CN108448620A - High permeability distributed generation resource assemblage classification method based on integrated performance index - Google Patents
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
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Abstract
The invention discloses the high permeability distributed generation resource assemblage classification methods based on integrated performance index, are related to the distribution network planning and control technology field of renewable energy source current, the index system of assemblage classification and the efficient algorithm of assemblage classification；The index definition of assemblage classification is integrated performance index, and integrated performance index includes the reactive balance degree index of modularity index ρ based on electrical distance, clusterWith the active balance degree index of clusterFor the objective requirement of the calculation expression and assemblage classification of adaptation integrated performance index system, the efficient algorithm of assemblage classification is to carry out distributed generation resource assemblage classification using genetic algorithm, simultaneously, improve basic genetic algorithmic, the coding mode of chromosome is devised according to the syntople of network, and uses adaptive crossover and mutation probability.The invention has the advantages that：The capacity of self-government that the complementarity and cluster between node can be given full play to, is beneficial to the consumption to extensive regenerative resource and control.
Description
Technical field
The present invention relates to the distribution network planning of renewable energy source current and control technology field, it is more particularly to based on synthesis
The high permeability distributed generation resource assemblage classification method of performance indicator.
Background technology
The increase in demand of regenerative resource and people promote the fast of renewable energy power generation to the concern of environmental problem
Speed development.It is a large amount of distributed with the further reinforcing of national new energy help-the-poor policy especially in backwoodsman power distribution network
Regenerative resource accesses power grid, and the case where permeability is more than 100% have occurred in some areas, to local power grid planning,
Operation and control bring huge challenge.Distribution type renewable energy control methods common at present mainly have micro-capacitance sensor mould
Formula, centralized control and clustered control pattern.In backwoodsman low-voltage network, due to regenerative resource plant-grid connection
Single-machine capacity is small, quantity is more and geographical location disperses, and microgrid and centralized control run operating difficulties, and based on cluster
Control methods can make full use of the autonomous characteristic of cluster, ensure scale distributed power generation scale orderly, it is reliable, efficiently connect
Enter power grid, it has also become the grid-connected important solutions of scale regenerative resource.
This noun of cluster derives from Computer Subject field, is a series of autonomous workings but is connected by express network
They can be regarded as an entirety to manage by computer cluster, upper layer, in the case that system overall cost it is lower obtain it is higher
Performance And Reliability.In the power system, cluster can be defined as：Be made of a series of equipment, can independent operating again may be used
The working group of mutual co-ordination.Cluster is externally an entirety, has common objective, receives single instruction control, convenient for adjusting
Degree and management；And in cluster internal, each equipment is to complete common objective to cooperate, and efficiently plays the collaboration capabilities of equipment.
In recent years, field of power for cluster research with starting to cause to pay close attention to, the application scenarios of assemblage classification
Include mainly two fields：Systems organization and scheduling controlling.From the point of view of existing achievement in research, research work is mostly focused on tune
Degree control, such as：Network Partition for Voltage Control, sub-area division and group's tone group control etc., but considering system operation controlling behavior
Research can be divided into two problems：(1) criterion and index system of assemblage classification；(2) efficient algorithm and reality of assemblage classification
It is existing.Current division judges that using the coupling of cluster as index, i.e. cluster internal contact is close, is contacted between group sparse；It divides and calculates
Method can be divided into three classes：Clustering, the community discovery of complex network and optimization algorithm.Most simply and intuitively assemblage classification
It can be realized, but divided so excessively coarse according to geographical location or administrative region.It is drawn for this purpose, there are following several type of cluster
Point：With Euclidean between node away from for index, simplify distributed electrical source position in distribution network planning with hierarchical agglomerative clustering algorithm
It solves；With in group, the distance between group's intermediate node for foundation, show that system operation manages cluster by hierarchical cluster analysis；Fortune
With fuzzy clustering method division control partition, the electrical distance based on voltage magnitude between the sensitive definition node of reactive power,
Dynamic partition is found out with transitive closure hair, optimal classification is obtained finally by Counting statistics amount F；Utilize the community of complex network
Digging technology has carried out the division of Network Partition for Voltage Control, and is evaluated sub-area division quality using modularity as evaluation index；
From the angle of number optimization, electric power networks subregion is regarded as a combinatorial optimization problem, with Tabu search methods, realize voltage
Subregion of control etc..
