CN104600714A - Method and device for optimizing reactive power of power distribution network containing distributed generation - Google Patents

Abstract

The invention provides a method and a device for optimizing the reactive power of a power distribution network containing distributed generation. The method comprises the steps of setting up a power distribution network reactive power optimization genetic algorithm model according to the preset control parameters of the power distribution network containing distributed generation and a genetic algorithm model, performing load flow calculation on the preset control parameters of the power distribution network to determine the equality constraints of the reactive power optimization of the power distribution network reactive power optimization genetic algorithm model, determining the inequality constraints of the power distribution network reactive power optimization genetic algorithm model according to the positions of controllable transformer taps, each node voltage, the output power of parallel reactive compensation capacity generators and the power factor of the high voltage side of each node distribution transformer in the control parameters of the power distribution network, and generating a reactive power optimization result according to a preset priority parameter, the equality and inequality constraints of the reactive power optimization and the reactive power optimization genetic algorithm model. The method and the device for optimizing the reactive power of the power distribution network containing distributed generation in real time have the advantages that less information is requested for solving problems, the solving process is simple and the optimal solution or the second-belt solution can be obtained with a great probability.

Description

Containing var Optimization Method in Network Distribution and the device of distributed power source

Technical field

The present invention relates to power technology, is a kind of var Optimization Method in Network Distribution containing distributed power source and device concretely.

Background technology

At present, with the increase day by day of power consumption, stable, the economical operation of electrical network come into one's own day by day, and the quality of power supply of electrical network is poor, not only affects electric power enterprise itself, affects the safety of electric power system simultaneously.The wattles power economic equivalent of electric power system and reactive power compensation are the important component parts of safe operation of power system, to the reasonable disposition of electric power system and the optimal compensation of load or burden without work, not only can improve the stability of power system operation, can also network loss be reduced, make power system security, economical operation.

Idle work optimization be exactly when system network architecture and system loading given, by regulable control variable be system meet various constraint regulate under network loss reach minimum, not only make whole power run near rated value by idle work optimization, and electrical network economy benefit, the quality of power supply can be improved.

In prior art, the traditional algorithm for Reactive Power Optimazation Problem is divided into linear processes to plan two kinds, its shortcoming be target function and constraints must continuously, can be micro-, higher to the requirement of initial point.In addition, more difficult to discrete variable.

The genetic algorithm of prior art judges its excellent degree with the size of the fitness of each individuality, and fitness is larger, and individuality is better.But the determination of the improved adaptive GA-IAGA fitness function in the present invention is based on the minimum value solving target function.

Optionally, in an embodiment of the present invention, mutation probability P _mwith crossover probability P _cfor:

$P_c=\left\{\begin{array}{cc}\frac{k_1(f_{\max }-f)}{f_{\max }-f_{\mathrm{avg}}},& f\&GreaterEqual;f_{\mathrm{avg}}\\ k_2,& f<f_{\mathrm{avg}}\end{array}\right.$

$P_m=\left\{\begin{array}{cc}\frac{k_3(f_{\max }-f^{\&prime;})}{f_{\max }-f_{\mathrm{avg}}},& f^{\&prime;}\&GreaterEqual;f_{\mathrm{avg}}\\ k_4,& f^{\&prime;}<f_{\mathrm{avg}}\end{array}\right.$

F _avgfor kind of a group mean adaptive value; F' is the fitness value of variation individuality; f _maxfor maximum adaptation value in population; F is fitness value larger in two individualities that will intersect; k ₁, k ₂, k ₃and k ₄for constant.

Selecting operation to implement criterion is that outstanding individuality has larger probability and is chosen as parent to produce the next generation, adopts the method for championship to select.The method selection uniform crossover intersected, because the effect of uniform crossover is intersected than any usually or two-point crossover is good; Crossover probability and mutation probability are calculated by adaptive approach, the position of variation can not only one, population diversity can be caused so not enough, but variable position can cause convergence in population slow too much, if so population maximum adaptation value and minimum adaptive value are equal, then only has the position that makes a variation, if not the number of stochastic generation variation position, this position made a variation several times of stochastic generation, makes a variation subsequently again.

Optionally, in an embodiment of the present invention, second mutation process is:

When repeating the half that individual amount is more than or equal to population number in certain generation, these are repeated individual taking-up, only retain one by one body in original seed group;

All the other individualities add 1 or subtract 1 in last position of each variable, add deduct and determine at random.

Step 2: according to the Mathematical Modeling of idle work optimization, carries out solving of optimal solution based on Revised genetic algorithum;

Optionally, in an embodiment of the present invention, the algorithm steps solved carrying out optimal solution based on Revised genetic algorithum comprises:

Step 1: initialization of population.Comprise setup parameter, to the variable coding solved and generation initial population, and the result of subregion is added in initial population.

Step 2:

A) Load flow calculation is carried out.

B) network loss is calculated.

C) by capacitor and transformer action number, the out-of-limit value of each variable and the network loss target function calculating target function value according to Reactive Power Optimazation Problem.

D) fitness value is generated by target function value.

Step 3: if meet convergence criterion, Output rusults, namely exports optimum individual; Otherwise, carry out next step.

Step 4: perform and select operation, interlace operation and mutation operation.

Step 5: check and repeat the half whether individual amount equals or exceeds population quantity, if meet, carry out second mutation; Otherwise do not carry out second mutation.

Step 6: retain optimum individual

Step 7: return step 2.

The each result of calculation of genetic algorithm is not identical with convergence situation, if be provided with fixing algebraically, then likely do not restrain and just stops optimizing, so the target function value of setting continuous multi-generation does not reduce or decreasing value is minimum, then represents and has restrained.And be provided with minimum of computation algebraically to avoid precocity.But because real-time reactive power optimization requires that speed wants fast, so set max calculation algebraically.Therefore the condition of convergence be set to continuous 5 generation target function value do not reduce or the minimum and computer algebra of decreasing value between minimum and maximum computer algebra.

Three, sample calculation analysis is carried out in conjunction with the scheme of the present embodiment as follows:

Table 1 control variables parameter list

Compare with IEEE-33 node calculate.Add distributed power source and distribution transformer in systems in which, 13 node access blower fans, capacity is 300kVA, 23 node access photovoltaics, capacity is 300kVA, 31 node access miniature gas turbines, access capacity is 300kVA, 11 and 29 node access capacitors, access capacity 20kVA, add distribution transformer and capacitor at 3,19 and 26 nodes, the capacity of each capacitor is 20kVA.Control variables and scope are as shown above.Namely according to the control variables in the coding structure formula (5) of genetic algorithm and table 1, determine the individuality in Genetic Algorithm Model, then determine the optimum individual in individuality according to the calculating of aforesaid target function, fitness value, the optimum individual according to determining is optimized control to electrical network parameter.

Table 2 computer algebra statistical form

By the method controlling space cluster analysis based on reactive source, 33 node systems are divided into 4 districts, 33,1,18,19,36 nodes connecing distribution transformer for 20,21 and 19 times are 1st districts, 2,3,4,22,23,35 nodes of the distribution transforming transformer connect under 24 and 3 nodes are 2nd districts, and are 3rd districts from 7 nodes to 17 nodes, 27 to 32 nodes are 4th districts.The genetic algorithm algebraically statistics of table 2 for adopting multi-Vari position adaptive genetic algorithm and the solution of the present invention to be optimized respectively, the multi-Vari position adaptive genetic algorithm (hereinafter referred to as MMAdapGA) of prior art does not add partition information, second mutation and elite's retention strategy.And compare with method herein and the calculating of multi-Vari position adaptive genetic algorithm.Because the algebraical sum the possibility of result that genetic algorithm calculates is different at every turn, can only compare by calculating statistics in a large number.Prevent precocity, arranging minimal algebra was 10 generations, and prevent again operation time long, maximum algebraically was set to for 30 generations, and population size is all 50.Two kinds of all the other conditions of method and parameter all consistent.Respectively calculate 100 times, the statistics of precocious rate and computer algebra as shown above,

Table 3 optimizes rear network loss, action number and out-of-limit amount statistical form

As can be seen from the above table, average loss after adopting method used herein to optimize is better than the optimum results of MMAdapGA far away, because network loss is the 3rd priority, so before sometimes network loss can be greater than optimization on the contrary, although this makes system voltage not out-of-limit, action number is as far as possible little, but economic benefit neither be very high, and the result that method herein draws is when the action number of transformer and capacitor is minimum, make voltage not out-of-limit very little with network loss, and the minimum network loss that context of methods calculates and maximum network loss only have the gap of 0.0018, illustrate method herein comparatively MMAdapGA optimizing performance and stability better.Although the network loss after context of methods optimization is good not as Fmincon function optimization, be more or less the same, and the time of optimization is well below the optimization time of Fmincon.

