different relevant characteristics are reflected in statistical data.
The actual relevant characteristics of DG output data are described by drawing a scatter diagram method, and analysis shows that the relevant characteristics of different DG combined outputs are different: the wind speed shows strong positive correlation at the upper tail, the output of the photovoltaic power supply shows certain positive symmetry, and the output of the wind and light combined power generation system shows negative correlation.
(2) DG output modeling:
the DG output modeling mainly comprises an indirect method and a direct method. The indirect method is to indirectly obtain the DG output model by researching probability models of main factors (such as wind speed, solar irradiance or sky clear coefficient) influencing the DG according to the relationship between the factors and the DG output. The direct method is a method for studying data distribution characteristics completely from a DG output data sample, wherein the most used method is a non-parametric kernel density estimation method. The nonparametric kernel density estimation does not need to carry out distribution hypothesis on a research object, comprehensively considers various factors influencing the output, and more comprehensively reflects the probability characteristic of DG output. In the actual modeling process, different modeling modes are adopted according to the influence of various factors and the requirements of calculation accuracy and speed.
(3) And (3) correlation modeling:
the Copula function can accurately describe the nonlinear relation among random variables, has no requirement on the distribution type of the random variables, and is suitable for establishing a joint probability distribution model of a plurality of DGs. According to the Copula theory, the construction of the Copula model is divided into the following two steps: (1) the edge distribution of the DG output is determined and can be obtained by adopting a parametric model or a non-parametric kernel density estimation method. (2) An appropriate Copula function is determined to describe the correlation structure between random variables. Firstly, selecting a plurality of commonly used Copula functions and estimating parameters of the commonly used Copula functions; then, an optimal Copula function is selected according to a shortest euclidean distance method between an Empirical Copula (EMC) function and a Theoretical Copula (THC) function.
EMC can be expressed as:
wherein I (·) is an indicative function, and if the condition in parentheses is satisfied, I ═ 1, whereas I ═ 0; v { (v)1,j,...,vN,j) J 1,2,. N } is an N-dimensional observation sample with the capacity of N; n is the element number of the Copula function;
is an order statistic and 1 is less than or equal to i1,...,iN≤n。
The euclidean distance of EMC from THC is calculated by:
the output data of DG is simulated through a Copula model and compared with the simulation data of a TMM method;
the comparison result shows that the simulation data obtained by TMM simulation and the original data present obviously different data correlation structures, a certain number of negative numbers appear, and the correlation relationship is irregular. The simulation data obtained by the Copula model keeps better consistency with the original data. The Proavailability-Proavailability (P-P) graph was used to further verify the ability of the Copula model to fit the joint Probability distribution. The P-P diagram is a graph formed from the relationship between the cumulative proportion of random variables and the cumulative proportion of a given distribution, and is a graphical method for verifying whether data obeys a certain distribution. According to the P-P diagram theory, if the two multivariate distributions are from the same distribution, the theoretical distribution and the empirical distribution based on the data are in (x)i,yi) The probability scatter plot of (a) should lie on the diagonal.
Two-dimensional empirical distribution Fe(x, y) the following formula can be used:
(4) and (3) evaluating the probability of the voltage unbalance of the power distribution network:
and (3) performing probability evaluation analysis on the voltage unbalance degrees of the power distribution network containing different DGs in an improved IEEE34 node feeder test system by using the Copula simulated data and adopting a Monte Carlo simulation method (LHS-MCS) based on the Latin hypercube sampling technology, and comparing the probability evaluation analysis with a TMM method. And comparing the VUF of each node obtained by calculation with the national standard, and accurately evaluating the voltage quality and the running state of the power distribution network.
To facilitate comparison of the effects of two different methods on VUF, the following average error indicators were defined:
the average indices of the two methods are compared as shown in table 1:
TABLE 1 average error comparison
The Copula model fully reflects the influence of the relevant characteristics of different DGs on the power distribution network voltage unbalance probability evaluation, and the validity and the correctness of the Copula model are verified by comparing the Copula model with the TMM.
In the actual DG modeling process, different modeling modes are adopted according to the influence of random factor factors influencing the DG output and the requirements on calculation accuracy and speed, and the comprehensive influence among various DGs is considered in the analysis of a power distribution network containing various DGs; the invention provides a method for drawing a scatter diagram of wind power generation output, photovoltaic power generation output and wind-solar combined power generation output to obtain different relevant characteristics of different DG combined outputs; the invention analyzes the similarities and differences of various correlation indexes, and provides a Copula model method and a Copula model step for building DG output on the basis of a Copula theory; the model has no requirement on the distribution type of DG random variables, can meet various DG output modeling requirements, and can be applied to a non-normal distribution parameter model and a model obtained by a non-parameter kernel density estimation method; when the model carries out the nonlinear transformation with the same monotonicity on the DG model, the correlation relation among random variables is unchanged, and the mathematical basis is laid for the further analysis of the DG output; different Copula functions are selected according to the Euclidean distance to capture different correlation characteristics among random variables, and different correlation characteristics among DGs are reflected; the correctness of the Copula model is verified by applying a P-P diagram; through example analysis, the validity of the Copula model is verified, and the method has important practical significance for accurate evaluation of the voltage unbalance probability of the power distribution network.
Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art can make modifications and equivalents to the embodiments of the present invention without departing from the spirit and scope of the present invention, which is set forth in the claims of the present application.
Claims (5)
1. A method for evaluating the voltage unbalance probability of a power distribution network with distributed power sources is provided, wherein the power distribution network comprises a distributed power generation system; the distributed power generation system is a wind power generation system, a photovoltaic power generation system or a wind-solar combined power generation system; characterized in that the method comprises the following steps:
step 1, determining the type and the relevance of the distributed power generation system;
step 2, constructing an edge distribution function model of the distributed power generation system;
step 3, constructing a joint probability distribution model of the distributed power generation systems by using a Copula function according to the correlation among the distributed power generation systems and the edge distribution function model;
step 4, carrying out unbalance degree probability evaluation on the power distribution network voltage comprising the distributed power generation system according to the joint probability distribution model;
if the distributed power generation system is a wind power generation system, the step 1 includes:
according to the measurement data of the wind power generation systems, drawing a wind speed scatter diagram between two adjacent wind power generation systems to obtain that the correlation of the wind speeds between the wind power generation systems is a tail-up positive correlation;
if the distributed power generation system is a wind power generation system, the step 2 includes:
according to the wind speed measurement data of the wind power generation system and the power curve of the wind turbine generator set, an edge distribution function model of the output of the wind power generation system, which is composed of the wind speed measurement data and the power curve of the wind turbine generator set, is established by an indirect method;
the indirect method comprises the following steps:
a. establishing a Weibull parameter model of the wind speed, and estimating parameters lambda and k by using a maximum likelihood method;
the Weibull probability density function of the wind speed v is as follows:
wherein k > 0 is a shape parameter and λ > 0 is a scale parameter;
wherein, the likelihood function of the maximum likelihood method is constructed as follows:
in the formula, viN measured wind speeds, i ═ 1,2, …, n for the wind power system;
the likelihood estimation values of k and lambda are obtained by solving the following formula:
b. combining the Weibull parameter model of the wind speed with the power curve of the wind turbine generator set to obtain an edge distribution function model of the output of the wind power generation system:
the power curve of the wind turbine is represented as:
in the formula, PrRated power v for wind turbineciFor cutting into the wind speed, vcoTo cut out wind speed, vrFor rated wind speed, P (v) for output power, A, B, C for model parameters, the calculation is as follows:
the step 4 comprises the following steps:
and according to the combined probability distribution model, performing probability evaluation on the voltage unbalance degree of the power distribution network comprising the wind-solar combined power generation system by adopting a Monte Carlo simulation method based on a Latin hypercube sampling technology.
2. The method of claim 1, wherein if the distributed power generation system is a photovoltaic power generation system, the step 1 comprises:
and drawing a photovoltaic power output scatter diagram between two adjacent photovoltaic power generation systems according to the measurement data of the photovoltaic power generation systems, and obtaining the output correlation of the photovoltaic power between the photovoltaic power generation systems as the positive symmetry.
3. The method according to claim 1, wherein if the distributed generation system is a wind-solar hybrid generation system, the step 1 comprises:
and drawing a sequence scatter diagram of the wind-solar hybrid power generation system according to the measurement data of the wind-solar hybrid power generation system to obtain that the correlation between the wind speed of the wind power generation system in the wind-solar hybrid power generation system and the output of the photovoltaic power generation system is negative correlation.
4. The method of claim 1, wherein if the distributed power generation system is a photovoltaic power generation system, the step 2 comprises:
according to the measurement data of the photovoltaic power generation system output, an edge distribution function model of the photovoltaic power generation system output is constructed by adopting a direct method:
the direct method is an nonparametric nuclear density estimation method; the non-parametric nuclear density estimation method comprises the following steps:
in the formula, piN measurement samples of the photovoltaic power generation system output p, i is 1, 2. The probability density function of the contribution p is f (p),
for its estimate, h is the bandwidth, n is the number of samples, and K () is the kernel function.
5. The method of claim 4, wherein if the distributed generation system is a wind-solar combined generation system, the step 2 comprises:
and determining an edge distribution function model of the output of the wind-solar combined power generation system by adopting the Weibull parameter model or the nonparametric kernel density estimation method according to the wind speed measurement data, the power curve of the wind turbine generator and the photovoltaic output measurement data of the wind-solar combined power generation system.
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