CN108092320A - The method and system for planning of the grid-connected allowed capacity of distributed photovoltaic - Google Patents
The method and system for planning of the grid-connected allowed capacity of distributed photovoltaic Download PDFInfo
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Abstract
The present invention is suitable for distribution network planning technical field, discloses a kind of method and system for planning of the grid-connected allowed capacity of distributed photovoltaic, the described method includes：Photo-voltaic power supply is contributed and load output is determined as stochastic variable, establishes the comprehensive probability model of photo-voltaic power supply output and the probabilistic model of load output；Using the Latin Hypercube Sampling method based on equiprobability conversion principle and square-root method, mutually independent random vector is generated；It determines Chance-Constrained Programming Model when the sum of the distributed photovoltaic power allowed capacity of distribution network system access is maximum, Chance-Constrained Programming Model is solved using the cuckoo searching algorithm based on particle cluster algorithm, obtains the grid-connected maximum penetration level of distributed photovoltaic；The grid-connected capacity of each node in distribution network system is planned according to distributed photovoltaic grid-connected maximum penetration level.The present invention considers the characteristics of photo-voltaic power supply output randomness and fluctuation, improves node voltage level distribution, ensure that power distribution network safe and stable operation.
Description
Technical field
The invention belongs to the planning of distribution network planning technical field more particularly to a kind of grid-connected allowed capacity of distributed photovoltaic
Method and system.
Background technology
The access of distributed photovoltaic changes network structure in power grid so that power grid becomes double from original single-ended radial pattern
End and loop network, this just brings series of challenges to the safe and stable operation of power grid.It is distributed for China's distribution network load
Diversity feature, it is ensured that electrical network economy is stablized, and reliability service determines that power distribution network is admissible under the typical load regularity of distribution
The grid-connected allowed capacity of distributed photovoltaic is very necessary.
At present, determining the method for the grid-connected allowed capacity of distributed photovoltaic mainly has three classes：The first kind is dynamic simulation method, the
Two classes are mathematical analysis methods, and three classes are optimization algorithms.However, three of the above method can not all be included in photo-voltaic power supply output
Randomness and fluctuation feature, and met under specific condition even extreme condition obtained from electricity net safety stable constraint
As a result, cause the definitive result of the grid-connected allowed capacity of distributed photovoltaic unreasonable, inaccurate.
The content of the invention
In view of this, an embodiment of the present invention provides a kind of grid-connected allowed capacity of distributed photovoltaic planing method and be
System to solve that the randomness of photo-voltaic power supply output and fluctuation feature can not be included in the prior art, and is all to meet special item
As a result, causing determining for the grid-connected allowed capacity of distributed photovoltaic obtained from electricity net safety stable constraint under part even extreme condition
As a result the problem of unreasonable, inaccurate.
The first aspect of the embodiment of the present invention provides a kind of planing method of the grid-connected allowed capacity of distributed photovoltaic, bag
It includes：
Photo-voltaic power supply output is determined as the first stochastic variable, the comprehensive of photo-voltaic power supply output is established according to the first stochastic variable
Probabilistic model is closed, load output is determined as the second stochastic variable, the probability mould of load output is established according to the second stochastic variable
Type；
Target typical load distribution function is obtained from the typical load distribution function to prestore；
The combined chance mould contributed according to related coefficient, the photo-voltaic power supply between the first stochastic variable and the second stochastic variable
The probabilistic model that type and load are contributed, using the Latin Hypercube Sampling method based on equiprobability conversion principle and square-root method,
Generate the mutually independent random vector that photo-voltaic power supply is contributed with load output；
Confidence level is set, according to confidence level and target typical load distribution function, determines distribution network system access
Chance-Constrained Programming Model when the sum of distributed photovoltaic power allowed capacity is maximum；
According to the mutually independent random vector that photo-voltaic power supply output is contributed with load, using the cloth based on particle cluster algorithm
Paddy bird searching algorithm solves Chance-Constrained Programming Model, obtains the grid-connected maximum penetration level of distributed photovoltaic；
According to the grid-connected maximum penetration level of distributed photovoltaic to the grid-connected capacity of each node in distribution network system into
Professional etiquette is drawn.
The second aspect of the embodiment of the present invention provides a kind of planning system of the grid-connected allowed capacity of distributed photovoltaic, bag
It includes：
Probabilistic model establishes module, for photo-voltaic power supply output to be determined as the first stochastic variable, according to the first random change
Amount establishes the comprehensive probability model of photo-voltaic power supply output, and load output is determined as the second stochastic variable, according to the second random change
Amount establishes the probabilistic model of load output；
Acquisition module, for obtaining target typical load distribution function from the typical load distribution function to prestore；
Generating random vector module, for according to related coefficient, the light between the first stochastic variable and the second stochastic variable
The comprehensive probability model of power supply output and the probabilistic model of load output are lied prostrate, using based on equiprobability conversion principle and square-root method
Latin Hypercube Sampling method, generation photo-voltaic power supply contribute with load contribute mutually independent random vector；
Chance-Constrained Programming Model determining module, for setting confidence level, according to confidence level and target typical load
Distribution function determines chance constrained programming mould when the sum of the distributed photovoltaic power allowed capacity of distribution network system access is maximum
Type；
Maximum penetration level solve module, for according to photo-voltaic power supply contribute with load contribute it is mutually independent at random to
Amount, solves Chance-Constrained Programming Model using the cuckoo searching algorithm based on particle cluster algorithm, obtains distributed light
Lie prostrate grid-connected maximum penetration level；
Planning module, for the light according to the grid-connected maximum penetration level of distributed photovoltaic to each node in distribution network system
Volt grid connection capacity is planned.
The third aspect of the embodiment of the present invention provides a kind of terminal device, including memory, processor and is stored in
In memory and the computer program that can run on a processor, processor realize distribution as described above when performing computer program
The step of planing method of the grid-connected allowed capacity of formula.
The fourth aspect of the embodiment of the present invention provides a kind of computer readable storage medium, computer readable storage medium
Computer program is stored with, distributed photovoltaic as described above grid-connected allowed capacity is realized when computer program is executed by processor
The step of planing method.
Existing advantageous effect is the embodiment of the present invention compared with prior art：Distribution light provided in an embodiment of the present invention
The method and system for planning of grid-connected allowed capacity is lied prostrate, being determined as stochastic variable with load output by contributing photo-voltaic power supply, building
The probabilistic model that the comprehensive probability model and load that vertical photo-voltaic power supply is contributed are contributed, obtains target typical load distribution function, adopts
With the Latin Hypercube Sampling method based on equiprobability conversion principle and square-root method, generation photo-voltaic power supply is contributed contributes with load
Mutually independent random vector, determine machine when the sum of distributed photovoltaic power allowed capacity of distribution network system access is maximum
Plan model can be constrained, and Chance-Constrained Programming Model is solved using the cuckoo searching algorithm based on particle cluster algorithm, is obtained
To the grid-connected maximum penetration level of distributed photovoltaic, according to the grid-connected maximum penetration level of distributed photovoltaic to each in distribution network system
The grid-connected capacity of node planned, the characteristics of so as to take into full account randomness and fluctuation that photo-voltaic power supply is contributed,
It ensure that the reasonability and accuracy of the definitive result of the grid-connected allowed capacity of distributed photovoltaic, improve node voltage distribution water
It is flat, it ensure that power distribution network safe and stable operation.