In addition, as shown in Figure 2, present invention also offers a kind of GA for reactive power optimization device containing distributed power source, comprising:

Model building module 201, for setting up the GA for reactive power optimization Genetic Algorithm Model containing distributed power source according to the power distribution network controling parameters containing distributed power source preset and Genetic Algorithm Model;

Equality constraint determination module 202, for carrying out to the described default power distribution network controling parameters containing distributed power source the equality constraint that Load flow calculation determines the idle work optimization of the GA for reactive power optimization Genetic Algorithm Model containing distributed power source;

Inequality constraints determination module 203, for determining the inequality constraints of the GA for reactive power optimization Genetic Algorithm Model containing distributed power source according to the described position containing the controllable load tap changer in the power distribution network controling parameters of distributed power source, each node voltage, parallel reactive compensation capacity generator output and the on high-tension side power factor of each node distribution transforming;

Optimum results generation module 204, generates idle work optimization result for the equality constraint according to the priority parameters preset, described idle work optimization, inequality constraints and the GA for reactive power optimization Genetic Algorithm Model containing distributed power source.

Summary of the invention

For simplifying the computational process of the GA for reactive power optimization containing distributed power source based on Genetic Algorithm Model, ensureing carrying out fast of idle work optimization, embodiments providing a kind of var Optimization Method in Network Distribution containing distributed power source, comprising:

Step 1, sets up the GA for reactive power optimization Genetic Algorithm Model containing distributed power source according to the power distribution network controling parameters containing distributed power source preset and Genetic Algorithm Model;

Step 2, carries out to the described default power distribution network controling parameters containing distributed power source the equality constraint that Load flow calculation determines the idle work optimization of the GA for reactive power optimization Genetic Algorithm Model containing distributed power source;

Step 3, determines the inequality constraints of the GA for reactive power optimization Genetic Algorithm Model containing distributed power source according to the described position containing the controllable load tap changer in the power distribution network controling parameters of distributed power source, each node voltage, parallel reactive compensation capacity generator output and the on high-tension side power factor of each node distribution transforming;

Step 4, generates idle work optimization result according to the priority parameters preset, the equality constraint of described idle work optimization, inequality constraints and GA for reactive power optimization Genetic Algorithm Model.

In addition, present invention also offers a kind of GA for reactive power optimization device containing distributed power source, device comprises:

Model building module, for setting up the GA for reactive power optimization Genetic Algorithm Model containing distributed power source according to the power distribution network controling parameters containing distributed power source preset and Genetic Algorithm Model;

Model building module, for setting up the GA for reactive power optimization Genetic Algorithm Model containing distributed power source according to the power distribution network controling parameters preset and Genetic Algorithm Model;

Equality constraint determination module, for carrying out to the described default power distribution network controling parameters containing distributed power source the equality constraint that Load flow calculation determines the idle work optimization of the GA for reactive power optimization Genetic Algorithm Model containing distributed power source;

Inequality constraints determination module, for determining the inequality constraints of the GA for reactive power optimization Genetic Algorithm Model containing distributed power source according to the described position containing the controllable load tap changer in the power distribution network controling parameters of distributed power source, each node voltage, parallel reactive compensation capacity generator output and the on high-tension side power factor of each node distribution transforming;

Optimum results generation module, generates idle work optimization result for the equality constraint according to the priority parameters preset, described idle work optimization, inequality constraints and the GA for reactive power optimization Genetic Algorithm Model containing distributed power source.

The present invention contains power distribution network real-time reactive power optimization method and the device of distributed power source, compared with prior art, the information required during genetic algorithm for solving problem is few, solution procedure is also uncomplicated, can larger probability obtain optimal solution or suboptimal solution, and conveniently add division result, reactive power optimization of power system field can be widely used in.For above and other object of the present invention, feature and advantage can be become apparent, preferred embodiment cited below particularly, and coordinate institute's accompanying drawings, be described in detail below.

Accompanying drawing explanation

In order to be illustrated more clearly in the embodiment of the present invention or technical scheme of the prior art, be briefly described to the accompanying drawing used required in embodiment or description of the prior art below, apparently, accompanying drawing in the following describes is only some embodiments of the present invention, for those of ordinary skill in the art, under the prerequisite not paying creative work, other accompanying drawing can also be obtained according to these accompanying drawings.

Fig. 1 is the flow chart of a kind of var Optimization Method in Network Distribution containing distributed power source provided by the invention;

Fig. 2 is the block diagram of a kind of GA for reactive power optimization device containing distributed power source provided by the invention.

Embodiment

Below in conjunction with the accompanying drawing in the embodiment of the present invention, be clearly and completely described the technical scheme in the embodiment of the present invention, obviously, described embodiment is only the present invention's part embodiment, instead of whole embodiments.Based on the embodiment in the present invention, those of ordinary skill in the art, not making the every other embodiment obtained under creative work prerequisite, belong to the scope of protection of the invention.

As shown in Figure 1, the invention provides the var Optimization Method in Network Distribution containing distributed power source, comprising:

Step S101, sets up the GA for reactive power optimization Genetic Algorithm Model containing distributed power source according to the power distribution network controling parameters containing distributed power source preset and Genetic Algorithm Model;

Step S102, carries out to the described default power distribution network controling parameters containing distributed power source the equality constraint that Load flow calculation determines the idle work optimization of the GA for reactive power optimization Genetic Algorithm Model containing distributed power source;

Step S103, determines the inequality constraints of the GA for reactive power optimization Genetic Algorithm Model containing distributed power source according to the described position containing the controllable load tap changer in the power distribution network controling parameters of distributed power source, each node voltage, parallel reactive compensation capacity generator output and the on high-tension side power factor of each node distribution transforming;

Step S104, generates idle work optimization result according to the priority parameters preset, the equality constraint of described idle work optimization, inequality constraints and the GA for reactive power optimization Genetic Algorithm Model containing distributed power source.

In specific embodiment, set up GA for reactive power optimization Genetic Algorithm Model according to the power distribution network controling parameters containing distributed power source preset and Genetic Algorithm Model and comprise:

The target function of the GA for reactive power optimization Genetic Algorithm Model containing distributed power source is set up according to formula (1),

$\min f=p_1*\{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{\left(\frac{\mathrm{\&Delta;}{U}_i}{\mathrm{\&Delta;}{U}_{m_i}}\right)}^2+\underset{i=1}{\overset{m}{\mathrm{\&Sigma;}}}{\left(\frac{\mathrm{\&Delta;Q}{G}_i}{\mathrm{\&Delta;}{G}_{m_i}}\right)}^2\}+p_2*t+p_3*\mathrm{\&Delta;P}+\mathrm{\&lambda;\&Delta;}\cos {\mathrm{\&theta;}}_i---\left(1\right)$

Wherein, $\mathrm{\&Delta;}{U}_i=\left\{\begin{array}{cc}{U}_{\min i}-U_i;& U_i<U_{\min i}\\ 0;& U_{\min i}<U_i<U_{\max i}\\ U_{\max i}-U_i;& U_i>U_{\max i}\end{array}\right.$

$\mathrm{\&Delta;}{U}_{m_i}=U_{\max i}-U_{\min i}$

$\mathrm{\&Delta;}{\mathrm{QG}}_i=\left\{\begin{array}{cc}{\mathrm{QG}}_{\min i}-{\mathrm{QG}}_i;& {\mathrm{QG}}_i<{\mathrm{QG}}_{\min i}\\ 0;& {\mathrm{QG}}_{\min i}<{\mathrm{QG}}_i<{\mathrm{QG}}_{\max i}\\ {\mathrm{QG}}_i-Q{G}_{\max i};& {\mathrm{QG}}_i>{\mathrm{QG}}_{\max i}\end{array}\right.$

ΔQG _i＝QG _maxi-QG _mini

$\mathrm{\&Delta;P}=\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{V}_i\underset{j=1}{\overset{n}{\mathrm{\&Sigma;}}}{V}_j(G_{\mathrm{ij}}\cos {\mathrm{\&xi;}}_{\mathrm{ij}}+B_{\mathrm{ij}}\sin {\mathrm{\&xi;}}_{\mathrm{ij}})$

Wherein, p ₁, p ₂, p ₃for the described priority parameters preset;

N is node total number, and m is the number of distributed power source;

U _i, U _miniand U _maxibe respectively the voltage magnitude of node, minimum permission voltage and maximum permissible voltage;

QG _i, QG _miniand QG _maxirepresent reactive power, idle lower limit and the idle upper limit of exerting oneself of exerting oneself of generator node respectively;

T is the total adjustment number of transformer and capacitor;

ξ _ijfor voltage phase angle;

G _ijand B _ijnode admittance matrix element is drawn for being solved by electrical network parameter.

