CN109102146A - Study of Risk Evaluation Analysis for Power System accelerated method based on multi-parameter linear programming - Google Patents
Study of Risk Evaluation Analysis for Power System accelerated method based on multi-parameter linear programming Download PDFInfo
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Abstract
The invention discloses one kind to be based on multi-parameter linear programming Study of Risk Evaluation Analysis for Power System accelerated method, and this method establishes the corresponding cutting load amount minimum Optimized model of operation states of electric power system and its corresponding multi-parameter linear programming model；Establish line status dictionary collection；For a certain operation states of electric power system for sampling and obtaining, retrieves and the key matched in its corresponding line state dictionary element of set element judges region collection；If matching is unsuccessful, optimization software is used to solve the corresponding Optimized model with cutting load loss reduction of the operating status, and establish new key with solving result and judge region；If successful match, the corresponding each node of the operating status is directly calculated using the characteristic information of corresponding crucial critical region and loses load.Study of Risk Evaluation Analysis for Power System efficiency is improved using this method, the risk assessment speed of power grid is improved, provides decision references and support for Electric Power Network Planning and operations staff.
Description
Technical field
The present invention relates to Power System Analysis technical fields, more particularly to the electric system based on multi-parameter linear programming
Risk assessment accelerated method.
Background technique
Exist in electric system now a large amount of uncertain.These probabilistic cumulative and interactions, so that electric
Force system real time execution is among risk.For example, generator has the probability stopped transport in power plant, system-wide hair may cause
Electric scarce capacity, so as to cause being forced to have a power failure；There are probability out of service for transmission line, so that total system occurs trend and turns
It moves, influences the transmission distribution of power, may cause and be unable to satisfy workload demand；A variety of probabilistic interactions, are more likely to
Lead to the generation of major accident.
Therefore it needs to carry out risk assessment to electric system, i.e., by probabilistic method, to related uncertain in electric system
Property is modeled, according to uncertainties model as a result, assessing system-wide power failure risk.According to time scale, risk
Assessment can be divided into the assessment of long-term and middle or short term.Long-term risk assessment result there is directiveness to make the planning of electric system
With, and the risk assessment of middle or short term with auxiliary power system management and running personnel's decision, can then improve the operation peace of electric system
Quan Xing.
Due to during Study of Risk Evaluation Analysis for Power System, needing to generate a large amount of sample for simulating Future Power System
Randomness, and the cutting load situation of each sample is judged using optimal load flow method, therefore its is computationally intensive, analysis speed
Degree constrains application of the methods of risk assessment in actual electric network slowly.In order to overcome this difficulty, a large amount of improve assesses efficiency
Method is suggested.In sampling technique, including truncation sampling, stratified sampling, state space pruning, intelligent search, importance are adopted
Sample is suggested based on all kinds of efficient sampling techniques such as cross-entropy method, for improving sampling efficiency, with less sample come mould
Quasi- electric system randomness, obtains preferable convergence.By reducing the sample size needed, to accelerate risk assessment processes.
On the assessment technology of sample, existing methods and techniques more lack.A part is based on artificial neural network, support vector machines
The method of equal machine learning techniques is suggested, but they are all based on system branch (transmission line of electricity, cable, transformer and connection
The transmission facility of two buses is defined as " branch ") it will not be out of service it is assumed that this does not conform to the actual conditions.
Therefore, it is desirable to there is a kind of Study of Risk Evaluation Analysis for Power System accelerated method, commented with solving electric system risk in the prior art
Estimate the low problem of method computational efficiency.
Summary of the invention
The purpose of the present invention is to provide a kind of Study of Risk Evaluation Analysis for Power System accelerated method based on multi-parameter linear programming,
This method utilizes multi-parameter linear programming method, judges Region Theory according to key, using dynamic study method, in accelerator
Samples Estimates speed promote the real-time assessment of electric system risk to improve computational efficiency.
