CN109149596A - A kind of power distribution network capacitor group optimization constant volume configuration method towards loss minimization - Google Patents
A kind of power distribution network capacitor group optimization constant volume configuration method towards loss minimization Download PDFInfo
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Abstract
This application discloses a kind of, and the power distribution network capacitor group towards loss minimization optimizes constant volume configuration method, this method comprises: determining distribution network structure structured data, the node location of capacitor group to be installed and specification and the relevant parameter for improving discrete particle cluster algorithm；According to the new position of each particle identified after iteration, the target function value of each particle is calculated；According to the target function value of each particle, the global optimum position for the optimal location and all particles that each particle is lived through is determined；According to the position of optimal location and all particles of global optimum's location updating；Cross and variation operation is carried out to the particle of half in group；Whether determining program restrains；If it is, exporting current optimal location objective function corresponding with its；If it is not, then the position to all particles carries out integral quantization, next round iteration is carried out.This method improves discrete particle cluster algorithm using intersection and mutation operation, improves its population diversity, greatly reduces a possibility that falling into local optimum.
Description
Technical field
This application involves technical field of power systems more particularly to a kind of power distribution network capacitor groups towards loss minimization
Optimize constant volume configuration method.
Background technique
With the growth of electric load, in order to supply these loads, power distribution network is also correspondingly developed therewith.Load increases
Length also results in the increase of line loss and the decline of distribution network voltage, and capacitor group can be used to as a kind of reactive power compensator
Such issues that solution.
The formation of capacitor group investment mount scheme needs to comprehensively consider the optimal of a variety of constraint conditions and economic benefit,
And capacitor group rated capacity as optimized variable when be discrete variable, therefore, the solution of the optimization problem be easily trapped into part most
It is excellent.Therefore the foundation of capacitor group configuration scheme optimization mathematical model and solution value are worth further research.
Summary of the invention
This application provides a kind of, and the power distribution network capacitor group towards loss minimization optimizes constant volume configuration method, by distribution
Capacitor group is configured in net to reduce the allocation problem of line loss, is modeled as mathematic optimal model, and using improvement discrete particle cluster
Algorithm (MDPSO) is solved, and provides foundation for distribution system programmed decision-making.
To achieve the goals above, technical solution provided by the present application is as follows:
The embodiment of the present application discloses a kind of power distribution network capacitor group optimization constant volume configuration method towards loss minimization,
The described method includes:
Determine distribution network structure structured data, the node location of capacitor group to be installed, capacitor group specification and improve from
The relevant parameter of shot swarm optimization；
Determine improve discrete particle cluster algorithm maximum number of iterations, generate group in each particle initial position and just
Beginning speed, and initialize the number of iterations；
According to the new position of each particle identified after iteration, the target function value of each particle is calculated；
According to the target function value of each particle, the optimal location pbest and institute that each particle is lived through are determined
There is the global optimum position gbest of particle；
All particles are updated according to the optimal location pbest of each particle and the global optimum position gbest
Position；
Cross and variation operation is carried out to the particle of half in group；
Whether determining program restrains；
If it is, exporting current optimal location objective function corresponding with its；
If it is not, then the position to all particles carries out integral quantization, next round iteration is carried out.
Optionally, determine distribution network structure structured data, the node location of capacitor group to be installed, capacitor group specification and
Improve the relevant parameter of discrete particle cluster algorithm, comprising:
The relevant parameter for improving discrete particle cluster algorithm includes individual sum NP, maximum number of iterations in population
Itermax, weight factor maximin ωmaxAnd ωmin, particle rapidity update when accelerator coefficient c1And c2, violate constraint item
The penalty factor λ of part.
Optionally, according to the new position of each particle identified after iteration, the target function value of each particle is calculated
The mathematical model for improving the capacitor group allocation problem that discrete particle cluster algorithm solves is established according to formula；
s.t.0.95pu≤Vbus≤1.05pu
If≤If rated
Wherein, CCAPITALAnd CO&MIt is the investment and O&M cost of capacitor group, CLOSSIt is line loss cost, r is society
Discount rate, T are the research time limit of allocation problem, VbusIt is busbar voltage, IfAnd If ratedIt is the actual current and specified electricity of feeder line
Stream；
Constraint condition is brought into objective function using penalty function method, forms objective function OF.
