CN102170137B - ORP (optimal reactive power) method of distribution network of electric power system - Google Patents

Abstract

The invention discloses an ORP (optimal reactive power) method of a distribution network of an electric power system in the technical field of ORP of the distribution network of the electric power system. The method comprises the following main steps: introducing an accelerated evolution operation and an investigation operation in an ABC (artificial bee colony) algorithm to a basic differential evolution operation; and judging whether conditions of convergence of a hybrid algorithm are met, and ending the optimization and outputting the optimal result if the conditions of convergence are met. The hybrid algorithm for solving the ORP problem exerts the advantages that the operation is simple, robustness is good and the like, of the differential evolution algorithm, and can be used to shorten the running time of the algorithm and improve the probability of finding out the global optimal value.

Description

A kind of idle work optimization method of system for distribution network of power

Technical field

The invention belongs to the idle work optimization technical field of system for distribution network of power, relate in particular to a kind of idle work optimization method of system for distribution network of power.

Background technology

Idle work optimization, be exactly to regularly when the structural parameters of system and load condition, by the optimization to some control variables, that can find is satisfying under all prerequisites of specifying constraintss, the idle regulating measure when making some or a plurality of performance index of system reach optimum.Reactive Power Optimazation Problem is a branch problem that differentiates gradually from the development of optimal load flow.In electric power system, power distribution network being carried out idle work optimization can control voltage levvl and reduce active loss.REACTIVE POWER/VOLTAGE CONTROL means commonly used comprise the regulator generator set end voltage, adjust the on-load transformer tap changer position, regulate shunt capacitor and reactor switching group number etc.Reactive power operation planning is to utilize reactive-load compensation equipment to improve the System Reactive Power operation conditions, namely controls voltage levvl and reduces active loss.

On mathematics, idle work optimization is typical nonlinear programming problem, has the characteristics such as non-linear, discontinuous, that uncertain factor is more.The mixing nonlinear programming problem of multivariable, multiple constraint, its control variables, existing continuous variable (generator terminal voltage), discrete variable (Loading voltage regulator tap gear is arranged again, the switching group number of compensation condenser, reactor), find the solution difficulty very large, differential evolution algorithm is a kind of more method of using in idle work optimization.

Differential evolution algorithm is that Rainer Storn and Kenneth Price proposed for finding the solution Chebyshev polynomials in nineteen ninety-five, it is a kind of emerging evolutionary computation technique, be a kind of probabilistic search method that the biological evolutionary phenomena of simulation (select, intersect, make a variation) shows complicated phenomenon, fast and effeciently solve all difficulties problem to reach.The procreation of differential evolution algorithm is to be driven by the gene difference between the individuality of stochastical sampling in current population.

The differential evolution algorithm principle is simple, Operating Complexity is low, has advantages of that parameter arranges simply, amount of calculation is little and robustness good.Although differential evolution algorithm is simple to operate, be easy to realize, and has been applied to solve the reactive power optimization of power system problem preferably, it is difficult to accomplish the balance between diversity and convergence, and is easy to converge on local optimum.The present invention has introduced accelerated evolutionary and the thought of expanding space in the artificial bee colony algorithm in order to make up the defective of differential evolution algorithm in differential evolution algorithm, can shorten Riming time of algorithm, improves the probability that searches global optimum.

Summary of the invention

Be difficult to accomplish balance between diversity and convergence for the differential evolution algorithm of mentioning in background technology, and be easy to converge on the deficiency of local optimum, the present invention proposes a kind of idle work optimization method of system for distribution network of power.

Technical scheme of the present invention is that a kind of idle work optimization method of system for distribution network of power is characterized in that said method comprising the steps of:

Step 1: input original power distribution network parameter;

Step 2: the individuality that structure is comprised of System Reactive Power optimal control variable, initialization population;

Step 3: carry out according to initial population and electrical network parameter that trend is calculated and carry out fitness evaluation;

Step 4: differential evolution algorithm and artificial bee colony algorithm hybrid optimization;

Step 5: optimizing process finishes, the output optimum results.