The present invention is based on the Study of Risk Evaluation Analysis for Power System accelerated method of multi-parameter linear programming, define transmission line of electricity, cable,
Transformer and the transmission facility for connecting two buses are branch；All kinds of generating equipments in definition system are unit；By electric power
System generator group and branch are referred to as element；Defining all buses in electric system is node；Define same multi-parameter rule
The crucial critical region collection for the problem of drawing is combined into crucial critical region collection, each of crucial critical region collection key critical region
For its element；Definition stores multiple line status and the corresponding parameters of electric power system of each line status and crucial differentiation
The collection of region collection is combined into line status word allusion quotation collection, each line status and its corresponding electric power that line status dictionary is concentrated
System parameter and crucial critical region collection are its element.
Study of Risk Evaluation Analysis for Power System accelerated method based on multi-parameter linear programming, which is characterized in that the electric system
Risk assessment accelerated method the following steps are included:
Step 1: within the period for needing to carry out risk assessment, using the fortune of monte carlo method stochastical sampling electric system
Row state, wherein the operating status of electric system includes: the normal or stoppage in transit of the normal or stoppage in transit state of each branch, generating set
State and each node load size of electric system, and the operating status of electric system is established with the excellent of cutting load loss reduction
Change the corresponding multi-parametric programming model of Optimized model of model and cutting load loss reduction；
Step 2: being greater than the electric system branch sample states of certain threshold value for sampled probability, establish line status dictionary
Collection, each element that line status dictionary is concentrated is corresponded to each other with electric system branch sample states, and stores power train according to this
The transfer distribution factor matrix and key for branch sample states of uniting judge region collection characteristic information；
Step 3: the element that operation states of electric power system and line status dictionary that sampling obtains are concentrated is subjected to retrieval
Match, if matching is unsuccessful, be transferred to step 4, if successful match, is transferred to step 5；
Step 4: the corresponding Optimized model with cutting load loss reduction of operation states of electric power system that sampling obtains is solved,
It obtains each node and loses load, be transferred to step 6；
Step 5: by the set state and load level state key corresponding with line status dictionary concentration in current sample
The element that critical region is concentrated is matched, if matching is unsuccessful, it is corresponding negative to cut to solve the operation states of electric power system
The Optimized model of lotus loss reduction obtains each node and loses load, calculates the corresponding crucial differentiation of the operation states of electric power system
Region supplement is entered corresponding crucial critical region collection in line status dictionary element of set element, and records its characteristic information by region；
If successful match, the characteristic information of corresponding crucial critical region is concentrated directly to calculate the electricity using corresponding crucial critical region
The corresponding each node of Force system operating status loses load, is transferred to step 6；
Step 6: obtaining next operating status that sampling obtains, be transferred to step 3, until completing Study of Risk Evaluation Analysis for Power System
It calculates.
Preferably, the operating status to electric system in the step 1 is established with the optimization mould of cutting load loss reduction
Type specifically includes:
The Optimized model with cutting load loss reduction for meeting multi-parameter linear programming hypothesis is established, such as formula (4):
In objective function, DdFor each node cutting load vector, P is each node active injection；c1And c2For corresponding weight to
Amount；Constraint condition part, 1T× P=0 represents system-wide power-balance constraint, i.e., in the case where not considering network loss, complete set
The sum of injecting power of uniting is 0；In, G is transfer distribution factor matrix, and G × P is route effective power flow, line
Road trend is by PlineWithConstraint；
It is constrained for node injecting power,For the generator output upper limit, D bears for each node
Lotus, W is unit node connection matrix, if unit j is connected to node i, Wi,j=1；Otherwise Wi,j=0；For each node, hair
Electric power generation power, after the true load for subtracting this node, the power P as injected to power grid；0≤Dd≤ D be cutting load about
Beam, i.e. cutting load amount is non-negative, and size is no more than this node load demand maximum value.
Preferably, the multi-parametric programming model in the step 1 specifically includes: the multi-parameter of building such as formula (5) is linear
Parameter vector in planning:
The variation of the variation of the set state and load level is indicated by the variation of parameter θ, is established
Determine the set state and load variations under line status, the multi-parameter linear programming model such as formula (6) of power grid risk assessment
It is shown:
Preferably, the step 2 specifically includes:
Line status for all routes quantity out of service less than or equal to α establishes the line status dictionary collection, i.e.,
It is seriously concentrated to the accident of N- α in the line status dictionary and establishes index, wherein N is number of, lines total in system, and α is
Number of, lines out of service, the centrally stored line status total number of the line status dictionary are formula (7):
Store all line status and relevant parameter that the line status dictionary is concentrated；Wherein, relevant parameter includes: electricity
It include the transfer distribution factor matrix G under current line state, line transmission capacity limit in terms of Force system parameterWithPline；Multi-parameter linear programming for solution technical aspect includes the corresponding crucial critical region information of storage.