Description of the drawings
It to describe the technical solutions in the embodiments of the present invention more clearly, below will be to embodiment or description of the prior art
Needed in attached drawing be briefly described, it should be apparent that, the accompanying drawings in the following description be only the present invention some
Embodiment, for those of ordinary skill in the art, without having to pay creative labor, can also be according to these
Attached drawing obtains other attached drawings.
Fig. 1 is the realization flow of the planing method for the grid-connected allowed capacity of distributed photovoltaic that the embodiment of the present invention one provides
Figure；
Fig. 2 is the structural representation of the planning system of the grid-connected allowed capacity of distributed photovoltaic provided by Embodiment 2 of the present invention
Figure；
Fig. 3 is the schematic diagram for the terminal device that the embodiment of the present invention three provides.
Specific embodiment
In being described below, in order to illustrate rather than in order to limit, it is proposed that such as tool of particular system structure, technology etc
Body details, to understand thoroughly the embodiment of the present invention.However, it will be clear to one skilled in the art that there is no these specifically
The present invention can also be realized in the other embodiments of details.In other situations, omit to well-known system, device, electricity
Road and the detailed description of method, in case unnecessary details interferes description of the invention.
Term " comprising " and their any deformations in description and claims of this specification and above-mentioned attached drawing, meaning
Figure is to cover non-exclusive include.Such as process, method or system comprising series of steps or unit, product or equipment do not have
The step of having listed or unit are defined in, but optionally further includes the step of not listing or unit or optionally also wraps
It includes for the intrinsic other steps of these processes, method, product or equipment or unit.In addition, term " first ", " second " and
" 3rd " etc. is for distinguishing different objects, not for description particular order.
In order to illustrate technical solutions according to the invention, illustrated below by specific embodiment.
Embodiment one
With reference to Fig. 1, Fig. 1 shows the planing method for the grid-connected allowed capacity of distributed photovoltaic that the embodiment of the present invention one provides
Realization flow, the flow executive agent of the present embodiment can be terminal device, and details are as follows for process：
S101：Photo-voltaic power supply output is determined as the first stochastic variable, establishing photo-voltaic power supply according to the first stochastic variable goes out
The comprehensive probability model of power, is determined as the second stochastic variable by load output, and load output is established according to the second stochastic variable
Probabilistic model.
Wherein, the comprehensive probability model that photo-voltaic power supply is contributed is included based on the parameter Beta probabilistic models being distributed and based on non-
The probabilistic model of parameter Density Estimator.
Photo-voltaic power supply contribute it is closely related with the factors such as intensity of illumination, temperature, photovoltaic array area and photoelectric conversion efficiency,
The change of any of which factor can all make photovoltaic output change.Photo-voltaic power supply output is different under the conditions of different weather, day of the same race
Gas different periods photo-voltaic power supply output is also different, and analysis reason is understood, the variation of weather and time can change cloud layer reflection and suction
The intensity of solar radiation of receipts and the temperature of environment, so that photo-voltaic power supply, which is contributed, is presented randomness and fluctuation.
In order to embody the randomness of photo-voltaic power supply output and fluctuation feature, the photo-voltaic power supply in certain period of time is contributed
It regards as and meets the Beta distributions of certain parameter and the comprehensive probability model of nonparametric probability.Wherein, parameter Beta is met
The probabilistic model of distribution simply easily realizes that the probabilistic model of nonparametric probability has well adapting to property and simulation accurately
Degree.
Based on the probabilistic model of parameter Beta distributions, density function is：
In formula (1), PPV、PmaxThe random output and maximum output of distributed photovoltaic power in respectively certain period；Γ is
Gamma functions；α, β are the form parameter of intensity of illumination Beta distributions, by the mean μ and standard of intensity of illumination in certain period of time
Poor σ is obtained, as shown in formula (2)：
Probabilistic model based on nonparametric probability, probability density theoretical from nonparametric probability
Function is：
In formula (3), q is photovoltaic data sample number；K () is kernel function, chooses Gaussian function conduct in the present embodiment
Kernel function；H is bandwidth, can be obtained by empirical algorithms.
Load, which is contributed, equally has randomness and fluctuation, the active power and reactive power of different weather same time period
Probabilistic model meet normal distribution, probability density function is respectively：
In formula (4) and formula (5), μP、μQRespectively load active power average and reactive load power average；σP、σQRespectively
Load active power standard deviation and reactive load power standard are poor.
S102：Target typical load distribution function is obtained from the typical load distribution function to prestore.
Wherein, the typical load distribution function to prestore includes：End integrated distribution function, uniformly distributed function incrementally divide
Cloth function, the distribution function that successively decreases, the small distribution function in both ends broad in the middle and the intermediate small big distribution function in both ends.Target typical load point
Cloth function can be any one of above-mentioned six kinds of distribution functions.
S103：The synthesis contributed according to related coefficient, the photo-voltaic power supply between the first stochastic variable and the second stochastic variable
The probabilistic model that probabilistic model and load are contributed, using the Latin Hypercube Sampling based on equiprobability conversion principle and square-root method
Method, the mutually independent random vector that generation photo-voltaic power supply is contributed with load output.
In order to accurately obtain load output grid-connected maximum access of distributed photovoltaic under target typical load distribution function
Capacity, the comprehensive probability model that not only establish photo-voltaic power supply output introduce the first stochastic variable, establish the probability of load output
Model introduces the second stochastic variable, it is also contemplated that the correlation feature between the first stochastic variable and the second stochastic variable, at this
In embodiment, the related coefficient that introduces between the first stochastic variable and the second stochastic variable represents the first stochastic variable and second
Correlation feature between stochastic variable.
It is as follows to the correlation processing procedure between the first stochastic variable and the second stochastic variable：
1) obtain the quantity for the first stochastic variable for meeting the probabilistic model based on parameter Beta distributions, meet based on non-ginseng
Second for counting the quantity of the first stochastic variable of the probabilistic model of Density Estimator and meeting the probabilistic model of load output is random
The quantity of variable.
2) basis meets the quantity of the first stochastic variable of the probabilistic model based on parameter Beta distributions, meets based on non-ginseng
Second for counting the quantity of the first stochastic variable of the probabilistic model of Density Estimator and meeting the probabilistic model of load output is random
The quantity of variable, the random vector contributed with load of being contributed using Latin Hypercube Sampling method generation photo-voltaic power supply.
Assuming that the quantity of the first stochastic variable and the second stochastic variable sum is m, given birth to using Latin Hypercube Sampling method
The random vector contributed with load of contributing into photo-voltaic power supply is X=(x1,x2,…,xm)。
3) according to equiprobability conversion principle, the random vector contributed with load that photo-voltaic power supply is contributed is converted to photo-voltaic power supply
The random vector contributed with the standardized normal distribution of load output.
Assuming that photo-voltaic power supply is contributed, the random vector for the standardized normal distribution contributed with load is H=(h1,h2,…,hm)。
Random vector X=(the x that photo-voltaic power supply is contributed with load output1,x2,…,xm) according to equiprobability conversion principle, i.e. formula (6), turn
It is changed to the random vector H=(h that photo-voltaic power supply is contributed with the standardized normal distribution of load output1,h2,…,hm)。
In formula (6), Φ (hi) it is standard normal Cumulative Distribution Function；F(xi) it is stochastic variable xiCumulative probability density letter
Number；Fi -1() is F (xi) inverse function.
