CN107465197A - A kind of var Optimization Method in Network Distribution based on dynamic particle cluster algorithm on multiple populations - Google Patents
A kind of var Optimization Method in Network Distribution based on dynamic particle cluster algorithm on multiple populations Download PDFInfo
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- CN107465197A CN107465197A CN201710645678.9A CN201710645678A CN107465197A CN 107465197 A CN107465197 A CN 107465197A CN 201710645678 A CN201710645678 A CN 201710645678A CN 107465197 A CN107465197 A CN 107465197A
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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
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
- H02J3/16—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by adjustment of reactive power
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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
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
- Y02E40/30—Reactive power compensation
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Abstract
The present invention relates to a kind of var Optimization Method in Network Distribution based on dynamic particle cluster algorithm on multiple populations, belong to reactive power optimization of power system control technology field.This method performs following steps：1) population is initialized, generates one group of population at random;2) division is carried out to particle according to roulette algorithm and generates some subgroups；3) particle in 2) is updated using velocity location more new formula, and judges the quality of particle and particle before renewal after renewal;4) continue to operate to 3) middle, carry out R times；5) judge whether particle reaches maximum iterationc, if so, output optimal result；If not reaching, note current optimal particle is optimal result；6) judge whether the particle of optimal result changes after a suboptimization, if nothing, optimal particle is final result；2) continue to optimize if so, then returning.The method of the present invention solves the limitation of Reactive Power Optimazation Problem under the insurmountable multi-constraint condition of prior art, and the result that carries out voltage and reactive power optimization is more excellent, the speed of service faster.
Description
Technical field
The present invention relates to a kind of var Optimization Method in Network Distribution based on dynamic particle cluster algorithm on multiple populations, belong to power train
System Reactive power control technical field.
Background technology
Power distribution network is the important step that electric energy is distributed in power system.With the fast development of social economy, system loading
Increasingly increase, the topological circuit more sophisticated of power distribution network, grid net loss is consequently increased.Reasonable disposition power distribution network is idle to equilibrium
Distribution power flow is distributed, and reduces network loss, and voltage plays vital effect at stable central concentrated load.
Particle cluster algorithm is a kind of optimized algorithm for handling nonlinear optimal problem, belongs to one kind of evolution algorithm, has
Fast convergence rate, calculate the advantages that simple, precision is high, easy acquisition global optimum.In signal transacting, neural metwork training, idle
Optimization etc. is widely used.Basic particle group algorithm is the single goal or multiple target for unconfined condition
The problem of optimization, and in actual applications, optimization aim often suffers from such-and-such constraints.Due to elementary particle group
Algorithm is applied to the optimization problem of unconfined condition, and search has blindness, the optimal solution for the constraints that is difficult to be met.Cause
This present invention proposes a kind of dynamic particle cluster algorithm on multiple populations for constraints optimization problem.
Disclosed in the patent of invention file of Application No. 201410222352.1 a kind of entitled " a kind of based on adaptive mixed
The technical scheme of the multi-objective reactive optimization method of ignorant particle cluster algorithm ".The patent is solved in processing multi-objective reactive optimization
The problem of controlling variable to be absorbed in locally optimal solution during problem, and rationally solve the problems, such as that solution optimal value speed is excessively slow.
But the proprietary algorithms are complex, and there is limitation when handling multiple constraint problem.Therefore need one kind can be in short-term
It is interior to obtain the optimal solution of voltage and reactive power optimization problem, and more comprehensively method when handling multi-constraint condition, it is able in work
It is widely applied among Cheng Shiji.
The content of the invention
The technical problem to be solved in the present invention is, in view of the shortcomings of the prior art, proposing that one kind can obtain in a short time
The optimal solution of voltage and reactive power optimization problem, and more comprehensively calculated based on dynamic population on multiple populations when handling multi-constraint condition
The var Optimization Method in Network Distribution of method.