Preferably, all line status that the line status dictionary is concentrated and relevant parameter are stored in the step 2
Specific steps:
It is concentrated in the line status dictionary, the information of each line status is stored by two parts, including number
And content；Number set in, storage be current line state number, realized using binary coding, each for pair
Answer the operating status of route；In properties collection, storage current line state is corresponding for judging system cutting load feature square
Battle array, includingWith
Preferably, in the step 5 that set state and load level state is corresponding with line status dictionary concentration crucial
The element that critical region is concentrated carries out matching and specifically includes:
1. crucial critical region collection matching, each of crucial critical region collection element, all include two features, and one
It is the attribute for indicating crucial critical region range, such as formula (8)；Another is for indicating in crucial critical region from parameter vector
To the mapping relations of optimal solution, such as formula (9):
Wherein,WithIt is the crucial critical region spy obtained when creating crucial critical region
Levy matrix；
When being matched, the corresponding parameter of current sample is calculated according to formula (5) firstThen
According to the characteristic parameter of each element in crucial critical region collection, successively judge whether θ belongs to the key critical region；Judgement
Mode are as follows: calculate vectorValue, judge whether its institute it is important be respectively less than 0；If right
In some crucial critical region, above-mentioned judgement is set up, then illustrates that θ belongs to the key critical region；If all invalid, illustrate
With unsuccessful, θ is not belonging to any one element in crucial critical region collection；
2. directly calculating each node loses load, in crucial critical region successful match, directly sentenced using key where θ
The mapping relations in other region, if each component of x* (θ) in formula (9) is that the corresponding each node of the operating status loses load
Amount；
3. running optimizatin obtains each node and loses load, and updates crucial critical region collection, matched in crucial critical region
When unsuccessful, then linear programming problem shown in solution formula (10), obtains each node cutting load amount；
Then, the Lagrange multiplier that each constrains in screening and optimizing model, by all Lagrange multipliers greater than 0
One set of constraint composition, the corresponding portion of constraint condition separately constituteWithMatrix；By all Lagrange multipliers
Constraint equal to 0 forms a set, and the corresponding portion of constraint condition separately constitutesWithMatrix；
According to acquisitionWithMatrix, formula (8) and (9), the corresponding key of calculating are sentenced
The characteristic information in other region；The crucial critical region that will newly obtain, supplement enter corresponding crucial in line status dictionary element of set element
Critical region collection records crucial critical region characteristic information represented by formula (8) and formula (9).
The multi-parameter linear programming Study of Risk Evaluation Analysis for Power System accelerated method disclosed by the invention that is based on is in existing power train
Unite on the basis of risk assessment, establish a kind of new Samples Estimates method, this method apply multi-parameter programming theory and
Line status matching assesses sample using multi-parameter linear programming method, relative to existing based on solution optimal load flow model
Method, this method is greatly improved in computational efficiency.In addition, this method does not have any restrictions for sampling technique,
Therefore there can be good applicability, by applying the efficient method of sampling, can further promote computational efficiency.Using we
Method can be realized the rapid evaluation of electric system risk, promote application of the risk assessment in large-scale complex electric system,
Electric Power Network Planning and operation side can play the role of corresponding, play decision references and supporting function for Electric Power Network Planning and operation.
Detailed description of the invention
Fig. 1 is the Study of Risk Evaluation Analysis for Power System method flow diagram using multi-parameter linear programming proposed in the present invention.
Fig. 2 is the schematic diagram of the line status dictionary collection proposed in the present invention.
Fig. 3 is I EEE RTS-79 power system network topological diagram in the present embodiment.
Fig. 4 is the effect of proposition method of the present invention in I EEE RTS-79 electric system in the present embodiment.