4) light is obtained according to the related coefficient between the first stochastic variable and the second stochastic variable and equiprobability conversion principle
Lie prostrate the correlation matrix that power supply is contributed with the random vector of the standardized normal distribution of load output.
Assuming that any two stochastic variable x in XiWith xjBetween related coefficient be ρij, any two stochastic variable h in Hi
With hjBetween related coefficient be ρhij, according to the definition of formula (6) and related coefficient, obtain ρijAnd ρhijFunctional relation such as formula
(7) shown in：
In formula (7), φ2(hi,hj,ρhij) for the joint density function of two-dimentional standardized normal distribution stochastic variable.
The related coefficient of the random vector H for the standardized normal distribution that photo-voltaic power supply is contributed and load is contributed is obtained by formula (7)
Matrix ρh, wherein ρhAs shown in formula (8)：
5) correlation matrix is decomposed according to square-root method to obtain lower triangular matrix.
According to square-root method, that is, Cholesky decomposition techniques, by correlation matrix ρhIt is decomposed to obtain lower triangular matrix
C, as shown in formula (9)：
ρh=CCT (9)
6) according to lower triangular matrix, the standardized normal distribution random vector contributed with load that photo-voltaic power supply is contributed is converted to
The mutually independent random vector that photo-voltaic power supply is contributed with load output.
According to formula (10), photo-voltaic power supply is contributed the standardized normal distribution random vector H=(h to contribute with load1,h2,…,
hm) be converted to the mutually independent random vector Y=(y that photo-voltaic power supply is contributed with load output1,y2,…,ym)。
Y=C-1H (10)
S104：Confidence level is set, according to confidence level and target typical load distribution function, determines that distribution network system connects
Chance-Constrained Programming Model when the sum of distributed photovoltaic power allowed capacity entered is maximum.
Wherein, Chance-Constrained Programming Model includes first object function and constraints, and constraints includes trend equation
Constraint, chance constraint and inequality constraints, chance constraint include quality of voltage chance constraint, line energizing flow amount chance constraint and match somebody with somebody
Grid power is forbidden sending bulk power grid chance constraint, and inequality constraints includes the constraint of photovoltaic power factor, node optical volt-ampere dressing
Amount constraint and the constraint of capacity permeability.
In view of index is allowed to be unsatisfactory for constraints to a certain extent when rough sledding occurs, therefore confidence is set
Horizontal ε carrys out characteristic index Qualification, and wherein ε is a nonnegative number no more than 1, and ε wants qualified rate closer to 1
It asks stringenter, index is not allowed to get over line when taking ε=1.
Specifically, first object function is：
In formula (11), ωi=1 represents node i access photo-voltaic power supply, ωi=0 expression node i does not access photo-voltaic power supply；N
For distribution network system interior joint sum；υiRepresent load contribute under target typical load distribution function photo-voltaic power supply in node i
The proportionality coefficient of access capacity；Spv.iFor photo-voltaic power supply node i access capacity.
Trend equality constraint, that is, power flow equation is：
In formula (12), PLi、QLiLoad active power and reactive load power respectively at node i；PPVi、QPViRespectively
Photovoltaic active power and photovoltaic reactive power at node i；U is voltage magnitude；R, X is respectively resistance and the reactance of circuit.
In chance constraint, quality of voltage chance constraint is：
In formula (13), s is Latin Hypercube Sampling number realization；εUFor voltage confidence level；In jth time simulation, when
Voltage UiIn bound Ui.min、Ui.maxWithin when, gj=1, otherwise gj=0；Meeting for s lower quality of voltage of simulation will
The probability value asked.
Line energizing flow amount chance constraint is：
In formula (14), IkTo flow through kth line current amplitude；εlFor circuit confidence level；In jth time simulation, work as electricity
Flow IkLess than lower limit Ik.maxWhen, gj=1, otherwise gj=0；The probability value met the requirements for s lower line current of simulation.
Distribution power forbids sending the bulk power grid chance constraint be：
In formula (15), PPV、PGTotal active power that respectively grid-connected total active power and major network provide；εGridTo match somebody with somebody
Net forbids sending the confidence level of power；In jth time simulation, work as PPVMore than or equal to PGWhen, gj=1, otherwise gj=0；The probability value of power is not sent to bulk power grid for s lower distribution of simulation.
In inequality constraints, photovoltaic power factor is constrained to：
In formula (16),The respectively upper and lower bound of photovoltaic operation power factor.
Node optical volt installed capacity is constrained to：
0≤SPV.i≤SPV.i.max (17)
In formula (17), SPV.i、SPV.i.maxPhotovoltaic installed capacity and maximum allowable installed capacity respectively at node i.
Capacity permeability is constrained to：
In formula (18), PPV.max、PL.maxThe total total active sample of active sample value and load of photovoltaic respectively in certain time period
Value；PSE.maxThe photovoltaic maximum permeability allowed for power grid.
S105：According to the mutually independent random vector that photo-voltaic power supply output is contributed with load, calculated using based on population
The cuckoo searching algorithm of method solves Chance-Constrained Programming Model, obtains the grid-connected maximum penetration level of distributed photovoltaic.
Specific solution procedure is as follows：
Step 1：Obtain photo-voltaic power supply quantity m, Latin Hypercube Sampling number realization s, bird's nest quantity n, greatest iteration time
Number T, current iteration number t, bird egg are found Probability p, weight upper limit ωmax, weight lower limit ωmin, the first Studying factors η and
Two Studying factors
When initial, current iteration number t is 0.
Step 2：Being contributed according to bird's nest quantity n, photo-voltaic power supply quantity m and photo-voltaic power supply, it is mutually independent to contribute with load
Random vector, the random first position matrix for generating photo-voltaic power supply and contributing with load outputWith First Speed matrix V1 t。
Wherein, first position matrixWith First Speed matrix V1 tAll it is the matrix of n rows m row.
The mutually independent random vector group that first position matrix is contributed by n different photo-voltaic power supplies with load output
Into First Speed matrix generates at random.
Step 3：If current iteration number is less than maximum iteration, according to Latin Hypercube Sampling number realization and tide
Equality constraint is flowed, Load flow calculation is carried out to first position matrix, calculation of tidal current is obtained, and whether judges calculation of tidal current
Meet constraints.
If current iteration number is less than maximum iteration, according to trend equality constraint, that is, formula (12) to first position square
Battle array carries out s Load flow calculation, obtains calculation of tidal current, and judges whether calculation of tidal current meets constraints；Otherwise, it is defeated
The group gone out at this time is optimal, the optimal as grid-connected maximum penetration level of distributed photovoltaic of group at this time.
Wherein, calculation of tidal current includes quality of voltage, line energizing flow amount and power distribution network power；Judge calculation of tidal current
Whether constraints is met, including：
Judge whether quality of voltage meets quality of voltage chance constraint, whether line energizing flow amount meets line energizing flow amount chance
Whether constraint and power distribution network power, which meet power distribution network power, is forbidden sending bulk power grid chance constraint；
If quality of voltage meets quality of voltage chance constraint, line energizing flow amount meets line energizing flow amount chance constraint and distribution
Net power, which meets power distribution network power, to be forbidden sending bulk power grid chance constraint, then judges that calculation of tidal current meets constraints；
If quality of voltage is unsatisfactory for quality of voltage chance constraint or line energizing flow amount is unsatisfactory for line energizing flow amount chance about
Beam or power distribution network power, which are unsatisfactory for power distribution network power, to be forbidden sending bulk power grid chance constraint, then judges calculation of tidal current not
Meet constraints.