In described step 1, original power distribution network parameter comprises:

A. power distribution network inherent data: comprise that under power distribution network network structure, a circuit-switched data, various operational mode, each node load and generated power are exerted oneself;

B. the generator terminal voltage of adjustable voltage;

C. transformer voltage ratio;

D. the position of reactive-load compensation equipment and capacity;

E. all control variables constraint conditions and state variable constrain condition.

In described step 2, the idle work optimization control variables comprises:

A. generator terminal voltage;

B. on-load transformer tap changer position;

C. shunt capacitor and reactor switching group number.

Described step 2 comprises the following steps:

Step 2.1: form individual vector by System Reactive Power optimal control variable;

Step 2.2: all individualities in population are generated respectively the initial value that meets constraints at random.

Described step 3 comprises the following steps:

Step 3.1: carry out trend calculating according to initial population and electrical network parameter;

Step 3.2: calculate all individual fitness fit of initial population _i

Step 3.3: the optimum individual x that records initial population _BestWith fitness optimal value fit _Best

The computing formula that in described step 3.1, trend is calculated is:

$\left\{\begin{array}{c}{P}_{G_i}-P_{L_i}=U_i\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{U}_j(G_{\mathrm{ij}}\cos {\mathrm{\δ}}_{\mathrm{ij}}+B_{\mathrm{ij}}\sin {\mathrm{\δ}}_{\mathrm{ij}})\\ Q_{G_i}+Q_{C_i}-Q_{L_i}=U_i\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{U}_j(G_{\mathrm{ij}}\sin {\mathrm{\δ}}_{\mathrm{ij}}+B_{\mathrm{ij}}\cos {\mathrm{\δ}}_{\mathrm{ij}})\end{array}\right.;$ $i\⋐N$

Wherein:

Active power for the node i injection;

Reactive power for the node i injection;

Active power for the node i load;

Reactive power for the node i load;

Be the reactive compensation capacity of node i, by shunt capacitor switching group numerical control system;

U _iVoltage for node i;

U _jVoltage for node j;

G _ijFor the electricity between node i and node j is led;

B _ijBe the susceptance between node i and node j;

δ _ijBe the phase difference of voltage between node i and node j;

N is the node set of distribution network system.

Fitness fit in described step 3.2 _iComputing formula be:

${\mathrm{fit}}_i=\underset{k=1}{\overset{n_1}{\mathrm{\Σ}}}{G}_{k(i,j)}[U_i^2+U_j^2-{2U}_i{U}_j\cos ({\mathrm{\δ}}_i-{\mathrm{\δ}}_j)]$

Wherein:

fit _iFitness for node i;

n ₁Be network general branch way;

G _{K (i, j)}For branch road i leads to the electricity of branch road j;

δ _iPhase angle for node i;

δ _jPhase angle for node j.

Described step 4 specifically comprises the following step:

Step 4.1: operate in differential evolution and produce population of new generation on the basis of former generation population;

Step 4.2: calculate all individual fitness distribution proportions in population;

Step 4.3: calculate individual accelerated evolutionary number of times according to fitness distribution proportion and the population at individual quantity of individuality;

Step 4.4: artificial bee colony accelerated evolutionary operation;

Step 4.5: the optimum individual x that records population _BestWith fitness optimal value fit _Best

Step 4.6: judge whether to exist discarded individuality, if exist, execution in step 4.7, otherwise execution in step 4.8;

Step 4.7: artificial bee colony investigation operation;

Step 4.8: judge whether the swarm optimization end condition satisfies, if the condition of convergence satisfies, ending step 4, otherwise, return to step 4.1.

In described step 4.2, the computing formula of fitness distribution proportion is:

$P_i=\frac{{\mathrm{fit}}_i}{\underset{n=1}{\overset{\mathrm{NP}}{\mathrm{\Σ}}}{\mathrm{fit}}_n}$

Wherein:

P _iBe i individual fitness distribution proportion;

NP is population scale.

In described step 4.3, the computing formula of individual accelerated evolutionary number of times is:

N _i＝P _i×NP

Wherein:

N _iBe i individual accelerated evolutionary number of times.

The inventive method is when having brought into play differential evolution algorithm and having had superiority, overcome the defective that differential evolution algorithm easily obtains local extremum, and reached a balance preferably between diversity and convergence, and shortened algorithm computing time, improved the probability of search global optimum.

Description of drawings

Fig. 1 is the idle work optimization method flow chart.