Specific embodiment
To keep the purposes, technical schemes and advantages of the invention implemented clearer, below in conjunction in the embodiment of the present invention
Attached drawing, technical solution in the embodiment of the present invention is further described in more detail.In the accompanying drawings, identical from beginning to end or class
As label indicate same or similar element or element with the same or similar functions.Described embodiment is the present invention
A part of the embodiment, instead of all the embodiments.The embodiments described below with reference to the accompanying drawings are exemplary, it is intended to use
It is of the invention in explaining, and be not considered as limiting the invention.Based on the embodiments of the present invention, ordinary skill people
Member's every other embodiment obtained without creative efforts, shall fall within the protection scope of the present invention.
Study of Risk Evaluation Analysis for Power System method proposed by the present invention using multi-parameter linear programming, wherein defining power transmission line
Road, cable, transformer and connect two buses transmission facility be " branch "；All kinds of generating equipments in definition system are
" unit "；Electric system generator group and branch are referred to as " element "；Defining all buses in electric system is " node "；
The crucial critical region collection for defining same multi-parametric programming problem is combined into " crucial critical region collection ", in " crucial critical region collection "
Each crucial critical region be its one " element "；Definition stores multiple line status and each line status is corresponding
The collection of parameters of electric power system and crucial critical region collection is combined into " line status dictionary collection ", each in " line status dictionary collection "
A line status and its corresponding parameters of electric power system and crucial critical region collection are its one " element ".The reality of this method
Flow chart is applied as shown in Figure 1, this method step-by-step procedures is as follows:
1) a series of using monte carlo method stochastical sampling electric system within the period for needing to carry out risk assessment
Operating status, each operating status include the normal or stoppage in transit state of each branch, the normal or stoppage in transit state of generating set and
Each node load size of electric system；To each operating status of electric system establish with the Optimized model of cutting load loss reduction with
And its corresponding multi-parametric programming model；It specifically includes:
It establishes shown in the optimal DC flow model such as formula (1) of cutting load loss reduction:
Wherein, c is the cost vector of cutting load.In this method, it is taken as a constant vector herein.The objective function of model
As, the sum of cutting load cost or cutting load amount on all nodes are minimized.
Equality constraint part, the first row represents system-wide power-balance constraint, i.e., the case where not considering network loss
Under, the sum of total system injecting power is 0.Second row application transfer distribution factor matrix G, building Line Flow and node inject function
Equilibrium relationships between rate.The calculation method of third behavior node injecting power, PgenFor generator output, W is machine group node company
Matrix is connect, if unit j is connected to node i, Wi,j=1；Otherwise Wi,j=0.On each node, electrical power generators power is W
×Pgen, workload demand D, in consideration cutting load DdAfterwards, the true load on node is D-Dd.For each node, generator
Generated output, after the true load for subtracting this node, the power P as injected to power grid.The power of injection can be positive, i.e., very
It is real active to system injection；Or it is negative, i.e., it is obtained from power grid active.
Inequality constraints condition part, fourth line are generator output constraint, whereinFor the generator output upper limit.The
The transmission of five behavior routes constrains, i.e., the trend on route is no more than line transmission limit PlineWith6th behavior is cut negative
Lotus constraint, i.e., the size of cutting load amount is no more than this node load demand maximum value, while not can be carried out negative cutting load.
Above-mentioned model can not apply multi-parameter linear programming method in many cases, it is therefore desirable to improve.In order to
Solve the problems, such as above-mentioned more solutions, thus using multi-parameter Linear Programming Techniques, on the basis of above-mentioned model, for objective function into
Row improves, such as formula (2):
Min z=c1 T×Dd+c2 T×P (2)
Explicitly increase node injecting power P in objective function, and applies vector c1, c2To respectively indicate cutting load amount
DdWith weight of the node injecting power P in objective function.Vector c is determined using following method1, c2The value of each component,
Such as formula (3):
Wherein, M is one relative to the biggish constant of system node number, it ensure that the levels of precision of above-mentioned model.Together
When, c1And c2So that different nodes possesses different weights in a model, generated output injection and cutting load distribution are being considered
When, there are priority between different nodes, avoid the more solution problems for being possible to occur in archetype.
Hereafter, following abbreviation and variable replacement are carried out to model.With DdWith P as decision variable, will own in model
Other intermediate variables are indicated by decision variable, abbreviation constraint condition, the risk evaluation model after being simplified, such as
Formula (4).