Step 4：If it is determined that calculation of tidal current meets constraints, then first is calculated according to first object function
The desired value of each bird's nest in matrix is put, determines that for current goal function, otherwise, first object function is added for first object function
Upper penalty term obtains the second object function, and the target of each bird's nest in the matrix of first position is calculated according to the second object function
Value, it is current goal function to determine the second object function.
If it is determined that calculation of tidal current meets constraints, then according to first object function, i.e. formula (11), is calculated respectively
One location matrixThe desired value of middle n bird's nest, and determine that first object function is current goal function；Otherwise, by the first mesh
Scalar functions obtain the second object function plus penalty term, and as shown in formula (19), first is calculated respectively according to the second object function
Put matrixThe desired value of middle n bird's nest, and determine that the second object function is current goal function.
In formula (19) ,-B is penalty term.
Step 5：According to the desired value of each bird's nest in the matrix of first position, it is optimal optimal with group to obtain individual.
When initial, the optimal G of individualbestWith the optimal Z of groupbestIt is identical, all for first position matrix n bird's nest target
Maximum desired value in value.
Step 6：Dynamic is calculated according to the weight upper limit, weight lower limit, maximum iteration and current iteration number to weigh
Weight ω, according to changeable weight, the first Studying factors, the second Studying factors, individual is optimal, group is optimal and First Speed matrix obtains
To second speed matrixSecond position matrix is obtained according to second speed matrix and first position matrix
Wherein, shown in the calculation formula of changeable weight such as formula (20)：
ω=ωmax-(ωmax-ωmin)×t/T (20)
Second position matrix is obtained according to formula (21), second position matrix is obtained according to formula (22).
In formula (21), randn is the random number of normal distribution.
Step 7：The desired value of each bird's nest in the matrix of the second position is calculated according to current goal function, according to first
The target of each bird's nest is worth to the third place matrix in the desired value of each bird's nest and second position matrix in location matrix.
The desired value of n bird's nest in the matrix of the second position is calculated respectively according to current goal function.Compare successively
The larger bird's nest of desired value is chosen in the desired value of the bird's nest of same position in one location matrix and second position matrix, each position
Form the third place matrix
Step 8：The equally distributed superseded probability of a random obedience is assigned for each bird's nest in the third place matrix,
It forms and eliminates probability matrix, probability and the third place matrix are found according to superseded probability matrix, bird egg, obtain the 4th position square
Battle array.
Wherein, it is r=(r to eliminate probability matrix1,r2,…,rn), the 4th location matrix is obtained by formula (23)
Step 9：The desired value of each bird's nest and in the third place matrix is calculated according to current goal function respectively
The desired value of each bird's nest in four location matrixs, according to the desired value of each bird's nest in the third place matrix and the 4th location matrix
In the target of each bird's nest be worth to the 5th location matrix.
The desired value of n bird's nest in the third place matrix is calculated respectively according to current goal function, according to current goal letter
Number calculates the desired value of n bird's nest in the 4th location matrix respectively, compares successively in the third place matrix and the 4th location matrix
The desired value of the bird's nest of same position, each position choose the larger bird's nest of desired value and form the 5th location matrix
Step 10：The desired value of each bird's nest in the 5th location matrix is calculated according to current goal function, obtains the 5th
The bird's nest of desired value maximum in matrix is put, and judges whether the desired value of the bird's nest of desired value maximum reaches error requirements.
Wherein, error requirements are 1 percent.
Step 11：If the desired value of the bird's nest of desired value maximum reaches error requirements, according to the bird's nest of desired value maximum
The grid-connected maximum penetration level of distributed photovoltaic is obtained, otherwise, the individual is updated according to the desired value of the bird's nest of desired value maximum
Optimal, optimal by comparing the desired value and group of the bird's nest of desired value maximum, update group is optimal, with the 5th location matrix generation
For first position matrix, First Speed matrix is replaced with second speed matrix, first object function is replaced with current goal function,
Current iteration number is added one, if returning to current iteration number is less than maximum iteration, according to Latin Hypercube Sampling mould
Intend number and trend equality constraint, Load flow calculation is carried out to first position matrix, calculation of tidal current is obtained, and judges trend meter
Calculate whether result meets constraints.
If the desired value of the bird's nest of desired value maximum reaches error requirements, the desired value of the bird's nest of desired value maximum is
For the grid-connected maximum penetration level of distributed photovoltaic；Otherwise, more new individual is optimal, and individual at this time is optimal maximum for the desired value
The desired value of bird's nest, update group is optimal, the desired value of the optimal bird's nest for desired value maximum of group at this time and before
Higher value among the optimal the two of group.Order T=t+1 replaces first object with current goal function
Function, return to step 3 continue iteration.
S106：According to the grid-connected maximum penetration level of distributed photovoltaic to the grid-connected appearance of each node in distribution network system
Amount is planned.
One step explanation is carried out to the advantageous effect of the embodiment of the present invention below by simulated example.
The present embodiment chooses IEEE33 node systems and carries out Simulation Example analysis, access line and load data, and by net
Load output is allocated by load according to target typical load distribution function in network.During Load flow calculation, what photo-voltaic power supply was contributed
The form parameter of Beta distributions is respectively 1.886 and 1.141, chooses the output power of nonparametric probability model, node
The restriction range of voltage is 0.93~1.07.
Respectively choose power distribution network in most common typical load distribution function, that is, uniformly distributed function, be incremented by distribution function and
The distribution function that successively decreases carries out simulation analysis for target typical load distribution function.
1) photovoltaic allowed capacity analysis when target typical load distribution function is uniformly distributed function
When target typical load distribution function is uniformly distributed function, under multi objective constraints condition of opportunity and confidence level
When different, the simulation result of grid-connected allowed capacity is as shown in table 1, and 5 photo-voltaic power supplies is selected to disperse to access power grid, are connect
Access point is 2,17,21,25, No. 32 nodes.As shown in Table 1, during multiple photo-voltaic power supply access power grids, confidence level is bigger, i.e., electric
Net index request is stringenter, and photovoltaic allowed capacity is smaller, and is improved in distributed photovoltaic power access posterior nodal point voltage, and
And confidence level is different, voltage's distribiuting situation is different.
Grid-connected allowed capacity when 1 load of table is uniformly distributed
2) target typical load distribution function is photovoltaic allowed capacity analysis when being incremented by distribution function
Target typical load distribution function be incremented by distribution function when, confidence level, photovoltaic access point with target allusion quotation
When type power load distributing function is identical under the conditions of uniformly distributed function, the photovoltaic allowed capacity result such as table 2 that acquires.It can by table 2
Know, confidence level is bigger, and grid-connected capacity is smaller；Comparison sheet 1 understands that target typical load distribution function is different, respectively with table 2
Node photovoltaic grid connection capacity is different.
Grid-connected allowed capacity when 2 load of table is incrementally distributed
Photovoltaic concentrates grid-connected allowed capacity when 3 load of table is incrementally distributed
When target typical load distribution function is still to be incremented by distribution function, 5 photo-voltaic power supply Relatively centralizeds access power grid
Allowed capacity result such as table 3, access point 9,10,12,14,15.As shown in Table 3, distributed photovoltaic is accurate under different confidence levels
It is similar with the situation of scattered access power grid to enter capacity；Comparison sheet 2 understands that distributed photovoltaic power Relatively centralized accesses ratio with table 3
Grid-connected allowed capacity reduces during scattered access.