Δ P is system active power loss.

λ is the penalty function factor, Δ cos θ _iall out-of-limit amounts of power factor connecing the node of distribution transforming.

In this embodiment of the present invention, when adding distribution transforming transformer in 10kV circuit, for ensureing that the power factor of the node of distribution transforming is in the scope of 0.95 to 0.98, so also add a penalty function to make to connect the power factor of the node of distribution transforming in the scope of 0.95 to 0.98 in target function.

In specific embodiment, Load flow calculation is carried out to the power distribution network controling parameters containing distributed power source preset and determines that the equality constraint of the idle work optimization of the GA for reactive power optimization Genetic Algorithm Model containing distributed power source comprises: carry out according to formula (2), formula (3) equality constraint that Load flow calculation determines the idle work optimization of the GA for reactive power optimization Genetic Algorithm Model containing distributed power source;

$P_i=V_i\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{V}_j(G_{\mathrm{ij}}\cos {\mathrm{\&delta;}}_{\mathrm{ij}}+B_{\mathrm{ij}}\sin {\mathrm{\&delta;}}_{\mathrm{ij}})---\left(2\right)$

$Q_i=V_i\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{V}_j(G_{\mathrm{ij}}\sin {\mathrm{\&delta;}}_{\mathrm{ij}}+B_{\mathrm{ij}}\cos {\mathrm{\&delta;}}_{\mathrm{ij}})---\left(3\right)$

Wherein, P _ifor effective power flow

Q _ifor reactive power flow

V _ithe voltage of node i, G _ijand B _ijfor node admittance matrix element, δ _ijfor node voltage differential seat angle.

In specific embodiment, determine the inequality constraints of the GA for reactive power optimization Genetic Algorithm Model containing distributed power source according to formula (4);

$\left\{\begin{array}{c}{T}_{i\min }\&le;T_i\&le;T_{i\max }\\ Q_{\mathrm{ci}\min }\&le;Q_{\mathrm{ci}}\&le;Q_{\mathrm{ci}\max }\\ V_{i\min }\&le;V_i\&le;V_{i\max }\\ Q_{\mathrm{Gi}\min }\&le;Q_{\mathrm{Gi}}\&le;Q_{\mathrm{Gi}\max }\\ {\mathrm{\&eta;}}_{i\min }\&le;{\mathrm{\&eta;}}_i\&le;{\mathrm{\&eta;}}_{i\max }\end{array}\right.---\left(4\right)$

Wherein, T _iit is the position of controllable load tap changer; V _ithe voltage of node i; Q _ciit is parallel reactive compensation capacity; η _iit is the on high-tension side power factor of node i distribution transforming; Q _giit is generator output.

In specific embodiment, generate idle work optimization result according to the priority parameters preset, the equality constraint of described idle work optimization, inequality constraints and the GA for reactive power optimization Genetic Algorithm Model containing distributed power source and comprise:

Carry out genetic algorithm encoding according to the described GA for reactive power optimization Genetic Algorithm Model containing distributed power source, carry out constructing fitness function, mutation probability, crossover probability and second mutation and calculate the result of calculation generating fitness function, mutation probability, crossover probability and second mutation;

The result of calculation that generates fitness function, mutation probability, crossover probability and the second mutation optimal solution containing the determination GA for reactive power optimization Genetic Algorithm Model of distributed power source is calculated according to the coding of the equality constraint of described default priority parameters, idle work optimization, inequality constraints, Genetic Algorithm Model, crossover probability and second mutation;

Optimal solution according to the described GA for reactive power optimization Genetic Algorithm Model containing distributed power source generates idle work optimization result.

In specific embodiment, carry out genetic algorithm encoding according to the described GA for reactive power optimization Genetic Algorithm Model containing distributed power source and comprise:

Adopt genetic algorithm binary coding, the coding structure of generation is formula (5);

X＝[V _G1,...,V _GN1|T _K1,...,T _KN2|Q _C1,...,Q _CN2] (5)

Wherein, V _g1for generator 1 set end voltage, V _gN1for generator N1 set end voltage, T _k1for transformer 1 gear, T _kN2for transformer N2 gear, Q _c1for capacitor 1 reactive compensation capacity, Q _cN2for capacitor N2 reactive compensation capacity.

In specific embodiment, the result of calculation carrying out constructing fitness function, mutation probability, crossover probability and second mutation calculating generation fitness function, mutation probability, crossover probability and second mutation comprises:

The calculating of fitness function is carried out according to formula (6),

f _i＝1/F _i(6)

Wherein, f _ifor the fitness of individual i; F _ifor the target function value of individual i;

According to larger fitness value in the individual fitness value of variation, population maximum adaptation angle value, population average fitness value, two individualities that will intersect and be (7), formula (8) calculates described mutation probability and crossover probability;

$P_c=\left\{\begin{array}{cc}\frac{k_1(f_{\max }-f)}{f_{\max }-f_{\mathrm{avg}}},& f\&GreaterEqual;f_{\mathrm{avg}}\\ k_2,& f<f_{\mathrm{avg}}\end{array}\right.---\left(7\right)$

$P_m=\left\{\begin{array}{cc}\frac{k_3(f_{\max }-f^{\&prime;})}{f_{\max }-f_{\mathrm{avg}}},& f^{\&prime;}\&GreaterEqual;f_{\mathrm{avg}}\\ k_4,& f^{\&prime;}<f_{\mathrm{avg}}\end{array}\right.---\left(8\right)$

Wherein, f _avgfor kind of a group mean adaptive value; F' is the fitness value of variation individuality; f _maxfor maximum adaptation value in population; F is fitness value larger in two individualities that will intersect; k ₁, k ₂, k ₃and k ₄for constant.

In the present embodiment, the effect carrying out second mutation is the half that will repeat individual amount in certain generation and be more than or equal to population number, and these are repeated individual taking-up, only retain one by one body in original seed group, all the other individualities add 1 in last position of each variable, or subtract 1, add deduct and determine at random.This method creates the new individuality near a former individuality, strengthens local optimal searching ability.

In specific embodiment, the optimal solution calculating the GA for reactive power optimization Genetic Algorithm Model that the result of calculation generating fitness function, mutation probability and crossover probability is determined containing distributed power source according to the coding of the equality constraint of described default priority parameters, idle work optimization, inequality constraints, Genetic Algorithm Model and crossover probability comprises:

Initialization of population also generates initial population according to the coding structure of formula (5);

According to the idle work optimization equality constraint of formula (2), formula (3), the inequality constraints of formula (4) and formula (1) calculating target function value;

Fitness value is determined according to described target function value and formula (6);

Judge that described fitness value meets convergence criterion, retain elite individual, export genetic algorithm target function value, in the present embodiment, the condition of convergence of convergence criterion be set as continuous 5 generation target function value not reduce and computer algebra is greater than the minimal algebra of setting, be less than the maximum algebraically of setting simultaneously.

Judge that described fitness value does not meet convergence criterion, perform and select operation, interlace operation and mutation operation;

Judge that the repetition individual amount after performing selection operation, interlace operation and mutation operation is less than the half of population quantity, retain the optimal solution that optimum individual generates GA for reactive power optimization Genetic Algorithm Model.

Judge that the repetition individual amount after performing selection operation, interlace operation and mutation operation is not less than the half of population quantity, carry out second mutation, determine the optimal solution of GA for reactive power optimization Genetic Algorithm Model.

Below in conjunction with specific embodiment, a kind of power distribution network real-time reactive power optimization method provided by the invention is described in detail, present embodiments provides a kind of being described as follows containing DG power distribution network real-time reactive power optimization method based on improved adaptive GA-IAGA:

One, the Mathematical Modeling of idle work optimization is set up;

In embodiments of the present invention, set up the idle work optimization of the Mathematical Modeling of idle work optimization target function consider three priority:

First priority: node voltage and DG idle exert oneself not out-of-limit;

Second priority: the device action such as transformer and capacitor number;

3rd priority: system active power loss is minimum.

