CN113312833A - Distributed energy access power distribution network risk assessment method - Google Patents

Distributed energy access power distribution network risk assessment method Download PDF

Info

Publication number
CN113312833A
CN113312833A CN202110409935.5A CN202110409935A CN113312833A CN 113312833 A CN113312833 A CN 113312833A CN 202110409935 A CN202110409935 A CN 202110409935A CN 113312833 A CN113312833 A CN 113312833A
Authority
CN
China
Prior art keywords
power
new energy
sample
cluster
vector
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202110409935.5A
Other languages
Chinese (zh)
Inventor
谢桦
吐尔洪
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Beijing Jiaotong University
Original Assignee
Beijing Jiaotong University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Beijing Jiaotong University filed Critical Beijing Jiaotong University
Priority to CN202110409935.5A priority Critical patent/CN113312833A/en
Publication of CN113312833A publication Critical patent/CN113312833A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • G06F18/232—Non-hierarchical techniques
    • G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
    • G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0635—Risk analysis of enterprise or organisation activities
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/08—Probabilistic or stochastic CAD
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2113/00—Details relating to the application field
    • G06F2113/04—Power grid distribution networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2113/00—Details relating to the application field
    • G06F2113/06—Wind turbines or wind farms
    • 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]

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Human Resources & Organizations (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Economics (AREA)
  • General Physics & Mathematics (AREA)
  • Strategic Management (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Educational Administration (AREA)
  • Evolutionary Computation (AREA)
  • Marketing (AREA)
  • Tourism & Hospitality (AREA)
  • Data Mining & Analysis (AREA)
  • Development Economics (AREA)
  • General Business, Economics & Management (AREA)
  • Operations Research (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Health & Medical Sciences (AREA)
  • Quality & Reliability (AREA)
  • Artificial Intelligence (AREA)
  • General Engineering & Computer Science (AREA)
  • Game Theory and Decision Science (AREA)
  • Evolutionary Biology (AREA)
  • Probability & Statistics with Applications (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Medical Informatics (AREA)
  • Software Systems (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Geometry (AREA)
  • Computer Hardware Design (AREA)
  • Public Health (AREA)
  • Water Supply & Treatment (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Power Engineering (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

The invention relates to a risk assessment method for a distributed energy access power distribution network, which comprises the following steps: step 1: and inputting historical data of new energy output. Step 2: and calculating by adopting an analytical method according to the correlation of the historical output data of the new energy to obtain an input random variable with the correlation. And step 3: and clustering the input new energy output variables by adopting a K-means clustering algorithm to obtain a plurality of clustering sample clusters. And 4, step 4: and performing deterministic load flow calculation on the cluster center samples, performing linear load flow solution on other sample points in the same cluster according to the cluster center state variable and the Jacobian matrix, and calculating the state variables corresponding to all the new energy output samples by adopting a piecewise linearization method. And 5: and calculating risk indexes corresponding to the calculated system state variables. Step 6: and calculating probability density functions by adopting a statistical method according to the input new energy output on all risk index calculation results.