3) target typical load distribution function is photovoltaic allowed capacity analysis when successively decreasing distribution function
Target typical load distribution function for successively decrease distribution function when, when confidence level is taken as 0.99, distributed photovoltaic
During the form parameter difference for the Beta distributions that power supply is contributed, emulation acquires the grid-connected allowed capacity of distributed photovoltaic such as table 4.It selects
12:00—13:Form parameter α=2.034, β=1.251 of 00 period, with 13:00—14:The form parameter α of 00 period
=1.886, β=1.141；As shown in Table 4, Beta profile shape parameters are different, and photovoltaic allowed capacity is also different.
4 load of table successively decrease distribution when grid-connected allowed capacity
The photovoltaic allowed capacity Chance-Constrained Programming Model that the present embodiment is established considers not only photo-voltaic power supply output
Randomness and fluctuation feature, and ensure that power distribution network safe and stable operation, improve node voltage level distribution；To power distribution network
Typical load distribution function carries out the optimization analysis of grid-connected capacity, it is known that different power load distributing functions, grid-connected standard
Enter capacity difference, power load distributing function of the same race, photovoltaic disperses grid connection capacity increase when access is accessed than Relatively centralized；In chance about
Under beam plan model and various typical load distribution functions, confidence level is different, and grid-connected capacity is different, confidence level value
Bigger, grid-connected capacity is smaller；When photo-voltaic power supply output takes the form parameter of different periods, grid-connected capacity is different.
In the present embodiment, being determined as stochastic variable with load output by contributing photo-voltaic power supply, establishing photo-voltaic power supply
The probabilistic model that the comprehensive probability model and load of output are contributed obtains target typical load distribution function, using general based on waiting
The Latin Hypercube Sampling method of rate conversion principle and square-root method, generation photo-voltaic power supply are contributed mutual indepedent with load output
Random vector, determine chance constrained programming when the sum of distributed photovoltaic power allowed capacity of distribution network system access is maximum
Model, and Chance-Constrained Programming Model is solved using the cuckoo searching algorithm based on particle cluster algorithm, obtain distributed light
Grid-connected maximum penetration level is lied prostrate, according to the grid-connected maximum penetration level of distributed photovoltaic to the photovoltaic of each node in distribution network system
Grid connection capacity planned, the characteristics of so as to take into full account randomness and fluctuation that photo-voltaic power supply is contributed, ensure that distribution
The reasonability and accuracy of the definitive result of the grid-connected allowed capacity of formula, improve node voltage level distribution, ensure that and match somebody with somebody
Power network safety operation.
Embodiment two
The planning system of the grid-connected allowed capacity of distributed photovoltaic provided by Embodiment 2 of the present invention is shown with reference to Fig. 2, Fig. 2
200 structure diagram.The planning system 200 of the grid-connected allowed capacity of distributed photovoltaic in the present embodiment includes：Probabilistic model
Establish module 210, acquisition module 220, generating random vector module 230, Chance-Constrained Programming Model determining module 240, maximum
Allowed capacity solves module 250 and planning module 260.
Probabilistic model establishes module 210, random according to first for photo-voltaic power supply output to be determined as the first stochastic variable
Variable establishes the comprehensive probability model of photo-voltaic power supply output, and load output is determined as the second stochastic variable, random according to second
Variable establishes the probabilistic model of load output.
Wherein, the comprehensive probability model that photo-voltaic power supply is contributed is included based on the parameter Beta probabilistic models being distributed and based on non-
The probabilistic model of parameter Density Estimator.
Acquisition module 220, for obtaining target typical load distribution function from the typical load distribution function to prestore.
Generating random vector module 230, for according between the first stochastic variable and the second stochastic variable related coefficient,
The probabilistic model that the comprehensive probability model and load that photo-voltaic power supply is contributed are contributed, using based on equiprobability conversion principle and square root
The Latin Hypercube Sampling method of method, the mutually independent random vector that generation photo-voltaic power supply is contributed with load output.
Specifically, generating random vector module 230 includes：First parameter acquiring unit, the first generating random vector unit,
Second generating random vector unit, correlation matrix acquiring unit, lower triangular matrix acquiring unit and the life of the 3rd random vector
Into unit.
First parameter acquiring unit, for obtaining the first stochastic variable for meeting the probabilistic model based on parameter Beta distributions
Quantity, meet the probabilistic model based on nonparametric probability the first stochastic variable quantity and meet load output
The quantity of second stochastic variable of probabilistic model.
First generating random vector unit, for random according to meet the probabilistic model based on parameter Beta distributions first
The quantity of variable, meet the probabilistic model based on nonparametric probability the first stochastic variable quantity and meet load and go out
The quantity of second stochastic variable of the probabilistic model of power, using Latin Hypercube Sampling method generation photo-voltaic power supply output and load
The random vector of output.
Second generating random vector unit, for according to equiprobability conversion principle, photo-voltaic power supply being contributed and is contributed with load
Random vector be converted to photo-voltaic power supply contribute with load contribute standardized normal distribution random vector.
Correlation matrix acquiring unit, for according to the related coefficient between the first stochastic variable and the second stochastic variable
And equiprobability conversion principle obtains the related coefficient that photo-voltaic power supply is contributed with the random vector of the standardized normal distribution of load output
Matrix.
Lower triangular matrix acquiring unit, for being decomposed to obtain down three angular moments to correlation matrix according to square-root method
Battle array.
3rd generating random vector unit, for the mark contributed with load that according to lower triangular matrix, photo-voltaic power supply is contributed
Quasi normal distribution random vector is converted to the mutually independent random vector that photo-voltaic power supply is contributed with load output.
Chance-Constrained Programming Model determining module 240, it is negative according to confidence level and target typical case for setting confidence level
Lotus distribution function determines chance constrained programming when the sum of the distributed photovoltaic power allowed capacity of distribution network system access is maximum
Model.
Wherein, Chance-Constrained Programming Model includes first object function and constraints, and constraints includes trend equation
Constraint, chance constraint and inequality constraints, chance constraint include quality of voltage chance constraint, line energizing flow amount chance constraint and match somebody with somebody
Grid power is forbidden sending bulk power grid chance constraint, and inequality constraints includes the constraint of photovoltaic power factor, node optical volt-ampere dressing
Amount constraint and the constraint of capacity permeability.
Maximum penetration level solve module 250, for according to photo-voltaic power supply contribute with load contribute it is mutually independent with
Machine vector, solves Chance-Constrained Programming Model using the cuckoo searching algorithm based on particle cluster algorithm, is distributed
The grid-connected maximum penetration level of formula.
Specifically, maximum penetration level solves module 250 and includes：Second parameter acquiring unit, the generation of first position matrix
Unit, Load flow calculation unit, bird's nest desired value computing unit, the optimal acquiring unit of group, second position matrix acquiring unit,
Three location matrix acquiring units, the 4th location matrix acquiring unit, the 5th location matrix acquiring unit, top-quality bird's nest obtain
Take unit and maximum penetration level acquiring unit.
Second parameter acquiring unit, for obtaining photo-voltaic power supply quantity, Latin Hypercube Sampling number realization, bird's nest number
Amount, maximum iteration, current iteration number, bird egg be found probability, the weight upper limit, weight lower limit, the first Studying factors and
Second Studying factors.
