CN105184415A - Power distribution network reconstruction design method - Google Patents

Abstract

The invention discloses a power distribution network reconstruction design method, and belongs to the technical field of power distribution network optimization. The power distribution network reconstruction design method comprises the following steps that a, target normalization is performed on the three commonly used optimization indexes of a power distribution network, including a network loss target, a load balancing target and a voltage quality target; b, all target weights are set by utilizing an analytic hierarchy method; c, and a target function is constructed according to the steps a and b. The power distribution network operation mode of the power distribution network under the condition that multiple indexes of the power distribution network are optimal can be acquired via normalization of multiple targets and target weight setting under the premise that the voltage quality requirements, the line current requirements and the transformer capacity requirements of the power distribution network are met and radiation operation is realized so that the power distribution network is enabled to achieve the optimal operation efficiency.

Description

A kind of For Distribution Networks Reconfiguration method for designing

Technical field

The invention belongs to distribution network optimisation technique field, especially relevant with a kind of For Distribution Networks Reconfiguration method for designing.

Background technology

For Distribution Networks Reconfiguration, exactly meeting distribution network voltage quality requirements, line current requirement, transformer capacity require and under the prerequisite run radially, obtain the power distribution network method of operation under a certain index of power distribution network electrical network (as network network loss, quality of voltage or load balancing etc.) or multiple index optimal cases.The method of operation changing distribution network is realized by the block switch in operation power distribution network and interconnection switch, owing to there is a large amount of exercisable block switch and interconnection switch in power distribution network, therefore For Distribution Networks Reconfiguration is the hybrid optimization problem of a multi-target non-linear, solve the mathematic optimal model that first this problem will set up For Distribution Networks Reconfiguration, then utilize corresponding optimized algorithm to solve.

For Distribution Networks Reconfiguration is actually an optimization problem, and therefore its mathematical model is the same with other Optimized models, includes objective function and constraint condition.

1. constraint condition

In order to ensure the safe operation of distribution network, the following constraint condition of distribution network demand fulfillment in restructuring procedure:

(1) network topology constraint

Power distribution network is generally closed loop design, open loop operation, requires that the power distribution network after reconstruct is necessary for radial.

(2) power supply constraint

Network after reconstruct must meet burden requirement, can not have isolated node.

(3) voltage constraint V _{i, min}≤ V _i≤ V _{i, max}

In formula, V _{i, min}, V _{i, max}be respectively upper voltage limit and the lower voltage limit of node i permission.

(4) branch power constraint S _j≤ S _{j, max}

In formula, S _jrepresent the power that branch road j flows through, S _{j, max}represent peak power branch road j allowing flow through.

2. conventional objective function

(1) take loss minimization as target

Be that the objective function of target can be expressed as with loss minimization:

$f_1={\mathrm{minP}}_{loss}=min\underset{i=1}{\overset{b}{\Σ}}{k}_i{R}_i\frac{P_i^2+Q_i^2}{U_i^2}$

Wherein, k _irepresenting the state of branch road i, is that 1 expression branch road closes, and is that 0 expression branch road disconnects; R _ifor the resistance of branch road i; P _i, Q _ithe active power flow through for branch road i end and reactive power; U _ifor branch road i endpoint node voltage; B is branch road sum.

(2) take load balancing as target

Be that the objective function of target can be expressed as with load balancing:

$f_3={\mathrm{minLB}}_{sys}=min\left(\underset{i=1}{\overset{b}{\max }}\right(\frac{\left|S_i\right|}{\left|S_{i,max}\right|}\left)\right)$

Wherein LB _sysfor system loading equalization index, S _i, S _{i, max}be respectively the power on branch road i and maximum permission capacity thereof, b is branch road sum.

(3) take quality of voltage as target

Be that the objective function of target can be expressed as with quality of voltage:

$f_3={\mathrm{minV}}_o=min\left(\underset{i=1}{\overset{n}{\max }}\right(\left|V_i-V_N\right|\left)\right)$

Wherein V _irepresent the voltage of node i, n represents nodes, V _minrepresent minimum node voltage.