It constructs shown in the parameter vector such as formula (5) in multi-parameter linear programming.
Then the variation of set state and the variation of load level can be indicated by the variation of parameter θ.Using this
Parameter indicates after determining the unit under line status, load variations, is the matrix form shaped like (6) by model modification, obtains
The multi-parameter linear programming model of power grid risk assessment.
In fact, the actual physical meaning of parameter θ in a model is, after set state and load level determine, each
The active injection upper limit on node.
2) the biggish electric system branch sample states of sampled probability are directed to, line status dictionary collection, line status are established
Each of wordbook element corresponds to an electric system branch sample states, stores the state and shifts distribution factor accordingly
Matrix and key judge the characteristic informations such as region collection；It specifically includes:
Establish line status dictionary collection.In line status wordbook, a certain number of line status are stored.Although right
In the electric system for possessing m transmission lines, the line status being likely to occur is up to 2mKind, but every kind of state is in the sample
The probability of appearance is inconsistent, widely different.Actually due in reality the reliability of transmission line it is higher, many line status
The probability of appearance is extremely low, therefore only considers the higher line status of those probabilities of occurrence, is stored in line status dictionary concentration,
It just can cover the line condition of most numerical example.For example, considering that all routes exit fortune in line status wordbook
Line number amount is less than or equal to the line status of α, then seriously to N- α, (wherein N is number of, lines total in system, and α is out of service
Number of, lines) accident can be considered by line status dictionary collection, power grid occur it is seriously very low in the accident probability of N- α.
In this case, the centrally stored line status number of line status dictionary are as follows:
Each of line status dictionary line concentration line state wordbook line status, relevant parameter can all be deposited
Storage is got off.In terms of parameters of electric power system, including the transfer distribution factor matrix G under current line state, line transmission capacity
LimitationWithPline.Meanwhile in multi-parameter linear programming for solution technical aspect, corresponding crucial critical region letter is stored
Breath.
In line status wordbook, the information of each line status is stored actually by two parts.Its
Form is similar to the data structure of binary, is divided into " number " and " content ".In number set, storage is current line state
Number, can be realized by a string of binary codings, wherein each is the operating status of corresponding line.In properties collection
In, the relevant parameter under current line state is stored, particular content includes parameters of electric power system and multi-parameter linear programming for solution
Required parameter, schematic diagram are as shown in Figure 2.The data structure of line status dictionary collection is similar to dictionary.
3) for a certain operation states of electric power system for sampling and obtaining, by its route operating status and line status dictionary collection
In element carry out retrieval matching, if matching is unsuccessful, be transferred to step 4), if successful match, be transferred to step 5)；Specific packet
It includes:
It, only need to be by sample line when being matched the line status in sample with the element that line status dictionary is concentrated
State is compiled into corresponding binary coding, and it is out of service that 0 on each represents route, and 1 to represent route normal.Obtain two into
After system coding, the number in number set concentrated with line status dictionary is compared.If the binary coding and content
Some number in set is identical, then illustrates successful match；Otherwise, it matches unsuccessful.
4) it uses optimization software to solve the corresponding Optimized model with cutting load loss reduction of the operating status, obtains each section
Point loses load, is transferred to step 6)；
5) by the set state and load level state in current sample, crucial differentiation corresponding with line status dictionary concentration
The element that region is concentrated is matched, if matching is unsuccessful, uses optimization software to solve the operating status corresponding negative to cut
The Optimized model of lotus loss reduction obtains each node and loses load, the corresponding crucial critical region of the operating status is calculated, by this
Region supplement enters corresponding crucial critical region collection in line status dictionary element of set element, and records its characteristic information；If matching at
Function is concentrated the characteristic information of corresponding crucial critical region directly to calculate the operating status and is corresponded to using corresponding crucial critical region
Each node lose load.It is transferred to step 6)；It specifically includes:
5.1) crucial critical region collection matching
Each of crucial critical region collection element all includes two features, and one is to indicate crucial critical region model
The attribute enclosed, such as formula (8)；Another is used to indicate the mapping relations in crucial critical region from parameter vector to optimal solution, such as
Formula (9).