Description

Distributed energy access power distribution network risk assessment method
Technical Field
The invention relates to the field of research of power systems, in particular to a distributed energy access power distribution network risk assessment method.
Background
Renewable energy sources such as wind energy, solar energy and the like are connected into a power distribution network for power generation, so that the traditional power generation mode can be effectively replaced. With the integration of a large number of distributed renewable energy power generation, the power distribution network is changed from a traditional passive network to a bidirectional tidal active network. While providing benefits to distribution grid reliability, the randomness of these distributed power outputs may pose a risk to the safety of the distribution system. The risk assessment method provided by the invention can be used for analyzing the reliability of the power system.
As for the risk assessment method, the monte carlo simulation method is a widely adopted method that obtains a simulation result by generating a large number of probability distributions for calculating the relevant indexes, but the calculation amount is large and the calculation time is long. Further, there is a document that calculates the distribution of the relevant index from the sensitivity and the series expansion using an analysis method such as an accumulation-based analysis method. The existing method has the problems of very complex calculation process and difficult realization. The invention considers the risk evaluation of time severity and occurrence probability, and is more beneficial to making decision during system planning.
Uncertain factors in the distributed new energy accessed to the power distribution network are complex and various, and randomness caused by the output of various new energy is mainly considered in the text, for example, the wind speed has the characteristics of fluctuation, intermittence and the like, and the wind speed characteristics of different wind fields in different time periods are also different; and the correlation between the output of the distributed new energy sources, the wind power plant usually reaches the maximum output power at night, the photovoltaic power plant generates power in the daytime with sunlight, and the wind power and the photovoltaic power generation have correlation between the internal output but have no correlation between the output. The photovoltaic and wind-power output of the same area are also correlated within each other; and aiming at different output conditions of the new energy, the uncertain factors are used as input random variables of the system.
Disclosure of Invention
The randomness of renewable energy generation poses operational risks to the systems of the distribution network. The power distribution network risk assessment method considering the correlation and randomness of the new energy generated output is provided. Firstly, obtaining the correlation of a plurality of new energy power generation according to historical data of a real solar photovoltaic power generation system and a wind power plant, and respectively generating photovoltaic and wind power random output sample data with the correlation by adopting a Nataf inverse transformation and Chlorosky decomposition method. And secondly, calculating the power flow of the power distribution system in different running states according to the generated output of the new energy, and simplifying and calculating the uncertainty of the distributed new energy in the power distribution network by adopting a piecewise linearization method. The calculation then calculates a system risk index by multiplying the severity by the probability.
In order to achieve the above purposes, the technical scheme adopted by the invention is as follows:
a risk assessment method for a distributed energy access power distribution network comprises the following steps:
step 1: inputting new energy output historical data of a new energy device needing to be accessed in a power distribution network system;
step 2: performing correlation modeling according to the historical data of the new energy output input in the step 1, and calculating by adopting an analytical method to obtain sample data of the new energy output with correlation, wherein the sample data is used as an input random variable of the power distribution network system;
and step 3: clustering by using the new energy output sample obtained in the step (2) as a variable by adopting a K-means clustering algorithm to obtain a plurality of clustering sample clusters;
and 4, step 4: selecting a cluster sample cluster, performing deterministic load flow calculation on the cluster center sample, performing linear load flow solution on other sample points in the same cluster according to the cluster center state variable and the Jacobian matrix, calculating the state variables of the power distribution network system corresponding to all the new energy output samples by adopting a piecewise linear method until all the cluster samples are selected, and entering step 5;
and 5: calculating risk indexes corresponding to the state variables of the power distribution network system calculated in the step 4;
step 6: calculating probability density functions by adopting a statistical method for all risk index calculation results by combining the new energy output sample data obtained in the step 2;
on the basis of the scheme, the new energy device comprises: a wind power generator and a solar photovoltaic power generation system; the new energy output historical data comprises at least two groups of wind driven generator data and at least two groups of solar photovoltaic power generation system data; the installed capacity of the wind driven generator is 1 MW; the installed capacity of the solar photovoltaic power generation system is 1.5 MW;
on the basis of the scheme, the step 2 of performing correlation modeling according to the new energy output historical data input in the step 1 comprises the steps of establishing a probability distribution model of wind speed and establishing a photovoltaic sample model.
On the basis of the scheme, the establishment of the probability distribution model of the wind speed specifically comprises the following steps:
the probability distribution model of the wind speed can reflect the characteristics of the wind speed of the wind power plant, and the double-parameter Weibull distribution can reflect the actual wind speed of the wind power plant;
the probability density function of a two-parameter-compliant Weibull distribution is as follows:
Figure BDA0003023748600000041
in the formula: v is wind speed, and the unit is m/s; k is a shape parameter and can reflect the shape of the wind speed; and c is a scale parameter which can reflect the average wind speed.
The power of the wind driven generator is influenced by wind speed, and after the wind speed data with correlation is known, the output sample data of the wind driven generator with correlation can be obtained and used as an input random variable of a power distribution network system;