According to said method, consider that real-time reactive power optimization will meet fail safe, ageing and economy, when system voltage is not out-of-limit, system is failure to actuate, and time out-of-limit, makes its voltage qualified by regulating the variable in division result corresponding to out-of-limit region; Owing to being real-time reactive power optimization, load in frequent variations, in order to reduce the loss of equipment, so total adjustment number of transformer and capacitor will be made as far as possible little; Finally consider that active power loss is minimum.

In embodiments of the present invention, set up real-time reactive power optimization problem target function expression formula be:

$\min f=p_1*\{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{\left(\frac{\mathrm{\&Delta;}{U}_i}{\mathrm{\&Delta;}{U}_{m_i}}\right)}^2+\underset{i=1}{\overset{m}{\mathrm{\&Sigma;}}}{\left(\frac{\mathrm{\&Delta;Q}{G}_i}{\mathrm{\&Delta;}{G}_{m_i}}\right)}^2\}+p_2*t+p_3*\mathrm{\&Delta;P}+\mathrm{\&lambda;\&Delta;}\cos {\mathrm{\&theta;}}_i$

Wherein $\mathrm{\&Delta;}{U}_i=\left\{\begin{array}{cc}{U}_{\min i}-U_i;& U_i<U_{\min i}\\ 0;& U_{\min i}<U_i<U_{\max i}\\ U_{\max i}-U_i;& U_i>U_{\max i}\end{array}\right.$

$\mathrm{\&Delta;}{U}_{m_i}=U_{\max i}-U_{\min i}$

$\mathrm{\&Delta;}{\mathrm{QG}}_i=\left\{\begin{array}{cc}{\mathrm{QG}}_{\min i}-{\mathrm{QG}}_i;& {\mathrm{QG}}_i<{\mathrm{QG}}_{\min i}\\ 0;& {\mathrm{QG}}_{\min i}<{\mathrm{QG}}_i<{\mathrm{QG}}_{\max i}\\ {\mathrm{QG}}_i-Q{G}_{\max i};& {\mathrm{QG}}_i>{\mathrm{QG}}_{\max i}\end{array}\right.$

ΔQG _i＝QG _maxi-QG _mini

$\mathrm{\&Delta;P}=\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{V}_i\underset{j=1}{\overset{n}{\mathrm{\&Sigma;}}}{V}_j(G_{\mathrm{ij}}\cos {\mathrm{\&xi;}}_{\mathrm{ij}}+B_{\mathrm{ij}}\sin {\mathrm{\&xi;}}_{\mathrm{ij}})$

$\min f=p_1*\{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{\left(\frac{\mathrm{\&Delta;}{U}_i}{\mathrm{\&Delta;}{U}_{m_i}}\right)}^2+\underset{i=1}{\overset{m}{\mathrm{\&Sigma;}}}{\left(\frac{\mathrm{\&Delta;Q}{G}_i}{\mathrm{\&Delta;}{G}_{m_i}}\right)}^2\}+p_2*t+p_3*\mathrm{\&Delta;P}+\mathrm{\&lambda;\&Delta;}\cos {\mathrm{\&theta;}}_i$

Wherein, p ₁, p ₂, p ₃for the described priority parameters preset;

N is node total number, and m is the number of distributed power source;

U _i, U _miniand U _maxibe respectively the voltage magnitude of node, minimum permission voltage and maximum permissible voltage;

QG _i, QG _miniand QG _maxirepresent reactive power, idle lower limit and the idle upper limit of exerting oneself of exerting oneself of generator node respectively;

T is the total adjustment number of transformer and capacitor;

ξ _ijfor voltage phase angle;

G _ijand B _ijnode admittance matrix element is drawn for being solved by electrical network parameter.

Δ P is system active power loss.

λ is the penalty function factor, Δ cos θ _iall out-of-limit amounts of power factor connecing the node of distribution transforming.

Owing to adding distribution transforming transformer in 10kV circuit, so also add a penalty function to make to connect the power factor of the node of distribution transforming in the scope of 0.95 to 0.98 in target function.

In the present embodiment, the constraint of above-mentioned target function comprises: equality constraint and inequality constraints.

Concrete, the equality constraint of the real-time reactive power optimization problem in the embodiment of the present invention is:

$P_i=V_i\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{V}_j(G_{\mathrm{ij}}\cos {\mathrm{\&delta;}}_{\mathrm{ij}}+B_{\mathrm{ij}}\sin {\mathrm{\&delta;}}_{\mathrm{ij}})$

$Q_i=V_i\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{V}_j(G_{\mathrm{ij}}\sin {\mathrm{\&delta;}}_{\mathrm{ij}}+B_{\mathrm{ij}}\cos {\mathrm{\&delta;}}_{\mathrm{ij}})$

Wherein, P _ifor effective power flow, Q _ifor reactive power flow, δ _ijfor node voltage differential seat angle; G _ijand B _ijfor node admittance matrix element.

The inequality constraints of real-time reactive power optimization problem is:

$\left\{\begin{array}{c}{T}_{i\min }\&le;T_i\&le;T_{i\max }\\ Q_{\mathrm{ci}\min }\&le;Q_{\mathrm{ci}}\&le;Q_{\mathrm{ci}\max }\\ V_{i\min }\&le;V_i\&le;V_{i\max }\\ Q_{\mathrm{Gi}\min }\&le;Q_{\mathrm{Gi}}\&le;Q_{\mathrm{Gi}\max }\\ {\mathrm{\&eta;}}_{i\min }\&le;{\mathrm{\&eta;}}_i\&le;{\mathrm{\&eta;}}_{i\max }\end{array}\right.$

Wherein, T _iit is the position of controllable load tap changer; V _ithe voltage of i node; Q _ciit is parallel reactive compensation capacity; η _iit is the on high-tension side power factor of node i distribution transforming; Q _giit is generator output.

In embodiments of the present invention, the process of improved adaptive GA-IAGA comprises the coding of genetic algorithm, structure fitness function, selection, intersection and variation and second mutation process and determines.

Optionally, in an embodiment of the present invention, adopt binary coding, individual UVR exposure structure is:

Genetic algorithm encoding is determined:

X＝[V _G1,...,V _GN1|T _K1,...,T _KN2|Q _C1,...,Q _CN2]

Wherein, the precision of set end voltage V is 0.001, to meet its continually varying requirement.V _g1for generator 1 set end voltage, V _gN1for generator N1 set end voltage, T _k1for transformer 1 gear, T _kN2for transformer N2 gear, Q _c1for capacitor 1 reactive compensation capacity, Q _cN2for capacitor N2 reactive compensation capacity.

Have the control variables of two types in real-time reactive power optimization, the set end voltage of a kind of PV of being controllable type DG and idle the exerting oneself of PQ controllable type DG, be continuous variable, another kind is the gear of reactive compensation capacity and adjustable transformer, is discrete controlled variable.

In the embodiment of the present invention, fitness function is:

f _i＝1/F _i

In formula, f _iit is the fitness of individual i; F _iit is the target function value of individual i.

In specific embodiment, the GA for reactive power optimization Genetic Algorithm Model that model building module is set up containing distributed power source according to the power distribution network controling parameters preset and Genetic Algorithm Model comprises:

The target function of the GA for reactive power optimization Genetic Algorithm Model containing distributed power source is set up according to formula (1),

$\min f=p_1*\{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{\left(\frac{\mathrm{\&Delta;}{U}_i}{\mathrm{\&Delta;}{U}_{m_i}}\right)}^2+\underset{i=1}{\overset{m}{\mathrm{\&Sigma;}}}{\left(\frac{\mathrm{\&Delta;Q}{G}_i}{\mathrm{\&Delta;}{G}_{m_i}}\right)}^2\}+p_2*t+p_3*\mathrm{\&Delta;P}+\mathrm{\&lambda;\&Delta;}\cos {\mathrm{\&theta;}}_i---\left(1\right)$

Wherein, $\mathrm{\&Delta;}{U}_i=\left\{\begin{array}{cc}{U}_{\min i}-U_i;& U_i<U_{\min i}\\ 0;& U_{\min i}<U_i<U_{\max i}\\ U_{\max i}-U_i;& U_i>U_{\max i}\end{array}\right.$

$\mathrm{\&Delta;}{U}_{m_i}=U_{\max i}-U_{\min i}$

$\mathrm{\&Delta;}{\mathrm{QG}}_i=\left\{\begin{array}{cc}{\mathrm{QG}}_{\min i}-{\mathrm{QG}}_i;& {\mathrm{QG}}_i<{\mathrm{QG}}_{\min i}\\ 0;& {\mathrm{QG}}_{\min i}<{\mathrm{QG}}_i<{\mathrm{QG}}_{\max i}\\ {\mathrm{QG}}_i-Q{G}_{\max i};& {\mathrm{QG}}_i>{\mathrm{QG}}_{\max i}\end{array}\right.$

ΔQG _i＝QG _maxi-QG _mini

$\mathrm{\&Delta;P}=\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{V}_i\underset{j=1}{\overset{n}{\mathrm{\&Sigma;}}}{V}_j(G_{\mathrm{ij}}\cos {\mathrm{\&xi;}}_{\mathrm{ij}}+B_{\mathrm{ij}}\sin {\mathrm{\&xi;}}_{\mathrm{ij}})$

Wherein, p ₁, p ₂, p ₃for the described priority parameters preset;

N is node total number, and m is the number of distributed power source;

U _i, U _miniand U _maxibe respectively the voltage magnitude of node, minimum permission voltage and maximum permissible voltage;

QG _i, QG _miniand QG _maxirepresent reactive power, idle lower limit and the idle upper limit of exerting oneself of exerting oneself of generator node respectively;

T is the total adjustment number of transformer and capacitor;

ξ _ijfor voltage phase angle;

G _ijand B _ijnode admittance matrix element is drawn for being solved by electrical network parameter.