Optionally, according to the target function value of each particle, the optimal location that each particle is lived through is determined
The global optimum position gbest of pbest and all particles, comprising:
By target letter in all positions experienced before the objective function and the particle of each particle in kth time iteration
Several minimum values are compared, and the smaller position of the objective function is recorded as new pbest；
By the corresponding objective function of gbest of the minimum value and current record of all particle objective functions in kth time iteration
It is compared, the position of the smallest particle of objective function is recorded as new gbest.
Optionally, it is updated according to the optimal location pbest of each particle and global optimum position gbest all
The position of particle, comprising:
Optimal location pbest and global optimum position gbest the calculating kth of each particle obtained according to kth time iteration+
The speed of particle in 1 iteration；
Vj k+1=ωkVj k+c1r1(pbestj k-Xj k)+c2r2(gbestk-Xj k)
Wherein, Vj kRepresent speed of the particle j in kth time iteration, ωkFor the inertia weight factor in kth time iteration,
r1、r2For equally distributed random number in obedience [0,1]；
Xj k+1=Xj k+Vj k+1
The position of each particle is updated according to updated speed.
Optionally, cross and variation operation is carried out to the particle of half in group, comprising:
It selects a number at random in { 1,2 ..., NB }, is denoted as NC, wherein NB is that the node of capacitor group to be installed is total
Number；
It picks out NC number at random in { 1,2 ..., NB }, is denoted as { n1,n2,...,nNC}；
Enable Xi k+1In n-th1, n2..., nNCA component and Xj k+1Respective components exchange, formed two new particles.
Optionally, cross and variation operation is carried out to the particle of half in group, further includes:
It selects a number at random in { 1,2 ..., NB }, is denoted as NM, wherein NB is that the node of capacitor group to be installed is total
Number；
For Xi k+1The NM component, a numerical value is randomly generated in the bound of the component, for substituting
State the current value of component.
Optionally, whether determining program restrains, comprising:
The convergent condition of program are as follows: the number of iterations k reaches maximum number of iterations ItermaxOr after nearest 20 iteration
Optimal location does not change.
Compared with prior art, the application has the beneficial effect that
This application provides a kind of, and the power distribution network capacitor group towards loss minimization optimizes constant volume configuration method, this method
It comprises determining that distribution network structure structured data, the node location of capacitor group to be installed, capacitor group specification and improves discrete grain
The relevant parameter of swarm optimization；It determines the maximum number of iterations for improving discrete particle cluster algorithm, generates each particle in group
Initial position and initial velocity, and initialize the number of iterations；According to the new position of each particle identified after iteration, calculate each
The target function value of particle；According to the target function value of each particle, the optimal location pbest that each particle is lived through is determined
And the global optimum position gbest of all particles；According to the optimal location pbest of each particle and global optimum position gbest
Update the position of all particles；Cross and variation operation is carried out to the particle of half in group；Whether determining program restrains；If
It is then to export current optimal location objective function corresponding with its；If it is not, then the position to all particles carries out integral quantization,
Carry out next round iteration.Configuration method provided by the present application improves discrete particle cluster algorithm using intersection and mutation operation, improves
Its population diversity greatly reduces a possibility that falling into local optimum, while all joined " whole amount in every single-step iteration
Change " operation, it ensure that the correctness of configuration result, configuration result can work for distribution network planning and provide reference.
One kind provided by the embodiments of the present application is it should be understood that above general description and following detailed description is only
It is exemplary and explanatory, the application can not be limited.
Detailed description of the invention
In order to illustrate more clearly of the technical solution of the application, letter will be made to attached drawing needed in the embodiment below
Singly introduce, it should be apparent that, for those of ordinary skills, without creative efforts, also
Other drawings may be obtained according to these drawings without any creative labor.
