CN102170137A - 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 give regularly when the structural parameters and the load condition of system, 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.The idle work optimization problem is a branch problem that differentiates gradually from Development of optimal power flow.In electric power system power distribution network being carried out idle work optimization can control voltage levvl and reduce active loss.REACTIVE POWER 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 come the idle operation conditions of improvement system, promptly controls voltage levvl and reduces active loss.

On mathematics, idle work optimization is the typical nonlinear planning problem, has 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), it is very big to find the solution difficulty, and differential evolution algorithm is the more a kind of 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 the current population.

The differential evolution algorithm principle is simple, Operating Complexity is low, has the advantage that parameter is provided with simply, amount of calculation is little and robustness is good.Though 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 the convergence, and is easy to converge on local optimum.The present invention is in order to remedy the defective of differential evolution algorithm, and the thought of having introduced accelerated evolutionary in the artificial ant colony algorithm and expand space in differential evolution algorithm can shorten algorithm running time, improves the probability that searches global optimum.

Summary of the invention

Be difficult to accomplish balance between diversity and the convergence at the differential evolution algorithm of mentioning in the 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: import original power distribution network parameter;

Step 2: the individuality that structure is made up of system's idle work optimization control variables, initialization population;

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

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

Step 5: optimizing process finishes, and the result is optimized in output.

Original power distribution network parameter comprises in the described step 1:

A. power distribution network inherent data: comprise under power distribution network network structure, a circuit-switched data, the various operational mode each node load and generator is meritorious exerts 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 constraintss and state variable constraints.

The idle work optimization control variables comprises in the described step 2:

A. generator terminal voltage;

B. on-load transformer tap changer position;

C. shunt capacitor and reactor switching group number.

Described step 2 may further comprise the steps:

Step 2.1: form individual vector by system's idle work optimization control variables;

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

Described step 3 may further comprise the 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 of record initial population _BestWith fitness optimal value fit _Best

The computing formula that trend is calculated in the described step 3.1 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 the node j is led;

B _IjBe the susceptance between node i and the node j;

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

N is the node set of distribution network system.

Fitness fit in the 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 the population;

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

Step 4.4: artificial bee colony accelerated evolutionary operation;

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

Step 4.6: judge whether to exist discarded individuality, if exist, then 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, end step 4, otherwise, return step 4.1.

The computing formula of fitness distribution proportion is in the described step 4.2:

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

Wherein:

P _iBe i individual fitness distribution proportion;

NP is a population scale.

The computing formula of individual accelerated evolutionary number of times is in the described step 4.3:

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 obtains local extremum easily, and between diversity and convergence, reached a balance preferably, and shortened the algorithm computation 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 ant 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, be example with the IEEE14 node system of revising, idle work optimization method of the present invention is implemented to elaborate.Should be emphasized that following explanation only is 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.Among Fig. 1, method provided by the invention comprises following step:

Step 1: import original power distribution network parameter;

Original power distribution network parameter specifically comprises:

A. power distribution network inherent data: comprise each node load under power distribution network network structure, a circuit-switched data, the various operational mode, generator is meritorious exerts 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 constraintss, state variable constraints.

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, the adjustable scope of transformer voltage ratio is [0.90,1.10], and the on-load transformer tap changer gear is a 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 a balance node; Node 9,14 is the reactive power compensation node, and shunt capacitor is installed, and the reactive power adjustable scope of exerting oneself is [0,18], and shunt capacitor switching group number is a 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 made up of system's idle work optimization control variables, initialization population;

Step 2.1: form individual vector by system's idle work optimization control variables;

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 ₈, adjustable scope is [0.90,1.10]; The on-load transformer tap changer gear comprises: T ₄₇, T ₄₉And T ₅₆, this variable is an integer, adjustable scope is [0,20]; Shunt capacitor switching group number comprises: N ₉And N ₁₄, this variable is an integer, adjustable scope is [0,3].For convenience, unification is represented control variables with yi, system's idle work optimization control variables can be formed the individual vector of 10 dimensions be:

(y ₁，…，y _D)

Wherein: D=10.

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

According to control variables constraints initialization population, population scale is NP.At control variables restriction range [y _Jmin, y _Jmax] in get the individual x of random value initialization population _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 the formula:

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

x _i(0) represents i individuality in the initial population;

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

In the IEEE14 node system of revising, control variables restriction range [y _Jmin, y _Jmax] available step 2.1 described concrete data replacements, 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, in coding, will do rounding operation to random value.