Wherein,WithIt is the matrix obtained when creating crucial critical region.
When being matched, the corresponding parameter of current sample is constructed according to formula (5) firstThen root
According to the range attribute of each element in crucial critical region collection, successively judge whether θ belongs to the key critical region, the side of judgement
Formula are as follows: calculate correspondingJudge whether that its institute is important and is respectively less than 0.If to Mr. Yu
One crucial critical region, above-mentioned judgement are set up, then illustrate that θ belongs to the key critical region；If all invalid, illustrate matching not
Success, θ are not belonging to any one element in crucial critical region collection.
5.2) it directly calculates each node and loses load
In crucial critical region successful match, directly using the mapping relations of critical region crucial where θ, such as formula (9):Each component of x* (θ) is that the corresponding each node of the operating status loses load.
5.3) running optimizatin obtains each node and loses load, and updates crucial critical region collection
When the matching of crucial critical region is unsuccessful, each node directly can not be calculated using crucial critical region characteristic information
Lose load.The formalization of optimization problem shown in formula (6) is expressed as follows:
Optimizing application software solves linear programming problem shown in formula (10), obtains each node cutting load amount.Meanwhile it obtaining
The Lagrange multiplier that each constrains in Optimized model.Constraint by all Lagrange multipliers greater than 0 forms a set,
The corresponding portion of its constraint condition separately constitutesWithMatrix；Constraint by all Lagrange multipliers equal to 0 forms
One set, the corresponding portion of constraint condition separately constituteWithMatrix.
According to acquisitionWithMatrix calculates corresponding crucial differentiation with formula (8) and (9)
The characteristic information in region.The crucial critical region that will newly obtain, supplement enter corresponding key in line status dictionary element of set element and sentence
Crucial critical region characteristic information represented by other region collection, recording (8) and formula (9).
6) next operating status that sampling obtains is obtained, step 3) is transferred to, until completing Study of Risk Evaluation Analysis for Power System meter
It calculates.
According to above-mentioned Study of Risk Evaluation Analysis for Power System method, the efficiency of conventional electric power system risk assessment can be significantly improved,
Computing resource is saved, the rapid evaluation of electric system risk is promoted, provides and refers to and decision branch for operation of power networks and planning personnel
It holds.
Embodiment 2:
Illustrate that application proposed by the invention is more so that IEEE reliability standard tests electric system (IEEE RTS-79) as an example
The Study of Risk Evaluation Analysis for Power System method of parametric linear programming, and verify the effect that the present invention is realized.IEEE RTS-79 power train
It altogether include 24 nodes, 32 generating sets, 38 branches, maximum load 2850MW, installed capacity 3405MW.IEEE
RTS-79 power system network topological diagram is as shown in figure 3, generator parameter is as shown in table 1, each node load ratio such as 2 institute of table
Show, branch (route and transformer) parameter is as shown in table 3.
1 IEEE RTS-79 Generating Unit Operation Reliability data of table
2 IEEE RTS-79 node load ratio of table
Table 3 IEEE RTS-79 branch (route and transformer) parameter
Using method proposed by the present invention, corresponding program is write on software for calculation MATLAB R2017b, calls Cplex
12.4 solving optimization problem of version carries out risk assessment to IEEE RTS-79 system.The calculating equipment of application are as follows: one
Intel i7-7500U processor is configured, 16GB memory runs the Thinkpad T470 of 10 Professional operating system of Windows
Laptop.Sample phase in risk assessment has directlyed adopt basic Monte Carlo method.Certainly, more
The advanced and efficient method of sampling can have better improvement to effect.
In line status wordbook, the line status of consideration includes two class state of N and N-1.Therefore in sampling process
When obtaining the sample that line status is N or N-1, the appraisal procedure proposed by the present invention based on multi-parameter linear programming can be played
Effect；And when sample line state is not above situation, then it solves linear programming problem and sample is assessed.
Meanwhile in order to illustrate the validity of this method, the method that traditional application solves optimal DC flow model is made
For the control of this method.Fig. 4 is comparing result of the two methods in calculating speed.It can be seen that assessing sample in this method
Substantially in a linear relationship between assessment time-consuming, most of sample can all apply multi-parameter linear programming side in evaluation process
Method is solved.As shown in Figure 4, compared to conventional method, the computational efficiency of this method obtains in IEEE RTS-79 system
About 25 times of promotion.