Fig. 2 is the IEEE14 node winding diagram of revising.

Fig. 3 is differential evolution algorithm and artificial bee colony algorithm hybrid optimization flow chart.

Fig. 4 is once basic differential evolution flow chart.

Fig. 5 is artificial bee colony accelerated evolutionary flow chart.

Embodiment

Below in conjunction with accompanying drawing, take the IEEE14 node system revised as example, idle work optimization method of the present invention is implemented to elaborate.Should be emphasized that, following explanation is only exemplary, rather than in order to limit the scope of the invention and to use.

Fig. 1 is the idle work optimization method flow chart of a kind of system for distribution network of power provided by the invention.In Fig. 1, method provided by the invention comprises following step:

Step 1: input original power distribution network parameter;

Original power distribution network parameter specifically comprises:

A. power distribution network inherent data: comprise that under power distribution network network structure, a circuit-switched data, various operational mode, each node load, generated power are exerted oneself;

B. the generator terminal voltage of adjustable voltage;

C. transformer voltage ratio;

D. the position of reactive-load compensation equipment, capacity;

E. all control variables constraint conditions, state variable constrain condition.

Fig. 2 is the IEEE14 node winding diagram of revising, and whole system comprises 14 nodes, 20 branch roads.On branch road 4-7,4-9,5-6, on-load tap-changing transformer has been installed respectively, but the transformer voltage ratio modification scope is [0.90,1.10], the on-load transformer tap changer gear is discrete variable, and scope is [0,20].In 14 nodes, node 1,2,3,6,8 is the generator node, and wherein node 1 is balance node; Node 9,14 is the reactive power compensation node, and shunt capacitor is installed, and modification scope is [0,18] but reactive power is exerted oneself, and shunt capacitor switching group number is discrete variable, and scope is [0,3]; The voltage restriction range of all nodes is [0.90,1.10], and the adjustable voltage generator set end voltage also is subjected to this voltage constrained.

Step 2: the individuality that structure is comprised of System Reactive Power optimal control variable, initialization population;

Step 2.1: form individual vector by System Reactive Power optimal control variable;

The reactive power optimization of power system control variables mainly comprises: generator terminal voltage; The on-load transformer tap changer position; Shunt capacitor and reactor switching group number.As described in step 1,10 control variables are arranged in the IEEE14 node system of modification, generator terminal voltage comprises: U ₁, U ₂, U ₃, U ₆And U ₈But modification scope is [0.90,1.10]; The on-load transformer tap changer gear comprises: T ₄₇, T ₄₉And T ₅₆, this variable is integer, but modification scope is [0,20]; Shunt capacitor switching group number comprises: N ₉And N ₁₄, this variable is integer, but modification scope is [0,3].For convenient, unification represents control variables with yi, System Reactive Power optimal control variable can be formed the 10 individual vectors of dimension be:

(y ₁，…，y _D)

Wherein: D=10.

Step 2.2: all individualities in population are generated respectively the initial value that meets constraints at random;

According to control variables constraint condition initialization population, population scale is NP.At control variables constraint scope [y _Jmin, y _Jmax] in get random value initialization population at individual x _i(0):

$x_i\left(0\right)=(x_i^1\left(0\right),\·\·\·,x_i^D\left(0\right)),$ I={1 wherein ..., NP}

$x_i^j\left(0\right)=y_{j\min }+\mathrm{rand}\left[\mathrm{0,1}\right]\×(y_{j\max }-y_{j\min })$

Initial population is:

X(0)＝{x ₁(0)，x ₂(0)，…，x _NP(0)}

In formula:

y _Jmax, y _JminRepresent respectively control variables y _jHigher limit and lower limit;

x _i(0) represent i individuality in initial population;

Represent i individual j dimension variate-value in initial population, the digitized representation population algebraically in bracket, 0 namely represents initial population, wherein: j={1 ..., D}.

In the IEEE14 node system of revising, control variables constraint scope [y _Jmin, y _Jmax] the described concrete data replacement of available step 2.1, the restriction range of generator terminal voltage is [0.90,1.10]; The restriction range of on-load transformer tap changer gear is [0,3]; The restriction range of shunt capacitor switching group number is [0,3].For on-load transformer tap changer gear and the several two kinds of discrete variables of shunt capacitor switching group, will do rounding operation to random value in coding.