To sum up, existing assemblage classification method is to be based on some single index, for the planning of system, in operation and control
A certain process divided, for consider run and control planning type of cluster divide shortage system theory support and synthesis
Performance indicator.
Invention content
It is to be based on some single index technical problem to be solved by the present invention lies in existing assemblage classification method, for
The planning of system, a certain process in operation and control are divided, and the planning type of cluster that considers to run and control is divided
The theory support and integrated performance index of shortage system.
The present invention is that solution above-mentioned technical problem, specific technical solution are as follows by the following technical programs：
High permeability distributed generation resource assemblage classification method based on integrated performance index, including：The index of assemblage classification
The efficient algorithm of system and assemblage classification；The index definition of the assemblage classification is integrated performance index, and the comprehensive performance refers to
Mark includes the reactive balance degree index of modularity index ρ based on electrical distance, clusterWith the active balance degree index of clusterThe efficient algorithm of the assemblage classification is to carry out distributed generation resource assemblage classification using genetic algorithm；High permeability is distributed
Power supply assemblage classification method and step is as follows：
S1:The modularity index ρ is calculated, the expression of the modularity index ρ is as follows：
Wherein, e is the matrix of the weight composition on side, element eijFor the weight on the side of connecting node i and node j；For the sum of the side right on all sides of network；Indicate all sides being connected with node i weight it
With；Indicate all the sum of weights on side being connected with node j；δ (i, j)=1 indicates node i and node j same
In cluster, δ (i, j)=0 indicates node i and node j not in same cluster；
S2:Calculate the reactive balance degree indexThe reactive balance degree indexExpression it is as follows：
In formula, QiFor the reactive balance degree of the i-th cluster；C is cluster number；Qi' computational methods it is as follows：
In formula, nCkFor CkNode number in a cluster；Qsup,iFor the maximum value that node i reactive power provides, including
The reactive power Q that node i reactive power compensator providescAnd the reactive power Q that inverter is capable of providingt, i.e. Qsup,i=Qc+
Qt, wherein the maximum reactive power Q that inverter can be providedt maxIt is expressed as：
In formula,For the maximum power factor angle of inverter；T indicates a certain moment in typical time period scene, when typical
Between scene can determine on demand；PtIt contributes for t moment inverter active；SmaxFor inverter maximum capacity；Qt maxFor t moment inverter
Exportable maximum is idle；Pcut、PmaxExcision power is cut for inverter；Qneed,iFor node i without
The demand of the requirements of work(power, the node i reactive power includes the normal reactive requirement Q of nodeNWith node overvoltage institute
The minimum reactive power neededWherein, QVFor the minimum reactive power for adjusting needed for node i；ΔViFor the electricity of node i
Press variable quantity；SVQ,iiBe node i about the reactive voltage sensitivity of itself, then Qneed,i=QN+QV；
S3：Calculate the active balance degree indexThe active balance degree indexIt indicates as follows：
In formula, PiIt is the active balance degree of the i-th cluster, Pclu(t)iIt is net power of the cluster i under typical time period scene
Value, is expressed as [Pclu(1)i,Pclu(2)i,…,Pclu(t)i,…,Pclu(T)i], it is to be based on each node in typical time period scene
Lower performance number is added acquisition；T indicates the number of time point t in typical time period scene；C is cluster number；
S4：Distributed generation resource assemblage classification is carried out using genetic algorithm.
More specifically, using genetic algorithm progress distributed generation resource assemblage classification, steps are as follows in the S4：
1. the dividing mode of assemblage classification is solved as one, a solution is exactly an individual, according to set coding mode
Individual is encoded；Individual is generated using same coding mode, individual constitutes a population, wherein parameter N roots
It is voluntarily determined according to the scale of network；
2. genetic algorithm starts iteration using this individual as initial point, the suitable of each individual is calculated according to fitness calculation
It is that integrated performance index value γ, the integrated performance index value γ are to answer angle value, the fitness value：
Wherein, λ1、λ2、λ3For weight, value can be configured according to demand；
3. being selected, being intersected and being made a variation according to the algorithm flow of genetic algorithm；
4. repeating 2., 3., terminated until reaching maximum genetic iteration number, maximum of fitness value in gained population
Body is to be solved, that is, acquires optimal assemblage classification.
More specifically, the weight e on the side of the connecting node i in the S1 and node jijIt is calculated as follows acquisition：
Under moment t section, conventional Load Flow equation expression formula is as follows：
In formula, Δ δ, Δ V, Δ P, Δ Q are respectively the generator rotor angle of each node of power distribution network, electricity under typical day integral point moment t sections
Pressure, active power and reactive power increments of change, are the vector of n dimensions, and n is required partitioning site number；SδP、SVP、SVQ、SδQ
Respectively under moment t sections, generator rotor angle active po wer sensitivity coefficient matrix, voltage active po wer sensitivity coefficient matrix, voltage power-less are sensitive
Spend coefficient matrix and the idle sensitivity coefficient matrix of generator rotor angle；Matrix SVQIn the i-th row j column elements SVQ,ijIndicate node j reactive powers
Change the changing value of unit value corresponding node i voltages, then SVQ,iiIndicate node i reactive power variation unit value corresponding node i electricity
The changing value of pressure；Electrical distance is L, the electrical distance L between node i and node j between enabling nodeijFor：
Then, with the weight e of electrical distance L border rings between node, wherein the weight e on the side of node i and node jijIt indicates
For：
eij=1-Lij/max(L)。
More specifically, the determination of a certain moment t of typical time period scene is calculated as follows acquisition in the S2：
The regenerative resource output permeability highest moment carries out correlation computations, i.e. R (t)=P under typical time period scenere
(t)/Pload(t) it is calculated when maximum, wherein R (t) indicates the permeability of regenerative resource, Pre(t) indicate that t moment can be again
The power generating value of the raw energy, Pload(t) requirements of t moment load are indicated.
More specifically, the set coding mode in the S4 carries out as follows：
Electric power networks can regard the figure by putting and side forms as, and the number for counting side in figure is x, the gene of construction one x；
Each of gene represents certain a line in network, and the parameter of each represents the connection status of corresponding sides, only includes ginseng
Number 0,1,0 indicates that corresponding sides disconnect, and 1 indicates corresponding sides connection；
Coding mode is：Initial gene is constructed according to electric power networks connection status, to all progress in initial gene
Random sampling, and all parameters in the position selected are revised as 0, indicate that corresponding sides are gone off by being connected to, i.e. corresponding sides both ends
Two nodes by be connected go off；
New gene is formed after the completion of sampling, the new gene is the individual after a coding, also illustrates that a kind of collection
Group's division result.
More specifically, the intersection of genetic algorithm and mutation probability determine as follows in the S4：
In formula, Pc、PmIntersection, mutation probability are indicated respectively；Pc_max、Pc_min、Pm_max、Pm_minMaximum friendship is indicated respectively
Pitch probability, minimum crossover probability, maximum mutation probability, minimum mutation probability；F ' expressions need to carry out the two of crossover operation
Larger fitness value in individual；F indicates that the fitness value of the individual of mutation operation need to be carried out；favgIndicate being averaged for population
Fitness value.
The present invention has the following advantages compared with prior art：
1, the method for the present invention has considered the node electrical link and power-balance index of correlation of cluster internal, electrical
It gets in touch with, cluster internal node electrical link is close, is contacted between cluster loosely, is convenient for the operational management of cluster；It is flat in power
On weighing apparatus, cluster possesses certain idle deliverability, the certain capacity of self-regulation for making cluster more possess in limited time in node voltage,
Meanwhile outside cluster in characteristic, divides using characteristic complementation between node as principle, cluster can be given full play to regenerative resource
Self digestion capability, be conducive to subsequent cluster programming and group's tone group control.
2, assemblage classification is carried out using improved adaptive GA-IAGA, has not only adapted to the expression of integrated performance index；And it compares
In conventional partitioning algorithm, genetic algorithm is global optimization approach, and ability of searching optimum can ensure the increasing with iterations
Add and moves closer to globally optimal solution.
3, it is encoded based on syntople between node, assemblage classification is made to be combined with genetic algorithm, ensured
The division of the logic reasonability of assemblage classification result, i.e. cluster meets network structure demand, and isolated node is not present in cluster.This
Outside, according to the particular iteration process of algorithm, using the cross and variation probability of adaptive change so that the optimal solution search energy of algorithm
Power, convergence rate have certain optimization.
4, a certain moment for replacing conventional cluster division used with time-varying typical case's power producing characteristics of node certain time period is solid
Definite value so that itself of assemblage classification and node feature are closely connected, and division result is more reasonable.
Description of the drawings
Fig. 1 is the high permeability distributed generation resource assemblage classification method based on integrated performance index of the embodiment of the present invention
Flow chart of steps.
Fig. 2 is the high permeability distributed generation resource assemblage classification method based on integrated performance index of the embodiment of the present invention
Coding method.
Fig. 3 is the flow chart that distributed generation resource assemblage classification is carried out using genetic algorithm of the embodiment of the present invention.
Specific implementation mode
It elaborates below to the embodiment of the present invention, the present embodiment is carried out lower based on the technical solution of the present invention
Implement, gives detailed embodiment and specific operating process, but protection scope of the present invention is not limited to following implementation
Example.
As shown in Figure 1, the present invention is based on the collection of the high permeability distributed generation resource assemblage classification method of integrated performance index
Group's division methods are divided into：The index system of assemblage classification and the efficient algorithm of assemblage classification；The index definition of assemblage classification is comprehensive
Performance indicator is closed, integrated performance index includes the reactive balance degree index of modularity index ρ, clusterWith the active balance of cluster
Spend indexThe efficient algorithm of assemblage classification is to carry out distributed generation resource assemblage classification using genetic algorithm；High permeability is distributed
Formula power supply assemblage classification method and step is as follows：
S1:Computing module degree index is ρ, and the expression of modularity index ρ is as follows：
Wherein, e is the matrix of the weight composition on side, element eijFor the weight on the side of connecting node i and node j, for example, power
When refetching 1, the e when node i and node j are directly connected toij=1, e when being not attached toij=0, weight eijAlso it can be set as according to demand
Other values；For the sum of the side right on all sides of network；Indicate all sides being connected with node i
The sum of weight；Indicate all the sum of weights on side being connected with node j；δ (i, j)=1 indicates node i and node j
In same cluster, δ (i, j)=0 indicates node i and node j not in same cluster.
Wherein, the weight e on the side of connecting node i and node jijIt is calculated as follows acquisition：
Under moment t section, conventional Load Flow equation expression formula is as follows：
In formula, Δ δ, Δ V, Δ P, Δ Q are respectively the generator rotor angle of each node of power distribution network, electricity under typical day integral point moment t sections
Pressure, active power and reactive power increments of change, are the vector of n dimensions, and n is required partitioning site number；SδP、SVP、SVQ、SδQ
Respectively under moment t sections, generator rotor angle active po wer sensitivity coefficient matrix, voltage active po wer sensitivity coefficient matrix, voltage power-less are sensitive
Spend coefficient matrix and the idle sensitivity coefficient matrix of generator rotor angle；Matrix SVQIn the i-th row j column elements SVQ,ijIndicate node j reactive powers
Change the changing value of unit value corresponding node i voltages, then SVQ,iiIndicate node i reactive power variation unit value corresponding node i electricity
The changing value of pressure；Electrical distance is L, the electrical distance L between node i and node j between enabling nodeijFor：
Then, with the weight e of electrical distance L border rings between node, wherein the weight e on the side of node i and node jijIt indicates
For：
eij=1-Lij/max(L)。
S2:Calculate reactive balance degree indexReactive balance degree indexExpression it is as follows：
Wherein, QiFor the reactive balance degree of the i-th cluster；C is cluster number；Qi' computational methods it is as follows：
In formula, nCkFor CkNode number in a cluster；Qsup,iFor the maximum value that node i reactive power provides, including
The reactive power Q that node i reactive power compensator providescAnd the reactive power Q that inverter is capable of providingt, i.e. Qsup,i=Qc+
Qt, wherein the maximum reactive power Q that inverter can be providedt maxIt is expressed as：
Wherein,For the maximum power factor angle of inverter；T indicates a certain moment in typical time period scene, when typical
Between scene can determine on demand, typical day, Typical Year etc. can be selected, the regenerative resource output permeability under typical time period scene
The highest moment carries out correlation computations, i.e. R (t)=Pre(t)/Pload(t) it is calculated when maximum, wherein R (t) indicates renewable
The permeability of the energy, Pre(t) power generating value of t moment regenerative resource, P are indicatedload(t) requirements of t moment load are indicated；Pt
It contributes for t moment inverter active；SmaxFor inverter maximum capacity；Qt maxIt is idle for the exportable maximum of t moment inverter；Pcut、PmaxExcision power is cut for inverter；Qneed,iFor the requirements of node i reactive power, section
The requirements of point i reactive powers include the normal reactive requirement Q of nodeN, when also including that regenerative resource output permeability is excessively high,
Adjust the minimum reactive power needed for node overvoltageWherein, QVTo adjust the idle work(of minimum needed for node i
Rate；ΔViFor the voltage variety of node i；SVQ,iiBe node i about the reactive voltage sensitivity of itself, then Qneed,i=QN+QV；
S3：Calculate active balance degree indexActive balance degree indexIt indicates as follows：
In formula, PiIt is the active balance degree of the i-th cluster, Pclu(t)iIt is net power of the cluster i under typical time period scene
Value, is expressed as [Pclu(1)i,Pclu(2)i,…,Pclu(t)i,…,Pclu(T)i], it is to be based on each node in typical time period scene
Lower performance number is added acquisition；T indicates the number of time point t in typical time period scene；C is cluster number；
S4：Distributed generation resource assemblage classification is carried out using genetic algorithm：
1. the dividing mode of assemblage classification is solved as one, a solution is exactly an individual, according to set coding mode
Individual is encoded；Individual is generated using same coding mode, individual constitutes a population, wherein parameter N roots
It is voluntarily determined according to the scale of network；
2. genetic algorithm starts iteration using this individual as initial point, each individual is calculated according to fitness calculation
Fitness value, the fitness value be integrated performance index value γ, the integrated performance index value γ be：Wherein, λ1、λ2、λ3For weight, value can be configured according to demand；
3. being selected, being intersected and being made a variation according to the algorithm flow of genetic algorithm；
4. repeating 2., 3., terminated until reaching maximum genetic iteration number, maximum of fitness value in gained population
Body is to be solved, that is, acquires optimal assemblage classification.
Wherein, set coding mode is：Electric power networks can be regarded as by putting and the figure that forms of side, and the number on side is in meter figure
X, the gene of construction one x；Each of gene represents certain a line in network, and the parameter of each represents corresponding sides
Connection status only includes that parameter 0,1,0 indicates that corresponding sides disconnect, and 1 indicates corresponding sides connection；Coding method is：According to electric power
Network connection state constructs initial gene, to all progress random samplings in initial gene, and will own in the position selected
Parameter is revised as 0, indicates that two nodes are gone off by being connected；New gene is formed after the completion of sampling, this gene is a volume
Individual after code, also illustrates that a kind of assemblage classification result.For example, as shown in Fig. 2, in figure side number be 3, construct one 3
Gene, three sides are divided into 1,2,3 to be indicated with number, constructs initial gene according to network connection state, then initial gene
For [1 1 1], random sampling is then carried out, the side that number is 3 is drawn, and parameter in the position selected is revised as 0, is encoded
Afterwards, then it is [1 1 0].
Wherein, the intersection of genetic algorithm and mutation probability determine as follows：
In formula, Pc、PmIntersection, mutation probability are indicated respectively；Pc_max、Pc_min、Pm_max、Pm_minMaximum friendship is indicated respectively
Pitch probability, minimum crossover probability, maximum mutation probability, minimum mutation probability；F' expressions need to carry out the two of crossover operation
Larger fitness value in individual；F indicates that the fitness value of the individual of mutation operation need to be carried out；favgIndicate being averaged for population
Fitness value.
The flow chart of distributed generation resource assemblage classification is carried out using genetic algorithm, as shown in figure 3, input node parameter, and
Code construction initial population is carried out based on adjacency matrix, after initial population is constituted, is calculated individual adaptation degree and is preserved best
Individual, then judges whether cycle terminates, and exports optimized individual if recycling and terminating, and be decoded and obtain assemblage classification knot
Fruit；Selection individual is carried out if cycle is not over, intersects and makes a variation, then is calculated individual adaptation degree and preserved optimized individual,
Rejudge whether cycle terminates after calculating, if being unsatisfactory for the condition that cycle terminates will continue, if meeting cycle knot
Beam will not repeat the above process；It is final the result is that output optimized individual and decoding and obtaining assemblage classification result.
The present invention proposes to consider using the distribution network planning of the distribution type renewable energy containing high permeability as application scenarios
The division methods of cluster internal electrical link and power-balance degree, wherein the electrical link of cluster is used based on electrical distance
Modularity index ρ indicates that power-balance degree is with the reactive balance degree of clusterAnd active balance degreeFor index, construct
Integrated performance index system.Meanwhile the objective requirement of the calculation expression and assemblage classification for adaptation integrated performance index system,
Present invention improves over basic genetic algorithmics, and the coding mode of chromosome is devised according to the syntople of network, and use certainly
Adapt to cross and variation probability.New assemblage classification thought proposed by the present invention, can give full play to complementarity between node with
The capacity of self-government of cluster is beneficial to the consumption to extensive regenerative resource and control.
The low-voltage network of the power generation of distribution type renewable energy containing Thief zone is selected to be planned to application scenarios in the present invention, with
It is target to give full play to cluster capacity of self-government, realizes that process is thoroughly discussed to assemblage classification criterion, index and algorithm, the party
Method considers the complementarity and relevance between cluster internal node, on the basis of ensuring cluster endogenous lotus Proper Match, protects
Demonstrate,prove the coupling contact between node and pressure regulation ability.In addition, this method meets following principle of universality always：1, logic is former
Then：Isolated node is not present in cluster internal, is not present between cluster and overlaps node, must assure that connectivity between each node；2, it ties
Structure principle：On geographical space or in electrical couplings, cluster internal contact is close, is contacted between cluster sparse；3, function is former
Then：For the characteristic of cluster by each nodal properties Integrative expression in group, the node in group has good collaboration capabilities.
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 etc., should all be included in the protection scope of the present invention made by within refreshing and principle.
Claims (6)
1. the high permeability distributed generation resource assemblage classification method based on integrated performance index, which is characterized in that including：Cluster is drawn
The efficient algorithm of the index system and assemblage classification divided；The index definition of the assemblage classification is integrated performance index, described comprehensive
Close the reactive balance degree index that performance indicator includes modularity index ρ based on electrical distance, clusterActive with cluster is put down
Weighing apparatus degree indexThe efficient algorithm of the assemblage classification is to carry out distributed generation resource assemblage classification using genetic algorithm；Thief zone
Rate distributed generation resource assemblage classification method and step is as follows：
S1：The modularity index ρ is calculated, the expression of the modularity index ρ is as follows：
Wherein, e is the matrix of the weight composition on side, element eijFor the weight on the side of connecting node i and node j；For the sum of the side right on all sides of network；Indicate all sides being connected with node i weight it
With；Indicate all the sum of weights on side being connected with node j；δ (i, j)=1 indicates node i and node j same
In cluster, δ (i, j)=0 indicates node i and node j not in same cluster；
S2：Calculate the reactive balance degree indexThe reactive balance degree indexExpression it is as follows：
In formula, QiFor the reactive balance degree of the i-th cluster；C is cluster number；Qi' computational methods it is as follows：
In formula, nCkFor CkNode number in a cluster；Qsup,iFor the maximum value that node i reactive power provides, including node i
The reactive power Q that reactive power compensator providescAnd the reactive power Q that inverter is capable of providingt, i.e. Qsup,i=Qc+Qt,
In, maximum reactive power Q that inverter can be providedt maxIt is expressed as：
In formula,For the maximum power factor angle of inverter；T indicates a certain moment in typical time period scene, typical time period field
Scape can determine on demand；PtIt contributes for t moment inverter active；SmaxFor inverter maximum capacity；Qt maxIt can be defeated for t moment inverter
The maximum gone out is idle；Pcut、PmaxExcision power is cut for inverter；Qneed,iFor the idle work(of node i
The demand of the requirements of rate, the node i reactive power includes the normal reactive requirement Q of nodeNNeeded for node overvoltage
Minimum reactive powerWherein, QVFor the minimum reactive power for adjusting needed for node i；ΔViBecome for the voltage of node i
Change amount；SVQ,iiBe node i about the reactive voltage sensitivity of itself, then Qneed,i=QN+QV；
S3：Calculate the active balance degree indexThe active balance degree indexIt indicates as follows：
In formula, PiIt is the active balance degree of the i-th cluster, Pclu(t)iIt is net power values of the cluster i under typical time period scene, table
It is shown as [Pclu(1)i,Pclu(2)i,…,Pclu(t)i,…,Pclu(T)i], it is to be based on each node power under typical time period scene
Value is added acquisition；T indicates the number of time point t in typical time period scene；C is cluster number；
S4：Distributed generation resource assemblage classification is carried out using genetic algorithm.
2. the high permeability distributed generation resource assemblage classification method according to claim 1 based on integrated performance index,
It is characterized in that, using genetic algorithm progress distributed generation resource assemblage classification, steps are as follows in the S4：
1. the dividing mode of assemblage classification is solved as one, a solution is exactly an individual, according to set coding mode to a
Body is encoded；Individual is generated using same coding mode, individual constitutes a population, wherein parameter N is according to net
The scale of network and voluntarily determine；
2. genetic algorithm starts iteration using this individual as initial point, the adaptation of each individual is calculated according to fitness calculation
Angle value, the fitness value are that integrated performance index value γ, the integrated performance index value γ are：
Wherein, λ1、λ2、λ3For weight, value can be configured according to demand；
3. being selected, being intersected and being made a variation according to the algorithm flow of genetic algorithm；
4. repeating 2., 3., terminated until reaching maximum genetic iteration number, the maximum individual of fitness value is in gained population
To be solved, that is, acquire optimal assemblage classification.
3. the high permeability distributed generation resource assemblage classification method according to claim 1 based on integrated performance index,
It is characterized in that, the weight e on the side of connecting node i and node j in the S1ijIt is calculated as follows acquisition：
Under moment t section, conventional Load Flow equation expression formula is as follows：
In formula, Δ δ, Δ V, Δ P, Δ Q be respectively under typical day integral point moment t sections, the generator rotor angle of each node of power distribution network, voltage,
Active power and reactive power increments of change, are the vector of n dimensions, and n is required partitioning site number；SδP、SVP、SVQ、SδQRespectively
For under moment t section, generator rotor angle active po wer sensitivity coefficient matrix, voltage active po wer sensitivity coefficient matrix, voltage power-less sensitivity system
Matrix number and the idle sensitivity coefficient matrix of generator rotor angle；Matrix SVQIn the i-th row j column elements SVQ,ijIndicate the variation of node j reactive powers
The changing value of unit value corresponding node i voltages, then SVQ,iiIndicate node i reactive power variation unit value corresponding node i voltages
Changing value；Electrical distance is L, the electrical distance L between node i and node j between enabling nodeijFor：
Then, with the weight e of electrical distance L border rings between node, wherein the weight e on the side of node i and node jijIt is expressed as：
eij=1-Lij/max(L)。
4. the high permeability distributed generation resource assemblage classification method according to claim 1 based on integrated performance index,
Be characterized in that, in the S2 determination of a certain moment t of typical time period scene be calculated as follows acquisition：
The regenerative resource output permeability highest moment carries out correlation computations, i.e. R (t)=P under typical time period scenere(t)/
Pload(t) it is calculated when maximum, wherein R (t) indicates the permeability of regenerative resource, Pre(t) t moment renewable energy is indicated
The power generating value in source, Pload(t) requirements of t moment load are indicated.
5. the high permeability distributed generation resource assemblage classification method according to claim 2 based on integrated performance index,
It is characterized in that, the set coding mode in the S4 carries out as follows：
Electric power networks can regard the figure by putting and side forms as, and the number for counting side in figure is x, the gene of construction one x；Gene
Each represent certain a line in network, the parameter of each represents the connection status of corresponding sides, only include parameter 0,
1,0 indicates that corresponding sides disconnect, and 1 indicates corresponding sides connection；
Coding mode is：Initial gene is constructed according to electric power networks connection status, all in initial gene are carried out random
Sampling, and all parameters in the position selected are revised as 0, indicate that corresponding sides are gone off by being connected to, i.e. the two of corresponding sides both ends
Node is gone off by being connected；
New gene is formed after the completion of sampling, the new gene is the individual after a coding, also illustrates that a kind of cluster is drawn
Divide result.
6. the high permeability distributed generation resource assemblage classification method according to claim 2 based on integrated performance index,
It is characterized in that, the intersection of genetic algorithm and mutation probability determine as follows in the S4：
In formula, Pc、PmIntersection, mutation probability are indicated respectively；Pc_max、Pc_min、Pm_max、Pm_minIt indicates maximum respectively to intersect generally
Rate, minimum crossover probability, maximum mutation probability, minimum mutation probability；F' indicates that two of crossover operation need to be carried out
Larger fitness value in body；F indicates that the fitness value of the individual of mutation operation need to be carried out；favgIndicate the average adaptation of population
Angle value.
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