The technical scheme that in order to solve the above-mentioned technical problem proposes of the present invention is：One kind is based on dynamic demes particle cluster algorithm
Var Optimization Method in Network Distribution, perform following steps：
Step 1：Population is initialized, generates one group of population at random；
And calculate the fitness value f (x) and node i violation constraints m of each particle degree value gim(x),L is the branch road sum closed in power distribution network；For branch road b network active loss；
gi1(x)=Uimax-Ui；gi2(x)=Ui-Uimin；gi3(x)=Qimax-Qi；gi4(x)=Qi-Qimin；
Uimin≤Ui≤Uimax；Qimin≤Qi≤Qimax；
UiFor the voltage of the node i of power distribution network, UimaxTo run the node voltage amplitude upper limit of permission, U in power distribution networkimin
The node voltage amplitude lower limit allowed for distribution network operation, QiFor the reactive power of the compensation of node i, QimaxSet and mend for node i
Repay the idle upper limit, QiminFor the lower limit that node i setting compensation is idle, n is the number of load bus, QsetFor defined distribution
The upper limit of the compensating reactive power total capacity of net system；
Step 2：Calculate each particle in more dynamic demes and run counter to constraints degree qm, and according to roulette algorithm
Division is carried out to particle and generates some subgroups；
Optimization particle is chosen by tactful formula from the subgroup after distribution, the tactful formula is as follows,
xsubswarm(u, m)=ffind[ssort(Gmi(x),d)]
U=1,2 ..., k, k are subgroup sum；xsubswarm(U, m) represent to carry out m-th of constraints in u-th of subgroup
The particle of optimization；ssort(Gmi(x), d) represent to Gmi(x) it is ranked up from small to large, Gmi(x) violated for the particle in population
The degree of m-th of constraints, and choose Gmi(x) preceding d element, d are setting value in, and its size is less than total number of particles in subgroup
(value of the half as d is chosen in embodiment)；ffindFunction is used for the position for finding corresponding particle, and constraint journey is run counter to so as to select
Spend less preceding d particle；
Step 3：The speed for optimizing particle in step 2 is updated with position using velocity location more new formula, simultaneously
Calculate the fitness value f (x) of particle and violation constraints m degree value g after updatingim(x),
And compare the quality of particle and particle before renewal after renewal according to good and bad judgment rule,
If particle is more excellent after renewal,Otherwise
After the quality of completeer particle, it is optimal in subgroup according to good and bad judgment rule to seek regionAnd withThan
It is more good and bad, ifIt is more excellent, renewalForOtherwiseRemain as
Velocity location more new formula,
WithSpeed and position of the respectively particle j in the t+1 times iteration in e dimension spaces；c1、c2To accelerate
The factor, take nonnegative constant；r1、r2For the random arithmetic number between [0,1]；For particle j untill the t times iteration
E dimensions find the position where the optimal value of individual；For the position at the optimal place in subgroup；
Step 4：Continue to be updated the particle after updating in step 3, update R generations, if not updating R generations, return to step
Rapid 2；
Step 5：Judge whether particle reaches maximum iteration c, if so, (output optimal result；If not up to maximum changes
Generation number c, note current optimal particle are optimal result.
Step 6：Judge whether the particle of the optimal result does not change after by a suboptimization, if not changing,
Then the particle of the optimal mistake is final result；If changing, jump to step 2 and continue to optimize particle.
The improvement of above-mentioned technical proposal is：The packet of roulette algorithm in step 2,
Gmi(x)={ max { gmi, 0 }, (x) m=1,2 ..., 5 }；
Wherein, Gmi(x) degree of m-th of constraints, x are violated for the particle in populationjFor j-th in population x
Son, N are total number of particles in population.
The improvement of above-mentioned technical proposal is：Good and bad judgment rule in step 3,
1.fobj(a)=fobj(b)=0, if f (xa) ＜ f (xb)；Particle a is more excellent；
2.fobj(a)=fobj(b)=m, if gim(xa) ＜ gim(xb)or gim(xa)=gim(xb)&&f(xa) ＜ f (xb)；
Particle a is more excellent；
Define fobj(x)=m, represent that x-th of subgroup optimizes m-th of constraints；fobj(x) x-th of subgroup=0, is represented
Optimization object function, i.e.,
It is of the invention to be using the beneficial effect of above-mentioned technical proposal：To ensure that variation degree meets system safety and stability
The requirement of operation is, it is necessary to set the constraints of each node voltage.For improve reactive compensation system economy with avoid be
Unite idle surplus, each load bus reactive compensation capacity overall with system also has certain constraint.
In basic particle group algorithm, the flying speed of particle is by the optimal position of individual and global optimum position
Determine, and in dynamic particle cluster algorithm on multiple populations, because particle is in different subgroups, therefore the flying speed of particle will
Determined by the optimal position of the individual position optimal with subgroup.
So as to solve limitation of the particle cluster algorithm in the Reactive Power Optimazation Problem for solving multi-constraint condition, and dynamic
Particle cluster algorithm carry out voltage and reactive power optimization result is more excellent, the speed of service faster.
Brief description of the drawings
The invention will be further described below in conjunction with the accompanying drawings.