First position matrix generation unit, for being contributed and being born according to bird's nest quantity, photo-voltaic power supply quantity and photo-voltaic power supply
The mutually independent random vector that lotus is contributed, it is random to generate the first position matrix and first that photo-voltaic power supply is contributed and load is contributed
Rate matrices.
Load flow calculation unit, if being less than maximum iteration for current iteration number, according to Latin Hypercube Sampling
Number realization and trend equality constraint carry out Load flow calculation to first position matrix, obtain calculation of tidal current, and judge trend
Whether result of calculation meets constraints.
Wherein, calculation of tidal current includes quality of voltage, line energizing flow amount and power distribution network power.
Load flow calculation unit further includes：Judgment sub-unit, first judge that result subelement and second judges result subelement.
Judgment sub-unit, for judging it is whether full whether quality of voltage meets quality of voltage chance constraint, line energizing flow amount
Whether sufficient line energizing flow amount chance constraint and power distribution network power, which meet power distribution network power, is forbidden sending bulk power grid chance constraint.
First judges result subelement, if meeting quality of voltage chance constraint for quality of voltage, line energizing flow amount meets
Line energizing flow amount chance constraint and power distribution network power, which meet power distribution network power, to be forbidden sending bulk power grid chance constraint, then judges trend
Result of calculation meets constraints.
Second judges result subelement, if being unsatisfactory for quality of voltage chance constraint or line energizing flow for quality of voltage
Amount is unsatisfactory for line energizing flow amount chance constraint or power distribution network power is unsatisfactory for power distribution network power and forbids sending bulk power grid chance about
Beam then judges that calculation of tidal current is unsatisfactory for constraints.
Bird's nest desired value computing unit, for if it is determined that calculation of tidal current meets constraints, then according to first object
The desired value of each bird's nest in the matrix of first position is calculated in function, determines first object function as current goal function, no
Then, first object function is obtained into the second object function plus penalty term, first position is calculated according to the second object function
The desired value of each bird's nest in matrix, it is current goal function to determine the second object function.
For the desired value according to each bird's nest in the matrix of first position, it is optimal to obtain individual for the optimal acquiring unit of group
It is optimal with group.
Second position matrix acquiring unit, for according to the weight upper limit, weight lower limit, maximum iteration and current iteration
Changeable weight is calculated in number, and according to changeable weight, the first Studying factors, the second Studying factors, individual is optimal, group is optimal
Second speed matrix is obtained with First Speed matrix, second position square is obtained according to second speed matrix and first position matrix
Battle array.
The third place matrix acquiring unit, for each bird in the matrix of the second position to be calculated according to current goal function
The desired value of nest, according to the desired value of each bird's nest in the desired value of each bird's nest in the matrix of first position and second position matrix
Obtain the third place matrix.
4th location matrix acquiring unit, for assigning a random obedience for each bird's nest in the third place matrix
Equally distributed superseded probability forms and eliminates probability matrix, probability and the third place are found according to superseded probability matrix, bird egg
Matrix obtains the 4th location matrix.
5th location matrix acquiring unit, for being calculated respectively in the third place matrix respectively according to current goal function
The desired value of each bird's nest in the desired value of a bird's nest and the 4th location matrix, according to the mesh of each bird's nest in the third place matrix
The target of each bird's nest is worth to the 5th location matrix in scale value and the 4th location matrix.
Top-quality bird's nest acquiring unit, for calculating each bird's nest in the 5th location matrix according to current goal function
Desired value, obtain the bird's nest of desired value maximum in the 5th location matrix, and judge that the desired value of the bird's nest of desired value maximum is
It is no to reach error requirements.
Maximum penetration level acquiring unit, if the desired value for the bird's nest of desired value maximum reaches error requirements, root
The grid-connected maximum penetration level of distributed photovoltaic is obtained according to the bird's nest of desired value maximum, otherwise, according to the bird's nest of desired value maximum
The desired value update individual is optimal, optimal by comparing the desired value and group of the bird's nest of desired value maximum, updates group
Body is optimal, and first position matrix is replaced with the 5th location matrix, and the First Speed matrix is replaced with the second speed matrix,
First object function is replaced with current goal function, current iteration number is added one, if returning to current iteration number is less than maximum
Then according to Latin Hypercube Sampling number realization and trend equality constraint, trend meter is carried out to first position matrix for iterations
It calculates, obtains calculation of tidal current, and judge whether calculation of tidal current meets constraints.
Planning module 260, for according to the grid-connected maximum penetration level of distributed photovoltaic to each node in distribution network system
Grid-connected capacity planned.
In the present embodiment, by probabilistic model, establish module and be determined as photo-voltaic power supply output and load output at random
Variable establishes the comprehensive probability model of photo-voltaic power supply output and the probabilistic model of load output, passes through acquisition module, obtain target
Typical load distribution function, by generating random vector module, using the Latin based on equiprobability conversion principle and square-root method
The hypercube methods of sampling, the mutually independent random vector that generation photo-voltaic power supply is contributed with load output, is advised by chance constraint
Model determining module is drawn, determines chance constraint when the sum of the distributed photovoltaic power allowed capacity of distribution network system access is maximum
Plan model, by maximum penetration level solve module, using the cuckoo searching algorithm based on particle cluster algorithm to chance about
Beam plan model solves, and obtains the grid-connected maximum penetration level of distributed photovoltaic, grid-connected according to distributed photovoltaic by planning module
Maximum penetration level plans the grid-connected capacity of each node in distribution network system, so as to take into full account photovoltaic electric
Source contribute randomness and fluctuation the characteristics of, ensure that the grid-connected allowed capacity of distributed photovoltaic definitive result reasonability and
Accuracy improves node voltage level distribution, ensure that power distribution network safe and stable operation.
Embodiment three
With reference to Fig. 3, the embodiment of the present invention additionally provides a kind of terminal device 3, including memory 31, processor 30 and deposits
The computer program 32 that can be run in memory and on a processor is stored up, the processor 30 performs the computer program 32
The step in each method embodiment described in Shi Shixian such as above-described embodiment, such as step S101 shown in FIG. 1 is to step
S106.Alternatively, the processor 30 realizes that each system as described in above-described embodiment is real when performing the computer program 32
Apply the function of each module in example, such as the function of module 210 to 260 shown in Fig. 2.
The terminal device can be the computing devices such as desktop PC, notebook, palm PC and cloud server.
The terminal device may include, but be not limited only to, processor 30, memory 31.Such as the terminal device can also include it is defeated
Enter output equipment, network access equipment, bus etc..
Alleged processor 30 can be central processing unit (Central Processing Unit, CPU), can also be
Other general processors, digital signal processor (Digital Signal Processor, DSP), application-specific integrated circuit
(Application Specific Integrated Circuit, ASIC), field programmable gate array (Field-
Programmable Gate Array, FPGA) either other programmable logic device, discrete gate or transistor logic,
Discrete hardware components etc..General processor can be microprocessor or the processor can also be any conventional processor
Deng.
The memory 31 can be the internal storage unit of the terminal device, such as the hard disk of terminal device or interior
It deposits.The memory 31 can also be equipped on the External memory equipment of the terminal device, such as the terminal device insert
Connect formula hard disk, intelligent memory card (Smart Media Card, SMC), secure digital (Secure Digital, SD) card, flash memory
Block (Flash Card) etc..Further, the memory 31 can also both include the internal storage unit of terminal device or wrap
Include External memory equipment.The memory 31 is used to storing the computer program 32 and other needed for the terminal device
Program and data.The memory 31 can be also used for temporarily storing the data that has exported or will export.