Using relative error 1% as convergence target, independent Meng Teka twice is carried out with this method and conventional method respectively
Lip river analysis, result are as shown in the table.
4 IEEE RTS-79 risk evaluation result of table
By comparing it is found that the Study of Risk Evaluation Analysis for Power System method proposed by the present invention based on multi-parameter linear programming is can
It goes, efficiently, accurately.This method can effectively assess electric system risk, under the premise of guaranteeing precision, Ke Yicong
Very big improved efficiency is obtained in Samples Estimates level.
Finally it is noted that the above embodiments are merely illustrative of the technical solutions of the present invention, rather than its limitations.To the greatest extent
Present invention has been described in detail with reference to the aforementioned embodiments for pipe, those skilled in the art should understand that: it is still
It is possible to modify the technical solutions described in the foregoing embodiments, or part of technical characteristic is equally replaced
It changes；And these are modified or replaceed, the essence for technical solution of various embodiments of the present invention that it does not separate the essence of the corresponding technical solution
Mind and range.
Claims (6)
1. the Study of Risk Evaluation Analysis for Power System accelerated method based on multi-parameter linear programming, which is characterized in that the electric system wind
Danger assessment accelerated method the following steps are included:
Step 1: within the period for needing to carry out risk assessment, using the operation shape of monte carlo method stochastical sampling electric system
State, wherein the operating status of electric system includes: the normal or stoppage in transit state of each branch, the normal or stoppage in transit state of generating set
And each node load size of electric system, and the operating status of electric system is established with the optimization mould of cutting load loss reduction
The corresponding multi-parametric programming model of the Optimized model of type and cutting load loss reduction；
Step 2: it is greater than the electric system branch sample states of certain threshold value for sampled probability, establishes line status dictionary collection,
Each element that line status dictionary is concentrated is corresponded to each other with electric system branch sample states, and stores electric system branch according to this
The transfer distribution factor matrix and key of road sample states judge region collection characteristic information；
Step 3: the element that operation states of electric power system and line status dictionary that sampling obtains are concentrated is subjected to retrieval matching, if
It matches unsuccessful, is then transferred to step 4, if successful match, is transferred to step 5；
Step 4: solving the corresponding Optimized model with cutting load loss reduction of operation states of electric power system that sampling obtains, obtain
Each node loses load, is transferred to step 6；
Step 5: by current sample set state and load level state is corresponding with line status dictionary concentration crucial differentiates
The element that region is concentrated is matched, if matching is unsuccessful, solves that the operation states of electric power system is corresponding to be damaged with cutting load
The smallest Optimized model is lost, each node is obtained and loses load, calculates the corresponding crucial critical region of the operation states of electric power system,
Region supplement is entered into corresponding crucial critical region collection in line status dictionary element of set element, and records its characteristic information；If
With success, then the characteristic information of corresponding crucial critical region is concentrated directly to calculate the power train using corresponding crucial critical region
The corresponding each node of operating status of uniting loses load, is transferred to step 6；
Step 6: obtaining next operating status that sampling obtains, be transferred to step 3, until completing Study of Risk Evaluation Analysis for Power System meter
It calculates.
2. the Study of Risk Evaluation Analysis for Power System accelerated method according to claim 1 based on multi-parameter linear programming, feature
Be: the operating status to electric system in the step 1 is established to be specifically included with the Optimized model of cutting load loss reduction:
The Optimized model with cutting load loss reduction for meeting multi-parameter linear programming hypothesis is established, such as formula (4):
In objective function, DdFor each node cutting load vector, P is each node active injection；c1And c2For corresponding weight vectors；
Constraint condition part, 1T× P=0 represents system-wide power-balance constraint, i.e., in the case where not considering network loss, total system note
Entering the sum of power is 0；In, G is transfer distribution factor matrix, and G × P is route effective power flow, route tide
Stream byP lineWithConstraint；It is constrained for node injecting power,For the generator output upper limit, D
For each node load, W is unit node connection matrix, if unit j is connected to node i, Wi,j=1；Otherwise Wi,j=0；For
Each node, electrical power generators power, after the true load for subtracting this node, the power P as injected to power grid；0≤Dd≤D
For cutting load constraint, i.e. cutting load amount is non-negative, and size is no more than this node load demand maximum value.