Step 3: carry out according to initial population and electrical network parameter that trend is calculated and carry out fitness evaluation;

Step 3.1: carry out trend calculating according to individuality and the electrical network parameter of initial population;

$\left\{\begin{array}{c}{P}_{G_i}-P_{L_i}=U_i\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{U}_j(G_{\mathrm{ij}}\cos {\mathrm{\δ}}_{\mathrm{ij}}+B_{\mathrm{ij}}\sin {\mathrm{\δ}}_{\mathrm{ij}})\\ Q_{G_i}+Q_{C_i}-Q_{L_i}=U_i\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{U}_j(G_{\mathrm{ij}}\sin {\mathrm{\δ}}_{\mathrm{ij}}+B_{\mathrm{ij}}\cos {\mathrm{\δ}}_{\mathrm{ij}})\end{array}\right.;$ $i\⋐N$

Wherein:

Active power for the node i injection;

Reactive power for the node i injection;

Active power for the node i load;

Reactive power for the node i load;

Be the reactive compensation capacity of node i, by shunt capacitor switching group numerical control system;

U _iVoltage for node i;

U _jVoltage for node j;

G _ijFor the electricity between node i and node j is led;

B _ijBe the susceptance between node i and node j;

δ _ijBe the phase difference of voltage between node i and node j;

N is the node set of distribution network system.

The data that provide according to step 1 and step 2 are found the solution power flow equation to each of initial population is individual with the Newton-Raphson tidal current computing method, can obtain the value of all state variables, comprise voltage and the phase angle of each node.

Step 3.2: calculate all individual fitness fit of initial population _i

With the power distribution network active loss as the fitness function in optimizing process:

${\mathrm{fit}}_i=\underset{k=1}{\overset{n_1}{\mathrm{\&Sigma;}}}{G}_{k(i,j)}[{\mathrm{<figure-callout\; id="2"\; label="U\; i"\; filenames="HDA0000057560500000011.png"\; state="\{\{state\}\}">U</figure-callout>}}_i^{}+U_j^2-{2U}_i{U}_j\cos ({\mathrm{\&delta;}}_i-{\mathrm{\&delta;}}_j)]$

Wherein:

fit _iFitness for node i;

n ₁Be network general branch way;

G _{K (i, j)}For branch road i leads to the electricity of branch road j;

δ _iPhase angle for node i;

δ _jPhase angle for node j.

The value of all known variables all can obtain after the trend of step 3.1 is calculated, to all individual its fitness fit that calculates _i

Step 3.3: record initial population optimum individual x _BestWith fitness optimal value fit _Best

Constraints in the idle work optimization model comprises equality constraint and inequality constraints, and equality constraint namely satisfies power flow equation; The bound constraint of variable is mainly considered in inequality constraints.So should consider to have the situation of violating variable bound after trend is calculated when the selected population optimum individual.

Variable bound can be divided into state variable constrain and control variables constraint.The inequality constraints of state variable is:

$\left\{\begin{array}{c}{Q}_{G_{\min }}\&le;Q_G\&le;Q_{G_{\max }}\\ U_{{\mathrm{NG}}_{\min }}\&le;U_{\mathrm{NG}}\&le;U_{{\mathrm{NG}}_{\max }}\end{array}\right.,$

The inequality constraints of control variables is:

$\left\{\begin{array}{c}{Q}_{C_{\min }}\&le;Q_C\&le;Q_{C_{\max }}\\ U_{G_{\min }}\&le;U_G\&le;U_{G_{\max }}\\ T_{\min }\&le;T\&le;T_{\max }\end{array}\right.,$

In formula:

Q _GBe the reactive power of generator injection,

With

Be its upper bound, lower bound;

U _NGBe non-generator node voltage,

With

Be its upper bound, lower bound;

Q _CBe the reactive power of reactive power compensation node injection,

With

Be its upper bound, lower bound;

U _GBe generator node set end voltage,

With

Be its upper bound, lower bound;

T is on-load tap-changing transformer tap gear, T _maxAnd T _minBe its upper bound, lower bound.