Fig. 1 is that a kind of power distribution network capacitor group towards loss minimization provided by the embodiments of the present application optimizes constant volume configuration
The flow chart of method；
Fig. 2 is the curve changed using objective function when MDPSO algorithm optimization with the number of iterations；
Fig. 3 is the network topological diagram of 18 node system of IEEE.
Specific embodiment
In order to make those skilled in the art better understand the technical solutions in the application, below in conjunction with the application reality
The attached drawing in example is applied, the technical scheme in the embodiment of the application is clearly and completely described, it is clear that described implementation
Example is only some embodiments of the present application, rather than whole embodiments.Based on the embodiment in the application, the common skill in this field
The application protection all should belong in art personnel every other embodiment obtained without making creative work
Range.
It is fixed for a kind of power distribution network capacitor group optimization towards loss minimization provided by the embodiments of the present application referring to Fig. 1
Hold the flow chart of configuration method.
As shown in Figure 1, the power distribution network capacitor group optimization constant volume provided by the embodiments of the present application towards loss minimization is matched
The method of setting includes:
S100: distribution network structure structured data, the node location of capacitor group to be installed, capacitor group specification are determined and is changed
Into the relevant parameter of discrete particle cluster algorithm.
The node total number that capacitor group will likely be installed is denoted as NB, by power distribution network framework data and available capacitor group
Specification is input in algorithm, determines the load current value of maximum allowed pressure drop and feeder line as constraint condition.Specified MDPSO
The parameter of (discrete particle cluster algorithm), i.e., individual sum NP (NP=15 × NB), maximum number of iterations Iter in populationmax, weight
Factor maximin ωmaxAnd ωmin(ωmaxAnd ωminIt is taken as 0.9 and 0.4) respectively, accelerator coefficient when particle rapidity updates
c1And c2(c1And c2It takes and 0.5) violates the penalty factor λ of constraint condition (λ takes 1 × 1010)。
S200: determining the maximum number of iterations for improving discrete particle cluster algorithm, generates the initial bit of each particle in group
It sets and initial velocity, and initializes the number of iterations.
Enable the maximum number of iterations Iter of MDSPO (discrete particle cluster algorithm)max=1000, and remember primary iteration number k=
0.Random number is generated in the upper and lower limits of speed and position, as the initial position and speed of each particle, wherein speed
Bound take ± the 10% of location variable bound difference.Location variable needs " integral quantization " after generating, holding capacitor device group
Feature of the rated capacity as discrete variable.
S300: according to the new position of each particle identified after iteration, the target function value of each particle is calculated.
The mathematical model of the capacitor group allocation problem of MDSPO solution is established according to formula first, objective function is capacitor
The investment of device group, O&M cost and line loss cost summation, constraint condition is that node voltage and feeder current cannot get over line, and formula is such as
Under:
s.t.0.95pu≤Vbus≤1.05pu
If≤If rated
Wherein, CCAPITALAnd CO&MIt is the investment and O&M cost of capacitor group, CLOSSIt is line loss cost, r is society
Discount rate, T are the research time limit of allocation problem, VbusIt is busbar voltage, IfAnd If ratedIt is the actual current and specified electricity of feeder line
Stream, electric current and voltage are indicated with per unit value.
Then constraint condition is brought into objective function using penalty function method, forms the target that MDPSO is directly used
Function OF, it may be assumed that
Objective function OF and the relationship of the number of iterations k are as shown in Figure 2.
S400: according to the target function value of each particle, the optimal location pbest and institute that each particle is lived through are determined
There is the global optimum position gbest of particle.
First by mesh in all positions experienced before the objective function and the particle of each particle in kth time iteration
The minimum value of scalar functions is compared, and the smaller position of the objective function is recorded as new pbest.It is shown below:
Wherein, pbestj kBy the optimal location recorded after j-th of particle kth time iteration.