the function between the output power of the wind generator and the wind speed is as follows:
Figure BDA0003023748600000042
Figure BDA0003023748600000043
B＝-Avci (4)
in the formula, PwindIs the output power of the wind driven generator; prThe rated output power of the wind driven generator; v is the wind speed; v. ofciCutting wind speed for the wind driven generator; v. ofrThe rated wind speed of the wind driven generator; v. ofcoCutting wind speed for the wind driven generator; A. b is a parameter;
on the basis of the scheme, the building of the photovoltaic sample model specifically comprises the following steps:
taking a photovoltaic output power variable as an example, assuming that n solar photovoltaic power generation systems exist in a certain area, w is a photovoltaic historical data vector with correlation, and for each vector w of wiNormalization was performed as follows:
Figure BDA0003023748600000044
in the formula: w is aiIs pair wiNormalized vector, EwiIs wiThe mean value of the vector is calculated,
Figure BDA0003023748600000045
is wiStandard deviation of the vector;
the photovoltaic historical data vector w is converted into a random vector w ' after being normalized, a correlation coefficient matrix Cw ' of the random vector w ' is a positive definite matrix, the normalization process is that linear transformation does not change the correlation between the photovoltaic historical data, and decomposition is carried out by a Cholesky decomposition method:
Gw'＝G'G'T (6)
in the formula: g' is a lower triangular matrix, and T is the transpose of the corresponding matrix.
The linear combination of random vector w 'that can be converted to standard normal distribution vector Z' is as follows:
w'＝G'Z' (7)
in the formula: z' represents a standard normal distribution vector as an initial input variable of the random variable model.
Defining D as a diagonal matrix, and the expression of the diagonal matrix is as follows:
Figure BDA0003023748600000051
in the formula: nw is the number of random variables in the photovoltaic historical data vector w.
The relation between the photovoltaic historical data vector w and the uncorrelated standard normal distribution vector Z' can be obtained according to the formulas (5), (7) and (8) as follows:
Figure BDA0003023748600000052
in the formula: n ═ DG';
Figure BDA0003023748600000053
in conclusion, the photovoltaic power generation output power sample with correlation can be generated according to the relation between the photovoltaic historical data vector w and the non-correlation standard normal distribution vector Z' and the standard normal distribution random number of the upper limit and the lower limit determined by the installed capacity.
On the basis of the scheme, the step 3 specifically comprises the following steps:
and 2, converting according to the step 2 to obtain new energy output sample data represented as W (W)1,w2,…,wn) The input random variable is expressed as the output vector of a plurality of new energy sources accessed to the power distribution network system;
step 31: determining a value for a suitable number of clusters K
And evaluating the clustering effect by adopting the weighted average radius R, wherein the expression is as follows:
Figure BDA0003023748600000061
in the formula, piThe ratio of the number of samples in the ith cluster to the sample capacity is obtained; r isiCluster radius of the ith cluster;
step 32: randomly selecting initial cluster center
Randomly selecting a cluster center, wherein the cluster center is defined as follows
M0＝[m1,m2,...mj,...,mk,]T (15)
Figure BDA0003023748600000062
In the formula: m0For multidimensional distributed energy output power variable, all cluster centers are represented, mjIs the jth cluster center, K is the preset cluster number,
Figure BDA0003023748600000063
the average value corresponding to the ith input random variable in the jth cluster is obtained;
step 33: calculating Euclidean distance from all sample points to each cluster center
The Euclidean distance between each sample point and the clustering center is calculated by the formula (17):
Figure BDA0003023748600000064
in the formula: d (l, j) is the Euclidean distance between the ith point and the jth cluster center; the first point is denoted as [ x ]l1，xl2，...xli，...xln]；xliIs XiThe l value;
step 34, calculating the mean value of the samples in each cluster, and updating the clustering center;
step 35 repeats 33, 34 until the sample points in the cluster no longer change or the maximum number of iterations is reached;
on the basis of the above scheme, step 4 specifically includes the following steps:
the power flow equation power form of the power distribution network system is as follows:
S＝f(X)
Z＝g(X) (10)
in the formula, X is a state variable, S is active power and reactive power injected by a node, and Z is branch transmission active power and reactive power.
Setting j node injection distributed energy output power, and injecting j node injection quantity S according to distributed energy output powerjUniformly segmenting into m segments, and recording as Sj0-Sj1、Sj1-Sj2……Sjm-1-Sjm；
Calculate the expectation for each segment and record as Ej1、Ej2……Ejm(ii) a Expectation of a non-injected node is E1、E2……Ej-1、Ek、Ej+1……En. Note Ak＝(E1,E2…Ej-1,Ek,Ej+1…En) Wherein k is 1, 2 … … m; make the power flow equation at A1、A2……AmLinearizes and ignores the higher order term.
S＝Sk+△S＝f(Xk+△X)＝f(Xk)+Jk△X+......
Z＝Zk+△Z＝g(Xk+△X)＝g(Xk)+Gk△X+...... (11)
Wherein Δ S [. DELTA.P,. DELTA.Q [. DELTA.P ]]T，△X＝[△σ,△U]TFluctuation of corresponding variable; j. the design is a squarekIs the Jacobian matrix, G, from the last iterationkIs the derivative of the branch power to the state variable.
Obtaining the relation between the state variable and the active power:
Figure BDA0003023748600000071
Figure BDA0003023748600000072
in the formula, TkAnd
Figure BDA0003023748600000073
and
Figure BDA0003023748600000074
is a sensitivity coefficient matrix.
Obtaining the corresponding injection quantity per section Delta S from the formula (12)jFor Δ xiThe influence coefficient of the photovoltaic output sample is obtained, so that the fluctuation value of the state variable when the photovoltaic output sample fluctuates near the expected kth section is obtained;
Figure BDA0003023748600000081
in the formula (I), the compound is shown in the specification,
Figure BDA0003023748600000082
is corresponding to xiA sensitivity coefficient matrix within each segment;
the invention has the beneficial effects that:
according to the method, the output correlation among distributed energy sources is considered for the power distribution network accessed by the distributed energy sources, and the clustering-improvement-based piecewise linearization power distribution network operation risk assessment is provided. And the relevance is considered when the uncertainty of the output of the distributed energy is considered by the relevance among historical data, so that the input variable is ensured to be more practical. The method has the advantages that the accuracy of the input variable size and the relevance of the power distribution network is improved, meanwhile, the risk calculation is rapidly carried out on a large number of input samples by adopting a piecewise linearization method, the number of segments is optimized based on a clustering method, the risk is rapidly calculated, and meanwhile, the accuracy of the output variable is improved. Support is provided for operation risk assessment of distributed energy sources accessed to the power distribution network, and basis is provided for scheduling decision-making personnel.
Drawings
The invention has the following drawings:
FIG. 1 is a flow chart of the present invention.
Detailed Description
The present invention is described in further detail below with reference to fig. 1.
Correlation modeling of new energy output historical data
(1) Establishing a probability distribution model of wind speed
The probability distribution model of the wind speed can well reflect the characteristics of the wind speed of the wind field in a certain area, and the double-parameter Weibull distribution can well reflect the actual wind speed condition of the wind field in the certain area.
The probability density function of a two-parameter-compliant Weibull distribution is as follows:
Figure BDA0003023748600000091
in the formula: v is wind speed, and the unit is m/s; k is a shape parameter and can reflect the shape of the wind speed; and c is a scale parameter which can reflect the average wind speed.
The power of the wind driven generator is influenced by wind speed, and after the wind speed data with the correlation is known, the output power data of the wind driven generator with the correlation can be obtained to be used as an input random variable of the power distribution network system.
The function between the output power of the wind generator and the wind speed is as follows:
Figure BDA0003023748600000092
Figure BDA0003023748600000093
B＝-Avci (4)
in the formula, PwindIs the output power of the wind driven generator; prThe rated output power of the wind driven generator; v is the wind speed; v. ofciCutting wind speed for the wind driven generator; v. ofrThe rated wind speed of the wind driven generator; v. ofcoCutting wind speed for the wind driven generator; A. b is a parameter;
(2) establishing a photovoltaic sample model:
uncertain factors of distributed new energy accessed to a power distribution network are complex and various, and randomness of output of various new energy is mainly considered in the application, for example, wind speed has characteristics of fluctuation, intermittence and the like, and wind speed characteristics of different wind fields in different time periods are different; and the correlation between the distributed new energy output, the photovoltaic output and the wind power output in the same area have correlation inside each other; and aiming at different output conditions of the new energy, the uncertain factors are used as input random variables of the power distribution network system.
Two factors need to be considered in the distributed new energy power generation output modeling, namely randomness of new energy output and strong correlation between new energy power generation outputs in the same region. The invention analyzes the historical data by considering the analytic method, thereby generating the new energy output input random variable with correlation according to the correlation between the historical data.
Taking a photovoltaic output power variable as an example, assuming that n solar photovoltaic power generation systems exist in a certain area, w is a photovoltaic historical data vector with correlation, and for each vector w of wiNormalization was performed as follows:
Figure BDA0003023748600000101
in the formula: w is aiIs pair wiNormalized vector, EwiIs the mean of the vector of wi,
Figure BDA0003023748600000102
is the standard deviation of the wi vector.
The photovoltaic historical data vector w is converted into a random vector w ' after being normalized, a correlation coefficient matrix Cw ' of the random vector w ' is usually a positive definite matrix, the normalization process is that linear transformation does not change the correlation between the photovoltaic historical data, and the photovoltaic historical data can be decomposed by a Cholesky decomposition method:
Gw'＝G'G'T (6)
in the formula: g' is a lower triangular matrix, and T is the transpose of the corresponding matrix.
The linear combination of random vector w 'that can be converted to standard normal distribution vector Z' is as follows:
w'＝G'Z' (7)
in the formula: z' represents a standard normal distribution vector as an initial input variable of the random variable model.
Defining D as a diagonal matrix, and the expression of the diagonal matrix is as follows:
Figure BDA0003023748600000111
in the formula: nw is the number of random variables in the historical data vector w.
The relationship between the historical data w and the uncorrelated normal distribution vector Z' can be obtained according to equations (5), (7) and (8) as follows:
Figure BDA0003023748600000112
in the formula: n ═ DG';
Figure BDA0003023748600000113
in conclusion, the photovoltaic output sample data with correlation can be generated by the standard normal distribution random number with the upper and lower limits determined according to the maximum value of the historical data according to the relation between the historical data and the non-relevant standard normal distribution vector.
(II) load flow calculation based on piecewise linearization
In the risk assessment of the distribution network with the distributed power supply, the advantage of piecewise linearization is that for a large number of samples, input variables can be segmented, deterministic power flow calculation is carried out on each section of expected points, other sample points of the same section are obtained by derivation according to a linearization formula of injection quantity and state variables (voltage) of the distribution network system, and time for carrying out power flow calculation on the sample points of the same section is shortened.
The power flow equation power form of the power distribution network system is as follows:
S＝f(X)
Z＝g(X) (10)
in the formula, X is a state variable, S is active power and reactive power injected by a node, and Z is branch transmission active power and reactive power.
Setting j node injection distributed energy output power, and injecting j node injection quantity S according to distributed energy output powerjUniformly segmenting into m segments, and recording as Sj0-Sj1、Sj1-Sj2……Sjm-1-Sjm；
Calculate the expectation for each segment and record as Ej1、Ej2……Ejm(ii) a Expectation of a non-injected node is E1、E2……Ej-1、Ek、Ej+1……En. Note Ak＝(E1,E2…Ej-1,Ek,Ej+1…En) Wherein k is 1, 2 … … m; make the power flow equation at A1、A2……AmLinearizes and ignores the higher order term.
S＝Sk+△S＝f(Xk+△X)＝f(Xk)+Jk△X+......
Z＝Zk+△Z＝g(Xk+△X)＝g(Xk)+Gk△X+...... (11)