In these constraintss, generator node set end voltage U _G, on-load tap-changing transformer tap gear T and reactive power compensation node inject idle Q _CThe constraints of (being adjusted by shunt capacitor switching group number) all can satisfy in coding, and generator injects idle Q _GWith non-generator node voltage U _NGConstraints might be breached, the therefore definition penalty of crossing the border:

$F=\underset{j=1}{\overset{n}{\mathrm{\&Sigma;}}}\frac{|U_j-U_j^{\mathrm{spec}}|}{U_{j\max }-U_{j\min }}+\underset{k=1}{\overset{{\mathrm{<figure-callout\; id="1"\; label="n"\; filenames="HDA0000057560500000042.png"\; state="\{\{state\}\}">n</figure-callout>}}_1}{\mathrm{\&Sigma;}}}\frac{|Q_k-Q_k^{\mathrm{spec}}|}{Q_{k\max }-Q_{k\min }}$

In formula:

F is the penalty of crossing the border;

N is the set of all non-generator nodes;

n ₁For all can provide the idle set that goes out the power generator node;

U _JmaxAnd U _JminBe respectively voltage U _jThe upper bound, lower bound, U _jDuring greater than the upper bound, order

U _jDuring less than lower bound, order

Q _KmaxAnd Q _KminBe respectively the reactive power Q that node k injects _kThe upper bound, lower bound, Q _kDuring greater than the upper bound, order

Q _kDuring less than lower bound, order

Any two individualities are carried out odds than the time, selection strategy is as follows:

The first step: if two individualities have the situation of crossing the border, compare both fitness fit _i, take the smaller as excellent;

Second step: body has the situation of crossing the border if having one by one, and another situation of not crossing the border is take the individuality of the situation of not crossing the border as excellent;

The 3rd step: if two individualities have the situation of crossing the border, both penalty value F that cross the border relatively _i, take the smaller as excellent.

After comparing between individuality by above-mentioned selection strategy, record optimum individual x in initial population _BestAnd corresponding fitness optimal value fit _Best

Step 4: differential evolution algorithm and artificial bee colony algorithm hybrid optimization;

Fig. 3 has showed the detailed operating process of step 4.Step 4 specifically comprises the following step:

Step 4.1: operate in differential evolutions such as variation, intersection, selections and produce population of new generation on the basis of former generation population; Fig. 4 has showed the once detailed operating process of basic differential evolution operation;

The first step: mutation operation

To each the individual x in population _i(t), generate three mutually different random integers r1, r2, r3 ∈ 1,2 ..., NP}, and require three random integers all to be not equal to i, press following formula and generate the individual v of variation _i(t):

$v_i\left(t\right)=(v_i^1\left(0\right),...v_i^D\left(0\right)),$ I={1 wherein ... NP}

$v_i^j=v_{r_1}^j\left(t\right)+F(v_{r_2}^j\left(t\right)-v_{r_3}^j\left(t\right)),$ J={1 wherein ... D}

In formula:

Be that t is for i j dimension variate-value that variation is individual in population;

F is evolution parameter zoom factor, F ∈ (0,2).

Second step: interlace operation

At first generate a random integers j_rand ∈ 1,2 ..., D} is then to x _i(t) and v _i(t) press following formula and produce the individual u of test _i(t):

$u_i\left(t\right)=(u_i^1\left(t\right),\&CenterDot;\&CenterDot;\&CenterDot;,u_i^D\left(t\right)),$ I={1 wherein ..., NP}

J={1 wherein ..., D}

In formula:

Be that t is for i j dimension variate-value that test is individual in population;

CR is the evolution parameter intersection factor, CR ∈ (0,1).

The 3rd step: trend is calculated and fitness evaluation

To the individual u of all tests _i(t) carry out trend with reference to step 3 and calculate and calculate fitness.

The 4th step: select operation

j＝1，2，...，D

In formula:

x _i(t+1) be that the t+1 of t after for Evolution of Population is individual for i in population;

Fit (x _i(t)) be individual x _i(t) fitness value.

By the fitness of comparative test individuality and original individuality, select to have the individuality of more excellent fitness as a new generation's individuality.When basic differential evolution algorithm was applied to Reactive Power Optimazation Problem, the selection strategy in this step can be with reference to step 3.3.

Step 4.2: calculate all individual fitness distribution proportions in population

According to all ideal adaptation degree of step 4.1 record, calculate all individual fitness distribution proportions in population

Wherein: NP is population scale, fit _iRepresent i individual fitness value.

Step 4.3: according to the fitness distribution proportion of individuality and the times N of the individual accelerated evolutionary of population at individual quantity calculating _i=P _i* NP, P _iBe i individual fitness distribution proportion in population;

Step 4.4: artificial bee colony accelerated evolutionary operation;

Namely to individuality circulation N _iIt is individual that inferior differential evolution operation produces a new generation; Fig. 5 has showed the detailed operating process of artificial bee colony accelerated evolutionary operation.Specifically comprise the following step:

The first step: count initialized device, k=0;

Second step: judge that whether counter is less than individual accelerated evolutionary number of times, if k＜N _iSet up, entered for the 3rd step, otherwise finish this operating process;

The 3rd step: the first difference evolutional operation produces new individual, and operating procedure is with reference to step 4.1;

The 4th step: counter adds one, k=k+1, the redirect second step.

Step 4.5: record optimum individual x in population _BestWith fitness optimal value fit _Best

Carry out the quality contrast with reference to step 3.3 between individuality, record optimum individual x in population _BestWith fitness optimal value fit _Best

Step 4.6: judged whether discarded individual;

Definition evolution number of times upper limit limit=NP * D, wherein NP is population scale, D represents individual dimension, if individuality does not still improve after having carried out limit evolution test, this is individual for discarding individuality.As have discarded individuality, carry out step 4.7, otherwise directly enter step 4.8.

Step 4.7: artificial bee colony investigation operation;

Regenerate the random individual that meets constraints, discarded individuality is replaced with the random individual that regenerates, generate at random new individual operating procedure with reference to step 2.2, this individual evolution test number (TN) zero clearing simultaneously.

Step 4.8: judge whether the swarm optimization end condition satisfies, if the condition of convergence satisfies, ending step 4, otherwise, return to step 4.1.

The optimization end condition can be taken as evolutionary process and reaches certain algebraically, or continuous some generation evolution idle work optimization target function values do not improve.

Step 5: optimizing process finishes, the output optimum results;

Optimum results comprises value, system load flow level and the system's active loss etc. of optimizing rear each control variables, state variable.

The present invention compares with basic differential evolution algorithm observing in the artificial bee colony algorithm in honeybee and investigation honeybee operation introducing differential evolution algorithm, has shortened Riming time of algorithm, has improved the probability that searches global optimum.

The above; only for the better embodiment of the present invention, but protection scope of the present invention is not limited to this, anyly is familiar with those skilled in the art in the technical scope that the present invention discloses; the variation that can expect easily or replacement are within all should being encompassed in protection scope of the present invention.Therefore, protection scope of the present invention should be as the criterion with the protection range of claim.

Claims (9)

1. the idle work optimization method of a system for distribution network of power is characterized in that said method comprising the steps of:

Step 1: input original power distribution network parameter;

Step 2: the individuality that structure is comprised of System Reactive Power optimal control variable, initialization population;

Step 3: carry out according to initial population and electrical network parameter that trend is calculated and carry out fitness evaluation;

Step 4: differential evolution algorithm and artificial bee colony algorithm hybrid optimization specifically comprise the following step:

Step 4.1: operate in differential evolution and produce population of new generation on the basis of former generation population;

Step 4.2: calculate all individual fitness distribution proportions in population;

Step 4.3: calculate individual accelerated evolutionary number of times according to fitness distribution proportion and the population at individual quantity of individuality;

Step 4.4: artificial bee colony accelerated evolutionary operation;

Step 4.5: the optimum individual x that records population _BestWith fitness optimal value fit _Best

Step 4.6: judge whether to exist discarded individuality, if exist, execution in step 4.7, otherwise execution in step 4.8;

Step 4.7: artificial bee colony investigation operation;

Step 4.8: judge whether the swarm optimization end condition satisfies, if the condition of convergence satisfies, ending step 4, otherwise, return to step 4.1;

Step 5: optimizing process finishes, the output optimum results.

2. a kind of idle work optimization method of system for distribution network of power according to claim 1 is characterized in that in described step 1, original power distribution network parameter comprises:

A. power distribution network inherent data: comprise that under power distribution network network structure, a circuit-switched data, various operational mode, each node load and generated power are exerted oneself;

B. the generator terminal voltage of adjustable voltage;

C. transformer voltage ratio;

D. the position of reactive-load compensation equipment and capacity;

E. all control variables constraint conditions and state variable constrain condition.

3. a kind of idle work optimization method of system for distribution network of power according to claim 1 is characterized in that in described step 2, the idle work optimization control variables comprises:

A. generator terminal voltage;

B. on-load transformer tap changer position;

C. shunt capacitor and reactor switching group number.

4. the idle work optimization method of a kind of system for distribution network of power according to claim 1 is characterized in that described step 2 comprises the following steps:

Step 2.1: form individual vector by System Reactive Power optimal control variable;

Step 2.2: all individualities in population are generated respectively the initial value that meets constraints at random.

5. a kind of idle work optimization method of system for distribution network of power according to claim 1 is characterized in that described step 3 comprises the following steps:

Step 3.1: carry out trend calculating according to initial population and electrical network parameter;

Step 3.2: calculate all individual fitness fit of initial population _i

Step 3.3: the optimum individual x that records initial population _BestWith fitness optimal value fit _Best

6. a kind of idle work optimization method of system for distribution network of power according to claim 5 is characterized in that the computing formula that in described step 3.1, trend is calculated is:

$\left\{\begin{array}{c}{P}_{G_i}-P_{L_i}=U_i\underset{j=1}{\overset{n}{\mathrm{\&Sigma;}}}{U}_j(G_{\mathrm{ij}}\cos {\mathrm{\&delta;}}_{\mathrm{ij}}+B_{\mathrm{ij}}\sin {\mathrm{\&delta;}}_{\mathrm{ij}})\\ Q_{G_i}+Q_{C_i}-Q_{L_i}=U_i\underset{j=1}{\overset{n}{\mathrm{\&Sigma;}}}{U}_j(G_{\mathrm{ij}}\sin {\mathrm{\&delta;}}_{\mathrm{ij}}+B_{\mathrm{ij}}\cos {\mathrm{\&delta;}}_{\mathrm{ij}})\end{array}\right.;i\&Subset;N$

Wherein:

Active power for the node i injection;

Reactive power for the node i injection;

Active power for the node i load;

Reactive power for the node i load;

Be the reactive compensation capacity of node i, by shunt capacitor switching group numerical control system;

U _iVoltage for node i;

U _jVoltage for node j;

G _ijFor the electricity between node i and node j is led;

B _ijBe the susceptance between node i and node j;

δ _ijBe the phase difference of voltage between node i and node j;

N is the node set of distribution network system.

7. a kind of idle work optimization method of system for distribution network of power according to claim 6, is characterized in that fitness fit in described step 3.2 _iComputing formula be:

${\mathrm{fit}}_i=\underset{k=1}{\overset{n_1}{\mathrm{\&Sigma;}}}{G}_{k(i,j)}[U_i^2+U_j^2-2U_i{U}_j\cos ({\mathrm{\&delta;}}_i-{\mathrm{\&delta;}}_j)]$

Wherein:

fit _iFitness for node i;

n ₁Be network general branch way;

G _{K (i, j)}For branch road i leads to the electricity of branch road j;

δ _iPhase angle for node i;

δ _jPhase angle for node j.

8. a kind of idle work optimization method of system for distribution network of power according to claim 1 is characterized in that the computing formula of fitness distribution proportion in described step 4.2 is:

$P_i=\frac{{\mathrm{fit}}_i}{\underset{n=1}{\overset{\mathrm{NP}}{\mathrm{\&Sigma;}}}{\mathrm{fit}}_n}$

Wherein:

fit _iFitness for node i;

P _iBe i individual fitness distribution proportion;

NP is population scale.

9. a kind of idle work optimization method of system for distribution network of power according to claim 1 is characterized in that the computing formula of individual accelerated evolutionary number of times in described step 4.3 is:

N _i=P _i×NP

Wherein:

P _iBe i individual fitness distribution proportion;

NP is population scale;

N _iBe i individual accelerated evolutionary number of times.

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