Example IV
The embodiment of the present invention additionally provides a kind of computer readable storage medium, and computer-readable recording medium storage has meter
Calculation machine program realizes the step in each method embodiment as described in above-described embodiment when computer program is executed by processor
Such as step S101 shown in FIG. 1 to step S106 suddenly,.Alternatively, it is realized as above when the computer program is executed by processor
State the function of each module in each system embodiment described in embodiment, such as the function of module 210 to 260 shown in Fig. 2.
The computer program can be stored in a computer readable storage medium, and the computer program is by processor
During execution, it can be achieved that the step of above-mentioned each embodiment of the method.Wherein, the computer program includes computer program code,
The computer program code can be source code form, object identification code form, executable file or some intermediate forms etc..Institute
Stating computer-readable medium can include：Can carry the computer program code any entity or device, recording medium,
USB flash disk, mobile hard disk, magnetic disc, CD, computer storage, read-only memory (ROM, Read-Only Memory), arbitrary access
Memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium etc..It needs
It is bright, the content that the computer-readable medium includes can according in jurisdiction legislation and patent practice requirement into
The appropriate increase and decrease of row, such as in some jurisdictions, according to legislation and patent practice, computer-readable medium does not include being electricity
Carrier signal and telecommunication signal.
The steps in the embodiment of the present invention can be sequentially adjusted, merged and deleted according to actual needs.
Module or unit in system of the embodiment of the present invention can be combined, divided and deleted according to actual needs.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all essences in the present invention
All any modification, equivalent and improvement made within refreshing and principle etc., should all be included in the protection scope of the present invention.
Claims (10)
1. a kind of planing method of the grid-connected allowed capacity of distributed photovoltaic, which is characterized in that including：
Photo-voltaic power supply output is determined as the first stochastic variable, the photo-voltaic power supply is established according to first stochastic variable and is contributed
Comprehensive probability model, by load output be determined as the second stochastic variable, the load is established according to second stochastic variable
The probabilistic model of output；
Target typical load distribution function is obtained from the typical load distribution function to prestore；
It is contributed according to related coefficient, the photo-voltaic power supply between first stochastic variable and second stochastic variable comprehensive
The probabilistic model that probabilistic model and the load are contributed is closed, it is super vertical using the Latin based on equiprobability conversion principle and square-root method
The square methods of sampling generates the mutually independent random vector that the photo-voltaic power supply is contributed with load output；
Confidence level is set, according to the confidence level and the target typical load distribution function, determines that distribution network system connects
Chance-Constrained Programming Model when the sum of distributed photovoltaic power allowed capacity entered is maximum；
According to the mutually independent random vector that photo-voltaic power supply output is contributed with the load, using based on particle cluster algorithm
Cuckoo searching algorithm the Chance-Constrained Programming Model is solved, obtain the grid-connected maximum access of distributed photovoltaic and hold
Amount；
According to the grid-connected maximum penetration level of the distributed photovoltaic to the grid-connected appearance of each node in the distribution network system
Amount is planned.
2. the planing method of the grid-connected allowed capacity of distributed photovoltaic according to claim 1, which is characterized in that the photovoltaic
The comprehensive probability model that power supply is contributed is included based on the parameter Beta probabilistic models being distributed and based on nonparametric probability
Probabilistic model；
Related coefficient, the photo-voltaic power supply according between first stochastic variable and second stochastic variable is contributed
Comprehensive probability model and the load contribute probabilistic model, using the Latin based on equiprobability conversion principle and square-root method
The hypercube methods of sampling generates the mutually independent random vector that the photo-voltaic power supply is contributed with load output, including：
It obtains the quantity for first stochastic variable for meeting the probabilistic model based on parameter Beta distributions, meet the base
In first stochastic variable of the probabilistic model of nonparametric probability quantity and meet the probability that the load contributes
The quantity of second stochastic variable of model；
The quantity of first stochastic variable of probabilistic model based on parameter Beta distributions according to described meet, the symbol
Close first stochastic variable of the probabilistic model based on nonparametric probability quantity and it is described meet it is described negative
The quantity of second stochastic variable for the probabilistic model that lotus is contributed generates the light using the Latin Hypercube Sampling method
Lie prostrate the random vector that power supply is contributed with load output；
According to the equiprobability conversion principle, the random vector contributed with the load that the photo-voltaic power supply is contributed is converted to institute
State the random vector that photo-voltaic power supply is contributed with the standardized normal distribution of load output；
According to the related coefficient between first stochastic variable and second stochastic variable and the equiprobability conversion principle
Obtain the correlation matrix that the photo-voltaic power supply is contributed with the random vector of the standardized normal distribution of load output；
The correlation matrix is decomposed to obtain lower triangular matrix according to square-root method；
According to the lower triangular matrix, the standardized normal distribution random vector contributed with the load that the photo-voltaic power supply is contributed
Be converted to the mutually independent random vector that the photo-voltaic power supply is contributed with load output.
3. the planing method of the grid-connected allowed capacity of distributed photovoltaic according to claim 1, which is characterized in that the chance
Constraining plan model includes first object function and constraints, and the constraints includes trend equality constraint, chance constraint
And inequality constraints, the chance constraint include quality of voltage chance constraint, line energizing flow amount chance constraint and power distribution network power
Forbid sending bulk power grid chance constraint, the inequality constraints includes the constraint of photovoltaic power factor, node optical volt installed capacity about
Beam and the constraint of capacity permeability.