Δ P is system active power loss.

λ is the penalty function factor, Δ cos θ _iall out-of-limit amounts of power factor connecing the node of distribution transforming.

Owing to adding distribution transforming transformer in 10kV circuit, so also add a penalty function to make to connect the power factor of the node of distribution transforming in the scope of 0.95 to 0.98 in target function.

In specific embodiment, equality constraint determination module carries out Load flow calculation to the described default power distribution network controling parameters containing distributed power source and determines that the equality constraint of the idle work optimization of the GA for reactive power optimization Genetic Algorithm Model containing distributed power source comprises: carry out according to formula (2), formula (3) equality constraint that Load flow calculation determines the idle work optimization of the GA for reactive power optimization Genetic Algorithm Model containing distributed power source;

$P_i=V_i\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{V}_j(G_{\mathrm{ij}}\cos {\mathrm{\&delta;}}_{\mathrm{ij}}+B_{\mathrm{ij}}\sin {\mathrm{\&delta;}}_{\mathrm{ij}})---\left(2\right)$

$Q_i=V_i\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{V}_j(G_{\mathrm{ij}}\sin {\mathrm{\&delta;}}_{\mathrm{ij}}+B_{\mathrm{ij}}\cos {\mathrm{\&delta;}}_{\mathrm{ij}})---\left(3\right)$

Wherein, P _ifor effective power flow

Q _ifor reactive power flow

V _ithe voltage of node i, G _ijand B _ijfor node admittance matrix element, δ _ijfor node voltage differential seat angle.

In specific embodiment, inequality constraints determination module determines the inequality constraints of GA for reactive power optimization Genetic Algorithm Model according to formula (4);

$\left\{\begin{array}{c}{T}_{i\min }\&le;T_i\&le;T_{i\max }\\ Q_{\mathrm{ci}\min }\&le;Q_{\mathrm{ci}}\&le;Q_{\mathrm{ci}\max }\\ V_{i\min }\&le;V_i\&le;V_{i\max }\\ Q_{\mathrm{Gi}\min }\&le;Q_{\mathrm{Gi}}\&le;Q_{\mathrm{Gi}\max }\\ {\mathrm{\&eta;}}_{i\min }\&le;{\mathrm{\&eta;}}_i\&le;{\mathrm{\&eta;}}_{i\max }\end{array}\right.---\left(4\right)$

Wherein, T _iit is the position of controllable load tap changer; V _ithe voltage of node i; Q _ciit is parallel reactive compensation capacity; η _iit is the on high-tension side power factor of node i distribution transforming; Q _giit is generator output.

In specific embodiment, optimum results generation module comprises:

Genetic algorithm computing unit, for carrying out genetic algorithm encoding according to the described GA for reactive power optimization Genetic Algorithm Model containing distributed power source, carrying out constructing fitness function, mutation probability, crossover probability and second mutation and calculating the result of calculation generating fitness function, mutation probability, crossover probability and second mutation;

Optimal solution determining unit, calculates according to the coding of the equality constraint of described default priority parameters, idle work optimization, inequality constraints, Genetic Algorithm Model and crossover probability the optimal solution that the result of calculation generating fitness function, mutation probability, crossover probability and second mutation determines the GA for reactive power optimization Genetic Algorithm Model containing distributed power source;

Optimum results generation unit, the optimal solution according to the described GA for reactive power optimization Genetic Algorithm Model containing distributed power source generates idle work optimization result.

In specific embodiment, genetic algorithm computing unit carries out genetic algorithm encoding according to the described GA for reactive power optimization Genetic Algorithm Model containing distributed power source and comprises:

Adopt genetic algorithm binary coding, the coding structure of generation is formula (5);

X＝[V _G1,...,V _GN1|T _K1,...,T _KN2|Q _C1,...,Q _CN2] (5)

Wherein, V _g1for generator 1 set end voltage, V _gN1for generator N1 set end voltage, T _k1for transformer 1 gear, T _kN2for transformer N2 gear, Q _c1for capacitor 1 reactive compensation capacity, Q _cN2for capacitor N2 reactive compensation capacity.

In specific embodiment, the result of calculation that genetic algorithm computing unit carries out constructing fitness function, mutation probability, crossover probability and second mutation calculating generation fitness function, mutation probability, crossover probability and second mutation comprises:

The calculating of fitness function is carried out according to formula (6),

f _i＝1/F _i(6)

Wherein, f _ifor the fitness of individual i; F _ifor the target function value of individual i;

According to larger fitness value in the individual fitness value of variation, population maximum adaptation angle value, population average fitness value, two individualities that will intersect and be (7), formula (8) calculates described mutation probability and crossover probability;

$P_c=\left\{\begin{array}{cc}\frac{k_1(f_{\max }-f)}{f_{\max }-f_{\mathrm{avg}}},& f\&GreaterEqual;f_{\mathrm{avg}}\\ k_2,& f<f_{\mathrm{avg}}\end{array}\right.---\left(7\right)$

$P_m=\left\{\begin{array}{cc}\frac{k_3(f_{\max }-f^{\&prime;})}{f_{\max }-f_{\mathrm{avg}}},& f^{\&prime;}\&GreaterEqual;f_{\mathrm{avg}}\\ k_4,& f^{\&prime;}<f_{\mathrm{avg}}\end{array}\right.---\left(8\right)$

Wherein, f _avgfor kind of a group mean adaptive value; F' is the fitness value of variation individuality; f _maxfor maximum adaptation value in population; F is fitness value larger in two individualities that will intersect; k ₁, k ₂, k ₃and k ₄for constant.

Second mutation repeats in certain generation the half that individual amount is more than or equal to population number, and these are repeated individual taking-up, only retains one by one that body is in original seed group, and all the other individualities add 1 in last position of each variable, or subtract 1, add deduct and determine at random.This method creates the new individuality near a former individuality, strengthens local optimal searching ability.

In specific embodiment, optimal solution determining unit comprises:

Initialization unit, carries out initialization of population and generates initial population according to the coding structure of formula (5);

Target function value computing unit, according to the idle work optimization equality constraint of formula (2), formula (3), the inequality constraints of formula (4) and formula (1) calculating target function value;

Fitness value calculation unit, for determining fitness value according to described target function value and formula (6);

Judge that described fitness value meets convergence criterion, retain elite individual, export genetic algorithm target function value, the condition of convergence be set as continuous 5 generation target function value not reduce and computer algebra is greater than the minimal algebra of setting, be less than the maximum algebraically of setting simultaneously.

In specific embodiment, optimal solution determining unit also comprises:

Operation execution unit, when described judging unit judges that described fitness value does not meet convergence criterion, for performing selection operation, interlace operation and mutation operation;

Repeating number of individuals judging unit, for judging that the repetition individual amount after performing selection operation, interlace operation and mutation operation is less than the half of population quantity, retaining the optimal solution that optimum individual generates GA for reactive power optimization Genetic Algorithm Model.

In specific embodiment, optimal solution determining unit also comprises:

Second mutation performance element, for judging that the repetition individual amount after performing selection operation, interlace operation and mutation operation is not less than a half of population quantity, carrying out second mutation, determining the optimal solution of GA for reactive power optimization Genetic Algorithm Model.