When carrying out For Distribution Networks Reconfiguration, when independent consideration target is optimized, other targets often can not reach the optimum deterioration that even can cause other targets, therefore, in order to ensure the economical of distribution network simultaneously, reliability and security, often need to consider multiple target existing when carrying out multiple-objection optimization simultaneously, needs solve two problems: one is the normalization of target, because the dimension of each target differs, the order of magnitude is different, directly can not carry out summation process, therefore the desired value under being necessary to utilize distribution network original state is normalized each target, two be target weight arrange, because the result optimized is relevant with arranging of each target, the result optimized can be partial to the larger target of weight, modal weight method to set up is the state according to network, utilize the experience people of dispatcher for arranging, but the method subjectivity is too strong, is difficult to avoid artificial subjectivity to arrange mistake.

Summary of the invention

For the problem of above-mentioned optimization distribution network, object of the present invention aims to provide a kind of For Distribution Networks Reconfiguration method for designing.

For this reason, the present invention is by the following technical solutions: a kind of For Distribution Networks Reconfiguration method for designing, is characterized in that, described For Distribution Networks Reconfiguration method for designing comprises the following steps:

A, by the conventional three large optimizing index of distribution network: network loss target, load balancing target and quality of voltage target carry out target normalization, and objective definition Optimization Index is

${\mathrm{opt}}_i=\frac{{x_i}^{*}}{x_i},$

Wherein x _ii-th desired value, (i=1,2,3), opt _ithe Optimization Index of i-th target, x _i ^*be the optimal value of i-th target, thus the degree of optimization of target can be reflected to a certain extent;

B, analytical hierarchy process is utilized to arrange each target weight;

C, according to above-mentioned a, b step establishing target function:

$f=max(W_1\frac{{P_{loss}}^{*}}{P_{loss}}+W_2\frac{{{\mathrm{LB}}_{sys}}^{*}}{{\mathrm{LB}}_{sys}}+W_3\frac{V_N-{V_{\min }}^{*}}{V_N-V_{min}})$

In formula, W ₁for weight, the W of network loss objective function ₂for weight, the W of load balancing objective function ₃for the weight of quality of voltage objective function.

As to technique scheme supplement and perfect, the present invention also comprises following technical characteristic.

The importance functions of described network loss is:

The importance functions of described load balancing is:

The importance functions of described quality of voltage is:

The span of weighting function importance degree is at 1-9.

Described analytical hierarchy process comprises the following steps:

1) hierarchy Model is set up

On the basis analysing in depth practical problems, each relevant factor is resolved into some levels according to different attribute from top to down, the factors of same layer be subordinated to last layer factor or on upper strata because have impact, simultaneously again the lower one deck of domination factor or be subject to the effect of lower layer factors.The superiors are destination layer, and usually only have 1 factor, orlop is generally scheme or object layer, and can there be one or several level centre, is generally criterion or indicator layer.When criterion is too much, (such as more than 9) should decomposite sub-rule layer further.

2) Paired comparison matrix is constructed

From the 2nd layer of hierarchy Model, for the same layer factors being subordinated to (or impact) each factor of last layer, compare dimensional configurations Paired comparison matrix by Paired Comparisons and 1-9, until orlop.

An important feature of analytical hierarchy process is exactly the corresponding importance degree grade using the form of the ratio of importance degree between two to indicate two indices.Table 3 lists 9 importance rates and assignment thereof.Judgment matrix is called by the matrix that comparative result is formed between two.

3) calculate weight vector and do consistency check

Maximum characteristic root and character pair vector are calculated for each Paired comparison matrix, utilizes coincident indicator, random index and Consistency Ratio to do consistency check.

Use the present invention can reach following beneficial effect: the present invention meeting distribution network voltage quality requirements, line current requirement, transformer capacity require and under the prerequisite run radially, arrange by multiple target normalization with to the weight of target, obtain the power distribution network method of operation under the multiple index optimal cases of power distribution network electrical network, thus make electrical network reach maximum efficiency.