Wherein Δ S [. DELTA.P,. DELTA.Q [. DELTA.P ]]T，△X＝[△σ,△U]TFluctuation of corresponding variable; j. the design is a squarekIs the Jacobian matrix, G, from the last iterationkIs the derivative of the branch power to the state variable.
Therefore, the relationship between the state variable and the active power can be obtained:
Figure BDA0003023748600000121
Figure BDA0003023748600000122
in the formula, TkAnd
Figure BDA0003023748600000123
and
Figure BDA0003023748600000124
is a sensitivity coefficient matrix.
Obtaining the corresponding injection quantity per section Delta S according to the formulajFor Δ xiSo as to obtain the fluctuation value of the state variable when the photovoltaic output sample fluctuates near the expected k section
Figure BDA0003023748600000125
In the formula (I), the compound is shown in the specification,
Figure BDA0003023748600000126
is corresponding to xiA sensitivity coefficient matrix within each segment;
when voltages of different injection power calculation systems fluctuate, only deterministic load flow calculation is needed to be carried out on expected values of the segmented new energy output to obtain values of the voltage variables, and fluctuation values of the voltage variables corresponding to other new energy output samples in the same segment can be obtained through derivation according to the linearization formulas, so that the load flow calculation times in risk assessment are reduced.
(III) K-means-based piecewise linearization
In the piecewise linearization solving process, the new energy injection sample is segmented into average segmentation, the calculation efficiency and precision are influenced inevitably by the number of the non-optimal segmentation, the influence of input random variables on output state variables is comprehensively considered by adopting the piecewise linearization improved based on the K-means clustering technology, the sample fluctuation range is further reduced according to the Euclidean distance between calculated sample points during clustering, the selection of division references among uniform partitions is avoided, a foundation is laid for solving the output state variables in the high-accuracy linearization manner, and therefore the accuracy of solving the output state variables is improved.
A large number of new energy output samples obtained by conversion according to an analytical method are represented as W (W)1,w2,…,wn) And the input random variable is expressed as a vector of output of a plurality of new energy sources accessed into the system.
A K-means clustering technology is adopted to divide a distributed energy input random variable sample into a plurality of clusters, and the K-means clustering process is divided into the following five steps.
Step (1): cluster number determination
For the K-means clustering algorithm, the larger the value of the clustering cluster number K is, the better the clustering effect is, but the consumed time is in direct proportion to the selection of the value K, and the overlong calculation time can be caused by the overlarge value K. Therefore, it is very important to select an appropriate K value to ensure the clustering effect and the calculation efficiency, and the clustering effect is evaluated by using the weighted average radius, and the expression is as follows:
Figure BDA0003023748600000131
in the formula, piThe ratio of the number of samples in the ith cluster to the sample capacity is obtained; r isiCluster radius of ith cluster
The weighted average radius R is smaller and smaller along with the gradual increase of the number K of the clustering clusters, namely, the clustering effect is better, the reduction trend of the R is slowed down when the K reaches a certain numerical value, the K value is increased again, the clustering effect is not obvious, namely, the numerical value at the moment is selected as the K value, and the clustering effect is ensured while less time is consumed. However, the value of K is not selected uniquely, and can be balanced according to the actual clustering effect and the calculation time efficiency requirement.
Step (2): randomly selecting initial cluster center
Randomly selecting a cluster center, wherein the cluster center is defined as follows
M0＝[m1,m2,...mj,...,mk,]T (15)
Figure BDA0003023748600000141
In the formula: m0For a multidimensional distributed energy output power variable dimension, all cluster centers are represented, mjIs the jth cluster center, namely the jth cluster center, K is the preset cluster number,
Figure BDA0003023748600000142
and the average value corresponding to the ith input random variable in the jth cluster is obtained.
And (3): calculating Euclidean distances from all points to each cluster center
The Euclidean distance between each sample point and the clustering center is calculated by the formula (17):
Figure BDA0003023748600000143
in the formula: d (l, j) is the Euclidean distance between the ith point and the jth cluster center; the first point is denoted as [ x ]l1，xl2，...xli，...xln]；xliIs XiThe l value;
(4) calculating the mean value of the samples in each cluster, and updating a clustering center;
(5) repeating (3) and (4) until the point in the cluster is not changed or the maximum number of repetitions is reached;
(IV) distributed energy access power distribution network risk assessment process
According to the introduction, the k-means clustering-based optimized piecewise linearization risk assessment calculation method is a flow chart. As shown in fig. 1:
step 1: inputting new energy output historical data of a new energy device needing to be accessed in a power distribution network system;
step 2: performing correlation modeling according to the historical data of the new energy output input in the step 1, and calculating by adopting an analytical method to obtain sample data of the new energy output with correlation, wherein the sample data is used as an input random variable of the power distribution network system;
and step 3: clustering by using the new energy output sample obtained in the step (2) as a variable by adopting a K-means clustering algorithm to obtain a plurality of clustering sample clusters;
and 4, step 4: selecting a cluster sample cluster, performing deterministic load flow calculation on the cluster center sample, performing linear load flow solution on other sample points in the same cluster according to the cluster center state variable and the Jacobian matrix, calculating the state variables of the power distribution network system corresponding to all the new energy output samples by adopting a piecewise linear method until all the cluster samples are selected, and entering step 5;
and 5: calculating risk indexes corresponding to the state variables of the power distribution network system calculated in the step 4;
step 6: calculating probability density functions by adopting a statistical method for all risk index calculation results by combining the new energy output sample data obtained in the step 2;
those not described in detail in this specification are within the skill of the art.