Fig. 1 is a kind of var Optimization Method in Network Distribution based on dynamic particle cluster algorithm on multiple populations of the embodiment of the present invention
Schematic flow sheet.
Fig. 2 is certain taiwan area distribution line topology in the embodiment of the present invention.
Embodiment
Embodiment
A kind of GA for reactive power optimization side based on dynamic demes particle cluster algorithm of the present embodiment
Method, as shown in figure 1, performing following steps：
Step 1：Population is initialized, generates one group of population at random；
And calculate the fitness value f (x) and node i violation constraints m of each particle degree value gim(x),L is the branch road sum closed in power distribution network；For branch road b network active loss, pushed away before utilization
Back substitution calculating power system load flow can directly try to achieve network loss；
In particle cluster algorithm processing var Optimization Method in Network Distribution on multiple populations using dynamic, constraints is set,
gi1(x)=Uimax-Ui；gi2(x)=Ui-Uimin；gi3(x)=Qimax-Qi；gi4(x)=Qi-Qimin；
To ensure that variation degree meets the requirement of system safe and stable operation, it is necessary to set the pact of each node voltage
Beam condition.To improve the economy of reactive compensation system and avoiding System Reactive Power superfluous, each load bus and system totality
Reactive compensation capacity also has certain constraint.The constraints met needed for system is as follows：
Uimin≤Ui≤Uimax；Qimin≤Qi≤Qimax；
UiFor the voltage of the node i of power distribution network, UimaxTo run the node voltage amplitude upper limit of permission, U in power distribution networkimin
The node voltage amplitude lower limit allowed for distribution network operation, QiFor the reactive power of the compensation of node i, QimaxSet and mend for node i
Repay the idle upper limit, QiminFor the lower limit that node i setting compensation is idle, n is the number of load bus, QsetFor defined distribution
The upper limit of the compensating reactive power total capacity of net system；
Dynamically the thought of particle cluster algorithm on multiple populations be according to each constraints optimize complexity by particle dynamic
Packet, i.e., each particle are arranged to optimize a certain constraints.
Task identical particle is arranged in same subgroup, i.e. one constraints of the corresponding optimization in subgroup.
Step 2：The degree of difficulty optimized for objectively reaction particle to various boundary conditions, formula (10) define particle and disobeyed
Carry on the back the degree rate q of each constraintsm.The degree of difficulty that various boundary conditions are optimized for reaction particle.
Calculate each particle in more dynamic demes and run counter to constraints degree qm, and particle is carried out according to roulette algorithm
Division generates some subgroups, to be objective and reasonably particle is grouped.
According to roulette strategy, it is more difficult to which the constraints of optimization optimizes more subgroup is had to it.Define fobj
(x)=m, represent that x-th of subgroup optimizes m-th of constraints；fobj(x) x-th of subgroup optimization object function=0, is represented, i.e.,Because the particle for optimizing each constraints is randomly assigned, particle violates the degree of constraints
Also different sizes.To improve the optimizing ability of population, it should select to run counter to the less particle of constraints m degree and come to constraining bar
Part m is optimized.
Optimization particle is chosen by tactful formula from the subgroup after distribution, tactful formula is as follows,
xsubswarm(u, m)=ffind[ssort(Gmi(x),d)]
U=1,2 ..., k, k are subgroup sum；xsubswarm(U, m) represent to carry out m-th of constraints in u-th of subgroup
The particle of optimization；ssort(Gmi(x), d) represent to Gmi(x) it is ranked up from small to large, Gmi(x) violated for the particle in population
The degree of m-th of constraints, and choose Gmi(x) preceding d element, d are setting value in, and its size is less than total number of particles in subgroup
(value of the half as d is chosen in embodiment)；ffindFunction is used for the position for finding corresponding particle, and constraint journey is run counter to so as to select
Spend less preceding d particle；
Step 3：In basic particle group algorithm, the flying speed of particle is by the optimal position of individual and institute of global optimum
Determined in position, and in dynamic particle cluster algorithm on multiple populations, because particle is in different subgroups, therefore the flight of particle
Speed will be determined by the optimal position of the individual position optimal with subgroup.
The speed for optimizing particle in step 2 is updated with position using velocity location more new formula, calculated simultaneously
The fitness value f (x) of particle and violation constraints m degree value g after renewalim(x),
And compare the quality of particle and particle before renewal after renewal according to good and bad judgment rule,
If particle is more excellent after renewal,Otherwise
After the quality of completeer particle, it is optimal in subgroup according to good and bad judgment rule to seek regionAnd withThan
It is more good and bad, ifIt is more excellent, renewalForOtherwiseRemain as
Velocity location more new formula,
In voltage and reactive power optimization problem, it is assumed that the number of the total load bus of low-voltage network is N, Xi=(xi1,
xi2,…,xiN) for the positional information of i-th particle, represent the capacity of each load bus compensating reactive power of low-voltage network, Vi=
(vi1,vi2,…,viN) for the velocity information of i-th particle, represent the correction of positional information.
WithSpeed and position of the respectively particle j in the t+1 times iteration in e dimension spaces；c1、c2To accelerate
The factor, take nonnegative constant；r1、r2For the random arithmetic number between [0,1]；For particle j untill the t times iteration
E dimensions find the position where the optimal value of individual；For the position at the optimal place in subgroup.
Due to r1、r2For random arithmetic number, to ensure that particle flight speed is integer, random effect can be rounded up
Round.
Step 4：Continue to be updated the particle after updating in step 3, update R generations, if not updating R generations, return to step
Rapid 2；
Step 5：Judge whether particle reaches maximum iteration c, if so, (output optimal result；If not up to maximum changes
Generation number c, note current optimal particle are optimal result.
Step 6：Judge whether the particle of optimal result does not change after by a suboptimization, if not changing, most
The particle of excellent mistake is final result；If changing, jump to step 2 and continue to optimize particle.
The packet of roulette algorithm in the step 2 of the present embodiment,
Gmi(x)={ max { gmi, 0 }, (x) m=1,2 ..., 5 }；
Wherein, Gmi(x) degree of m-th of constraints, x are violated for the particle in populationjFor j-th in population x
Son, N are total number of particles in population.
Because particle need to complete object function and the double optimization of constraints, it is impossible to judge according only to the size of adaptive value
Quality, therefore set the quality of following rule judgment particle：Good and bad judgment rule in step 3,
1.fobj(a)=fobj(b)=0, if f (xa) ＜ f (xb)；Particle a is more excellent；
2.fobj(a)=fobj(b)=m, if gim(xa) ＜ gim(xb)or gim(xa)=gim(xb)&&f(xa) ＜ f (xb)；
Particle a is more excellent；
Define fobj(x)=m, represent that x-th of subgroup optimizes m-th of constraints；fobj(x) x-th of subgroup=0, is represented
Optimization object function, i.e.,
In order to show a kind of the excellent of var Optimization Method in Network Distribution based on dynamic demes particle cluster algorithm in the present embodiment
More property, is compared below in conjunction with real case.Voltage power-less complex optimum analysis is carried out to certain taiwan area distribution line.Circuit
Topology diagram is as shown in Figure 2.
The circuit shares 11 nodes, 10 branch roads, and branch road 1 is a S11-200 type distribution transformer, and range of regulation is
± 5 × 2.5%UN, now on-load tap changers of transformers is in 3 gear 100%U of centreNSide.Choosing power network reference capacity is
200kVA, high voltage side of transformer voltage reference value are 10kV, and low-pressure side voltage a reference value is 380V.Node 1 is chosen to save for balance
Point, voltage magnitude 10.4kV, phase angle 0.The design parameter of system is as shown in table 1.
The system branch parameter of table 1
Load flow calculation is carried out to circuit using forward-backward sweep method, it is 27.68kW to obtain system losses, the voltage knot of each node
Fruit is as shown in table 2.
The Load flow calculation node voltage of table 2
As shown in Table 2, the normal range (NR) of the node voltage of line end fatal voltage.To ensure power system security warp
Help the requirement of stable operation, set the constraints of voltage and reactive power optimization problem as:
0.9≤Ui≤1.05
0≤Qi≤60kvar
The result that Dynamic Packet particle cluster algorithm result obtains with basic particle group algorithm is compared, it is as a result as follows：
The Different Optimization algorithm speed of service of table 3 and optimum results
As shown in Table 3, the network loss being calculated by dynamic particle cluster algorithm on multiple populations is higher, but minimum voltage is also higher,
And there is the method in greatest differences, namely the present embodiment, its redundancy will be far smaller than basic population in run time
Algorithm.
Basic particle cluster algorithm is due to that can not solve the problems, such as Problem with Some Constrained Conditions as can be seen from the above table, to be met
The result of constraints can produce the redundant computation more than thousands of times, greatly reduce operating rate, it is impossible to meet voltage power-less
The requirement of promptness in optimization problem.
And dynamic particle cluster algorithm on multiple populations be method in the present embodiment because its algorithm is simple, the speed of service is fast, easily
In convergence, the result of voltage and reactive power optimization problem can be obtained in a short time, is had a wide range of applications among engineering reality
Prospect.
The present invention is not limited to above-described embodiment.All technical schemes formed using equivalent substitution, all falling within the present invention will
The protection domain asked.
Claims (3)
1. a kind of var Optimization Method in Network Distribution based on dynamic demes particle cluster algorithm, it is characterised in that perform following steps：
Step 1：Population is initialized, generates one group of population at random；
And calculate the degree value g that the fitness value f (x) of each particle and node i in population violate constraints mim(x),
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L is the branch road sum closed in power distribution network；For branch road b network active loss；
gi1(x)=Uimax-Ui；gi2(x)=Ui-Uimin；gi3(x)=Qimax-Qi；gi4(x)=Qi-Qimin；
Uimin≤Ui≤Uimax；Qimin≤Qi≤Qimax；
UiFor the voltage of the node i of power distribution network, UimaxTo run the node voltage amplitude upper limit of permission, U in power distribution networkiminFor with
The node voltage amplitude lower limit that operation of power networks allows, QiFor the reactive power of the compensation of node i, QimaxFor node i setting compensation without
The upper limit of work(, QiminFor the lower limit that node i setting compensation is idle, n is the number of load bus, QsetFor defined power distribution network system
The upper limit of the compensating reactive power total capacity of system；
Step 2：Calculate each particle in more dynamic demes and run counter to constraints degree qm, and according to roulette algorithm to grain
Son carries out division and generates some subgroups；
Optimization particle is chosen by tactful formula from the subgroup after distribution, the tactful formula is as follows,
xsubswarm(u, m)=ffind[ssort(Gmi(x),d)]
U=1,2 ..., k, k are subgroup sum；xsubswarm(u, m) represents to optimize m-th of constraints in u-th of subgroup
Particle；ssort(Gmi(x), d) represent to Gmi(x) it is ranked up from small to large, Gmi(x) violated m-th for the particle in population
The degree of constraints, and choose Gmi(x) preceding d element, d are setting value in, and its size is less than total number of particles in subgroup and (implemented
Value of the half as d is chosen in example)；ffindFunction is used for the position for finding corresponding particle, so as to select run counter to degree of restraint compared with
Small preceding d particle；
Step 3：The speed for optimizing particle in step 2 is updated with position using velocity location more new formula, calculated simultaneously
Go out the fitness value f (x) of particle and violation constraints m degree value g after updatingim(x),
And compare the quality of particle and particle before renewal after renewal according to good and bad judgment rule,
If particle is more excellent after renewal,Otherwise
After the quality of completeer particle, it is optimal in subgroup according to good and bad judgment rule to seek regionAnd withIt is more excellent
It is bad, ifIt is more excellent, renewalForOtherwiseRemain as
Velocity location more new formula,
WithSpeed and position of the respectively particle j in the t+1 times iteration in e dimension spaces；c1、c2For accelerated factor,
Take nonnegative constant；r1、r2For the random arithmetic number between [0,1]；Looked for for particle j untill the t times iteration in e dimensions
Position to where the optimal value of individual；For the position at the optimal place in subgroup；
Step 4：Continue to be updated the particle after updating in step 3, update R generations, if not updating R generations, return to step 2；
Step 5：Judge whether particle reaches maximum iteration c, if so, output optimal result；If not up to greatest iteration time
Number c, note current optimal particle is optimal result；
Step 6：Judge whether the optimal particle does not change after by a suboptimization, it is described optimal if not changing
Particle is final result；If changing, jump to step 2 and continue to optimize.
2. the var Optimization Method in Network Distribution according to claim 1 based on dynamic demes particle cluster algorithm, its feature exist
In：The packet of roulette algorithm in step 2,
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Gmi(x)={ max { gmi, 0 }, (x) m=1,2 ..., 5 }；
Wherein, Gmi(x) degree of m-th of constraints, x are violated for the particle in populationjFor j-th of particle in population x, N
For total number of particles in population.
3. the var Optimization Method in Network Distribution according to claim 1 based on dynamic demes particle cluster algorithm, its feature exist
In：Good and bad judgment rule in step 3,
1.fobj(a)=fobj(b)=0, if f (xa) ＜ f (xb)；Particle a is more excellent；
2.fobj(a)=fobj(b)=m, if gim(xa) ＜ gim(xb)or gim(xa)=gim(xb)&&f(xa) ＜ f (xb)；Particle
A is more excellent；
Define fobj(x)=m, represent that x-th of subgroup optimizes m-th of constraints；fobj(x) x-th of subgroup optimization=0, is represented
Object function, i.e.,
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