Accompanying drawing explanation

Fig. 1 is power distribution network index system schematic diagram of the present invention.

Fig. 2 is the schematic flow sheet of multiple goal weight of the present invention.

Fig. 3 is the importance functions schematic diagram of network loss.

Fig. 4 is load balancing importance functions schematic diagram.

Fig. 5 is lowest section point voltage schematic diagram.

Embodiment

Below in conjunction with accompanying drawing, the specific embodiment of the present invention is described in detail.

As shown in Fig. 1 ~ Fig. 2, for multiple-objection optimization, multiple goal is converted into single goal by the general method of target weighted sum that adopts, and objective function is expressed as:

$F=min\underset{i=1}{\overset{3}{\Σ}}{w}_i{f}_i$

Wherein w _ifor the weight of each target, f _ifor after normalization each desired value.

1, target normalization

For cost type optimization aim x _i(i=1,2,3), objective definition Optimization Index is

${\mathrm{opt}}_i=\frac{{x_i}^{*}}{x_i}$

Wherein x _ii-th desired value, opt _ithe Optimization Index of i-th target, x _i ^*be the optimal value of i-th target, because each target is cost type target, then have opt for any network state herein _i≤ 1, and opt _ithe effect of optimization being worth larger explanation target is better.Opt _ivalue can not only be normalized each target, and can reflect the degree of optimization of target to a certain extent.

According to above formula, establishing target function

$f=max(W_1\frac{{P_{loss}}^{*}}{P_{loss}}+W_2\frac{{{\mathrm{LB}}_{sys}}^{*}}{{\mathrm{LB}}_{sys}}+W_3\frac{V_N-{V_{\min }}^{*}}{V_N-V_{min}})$

In formula, W ₁, W ₂, W ₃be respectively the weight of each objective function, can arrange according to network actual conditions, as: the weight of load balancing target can be strengthened when the excessive i.e. network load of load balancing index is balanced not, and can the weight of high voltage quality objective when minimum node brownout.P _loss ^*, LB _sys ^*, V _min ^*be the optimal value of distribution network single object optimization.

2, target weight is arranged

In actual mechanical process, may there is very greatly the deviation of subjectivity according to the setting that network state carries out weight in dispatcher, can not objectively operate.Therefore consider utilize fuzzy membership function to build method and analytical hierarchy process (AnalyticHierarchyProcess, AHP) weight is calculated.

According to the related data provided in GDW565-2010 " urban power distribution network operation level and evaluation of power supply capability directive/guide ", (importance degree is larger can to define the importance degree of each target under different conditions, illustrate that this dbjective state is poorer, namely the target degree of concern of target in restructuring procedure is shown) as follows, in order to meet the definition rule of the comparator matrix between two of analytical hierarchy process, the scope of definition importance degree is 1 ~ 9.

As seen in figures 3-5, the importance functions of described network loss is:

The importance functions of load balancing is:

The importance functions of quality of voltage is:

Analytical hierarchy process:

So-called analytical hierarchy process, refer to a complicated decision-making problem of multi-objective as a system, be multiple target or criterion by goal decomposition, and then be decomposed into some levels of multi objective (or criterion, constraint), Mode of Level Simple Sequence (flexible strategy) and total sequence is calculated, using the systems approach as target (multi objective), multi-scheme Optimal Decision-making by qualitative index Fuzzy Quantifying.

Analytical hierarchy process be by decision problem by general objective, each straton target, interpretational criteria until the sequential breakdown of concrete standby throwing scheme is different hierarchical structure, then the handy way solving judgment matrix proper vector, try to achieve each element of each level to the priority weight of last layer time certain element, the method of last weighted sum again passs that rank merger is each standbyly selects the final weight of scheme to general objective, and this final weight the maximum is optimal case.Here so-called " priority weight " is a kind of measuring relatively, and it shows that each standby scheme of selecting is at the interpretational criteria of a certain feature or sub-goal, the relative measurement of the lower superior degree of mark, and each sub-goal is for the relative measurement of last layer target significance level.Analytical hierarchy process compares and is suitable for having layering and interlocks the goal systems of evaluation index, and desired value is difficult to again the decision problem of quantitative description.Its usage is Judgement Matricies, obtains its eigenvalue of maximum.And corresponding characteristic vector W, after normalization, be the relative importance weights of a certain level index for last layer time certain index of correlation.