4. the planing method of the grid-connected allowed capacity of distributed photovoltaic according to claim 3, which is characterized in that the basis
The mutually independent random vector that the photo-voltaic power supply is contributed with load output, using the cuckoo based on particle cluster algorithm
Searching algorithm solves the Chance-Constrained Programming Model, obtains the grid-connected maximum penetration level of distributed photovoltaic, including：
Obtain photo-voltaic power supply quantity, Latin Hypercube Sampling number realization, bird's nest quantity, maximum iteration, current iteration time
Number, bird egg are found probability, the weight upper limit, weight lower limit, the first Studying factors and the second Studying factors；
Being contributed according to the bird's nest quantity, the photo-voltaic power supply quantity and the photo-voltaic power supply, it is mutual only to contribute with the load
Vertical random vector generates first position matrix and First Speed square that the photo-voltaic power supply is contributed with load output at random
Battle array；
If the current iteration number is less than the maximum iteration, according to the Latin Hypercube Sampling number realization and
The trend equality constraint carries out Load flow calculation to the first position matrix, obtains calculation of tidal current, and judge the tide
Whether stream calculation result meets the constraints；
If it is determined that the calculation of tidal current meets the constraints, then according to being calculated the first object function
The desired value of each bird's nest in the matrix of first position determines the first object function as current goal function, otherwise, by described in
First object function obtains the second object function plus penalty term, and described first is calculated according to second object function
The desired value of each bird's nest in matrix is put, it is current goal function to determine second object function；
According to the desired value of each bird's nest in the first position matrix, it is optimal optimal with group to obtain individual；
It is calculated according to the weight upper limit, the weight lower limit, the maximum iteration and the current iteration number
Changeable weight, it is optimal, described according to the changeable weight, first Studying factors, second Studying factors, the individual
The optimal and described First Speed matrix of group obtains second speed matrix, according to the second speed matrix and the first position
Matrix obtains second position matrix；
The desired value of each bird's nest in the second position matrix is calculated according to the current goal function, according to described
The target of each bird's nest is worth to the third place in the desired value of each bird's nest and the second position matrix in one location matrix
Matrix；
The equally distributed superseded probability of a random obedience is assigned for each bird's nest in the third place matrix, forms and washes in a pan
Probability matrix is eliminated, probability and the third place matrix are found according to the superseded probability matrix, the bird egg, obtain the 4th
Location matrix；
The desired value of each bird's nest and described is calculated in the third place matrix according to the current goal function respectively
The desired value of each bird's nest in 4th location matrix, according to the desired value of each bird's nest in the third place matrix and described
The target of each bird's nest is worth to the 5th location matrix in four location matrixs；
The desired value of each bird's nest in the 5th location matrix is calculated according to the current goal function, obtains described 5th
The bird's nest of desired value maximum in matrix is put, and judges whether the desired value of the bird's nest of the desired value maximum reaches error requirements；
If the desired value of the bird's nest of the desired value maximum reaches the error requirements, according to the bird's nest of desired value maximum
The grid-connected maximum penetration level of distributed photovoltaic is obtained, otherwise, according to the update of the desired value of the bird's nest of desired value maximum
Individual is optimal, and optimal by comparing the desired value and the group of the bird's nest of desired value maximum, it is optimal to update the group,
The first position matrix is replaced with the 5th location matrix, the First Speed square is replaced with the second speed matrix
Battle array, replaces the first object function with the current goal function, the current iteration number is added one, if returning to the institute
Current iteration number is stated less than the maximum iteration, then according to the Latin Hypercube Sampling number realization and the trend
Equality constraint carries out Load flow calculation to the first position matrix, obtains calculation of tidal current, and judge the Load flow calculation knot
Whether fruit meets the constraints.
5. the planing method of the grid-connected allowed capacity of distributed photovoltaic according to claim 4, which is characterized in that the trend
Result of calculation includes quality of voltage, line energizing flow amount and power distribution network power；
It is described to judge whether the calculation of tidal current meets the constraints, including：
Judge whether the quality of voltage meets the quality of voltage chance constraint, whether the line energizing flow amount meets the line
Whether road-load flow chance constraint and the power distribution network power, which meet the power distribution network power, is forbidden sending bulk power grid chance constraint；
If the quality of voltage meets the quality of voltage chance constraint, the line energizing flow amount meets the line energizing flow amount machine
It can constrain and the power distribution network power meets the power distribution network power and forbids sending bulk power grid chance constraint, then judge the trend
Result of calculation meets the constraints；
If the quality of voltage is unsatisfactory for the quality of voltage chance constraint or the line energizing flow amount is unsatisfactory for the circuit
Current-carrying capacity chance constraint or the power distribution network power, which are unsatisfactory for the power distribution network power, to be forbidden sending bulk power grid chance constraint,
Then judge that the calculation of tidal current is unsatisfactory for the constraints.
6. a kind of planning system of the grid-connected allowed capacity of distributed photovoltaic, which is characterized in that including：
Probabilistic model establishes module, for photo-voltaic power supply output to be determined as the first stochastic variable, according to the described first random change
Amount establishes the comprehensive probability model that the photo-voltaic power supply is contributed, and load output is determined as the second stochastic variable, according to described the
Two stochastic variables establish the probabilistic model that the load is contributed；
Acquisition module, for obtaining target typical load distribution function from the typical load distribution function to prestore；
Generating random vector module, for according to the phase relation between first stochastic variable and second stochastic variable
The probabilistic model that the comprehensive probability model and the load that several, described photo-voltaic power supply is contributed are contributed, is converted using based on equiprobability
The Latin Hypercube Sampling method of principle and square-root method, generate the photo-voltaic power supply contribute with the load contribute it is mutual solely
Vertical random vector；
Chance-Constrained Programming Model determining module, for setting confidence level, according to the confidence level and target typical case
Power load distributing function determines chance constraint rule when the sum of the distributed photovoltaic power allowed capacity of distribution network system access is maximum
Draw model；
Maximum penetration level solve module, for according to the photo-voltaic power supply contribute with the load contribute it is mutually independent with
Machine vector, solves the Chance-Constrained Programming Model using the cuckoo searching algorithm based on particle cluster algorithm, obtains
The grid-connected maximum penetration level of distributed photovoltaic；
Planning module, for according to the grid-connected maximum penetration level of the distributed photovoltaic to each node in the distribution network system
Grid-connected capacity planned.
7. the planning system of the grid-connected allowed capacity of distributed photovoltaic according to claim 6, which is characterized in that the photovoltaic
The comprehensive probability model that power supply is contributed is included based on the parameter Beta probabilistic models being distributed and based on nonparametric probability
Probabilistic model；
The generating random vector module includes：
First parameter acquiring unit meets the described first random of the probabilistic model based on parameter Beta distributions for obtaining
The quantity of variable, meet the probabilistic model based on nonparametric probability first stochastic variable quantity and symbol
Close the quantity of second stochastic variable for the probabilistic model that the load is contributed；
First generating random vector unit, for meeting according to described in the probabilistic model based on parameter Beta distributions
The quantity of first stochastic variable, the described first random change for meeting the probabilistic model based on nonparametric probability
The quantity of the quantity of amount and second stochastic variable for meeting the probabilistic model that the load is contributed, using the Latin
The hypercube methods of sampling generates the random vector that the photo-voltaic power supply is contributed with load output；
Second generating random vector unit, for according to the equiprobability conversion principle, by the photo-voltaic power supply contribute with it is described
The random vector that load is contributed be converted to the photo-voltaic power supply contribute the standardized normal distribution contributed with the load it is random to
Amount；
Correlation matrix acquiring unit, for according to the correlation between first stochastic variable and second stochastic variable
Coefficient and the equiprobability conversion principle obtain the photo-voltaic power supply contribute the standardized normal distribution contributed with the load with
The correlation matrix of machine vector；
Lower triangular matrix acquiring unit, for being decomposed to obtain down three angular moments to the correlation matrix according to square-root method
Battle array；
3rd generating random vector unit, for according to the lower triangular matrix, the photo-voltaic power supply to be contributed and the load
The standardized normal distribution random vector of output be converted to the photo-voltaic power supply contribute with the load contribute it is mutually independent with
Machine vector.
8. the planning system of the grid-connected allowed capacity of distributed photovoltaic according to claim 6, which is characterized in that the chance
Constraining plan model includes first object function and constraints, and the constraints includes trend equality constraint, chance constraint
And inequality constraints, the chance constraint include quality of voltage chance constraint, line energizing flow amount chance constraint and power distribution network power
Forbid sending bulk power grid chance constraint, the inequality constraints includes the constraint of photovoltaic power factor, node optical volt installed capacity about
Beam and the constraint of capacity permeability.
9. a kind of terminal device, including memory, processor and it is stored in the memory and can be on the processor
The computer program of operation, which is characterized in that the processor realizes such as claim 1 to 5 when performing the computer program
The step of planing method of the grid-connected allowed capacity of any one distributed photovoltaic.
10. a kind of computer readable storage medium, the computer-readable recording medium storage has computer program, and feature exists
In the realization grid-connected access of the distributed photovoltaic as described in any one of claim 1 to 5 when the computer program is executed by processor
The step of planing method of capacity.
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