Then by the corresponding target of gbest of the minimum value and current record of all particle objective functions in kth time iteration
Function is compared, and the position of the smallest particle of objective function is recorded as new gbest.It is shown below:
Wherein, gbestkFor the global optimum position after kth time iteration.
S500: the position of all particles is updated according to the optimal location pbest of each particle and global optimum position gbest
It sets.
The optimal location pbest and global optimum position gbest of each particle obtained first according to kth time iteration are calculated
The speed of particle in+1 iteration of kth, is shown below:
Vj k+1=ωkVj k+c1r1(pbestj k-Xj k)+c2r2(gbestk-Xj k)
Wherein, Vj kRepresent speed of the particle j in kth time iteration, ωkFor the inertia weight factor in kth time iteration,
ωmaxAnd ωminRespectively its maximum value and minimum value, c1And c2For accelerator coefficient, r1、r2It is equally distributed in obedience [0,1]
Random number, ItermaxFor maximum number of iterations.
Then the position that each particle is updated according to updated speed, is shown below:
Xj k+1=Xj k+Vj k+1
S600: cross and variation operation is carried out to the particle of half in group.
Crossover operation is expressed as example using particle i and particle j:
Firstly, selecting a number at random in { 1,2 ..., NB }, it is denoted as NC, wherein NB is capacitor group to be installed
Node total number；Then, it picks out NC number at random in { 1,2 ..., NB }, is denoted as { n1,n2,...,nNC}；Finally, enabling Xi k+1
In n-th1, n2..., nNCA component and Xj k+1Respective components exchange, formed two new particles.
Carry out expression variance operation using particle j as example:
Firstly, selecting a number at random in { 1,2 ..., NB }, it is denoted as NM, wherein NB is capacitor group to be installed
Node total number；Then, for Xi k+1The NM component, a numerical value is randomly generated in the bound of the component, is used for
Substitute the current value of the component.
S700: whether determining program restrains.
The convergent condition of program are as follows: the number of iterations k reaches maximum number of iterations ItermaxOr after nearest 20 iteration
Optimal location does not change.I.e. if k=ItermaxOr the optimal location after nearest 20 iteration does not change, then
Meet program determination condition.
S800: if it is, exporting current optimal location objective function corresponding with its.
S900: if it is not, then the position to all particles carries out integral quantization, next round iteration is carried out.
If the number of iterations k reaches maximum number of iterations Itermax, and the optimal location after nearest 20 iteration does not occur
Change, then export current optimal location objective function corresponding with its, as distributing result rationally.
If the number of iterations k is less than maximum number of iterations ItermaxOr the optimal location after nearest 20 iteration occurs
Change, then " integral quantization " is carried out to the position of all particles, enable k=k+1, is transferred to step S300 and carries out next round iteration.
Illustrate the meaning of " integral quantization " operation herein:
If the rated capacity that one group of capacitor group is 300kvar, the value of each component of particle can only be with 300
Step-length, i.e., 300,600,900 ..., and so on.And certain components may be unsatisfactory for this requirement after each particle position updates,
Therefore 300,600,900 to be selected ... in the value closest with the component to replace these be unsatisfactory for " integral quantization " requirement
Component.
This application involves a kind of, and the power distribution network capacitor group towards loss minimization optimizes constant volume configuration method, especially relates to
And it is a kind of can count and node voltage and feeder current physical constraint it is excellent based on the capacitor group for improving discrete particle cluster algorithm
Change constant volume configuration method, improves discrete particle cluster algorithm with mutation operation using intersecting, improve its population diversity, substantially reduce
A possibility that falling into local optimum, while all joined " integral quantization " operation in every single-step iteration, it ensure that configuration result
Correctness, theories technique support can be provided for the planning of power distribution network.
The power distribution network capacitor group optimization constant volume configuration method towards loss minimization is exemplified below.
By taking 18 meshed network of IEEE as shown in Figure 3 as an example, capacity of capacitor bank is carried out using the method that the application proposes
Configuration.