This method neither pursues merely advanced mathematics, do not focus on behavior, logic, reasoning again not unilaterally, but quilitative method is organically combined with quantivative approach, make complicated system decomposition, can by the thought process mathematicization of people, systematization, be convenient to people accept, and the decision problem that multiple goal, multiple criteria are difficult to again whole quantification treatment can be turned to multi-level single-objective problem, by compare between two determine the relative last layer minor element of same layer minor element quantitative relation after, finally carry out simple mathematical operation.Even the people with medium schooling also can understand the ultimate principle of step analysis and grasp its basic step, calculate also often easy, and acquired results is simply clear and definite, easily for decision maker understands and grasp.But analytical hierarchy process quantitative data is less, qualitative composition is many, not easily convincing.

This project calculates the importance degree that uses the corresponding data provided according to GDW565-2010 " urban power distribution network operation level and evaluation of power supply capability directive/guide " to provide in the analytical hierarchy process used and non-artificially to provide for optimal reconfiguration target weight, data have higher science.

The basic step of analytical hierarchy process:

1) hierarchy Model is set up

On the basis analysing in depth practical problems, each relevant factor is resolved into some levels according to different attribute from top to down, the factors of same layer be subordinated to last layer factor or on upper strata because have impact, simultaneously again the lower one deck of domination factor or be subject to the effect of lower layer factors.The superiors are destination layer, and usually only have 1 factor, orlop is generally scheme or object layer, and can there be one or several level centre, is generally criterion or indicator layer.When criterion is too much, (such as more than 9) should decomposite sub-rule layer further.

2) Paired comparison matrix is constructed

From the 2nd layer of hierarchy Model, for the same layer factors being subordinated to (or impact) each factor of last layer, compare dimensional configurations Paired comparison matrix by Paired Comparisons and 1-9, until orlop.

An important feature of analytical hierarchy process is exactly the corresponding importance degree grade using the form of the ratio of importance degree between two to indicate two indices.Table 3 lists 9 importance rates and assignment thereof.Judgment matrix is called by the matrix that comparative result is formed between two.

In this project, importance rate is not limited to the natural number of 1 ~ 9, but extends to any real number between 1 ~ 9, makes the comparison scale of importance degree between each factor definitely.

3) calculate weight vector and do consistency check

Maximum characteristic root and character pair vector are calculated for each Paired comparison matrix, utilizes coincident indicator, random index and Consistency Ratio to do consistency check.If upcheck, proper vector (after normalization) is weight vector: if do not pass through, and need re-construct Paired comparison matrix.

The judgment matrix that the importance degree that the target membership function utilizing this project to propose calculates builds need not carry out consistency desired result, and the normalized proper vector therefore calculated is weight vectors.

Embodiment 1: the Network Loss Rate as certain network is 2.0798%, lowest section point voltage is 0.96173P.U., and maximum circuit load factor is 56.816%, and the step calculating target weight is as follows:

(1) importance degree of each target is calculated;

Can obtain network loss target importance degree 1.6933 according to the fuzzy membership function of target importance degree, quality of voltage target importance degree is 5.3737, and load balancing importance degree is 2.3632.

(2) establishing target judgment matrix;

	Network loss	Quality of voltage	Load balancing
				Network loss	1	1.6933/4.0616	1.6933/2.3632
Quality of voltage	5.3737/1.6932	1	5.3737/2.3632
				Load balancing	2.3633/1.6932	2.3633/5.3737	1

(3) judgment matrix eigenvalue of maximum characteristic of correspondence vector is asked for;

V＝[0.2771,0.8795,0.3868] ^T

(4) proper vector is normalized and obtains weight vectors.