3. the Study of Risk Evaluation Analysis for Power System accelerated method according to claim 2 based on multi-parameter linear programming, feature
Be: the multi-parametric programming model in the step 1 specifically includes: the ginseng in the building such as multi-parameter linear programming of formula (5)
Number vector:
The variation of the variation of the set state and load level is indicated by the variation of parameter θ, is established in determination
Set state and load variations under line status, shown in the multi-parameter linear programming model such as formula (6) of power grid risk assessment:
4. the Study of Risk Evaluation Analysis for Power System accelerated method according to claim 3 based on multi-parameter linear programming, feature
Be: the step 2 specifically includes:
Line status for all routes quantity out of service less than or equal to α establishes the line status dictionary collection, i.e., seriously
Accident to N- α concentrates foundation index in the line status dictionary, and wherein N is number of, lines total in system, and α is to exit
The number of, lines of operation, the centrally stored line status total number of the line status dictionary are formula (7):
Store all line status and relevant parameter that the line status dictionary is concentrated；Wherein, relevant parameter includes: power train
It include the transfer distribution factor matrix G under current line state, line transmission capacity limit in terms of parameter of unitingWithPline；
Multi-parameter linear programming for solution technical aspect includes the corresponding crucial critical region information of storage.
5. the Study of Risk Evaluation Analysis for Power System accelerated method according to claim 4 based on multi-parameter linear programming, feature
It is: stores the specific steps of all line status and relevant parameter that the line status dictionary is concentrated in the step 2:
Concentrated in the line status dictionary, the information of each line status stored by two parts, including number with it is interior
Hold；In number set, storage be current line state number, realized using binary coding, each is corresponding line
The operating status on road；In properties collection, storage current line state is corresponding for judging system cutting load eigenmatrix, wraps
It includesWith
6. the Study of Risk Evaluation Analysis for Power System accelerated method according to claim 5 based on multi-parameter linear programming, feature
It is: by set state and load level state crucial critical region collection corresponding with line status dictionary concentration in the step 5
In element carry out matching specifically include:
1. crucial critical region collection matching, each of crucial critical region collection element all include two features, one is table
The attribute for showing crucial critical region range, such as formula (8)；Another is for indicating in crucial critical region from parameter vector to most
The mapping relations of excellent solution, such as formula (9):
Wherein,WithIt is the crucial critical region feature square obtained when creating crucial critical region
Battle array；
When being matched, the corresponding parameter of current sample is calculated according to formula (5) firstThen according to pass
The characteristic parameter of each element in key critical region collection, successively judges whether θ belongs to the key critical region；Judgment mode
Are as follows: calculate vectorValue, judge whether its institute it is important be respectively less than 0；If to Mr. Yu
One crucial critical region, above-mentioned judgement are set up, then illustrate that θ belongs to the key critical region；If all invalid, illustrate matching not
Success, θ are not belonging to any one element in crucial critical region collection；
2. directly calculating each node loses load, in crucial critical region successful match, area directly is differentiated using crucial where θ
The mapping relations in domain, if each component of x* (θ) in formula (9) is that the corresponding each node of the operating status loses load；
3. running optimizatin obtains each node and loses load, and updates crucial critical region collection, the matching of crucial critical region not at
When function, then linear programming problem shown in solution formula (10), obtains each node cutting load amount；
Then, all Lagrange multipliers are greater than 0 constraint by the Lagrange multiplier that each constrains in screening and optimizing model
A set is formed, the corresponding portion of constraint condition separately constitutesWithMatrix；All Lagrange multipliers are equal to
0 constraint forms a set, and the corresponding portion of constraint condition separately constitutesWithMatrix；
According to acquisitionWithMatrix, formula (8) and (9), the corresponding key of calculating differentiate area
The characteristic information in domain；The crucial critical region that will newly obtain, supplement enter corresponding key in line status dictionary element of set element and differentiate
Region collection records crucial critical region characteristic information represented by formula (8) and formula (9).
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