The present invention proposes power distribution network real-time reactive power optimization compared to existing technologies, the information required during genetic algorithm for solving problem is few, solution procedure is also uncomplicated, can larger probability obtain optimal solution or suboptimal solution, and conveniently add division result, there is more advantages, be widely used in reactive power optimization of power system field.Because real-time reactive power optimization needs guarantee, speed is fast, one aspect of the present invention can jump out local optimum with larger probability compared with rapid convergence to make algorithm, subregion is carried out to system, self-adapted genetic algorithm utilizes existing partition information when forming initial population, adds second mutation to obtain less optimal solution on the other hand.

Those skilled in the art should understand, embodiments of the invention can be provided as method, system or computer program.Therefore, the present invention can adopt the form of complete hardware embodiment, completely software implementation or the embodiment in conjunction with software and hardware aspect.And the present invention can adopt in one or more form wherein including the upper computer program implemented of computer-usable storage medium (including but not limited to magnetic disc store, CD-ROM, optical memory etc.) of computer usable program code.

The present invention describes with reference to according to the flow chart of the method for the embodiment of the present invention, equipment (system) and computer program and/or block diagram.Should understand can by the combination of the flow process in each flow process in computer program instructions realization flow figure and/or block diagram and/or square frame and flow chart and/or block diagram and/or square frame.These computer program instructions can being provided to the processor of all-purpose computer, special-purpose computer, Embedded Processor or other programmable data processing device to produce a machine, making the instruction performed by the processor of computer or other programmable data processing device produce device for realizing the function of specifying in flow chart flow process or multiple flow process and/or block diagram square frame or multiple square frame.

These computer program instructions also can be stored in can in the computer-readable memory that works in a specific way of vectoring computer or other programmable data processing device, the instruction making to be stored in this computer-readable memory produces the manufacture comprising command device, and this command device realizes the function of specifying in flow chart flow process or multiple flow process and/or block diagram square frame or multiple square frame.

These computer program instructions also can be loaded in computer or other programmable data processing device, make on computer or other programmable devices, to perform sequence of operations step to produce computer implemented process, thus the instruction performed on computer or other programmable devices is provided for the step realizing the function of specifying in flow chart flow process or multiple flow process and/or block diagram square frame or multiple square frame.

Above-described specific embodiment; object of the present invention, technical scheme and beneficial effect are further described; be understood that; the foregoing is only specific embodiments of the invention; the protection range be not intended to limit the present invention; within the spirit and principles in the present invention all, any amendment made, equivalent replacement, improvement etc., all should be included within protection scope of the present invention.

Claims (20)

1., containing a var Optimization Method in Network Distribution for distributed power source, it is characterized in that, described method comprises:

Step 1, sets up the GA for reactive power optimization Genetic Algorithm Model containing distributed power source according to the power distribution network controling parameters containing distributed power source preset and Genetic Algorithm Model;

Step 2, carries out to the described default power distribution network controling parameters containing distributed power source the equality constraint that Load flow calculation determines the idle work optimization of the GA for reactive power optimization Genetic Algorithm Model containing distributed power source;

Step 3, determines the inequality constraints of the GA for reactive power optimization Genetic Algorithm Model containing distributed power source according to the described position containing the controllable load tap changer in the power distribution network controling parameters of distributed power source, each node voltage, parallel reactive compensation capacity generator output and the on high-tension side power factor of each node distribution transforming;

Step 4, generates idle work optimization result according to the priority parameters preset, the equality constraint of described idle work optimization, inequality constraints and GA for reactive power optimization Genetic Algorithm Model.

2. the var Optimization Method in Network Distribution containing distributed power source as claimed in claim 1, it is characterized in that, the power distribution network controling parameters containing distributed power source that described basis is preset and the GA for reactive power optimization Genetic Algorithm Model that Genetic Algorithm Model is set up containing distributed power source comprise:

The target function of the GA for reactive power optimization Genetic Algorithm Model containing distributed power source is set up according to formula (1),

$\min f=p_1*\{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{\left(\frac{{\mathrm{\&Delta;U}}_i}{{\mathrm{\&Delta;U}}_{m_i}}\right)}^2+\underset{i=1}{\overset{m}{\mathrm{\&Sigma;}}}{\left(\frac{{\mathrm{\&Delta;QG}}_i}{{\mathrm{\&Delta;QG}}_{m_i}}\right)}^2\}+p_2*t+p_3*\mathrm{\&Delta;P}+\mathrm{\&lambda;\&Delta;}\cos {\mathrm{\&theta;}}_i---\left(1\right)$

Wherein, ${\mathrm{\&Delta;U}}_i=\left\{\begin{array}{c}{U}_{\min i}-U_i;U_i<U_{\min i}\\ 0;U_{\min i}<U_i<U_{\max i}\\ U_{\max i}-U_i;U_i>U_{\max i}\end{array}\right.$

${\mathrm{\&Delta;U}}_{m_i}=U_{\max i}-U_{\min i}$

${\mathrm{\&Delta;QG}}_i=\left\{\begin{array}{c}{\mathrm{QG}}_{\min i}-{\mathrm{QG}}_i;{\mathrm{QG}}_i<{\mathrm{QG}}_{\min i}\\ 0;{\mathrm{QG}}_{\min i}<{\mathrm{QG}}_i<{\mathrm{QG}}_{\max i}\\ {\mathrm{QG}}_i-{\mathrm{QG}}_{\max i};{\mathrm{QG}}_i>{\mathrm{QG}}_{\max i}\end{array}\right.$

ΔQG _i＝QG _maxi-QG _mini

$\mathrm{\&Delta;P}=\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{V}_i\underset{j=1}{\overset{n}{\mathrm{\&Sigma;}}}{V}_j(G_{\mathrm{ij}}\cos {\mathrm{\&xi;}}_{\mathrm{ij}}+B_{\mathrm{ij}}\sin {\mathrm{\&xi;}}_{\mathrm{ij}})$

Wherein, p ₁, p ₂, p ₃for the described priority parameters preset;

N is node total number, and m is the number of distributed power source;

U _i, U _miniand U _maxibe respectively the voltage magnitude of node, minimum permission voltage and maximum permissible voltage;

QG _i, QG _miniand QG _maxirepresent reactive power, idle lower limit and the idle upper limit of exerting oneself of exerting oneself of generator node respectively;

T is the total adjustment number of transformer and capacitor;

ξ _ijfor voltage phase angle;

G _ijand B _ijfor the egress admittance matrix element solved by electrical network parameter;

Δ P is system active power loss;

λ is the penalty function factor, Δ cos θ _iall out-of-limit amounts of power factor connecing the node of distribution transforming.

3. the var Optimization Method in Network Distribution containing distributed power source as claimed in claim 2, it is characterized in that, described step 2 is carried out Load flow calculation to the described default power distribution network controling parameters containing distributed power source and is determined that the equality constraint of the idle work optimization of the GA for reactive power optimization Genetic Algorithm Model containing distributed power source comprises: carry out according to formula (2), formula (3) equality constraint that Load flow calculation determines the idle work optimization of the GA for reactive power optimization Genetic Algorithm Model containing distributed power source;

$P_i=V_i\underset{j=1}{\overset{n}{\mathrm{\&Sigma;}}}{V}_j(G_{\mathrm{ij}}\cos {\mathrm{\&delta;}}_{\mathrm{ij}}+B_{\mathrm{ij}}\sin {\mathrm{\&delta;}}_{\mathrm{ij}})---\left(2\right)$

$Q_i=V_i\underset{j=1}{\overset{n}{\mathrm{\&Sigma;}}}{V}_j(G_{\mathrm{ij}}\sin {\mathrm{\&delta;}}_{\mathrm{ij}}-B_{\mathrm{ij}}\cos {\mathrm{\&delta;}}_{\mathrm{ij}})---\left(3\right)$

Wherein, P _ifor effective power flow, Q _ifor reactive power flow, V _ithe voltage of node i, G _ijand B _ijfor the node admittance matrix element solved by electrical network parameter, δ _ijfor node voltage differential seat angle.

4. the var Optimization Method in Network Distribution containing distributed power source as claimed in claim 2, it is characterized in that, described step 3 is determined to comprise containing the inequality constraints of the GA for reactive power optimization Genetic Algorithm Model of distributed power source the inequality constraints determining the GA for reactive power optimization Genetic Algorithm Model containing distributed power source according to formula (4);

$\left\{\begin{array}{c}{T}_{i\min }\&le;T_i\&le;T_{i\max }\\ Q_{\mathrm{ci}\min }\&le;Q_{\mathrm{ci}}\&le;Q_{\mathrm{ci}\max }\\ V_{i\min }\&le;V_i\&le;V_{i\max }\\ Q_{\mathrm{Gi}\min }\&le;Q_{\mathrm{Gi}}\&le;Q_{\mathrm{Gi}\max }\\ {\mathrm{\&eta;}}_{i\min }\&le;{\mathrm{\&eta;}}_i\&le;{\mathrm{\&eta;}}_{i\max }\end{array}\right.---\left(4\right)$

Wherein, T _iit is the position of controllable load tap changer; V _ithe voltage of node i; Q _ciit is parallel reactive compensation capacity; η _iit is the on high-tension side power factor of node i distribution transforming; Q _giit is generator output.