W＝[W ₁,W ₂,W ₃] ^T＝[0.1796,0.5698,0.2506] ^T

The implementation method of distribution network multiple-objection optimization reconstruct

The present invention utilizes improved adaptive GA-IAGA to carry out network reconfiguration, is absorbed in the problem of local optimum in order to solve the easy precocity of genetic algorithm, introduces metropolis and improves genetic algorithm, algorithm is likely jumped out from local optimum, finds globally optimal solution.

3. Genetic Algorithms Theory

Genetic algorithm (GeneticAlgorithm, GA) is the computation model of the simulation natural selection of Darwinian evolutionism and the biological evolution process of genetic mechanisms, is a kind of method by simulating nature evolutionary process search optimum solution.Genetic algorithm is that a population is then made up of the individuality (individual) of the some of encoding through gene (gene) from representing a population (population) of the potential disaggregation of problem possibility.Each individuality is actually chromosome (chromosome) and is with characteristic entity.Chromosome is as the main carriers of inhereditary material, the i.e. set of multiple gene, its inner performance (i.e. genotype) is certain assortment of genes, which determines the external presentation of individual shape, and the feature as dark hair is determined by certain assortment of genes controlling this feature in chromosome.Therefore, need to realize from phenotype to genotypic mapping and coding work at the beginning.Owing to copying the work of gene code very complicated, we often simplify, as binary coding, after just producing for population, according to the principle of the survival of the fittest and the survival of the fittest, develop by generation (generation) and produce the approximate solution of becoming better and better, in every generation, select (selection) individual according to fitness (fitness) size individual in Problem Areas, and carry out combination intersection (crossover) and variation (mutation) by means of the genetic operator (geneticoperators) of natural genetics, produce the population representing new disaggregation.This process is more adapted to environment for population than former generation by causing the same rear life of kind of images of a group of characters natural evolution, and the optimum individual in last reign of a dynasty population, can as problem approximate optimal solution through decoding (decoding).

The fundamental operation process of genetic algorithm is as follows:

A) initialization: arrange evolutionary generation counter t=0, arranges maximum evolutionary generation T, and stochastic generation M individual as initial population P (0).

B) individual evaluation: the fitness calculating each individuality in colony P (t).

C) Selecting operation: selection opertor is acted on colony.The object selected the individuality optimized is genetic directly to the next generation or produces new individuality by pairing intersection be genetic to the next generation again.Selection operation is based upon on the Fitness analysis basis of individual in population.

D) crossing operation: crossover operator is acted on colony.What play the role of a nucleus in genetic algorithm is exactly crossover operator.

E) mutation operator: mutation operator is acted on colony.Namely be that the genic value on some locus of the individuality string in colony is changed.

Colony P (t) obtains colony P (t+1) of future generation after selection, intersection, mutation operator.

F) end condition judges: if t=T, then the maximum adaptation degree individuality that has obtained in evolutionary process exports as optimum solution, stops calculating.

Genetic algorithm is a kind of general-purpose algorithm solving search problem, can use for various common question.The common trait of searching algorithm is:

1. one group of candidate solution is first formed

2. the fitness of these candidate solutions is calculated according to some adaptability condition

3. retain some candidate solution according to fitness, abandon other candidate solutions

4. some operation is carried out to the candidate solution retained, generate new candidate solution.

In genetic algorithm, above-mentioned several feature is combined in a kind of special mode: based on the parallel search of chromosome complex, with the conjecture selection operation of character, swap operation and mutation operation.Genetic algorithm and other searching algorithm are distinguished and are come by this special array mode.

Genetic algorithm also has the feature of following several respects:

(1) genetic algorithm is searched for from the trail of solution, instead of from single solution.This is the very big difference of genetic algorithm and traditional optimized algorithm.Tradition optimized algorithm asks optimum solution from single initial value iteration; Easily be strayed into locally optimal solution.Genetic algorithm is searched for from trail, wide coverage, is beneficial to the overall situation preferentially.