It tests in example, the cost of electric energy is 0.06 $/kWh, and the installation cost of capacitor group is 4 $/kvar, annual O&M
Cost is the 8.75% of installation cost, and the capacity of capacitor group increases upwards using 300kvar as step-length, and electricity is considered in objective function
Container group can service 20 years.In the system, each node of residue 16 other than 50 and 51 two nodes is as installation capacitor group
Both candidate nodes, therefore the number of optimized variable is 16.Population Size is set as 15 times of optimized variable number, that is, includes 240
A particle.
Test macro data are as shown in Table 1 and Table 2.
1 IEEE of table, 18 bus test system node data
2 IEEE of table, 18 bus test system branch data
Distribute that the results are shown in Table 3 rationally.In order to embody the validity of this algorithm, which is not configured capacitor
Line loss and relevant cost when group provide together, are compared with the mentioned method of the application.
3 capacitor group of table distributes result rationally
Those skilled in the art will readily occur to its of the application after considering specification and practicing the disclosure invented here
His embodiment.This application is intended to cover any variations, uses, or adaptations of the invention, these modifications, purposes or
Person's adaptive change follows the general principle of the application and including the undocumented common knowledge in the art of the application
Or conventional techniques.The description and examples are only to be considered as illustrative, and the true scope and spirit of the application are wanted by right
The content asked is pointed out.
Above-described the application embodiment does not constitute the restriction to the application protection scope.
Claims (8)
1. a kind of power distribution network capacitor group towards loss minimization optimizes constant volume configuration method, which is characterized in that the method
Include:
It determines distribution network structure structured data, the node location of capacitor group to be installed, capacitor group specification and improves discrete grain
The relevant parameter of swarm optimization；
It determines the maximum number of iterations for improving discrete particle cluster algorithm, generates the initial position of each particle and initial speed in group
Degree, and initialize the number of iterations；
According to the new position of each particle identified after iteration, the target function value of each particle is calculated；
According to the target function value of each particle, the optimal location pbest and all grains that each particle is lived through are determined
The global optimum position gbest of son；
The position of all particles is updated according to the optimal location pbest of each particle and the global optimum position gbest；
Cross and variation operation is carried out to the particle of half in group；
Whether determining program restrains；
If it is, exporting current optimal location objective function corresponding with its；
If it is not, then the position to all particles carries out integral quantization, next round iteration is carried out.
2. the method according to claim 1, wherein determining distribution network structure structured data, capacitor to be installed
Node location, capacitor group specification and the relevant parameter for improving discrete particle cluster algorithm of group, comprising:
The relevant parameter for improving discrete particle cluster algorithm includes individual sum NP, maximum number of iterations Iter in populationmax,
Weight factor maximin ωmaxAnd ωmin, particle rapidity update when accelerator coefficient c1And c2, violate the punishment of constraint condition
Factor lambda.
3. the method according to claim 1, wherein being counted according to the new position of each particle identified after iteration
Calculate the target function value of each particle
The mathematical model for improving the capacitor group allocation problem that discrete particle cluster algorithm solves is established according to formula；
s.t.0.95pu≤Vbus≤1.05pu
If≤If rated
Wherein, CCAPITALAnd CO&MIt is the investment and O&M cost of capacitor group, CLOSSIt is line loss cost, r is social discount
Rate, T are the research time limit of allocation problem, VbusIt is busbar voltage, IfAnd If ratedIt is the actual current and rated current of feeder line；
Constraint condition is brought into objective function using penalty function method, forms objective function OF.
4. the method according to claim 1, wherein being determined each according to the target function value of each particle
The global optimum position gbest of optimal location pbest and all particles that a particle is lived through, comprising:
By objective function in all positions experienced before the objective function and the particle of each particle in kth time iteration
Minimum value is compared, and the smaller position of the objective function is recorded as new pbest；
The corresponding objective function of gbest of the minimum value and current record of all particle objective functions in kth time iteration is carried out
Compare, the position of the smallest particle of objective function is recorded as new gbest.