5. the var Optimization Method in Network Distribution containing distributed power source as described in claim 3 or 4, it is characterized in that, described step 4 generates idle work optimization result according to the priority parameters preset, the equality constraint of described idle work optimization, inequality constraints and the GA for reactive power optimization Genetic Algorithm Model containing distributed power source and comprises:

Carry out genetic algorithm encoding according to the described GA for reactive power optimization Genetic Algorithm Model containing distributed power source, carry out constructing fitness function, mutation probability, crossover probability and second mutation and calculate the result of calculation generating fitness function, mutation probability, crossover probability and second mutation;

The optimal solution that the result of calculation generating fitness function, mutation probability, crossover probability and second mutation determines the GA for reactive power optimization Genetic Algorithm Model containing distributed power source is calculated according to the coding of the equality constraint of described default priority parameters, idle work optimization, inequality constraints, Genetic Algorithm Model and crossover probability;

Idle work optimization result is generated according to the described optimal solution containing distributed power source GA for reactive power optimization Genetic Algorithm Model.

6. as claimed in claim 5 to it is characterized in that containing distributed power source var Optimization Method in Network Distribution, described carry out genetic algorithm encoding according to the described GA for reactive power optimization Genetic Algorithm Model containing distributed power source and comprise:

Adopt genetic algorithm binary coding, the coding structure of generation is formula (5);

X＝[V _G1,...,V _GN1|T _K1,...,T _KN2|Q _C1,...,Q _CN2] (5)

Wherein, V _g1for generator 1 set end voltage, V _gN1for generator N1 set end voltage, T _k1for transformer 1 gear, T _kN2for transformer N2 gear, Q _c1for capacitor 1 reactive compensation capacity, Q _cN2for capacitor N2 reactive compensation capacity.

7. the var Optimization Method in Network Distribution containing distributed power source as claimed in claim 6, it is characterized in that, described carrying out structure fitness function, mutation probability, crossover probability and second mutation calculate the result of calculation generating fitness function, mutation probability, crossover probability and second mutation and comprise:

The calculating of fitness function is carried out according to formula (6),

f _i＝1/F _i(6)

Wherein, f _ifor the fitness of individual i; F _ifor the target function value of individual i;

According to larger fitness value in the individual fitness value of variation, population maximum adaptation angle value, population average fitness value, two individualities that will intersect and be (7), formula (8) calculates described mutation probability and crossover probability;

$P_c=\left\{\begin{array}{c}\frac{k_1(f_{\max }-f)}{f_{\max }-f_{\mathrm{avg}}},f\&GreaterEqual;f_{\mathrm{avg}}\\ k_2,f<f_{\mathrm{avg}}\end{array}\right.---\left(7\right)$

$P_m=\left\{\begin{array}{c}\frac{k_3(f_{\max }-f^{\&prime;})}{f_{\max }-f_{\mathrm{avg}}},f^{\&prime;}\&GreaterEqual;f_{\mathrm{avg}}\\ k_4,f^{\&prime;}<f_{\mathrm{avg}}\end{array}\right.---\left(8\right)$

Wherein, f _avgfor kind of a group mean adaptive value; F' is the fitness value of variation individuality; f _maxfor maximum adaptation value in population; F is fitness value larger in two individualities that will intersect; k ₁, k ₂, k ₃and k ₄for constant.

8. the var Optimization Method in Network Distribution containing distributed power source as claimed in claim 7, it is characterized in that, the optimal solution that the coding of the described equality constraint according to described default priority parameters, idle work optimization, inequality constraints, Genetic Algorithm Model and crossover probability calculate the GA for reactive power optimization Genetic Algorithm Model that the result of calculation generating fitness function, mutation probability, crossover probability and second mutation is determined containing distributed power source comprises:

Initialization of population also generates initial population according to the coding structure of formula (5);

According to the idle work optimization equality constraint of formula (2), formula (3), the inequality constraints of formula (4) and formula (1) calculating target function value;

Fitness value is determined according to described target function value and formula (6);

Judge whether described fitness value meets convergence criterion, wherein, the condition of convergence of described convergence criterion be set as continuous 5 generation target function value not reduce and computer algebra is greater than the minimal algebra pre-set, be less than the maximum algebraically pre-set simultaneously;

Judge that described fitness value meets convergence criterion, retain optimum individual and export target function value.

9. the var Optimization Method in Network Distribution containing distributed power source as claimed in claim 8, it is characterized in that, described method also comprises:

Judge that described fitness value does not meet convergence criterion, perform and select operation, interlace operation and mutation operation;

Judge that the repetition individual amount after performing selection operation, interlace operation and mutation operation is less than the half of population quantity, retain the optimal solution that optimum individual generates the GA for reactive power optimization Genetic Algorithm Model containing distributed power source.

10. the var Optimization Method in Network Distribution containing distributed power source as claimed in claim 9, it is characterized in that, described method also comprises:

Judge that the repetition individual amount after performing selection operation, interlace operation and mutation operation is not less than the half of population quantity, carry out second mutation, determine the optimal solution of the GA for reactive power optimization Genetic Algorithm Model containing distributed power source.

11. 1 kinds of GA for reactive power optimization devices containing distributed power source, it is characterized in that, described device comprises:

Model building module, for setting up the GA for reactive power optimization Genetic Algorithm Model containing distributed power source according to the power distribution network controling parameters preset and Genetic Algorithm Model;

Equality constraint determination module, for carrying out to the described default power distribution network controling parameters containing distributed power source the equality constraint that Load flow calculation determines the idle work optimization of the GA for reactive power optimization Genetic Algorithm Model containing distributed power source;

Inequality constraints determination module, for determining the inequality constraints of the GA for reactive power optimization Genetic Algorithm Model containing distributed power source according to the described position containing the controllable load tap changer in the power distribution network controling parameters of distributed power source, each node voltage, parallel reactive compensation capacity generator output and the on high-tension side power factor of each node distribution transforming;

Optimum results generation module, generates idle work optimization result for the equality constraint according to the priority parameters preset, described idle work optimization, inequality constraints and the GA for reactive power optimization Genetic Algorithm Model containing distributed power source.

12. as claimed in claim 11 containing the GA for reactive power optimization device of distributed power source, it is characterized in that, described model building module is set up GA for reactive power optimization Genetic Algorithm Model according to the power distribution network controling parameters containing distributed power source preset and Genetic Algorithm Model and is comprised:

The target function of GA for reactive power optimization Genetic Algorithm Model is set up according to formula (1),

$\min f=p_1*\{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{\left(\frac{{\mathrm{\&Delta;U}}_i}{{\mathrm{\&Delta;U}}_{m_i}}\right)}^2+\underset{i=1}{\overset{m}{\mathrm{\&Sigma;}}}{\left(\frac{{\mathrm{\&Delta;QG}}_i}{{\mathrm{\&Delta;QG}}_{m_i}}\right)}^2\}+p_2*t+p_3*\mathrm{\&Delta;P}+\mathrm{\&lambda;\&Delta;}\cos {\mathrm{\&theta;}}_i---\left(1\right)$

Wherein, ${\mathrm{\&Delta;U}}_i=\left\{\begin{array}{c}{U}_{\min i}-U_i;U_i<U_{\min i}\\ 0;U_{\min i}<U_i<U_{\max i}\\ U_{\max i}-U_i;U_i>U_{\max i}\end{array}\right.$

${\mathrm{\&Delta;U}}_{m_i}=U_{\max i}-U_{\min i}$

${\mathrm{\&Delta;QG}}_i=\left\{\begin{array}{c}{\mathrm{QG}}_{\min i}-{\mathrm{QG}}_i;{\mathrm{QG}}_i<{\mathrm{QG}}_{\min i}\\ 0;{\mathrm{QG}}_{\min i}<{\mathrm{QG}}_i<{\mathrm{QG}}_{\max i}\\ {\mathrm{QG}}_i-{\mathrm{QG}}_{\max i};{\mathrm{QG}}_i>{\mathrm{QG}}_{\max i}\end{array}\right.$

ΔQG _i＝QG _maxi-QG _mini

$\mathrm{\&Delta;P}=\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{V}_i\underset{j=1}{\overset{n}{\mathrm{\&Sigma;}}}{V}_j(G_{\mathrm{ij}}\cos {\mathrm{\&xi;}}_{\mathrm{ij}}+B_{\mathrm{ij}}\sin {\mathrm{\&xi;}}_{\mathrm{ij}})$

Wherein, p ₁, p ₂, p ₃for the described priority parameters preset;

N is node total number, and m is the number of distributed power source;

U _i, U _miniand U _maxibe respectively the voltage magnitude of node, minimum permission voltage and maximum permissible voltage;

QG _i, QG _miniand QG _maxirepresent reactive power, idle lower limit and the idle upper limit of exerting oneself of exerting oneself of generator node respectively;

T is the total adjustment number of transformer and capacitor;

ξ _ijfor voltage phase angle;

G _ijand B _ijnode admittance matrix element is drawn for being solved by electrical network parameter;

Δ P is system active power loss;

λ is the penalty function factor; Δ cos θ _iall out-of-limit amounts of power factor connecing the node of distribution transforming.

13. as claimed in claim 12 containing the GA for reactive power optimization device of distributed power source, it is characterized in that, described equality constraint determination module carries out Load flow calculation to described default power distribution network controling parameters and determines that the equality constraint of the idle work optimization of the GA for reactive power optimization Genetic Algorithm Model containing distributed power source comprises: carry out according to formula (2), formula (3) equality constraint that Load flow calculation determines the idle work optimization of the GA for reactive power optimization Genetic Algorithm Model containing distributed power source;

$P_i=V_i\underset{j=1}{\overset{n}{\mathrm{\&Sigma;}}}{V}_j(G_{\mathrm{ij}}\cos {\mathrm{\&delta;}}_{\mathrm{ij}}+B_{\mathrm{ij}}\sin {\mathrm{\&delta;}}_{\mathrm{ij}})---\left(2\right)$

$Q_i=V_i\underset{j=1}{\overset{n}{\mathrm{\&Sigma;}}}{V}_j(G_{\mathrm{ij}}\sin {\mathrm{\&delta;}}_{\mathrm{ij}}-B_{\mathrm{ij}}\cos {\mathrm{\&delta;}}_{\mathrm{ij}})---\left(3\right)$

Wherein, P _ifor effective power flow, Q _ifor reactive power flow, V _ithe voltage of node i, G _ijand B _ijfor node admittance matrix element, δ _ijfor node voltage differential seat angle.

14. as claimed in claim 12 containing the GA for reactive power optimization device of distributed power source, it is characterized in that, described inequality constraints determination module determines the inequality constraints of the GA for reactive power optimization Genetic Algorithm Model containing distributed power source according to formula (4);

$\left\{\begin{array}{c}{T}_{i\min }\&le;T_i\&le;T_{i\max }\\ Q_{\mathrm{ci}\min }\&le;Q_{\mathrm{ci}}\&le;Q_{\mathrm{ci}\max }\\ V_{i\min }\&le;V_i\&le;V_{i\max }\\ Q_{\mathrm{Gi}\min }\&le;Q_{\mathrm{Gi}}\&le;Q_{\mathrm{Gi}\max }\\ {\mathrm{\&eta;}}_{i\min }\&le;{\mathrm{\&eta;}}_i\&le;{\mathrm{\&eta;}}_{i\max }\end{array}\right.---\left(4\right)$

Wherein, T _iit is the position of controllable load tap changer; V _ithe voltage of node i; Q _ciit is parallel reactive compensation capacity; η _iit is the on high-tension side power factor of node i distribution transforming; Q _giit is generator output.

15. as described in claim 13 or 14 containing the GA for reactive power optimization device of distributed power source, it is characterized in that, described optimum results generation module comprises:

Genetic algorithm computing unit, for carrying out genetic algorithm encoding according to the described GA for reactive power optimization Genetic Algorithm Model containing distributed power source, carrying out constructing fitness function, mutation probability, crossover probability and second mutation and calculating the result of calculation generating fitness function, mutation probability, crossover probability and second mutation;

Optimal solution determining unit, calculates according to the coding of the equality constraint of described default priority parameters, idle work optimization, inequality constraints, Genetic Algorithm Model and crossover probability the optimal solution that the result of calculation generating fitness function, mutation probability, crossover probability and second mutation determines the GA for reactive power optimization Genetic Algorithm Model containing distributed power source;

Optimum results generation unit, the optimal solution according to the described GA for reactive power optimization Genetic Algorithm Model containing distributed power source generates idle work optimization result.

16., as claimed in claim 15 containing the GA for reactive power optimization device of distributed power source, is characterized in that, described genetic algorithm computing unit carries out genetic algorithm encoding according to the described GA for reactive power optimization Genetic Algorithm Model containing distributed power source and comprises:

Adopt genetic algorithm binary coding, the coding structure of generation is formula (5);

X＝[V _G1,...,V _GN1|T _K1,...,T _KN2|Q _C1,...,Q _CN2] (5)

Wherein, V _g1for generator 1 set end voltage, V _gN1for generator N1 set end voltage, T _k1for transformer 1 gear, T _kN2for transformer N2 gear, Q _c1for capacitor 1 reactive compensation capacity, Q _cN2for capacitor N2 reactive compensation capacity.

17. as claimed in claim 16 containing the GA for reactive power optimization device of distributed power source, it is characterized in that, the result of calculation that described genetic algorithm computing unit carries out constructing fitness function, mutation probability, crossover probability and second mutation calculating generation fitness function, mutation probability, crossover probability and second mutation comprises:

The calculating of fitness function is carried out according to formula (6),

f _i＝1/F _i(6)

Wherein, f _ifor the fitness of individual i; F _ifor the target function value of individual i;

According to larger fitness value in the individual fitness value of variation, population maximum adaptation angle value, population average fitness value, two individualities that will intersect and be (7), formula (8) calculates described mutation probability and crossover probability;

$P_c=\left\{\begin{array}{c}\frac{k_1(f_{\max }-f)}{f_{\max }-f_{\mathrm{avg}}},f\&GreaterEqual;f_{\mathrm{avg}}\\ k_2,f<f_{\mathrm{avg}}\end{array}\right.---\left(7\right)$

$P_m=\left\{\begin{array}{c}\frac{k_3(f_{\max }-f^{\&prime;})}{f_{\max }-f_{\mathrm{avg}}},f^{\&prime;}\&GreaterEqual;f_{\mathrm{avg}}\\ k_4,f^{\&prime;}<f_{\mathrm{avg}}\end{array}\right.---\left(8\right)$

Wherein, f _avgfor kind of a group mean adaptive value; F' is the fitness value of variation individuality; f _maxfor maximum adaptation value in population; F is fitness value larger in two individualities that will intersect; k ₁, k ₂, k ₃and k ₄for constant.

18. as claimed in claim 17 containing the GA for reactive power optimization device of distributed power source, and it is characterized in that, described optimal solution determining unit comprises:

Initialization unit, carries out initialization of population and generates initial population according to the coding structure of formula (5);

Target function value computing unit, according to the idle work optimization equality constraint of formula (2), formula (3), the inequality constraints of formula (4) and formula (1) calculating target function value;

Fitness value calculation unit, for determining fitness value according to described target function value and formula (6);

Judge whether described fitness value meets convergence criterion, wherein, the condition of convergence of convergence criterion be set as continuous 5 generation target function value not reduce and computer algebra is greater than the minimal algebra pre-set, be less than the maximum algebraically pre-set simultaneously;

Judge that described fitness value meets convergence criterion, retain optimum individual and export target function value.

19. as claimed in claim 18 containing the GA for reactive power optimization device of distributed power source, and it is characterized in that, described optimal solution determining unit also comprises:

Operation execution unit, when described judging unit judges that described fitness value does not meet convergence criterion, for performing selection operation, interlace operation and mutation operation;

Repeat number of individuals judging unit, for judging that the repetition individual amount after performing selection operation, interlace operation and mutation operation is less than the half of population quantity, retain the optimal solution that optimum individual generates the GA for reactive power optimization Genetic Algorithm Model containing distributed power source.

20. as claimed in claim 19 containing the GA for reactive power optimization device of distributed power source, and it is characterized in that, described optimal solution determining unit also comprises:

Second mutation performance element, for judging that the repetition individual amount after performing selection operation, interlace operation and mutation operation is not less than a half of population quantity, carry out second mutation, determine the optimal solution of the GA for reactive power optimization Genetic Algorithm Model containing distributed power source.