(2) genetic algorithm processes the multiple individualities in colony simultaneously, namely assesses the multiple solutions in search volume, decreases the risk being absorbed in locally optimal solution, and algorithm itself is easy to realize parallelization simultaneously.

(3) genetic algorithm is substantially without knowledge or other supplementary of search volume, and only assesses individuality by fitness function value, carries out genetic manipulation on this basis.Fitness function is not only by continuously differentiable constraint, and its field of definition can set arbitrarily.This feature makes the range of application of genetic algorithm greatly expand.

(4) genetic algorithm is not adopt Deterministic rules, but adopts the transition rule of probability to instruct his direction of search.

(5) there is self-organization, self-adaptation and self-study habit.During the information self-organization search that genetic algorithm utilizes evolutionary process to obtain, the individuality that fitness is large has higher survival probability, and obtains the gene structure more conformed.

(6) in addition, algorithm itself also can adopt dynamic self-adapting technology, during evolution adjustment algorithm controling parameters and encoding precision, such as use fuzzy self-adaption method [2] automatically.

4.Metropolis criterion

According to Metropolis criterion, when under the environment of object at temperature T, be transitioned into new state j by current state i, if E _j<E _i, then accepting j is current state; Otherwise with probability receive status j, even probability be greater than [0,1) interval random number, then receive status j is current state; If be false, then reserved state i is current state.Interior energy when wherein E is object temperature T, E _j-E _ifor its knots modification, K is Boltzmann constant.

5. based on the application of improved adaptive GA-IAGA in For Distribution Networks Reconfiguration of Metropolis criterion

Traditional genetic algorithm is based on utilizing the design feature of power distribution network to carry out simplify processes, in process such as generation initial population, crossover and mutation etc., a large amount of infeasible solution can be produced, not only have a strong impact on the counting yield that heredity is calculated, and easily cause population scale shrink or occur degradation phenomena during evolution, reduce further speed of convergence.

Improved adaptive GA-IAGA adopts specific coding rule and genetic method based on loop analyze, setting up in the process such as initial population, crossover and mutation, all avoiding the generation of infeasible solution, substantially increasing the efficiency of genetic algorithm.

In improved adaptive GA-IAGA, based on following rule, simplify processes is carried out to chromosome:

(1) branch road be not included in any loop must close, otherwise there will be power supply isolated island;

(2) switch be connected with power supply node should close;

According to above rule, not exercisable switch is not encoded, and can reduce chromosomal length with this.During coding, adopt the cryptoprinciple of the branch road coding formation gene block in a loop.

Improved adaptive GA-IAGA correlation step is described below:

(1) initial population is produced

First produce the chromosome that everybody is 1 entirely, then in each gene block, Stochastic choice one is set to 0, and adds the chromosome determined by existing distribution net work structure, forms initial population.

(2) chromosome adaptive value is calculated

Due to formula build objective function be profit evaluation model target, consistent with the optimal anchor direction of fitness, therefore can target function value as chromosomal fitness value.

(3) genetic manipulation

The genetic manipulation strategies that improved adaptive GA-IAGA adopts is: gene block is regarded as an entirety by interlace operation, only carries out the exchange of corresponding gene block, and mutation operation only processes for some gene blocks.

Select operation: for guaranteeing convergence, adopt optimized individual retention strategy namely during evolution, current top n optimum individual does not carry out genetic manipulation and directly enters the next generation.Adopt this selection to operate, conveniently optimum solution is retained.

During interlace operation, the gene on chromosome does not adopt single-point to intersect, but gene block is integrally carried out cross processing.First produce a random number, determined the gene block needing to carry out exchanging by random number, then gene block corresponding in former generation's chromosome is exchanged.Owing to only having 1 to be 0 in each gene block of parent, still can ensure to only have 1 to be 0 in each gene block after carrying out interlace operation, not have infeasible solution to produce.

During mutation operation, first produce the gene block that a random number determines needs variation, then being made by all values in gene block is 1, and in order to ensure the feasibility of separating, finally producing a random number and made by the genic value of correspondence is 0.If the chromosome after variation is identical with the chromosome before variation, then continue variation; Otherwise genetic value is of future generation.

(4) Evolution of Population method

After genetic manipulation (comprising intersection, variation), select the individuality that fitness value is best, in order to avoid Premature Convergence, introduce metropolis criterion, accept poor solution with certain probability, algorithm is likely jumped out from local optimum, finds globally optimal solution.In order to make metropolis criterion be applicable to genetic algorithm, following change is done to metropolis criterion:

When evolving to N-Generation, the probability accepting new state is wherein F is individual fitness, F _j-F _ifor the change of fitness, K is constant.

(5) stop

According to the genetic algebra arranged, when reaching maximum genetic algebra, stop evolving; Or find that optimum individual is unchanged more than X generation (X generally gets 50), illustrate and reach optimum, stop evolving.

More than show and describe ultimate principle of the present invention and principal character and advantage of the present invention.The technician of the industry should understand; the present invention is not restricted to the described embodiments; what describe in above-described embodiment and instructions just illustrates principle of the present invention; without departing from the spirit and scope of the present invention; the present invention also has various changes and modifications, and these changes and improvements all fall in the claimed scope of the invention.Application claims protection domain is defined by appending claims and equivalent thereof.

Claims (5)

1. a For Distribution Networks Reconfiguration method for designing, is characterized in that: described For Distribution Networks Reconfiguration method for designing comprises the following steps:

A, by the conventional three large optimizing index of distribution network: network loss target, load balancing target and quality of voltage target carry out target normalization, and objective definition Optimization Index is

${\mathrm{opt}}_i=\frac{{x_i}^{*}}{x_i},$

Wherein x _ii-th desired value, i=1,2,3, opt _ithe Optimization Index of i-th target, x _i ^*it is the optimal value of i-th target;

B, analytical hierarchy process is utilized to arrange each target weight;

C, according to above-mentioned a, b step establishing target function:

$f=max(W_1\frac{{P_{loss}}^{*}}{P_{loss}}+W_2\frac{{{\mathrm{LB}}_{sys}}^{*}}{{\mathrm{LB}}_{sys}}+W_3\frac{V_N-{V_{min}}^{*}}{V_N-V_{min}})$

In formula, W ₁for weight, the W of network loss objective function ₂for weight, the W of load balancing objective function ₃for the weight of quality of voltage objective function.

2., according to a kind of For Distribution Networks Reconfiguration method for designing shown in claim 1, it is characterized in that:

The importance functions of described network loss is:

The importance functions of described load balancing is:

The importance functions of described quality of voltage is:

3. a kind of For Distribution Networks Reconfiguration method for designing according to claim 2, is characterized in that: the span of the function importance degree of weight is at 1-9.

4. a kind of For Distribution Networks Reconfiguration method for designing according to claim 3, is characterized in that: described analytical hierarchy process comprises the following steps:

1), each relevant factor is resolved into some levels according to different attribute from top to down, the factors of same layer be subordinated to last layer factor or on upper strata because have impact, arrange again the factor of lower one deck simultaneously or be subject to the effect of lower layer factors, obtaining the target importance degree that each index is relevant;

2), from the 2nd layer of hierarchy Model, for the same layer factors being subordinated to each factor of last layer, dimensional configurations Paired comparison matrix is compared by Paired Comparisons and 1-9, until orlop;

3), calculate weight vector and do consistency check

Maximum characteristic root and character pair vector are calculated for each Paired comparison matrix, utilizes coincident indicator, random index and Consistency Ratio to do consistency check.

5. a kind of For Distribution Networks Reconfiguration method for designing according to claim 4, it is characterized in that: the structural model the superiors are destination layer, destination layer is 1 factor, and orlop is generally scheme or object layer, and there is one or several level centre, is criterion or indicator layer.