Claims (7)

1. A risk assessment method for a distributed energy access power distribution network is characterized by comprising the following steps:
step 1: inputting new energy output historical data of a new energy device needing to be accessed in a power distribution network system;
step 2: performing correlation modeling according to the historical data of the new energy output input in the step 1, and calculating by adopting an analytical method to obtain sample data of the new energy output with correlation, wherein the sample data is used as an input random variable of the power distribution network system;
and step 3: clustering by using the new energy output sample obtained in the step (2) as a variable by adopting a K-means clustering algorithm to obtain a plurality of clustering sample clusters;
and 4, step 4: selecting a cluster sample cluster, performing deterministic load flow calculation on the cluster center sample, performing linear load flow solution on other sample points in the same cluster according to the cluster center state variable and the Jacobian matrix, calculating the state variables of the power distribution network system corresponding to all the new energy output samples by adopting a piecewise linear method until all the cluster samples are selected, and entering step 5;
and 5: calculating risk indexes corresponding to the state variables of the power distribution network system calculated in the step 4;
step 6: and (3) calculating probability density functions by adopting a statistical method for all risk index calculation results by combining the new energy output sample data obtained in the step (2).
2. The distributed energy access power distribution network risk assessment method according to claim 1, wherein the new energy device comprises: a wind power generator and a solar photovoltaic power generation system; the new energy output historical data comprises at least two groups of wind driven generator data and at least two groups of solar photovoltaic power generation system data; the installed capacity of the wind driven generator is 1 MW; the installed capacity of the solar photovoltaic power generation system is 1.5 MW.
3. The method for assessing risk of accessing the distributed energy resource to the power distribution network according to claim 2, wherein the step 2 of performing correlation modeling according to the historical new energy output data input in the step 1 comprises establishing a probability distribution model of wind speed and establishing a photovoltaic sample model.
4. The risk assessment method for the distributed energy access to the power distribution network according to claim 3, wherein the establishing of the probability distribution model of the wind speed specifically comprises the following steps:
the probability distribution model of the wind speed can reflect the characteristics of the wind speed of the wind power plant, and the double-parameter Weibull distribution can reflect the actual wind speed of the wind power plant;
the probability density function of a two-parameter-compliant Weibull distribution is as follows:
Figure FDA0003023748590000021
in the formula: v is wind speed, and the unit is m/s; k is a shape parameter and can reflect the shape of the wind speed; c is a scale parameter which can reflect the average wind speed;
the power of the wind driven generator is influenced by wind speed, and according to the wind speed data with correlation, the output sample data of the wind driven generator with correlation can be obtained and used as an input random variable of the power distribution network system;
the function between the output power of the wind generator and the wind speed is as follows:
Figure FDA0003023748590000022
Figure FDA0003023748590000023
B＝-Avci (4)
in the formula, PwindIs the output power of the wind driven generator; prThe rated output power of the wind driven generator; v is the wind speed; v. ofciCutting wind speed for the wind driven generator; v. ofrThe rated wind speed of the wind driven generator; v. ofcoCutting wind speed for the wind driven generator; A. b is a parameter.
5. The distributed energy access distribution network risk assessment method of claim 3, wherein establishing the photovoltaic sample model specifically comprises the steps of:
assuming that a certain area has n solar photovoltaic power generation systems, w is a photovoltaic historical data vector with correlation, and for each vector w of wiNormalization was performed as follows:
Figure FDA0003023748590000031
in the formula: w is aiIs pair wiNormalized vector, EwiIs wiThe mean value of the vector is calculated,
Figure FDA0003023748590000032
is wiStandard deviation of the vector;
the photovoltaic historical data vector w is converted into a random vector w ' after being normalized, a correlation coefficient matrix Cw ' of the random vector w ' is a positive definite matrix, the normalization process is that linear transformation does not change the correlation between the photovoltaic historical data, and decomposition is carried out by a Cholesky decomposition method:
Gw'＝G'G'T (6)
in the formula: g' is a lower triangular matrix, and T is the transposition of the corresponding matrix;
the linear combination of random vector w 'that can be converted to a standard normal distribution vector Z' is as follows:
w'＝G'Z' (7)
in the formula: z' is a standard normal distribution vector and is an initial input variable of a random variable model;
defining D as a diagonal matrix, and the expression of the diagonal matrix is as follows:
Figure FDA0003023748590000033
in the formula: nw is the number of random variables in the photovoltaic historical data vector w;
the relation between the photovoltaic historical data vector w and the uncorrelated standard normal distribution vector Z' can be obtained according to the formulas (5), (7) and (8) as follows:
Figure FDA0003023748590000041
in the formula: n ═ DG';
Figure FDA0003023748590000042
in conclusion, the standard normal distribution random number with the upper and lower limits determined according to the relation between the photovoltaic historical data vector w and the non-relevant standard normal distribution vector Z' and the installed capacity can generate the photovoltaic power generation output power sample with the relevant limits.
6. The distributed energy access power distribution network risk assessment method according to claim 1, wherein step 3 specifically comprises the steps of:
and 2, converting according to the step 2 to obtain new energy output sample data represented as W (W)1,w2,…,wn) The input random variable is expressed as the output vector of a plurality of new energy sources accessed to the power distribution network system;
step 31: determining a value for a suitable number of clusters K
And evaluating the clustering effect by adopting the weighted average radius R, wherein the expression is as follows:
Figure FDA0003023748590000043
in the formula, piThe ratio of the number of samples in the ith cluster to the sample capacity is obtained; r isiCluster radius of the ith cluster;
step 32: randomly selecting initial cluster center
Randomly selecting a cluster center, wherein the cluster center is defined as follows
M0＝[m1,m2,...mj,...,mk,]T (15)
Figure FDA0003023748590000044
In the formula: m0Representing all clusters for multidimensional distributed energy output power variablesHeart, mjIs the jth cluster center, K is the preset cluster number,
Figure FDA0003023748590000051
the average value corresponding to the ith input random variable in the jth cluster is obtained;
step 33: calculating Euclidean distance from all sample points to each cluster center
The Euclidean distance between each sample point and the clustering center is calculated by the formula (17):
Figure FDA0003023748590000052
in the formula: d (l, j) is the Euclidean distance between the ith point and the jth cluster center; the first point is denoted as [ x ]l1，xl2，...xli，...xln]；xliIs XiThe l value;
step 34, calculating the mean value of the samples in each cluster, and updating the clustering center;
step 35 repeats steps 33, 34 until the sample points in the cluster no longer change or the maximum number of iterations is reached.
7. The distributed energy access power distribution network risk assessment method of claim 1, wherein step 4 specifically comprises the steps of:
the power flow equation power form of the power distribution network system is as follows:
S＝f(X)
Z＝g(X) (10)
in the formula, X is a state variable, S is active power and reactive power injected by a node, and Z is branch transmission active power and reactive power;
setting j node injection distributed energy output power, and injecting j node injection quantity S according to distributed energy output powerjUniformly segmenting into m segments, and recording as Sj0-Sj1、Sj1-Sj2……Sjm-1-Sjm；
Calculate the expectation for each segment and record as Ej1、Ej2……Ejm(ii) a Expectation of a non-injected node is E1、E2……Ej-1、Ek、Ej+1……En(ii) a Note Ak＝(E1,E2…Ej-1,Ek,Ej+1…En) Wherein k is 1, 2 … … m; make the power flow equation at A1、A2……AmLinearize and ignore higher order terms
S＝Sk+△S＝f(Xk+△X)＝f(Xk)+Jk△X+......
Z＝Zk+△Z＝g(Xk+△X)＝g(Xk)+Gk△X+...... (11)
Wherein Δ S [. DELTA.P,. DELTA.Q [. DELTA.P ]]T，△X＝[△σ,△U]TFluctuation of corresponding variable; j. the design is a squarekIs the Jacobian matrix, G, from the last iterationkThe derivative of the branch power to the state variable;
obtaining the relation between the state variable and the active power:
Figure FDA0003023748590000066
Figure FDA0003023748590000061
in the formula, TkAnd
Figure FDA0003023748590000062
and
Figure FDA0003023748590000063
is a sensitivity coefficient matrix;
obtaining the corresponding injection quantity per section Delta S from the formula (12)jFor Δ xiFromObtaining a fluctuation value of the state variable of the photovoltaic output sample when the photovoltaic output sample fluctuates near the expected k section;
Figure FDA0003023748590000064
in the formula (I), the compound is shown in the specification,
Figure FDA0003023748590000065
is corresponding to xiWithin each segment is a sensitivity coefficient matrix.
CN202110409935.5A 2021-04-16 2021-04-16 Distributed energy access power distribution network risk assessment method Pending CN113312833A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110409935.5A CN113312833A (en) 2021-04-16 2021-04-16 Distributed energy access power distribution network risk assessment method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110409935.5A CN113312833A (en) 2021-04-16 2021-04-16 Distributed energy access power distribution network risk assessment method