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

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

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

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 the node j is led;

B _IjBe the susceptance between node i and the node j;

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

N is the node set of distribution network system.

According to the data that step 1 and step 2 provide, the inferior tidal current computing method of each body and function newton-pressgang of initial population is found the solution power flow equation, can obtain the value of all state variables, comprise the 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 the 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 be calculated the back in the trend of step 3.1 and obtain, 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 promptly satisfies power flow equation; The bound constraint of variable is mainly considered in inequality constraints.So should consider trend when selecting the population optimum individual calculates the back and has the situation of violating variable bound.

Variable bound can be divided into state variable constraint 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 the formula:

Q _GBe the reactive power of generator injection,

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 an 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 violated 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 the 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:, then compare both fitness fit if two individualities all have the situation of crossing the border _i, be excellent with the smaller;

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

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

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

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

Fig. 3 has showed the detail operations flow process of step 4.Step 4 specifically comprises the following step:

Step 4.1: evolving with difference such as variation, intersection, selections produces population of new generation on the basis operate in the former generation population; Fig. 4 has showed the detail operations flow process of once basic differential evolution operation;

The first step: mutation operation

To each the individual x in the 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 the formula:

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

F is an 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 the formula:

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

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

The 3rd step: trend is calculated and fitness evaluation

All are tested individual u _i(t) carry out trend with reference to step 3 and calculate and calculate fitness.

The 4th step: selection operation

j＝1，2，...，D

In the formula:

x _i(t+1) t+1 after to be t for population evolve is for i in population individuality;

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 the idle work optimization 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 the population

According to all ideal adaptation degree of step 4.1 record, calculate all individual fitness distribution proportions in the population Wherein: NP is a population scale, fit _iRepresent i individual fitness value.

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

Step 4.4: artificial bee colony accelerated evolutionary operation;

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

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

Second step: whether judge counter 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: first difference evolution operation produces new individual, and operating procedure is with reference to step 4.1;

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

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

Between individuality, carry out the quality contrast with reference to step 3.3, optimum individual x in the record 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 a population scale, and D represents individual dimension, and individual as if still not improving after having carried out limit evolution test, then this is individual for discarding individuality.As have discarded individuality, then 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 new individual operating procedure at random with reference to step 2.2, simultaneously should the zero clearing of individuality evolution test number (TN).

Step 4.8: judge whether the swarm optimization end condition satisfies, if the condition of convergence satisfies, end step 4, otherwise, return 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, and the result is optimized in output;

Optimize the result and comprise value, system load flow level and the system's active loss etc. of optimizing each control variables of back, state variable.

The present invention will be in the artificial ant colony algorithm observes honeybee and the operation of investigation honeybee is introduced in the differential evolution algorithm, compares with basic differential evolution algorithm, has shortened algorithm running time, has improved the probability that searches global optimum.

The above; only for the preferable embodiment of the present invention, but protection scope of the present invention is not limited thereto, and 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 all should be encompassed within 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 (10)

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: import original power distribution network parameter;

Step 2: the individuality that structure is made up of system's idle work optimization control variables, initialization population;

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

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

Step 5: optimizing process finishes, and the result is optimized in output.

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

A. power distribution network inherent data: comprise under power distribution network network structure, a circuit-switched data, the various operational mode each node load and generator is meritorious exerts 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 constraintss and state variable constraints.

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

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 may further comprise the steps:

Step 2.1: form individual vector by system's idle work optimization control variables;

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

5. according to the idle work optimization method of the described a kind of system for distribution network of power of claim 1, it is characterized in that described step 3 may further comprise the 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 of record initial population _BestWith fitness optimal value fit _Best

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

B _IjBe the susceptance between node i and the node j;

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

N is the node set of distribution network system.

7. according to the idle work optimization method of the described a kind of system for distribution network of power of claim 5, it is characterized in that fitness fit in the 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. according to the idle work optimization method of the described a kind of system for distribution network of power of claim 1, it is characterized in that 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 the population;

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

Step 4.4: artificial bee colony accelerated evolutionary operation;

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

Step 4.6: judge whether to exist discarded individuality, if exist, then 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, end step 4, otherwise, return step 4.1.

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

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

Wherein:

P _iBe i individual fitness distribution proportion;

NP is a population scale.

10. the idle work optimization method of described a kind of system for distribution network of power according to Claim 8 is characterized in that the computing formula of individual accelerated evolutionary number of times in the described step 4.3 is:

N _i＝P _i×NP

Wherein:

N _iBe i individual accelerated evolutionary number of times.

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