5. according to the method described in claim 2, it is characterized in that, according to the optimal location pbest of each particle and institute
State the position that global optimum position gbest updates all particles, comprising:
The optimal location pbest and global optimum position gbest of each particle obtained according to kth time iteration are calculated kth+1 time
The speed of particle in iteration；
Wherein, Vj kRepresent speed of the particle j in kth time iteration, ωkFor the inertia weight factor in kth time iteration, r1、r2For
Obey equally distributed random number in [0,1]；
Xj k+1=Xj k+Vj k+1
The position of each particle is updated according to updated speed.
6. the method according to claim 1, wherein in group half particle carry out cross and variation operation,
Include:
It selects a number at random in { 1,2 ..., NB }, is denoted as NC, wherein NB is the node total number of capacitor group to be installed；
It picks out NC number at random in { 1,2 ..., NB }, is denoted as { n1,n2,...,nNC}；
Enable Xi k+1In n-th1, n2..., nNCA component and Xj k+1Respective components exchange, formed two new particles.
7. according to the method described in claim 6, it is characterized in that, in group half particle carry out cross and variation operation,
Further include:
It selects a number at random in { 1,2 ..., NB }, is denoted as NM, wherein NB is the node total number of capacitor group to be installed；
For Xi k+1The NM component, a numerical value is randomly generated in the bound of the component, for substituting the component
Current value.
8. the method according to claim 1, wherein whether determining program restrains, comprising:
The convergent condition of program are as follows: the number of iterations k reaches maximum number of iterations ItermaxOr it is optimal after nearest 20 iteration
Position does not change.
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|---|---|---|---|---|
| CN117424294A (en) * | 2023-12-18 | 2024-01-19 | 国网辽宁省电力有限公司经济技术研究院 | Efficient reactive power planning method and system for power distribution network |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2000116003A (en) * | 1998-09-29 | 2000-04-21 | Kansai Electric Power Co Inc:The | Method for controlling reactive voltage power |
| CN108306346A (en) * | 2017-12-30 | 2018-07-20 | 国网江苏省电力公司常州供电公司 | A kind of distribution network var compensation power-economizing method |
| CN108399450A (en) * | 2018-02-02 | 2018-08-14 | 武汉理工大学 | Improvement particle cluster algorithm based on biological evolution principle |
-
2018
- 2018-09-26 CN CN201811124696.3A patent/CN109149596A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2000116003A (en) * | 1998-09-29 | 2000-04-21 | Kansai Electric Power Co Inc:The | Method for controlling reactive voltage power |
| CN108306346A (en) * | 2017-12-30 | 2018-07-20 | 国网江苏省电力公司常州供电公司 | A kind of distribution network var compensation power-economizing method |
| CN108399450A (en) * | 2018-02-02 | 2018-08-14 | 武汉理工大学 | Improvement particle cluster algorithm based on biological evolution principle |
Non-Patent Citations (6)
| Title |
|---|
| 余健明: "基于改进DPSO的含风力发电的配电网无功优化规划", 《西安理工大学学报》 * |
| 朱诺: "《交通运输业与人工智能的深度融合：遗传算法在交通领域的应用》", 30 December 2016, 东北林业大学出版社 * |
| 杨英杰: "《粒子群算法及其应用研究》", 31 March 2017, 北京理工大学出版社 * |
| 沈林成: "离散粒子群优化算法研究现状综述", 《系统工程与电子技术》 * |
| 范晋伟: "PSO-GA组合算法优化PID参数及可视化平台设计", 《机械设计与制造》 * |
| 边伟: "《非光滑优化及其变分分析》", 31 March 2014, 哈尔滨工业大学出版社 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117424294A (en) * | 2023-12-18 | 2024-01-19 | 国网辽宁省电力有限公司经济技术研究院 | Efficient reactive power planning method and system for power distribution network |
| CN117424294B (en) * | 2023-12-18 | 2024-03-01 | 国网辽宁省电力有限公司经济技术研究院 | Efficient reactive power planning method and system for power distribution network |
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