Publications (1)

Publication Number Publication Date
CN113312833A true CN113312833A (en) 2021-08-27

Family

ID=77372494

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110409935.5A Pending CN113312833A (en) 2021-04-16 2021-04-16 Distributed energy access power distribution network risk assessment method

Country Status (1)

Country Link
CN (1) CN113312833A (en)

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107730111A (en) * 2017-10-12 2018-02-23 国网浙江省电力公司绍兴供电公司 A kind of distribution voltage risk evaluation model for considering customer charge and new energy access
US20190305589A1 (en) * 2016-11-10 2019-10-03 China Electric Power Research Institute Company Limited Distribution network risk identification system and method and computer storage medium
CN110795691A (en) * 2019-08-25 2020-02-14 南京理工大学 Power distribution network risk assessment method based on improved Monte Carlo simulation method
CN110795841A (en) * 2019-10-24 2020-02-14 北京交通大学 Mathematical modeling method for uncertainty of intermittent energy output
CN111628499A (en) * 2020-06-02 2020-09-04 国网浙江省电力有限公司湖州供电公司 Method for evaluating new energy consumption capability of power distribution network considering multiple risk factors
CN111668845A (en) * 2020-06-16 2020-09-15 广东工业大学 Probability load flow calculation method considering photovoltaic correlation

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20190305589A1 (en) * 2016-11-10 2019-10-03 China Electric Power Research Institute Company Limited Distribution network risk identification system and method and computer storage medium
CN107730111A (en) * 2017-10-12 2018-02-23 国网浙江省电力公司绍兴供电公司 A kind of distribution voltage risk evaluation model for considering customer charge and new energy access
CN110795691A (en) * 2019-08-25 2020-02-14 南京理工大学 Power distribution network risk assessment method based on improved Monte Carlo simulation method
CN110795841A (en) * 2019-10-24 2020-02-14 北京交通大学 Mathematical modeling method for uncertainty of intermittent energy output
CN111628499A (en) * 2020-06-02 2020-09-04 国网浙江省电力有限公司湖州供电公司 Method for evaluating new energy consumption capability of power distribution network considering multiple risk factors
CN111668845A (en) * 2020-06-16 2020-09-15 广东工业大学 Probability load flow calculation method considering photovoltaic correlation

Similar Documents

Publication Publication Date Title
Hu et al. A new clustering approach for scenario reduction in multi-stochastic variable programming
Singh et al. An efficient technique for reliability analysis of power systems including time dependent sources
CN111628499B (en) Method for evaluating new energy consumption capability of power distribution network considering multiple risk factors
Eryilmaz et al. Reliability based modeling of hybrid solar/wind power system for long term performance assessment
CN105825002B (en) A kind of wind power plant dynamic equivalent modeling method based on dynamic Gray Association Analysis
CN111860950B (en) Probability assessment method for power adjustability of virtual power plant
CN112186761B (en) Wind power scene generation method and system based on probability distribution
CN109740962A (en) Voltage stabilization probability evaluation method of failure based on scene subregion and cumulant
CN109272258B (en) Regional wind and solar power generation resource evaluation method based on K-means clustering
CN105656084A (en) Improved stochastic load flow algorithm involved with new energy power generation prediction errors
Huang et al. An efficient probabilistic approach based on area grey incidence decision making for optimal distributed generation planning
CN111859283B (en) Scene generation method considering multi-energy charge time sequence and correlation
CN107834540B (en) Method for determining distributed photovoltaic access capacity based on probability constraint
Teferra et al. Fuzzy-based prediction of solar PV and wind power generation for microgrid modeling using particle swarm optimization
CN114021483A (en) Ultra-short-term wind power prediction method based on time domain characteristics and XGboost
CN115907339A (en) Power distribution network photovoltaic and electric vehicle charging station collaborative planning method based on GMM
CN104951654A (en) Method for evaluating reliability of large-scale wind power plant based on control variable sampling
Zhao et al. Probabilistic voltage stability assessment considering stochastic load growth direction and renewable energy generation
CN108694475B (en) Short-time-scale photovoltaic cell power generation capacity prediction method based on hybrid model
CN107276093B (en) The Probabilistic Load calculation method cut down based on scene
CN111861256B (en) Active power distribution network reconstruction decision method and system
CN113312833A (en) Distributed energy access power distribution network risk assessment method
Chenxu et al. An improved cumulant method for probabilistic load flow calculation
Khalghani et al. Demonstrating the importance of applying a new probabilistic power flow strategy to evaluate power systems with high penetration of wind farms
Zhang et al. Data-Driven Distributionally Robust Optimization-Based Coordinated Dispatching for Cascaded Hydro-PV-PSH Combined System

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination