CN104484832A - Method for evaluating total supplying capability of 220KV Lashou net - Google Patents

Abstract

The invention provides a method for evaluating the total supplying capability of a 220KV Lashou net. The method comprises the following steps of obtaining a space truss structure of the 220KV Lashou net, determining the network node number and the branch number of the space truss structure, and numbering the nodes and the branches of the space truss structure; reading the parameter and the load parameter of the space truss structure as well as the voltage amplitude and the phase angle of a balance node for the numbered space truss structure of the 220KV Lashou net; according to the constraint of the preset node voltage Vi and the phase angle difference deltaij as well as a PQnode and a balance node, obtaining an initialized Newton-Ralfson's method; obtaining an initialized self-adaptive differential evolutionary algorithm; according to the Newton-Ralfson's method, solving the tidal current of the 220KV Lashou net, and processing the tidal current by the self-adaptive differential evolutionary algorithm to obtain the total supplying capability of 220KV Lashou net under a N-1 constraint. According to the method, a district can be taken as a minimum unit to evaluate the total supplying capability, and the total supplying capability of the 220KV Lashou net can be quickly and accurately obtained when the N-1 condition is satisfied.

Description

The method of assessment 220KV handle net net capability

Technical field

The present invention relates to mains supply enabling technology field, particularly relate to a kind of method assessing 220KV handle net net capability.

Background technology

Along with China's expanding economy, living standards of the people improve constantly, and the demand for electric energy also constantly increases thereupon.The growth of electric load is obviously accelerated, and causes the quality of power supply, power supply capacity and power supply reliability to can not meet the electricity needs of user, defines bottleneck of much powering.And urban distribution network has substantially built up at present, if the underpass wanting the site and new feeder line obtaining new transformer station from the planning and reconstruction of system is very difficult.Therefore, when not building the underpass in new power station and feeder line, the net capability of research electrical network becomes very important.

Electrical network net capability (Total Supplying Capacity, TSC) refers to that in certain power supply area, electrical network meets N-1 safety criterion, and considers the peak load deliverability under network practical operation situation.Solve the linear law of planning of common method of electrical network net capability, interior point method, hit-and-miss method and peak load method of multiplicity etc.Compared with net capability, available transmission capacity (Available Transfer Capacity, ATC) refers on existing transmission of electricity Contract basis, remaining in actual physics power transmission network, to can be used for business use transmission capacity.TSC it is emphasised that electrical network to meet under certain constraint can with peak load, what ATC paid close attention to is deduct basic trend and adequate allowance on the basis of maximum transmitted ability in power transmission network after, the maximum power also can transmitted.

At present, the linear law of planning of the common method of electrical network net capability, hit-and-miss method and peak load method of multiplicity etc. are determined.

Linear programming technique, based on DC flow model, does not consider impact that is idle and voltage, so can affect accuracy and the validity of result; Hit-and-miss method solution procedure is quite consuming time, and the accuracy of result is difficult to ensure; Peak load method of multiplicity solving speed is fast, but it supposes that each node load all increases with same ratio, and accuracy can decrease.

Summary of the invention

Based on this, the invention provides a kind of method assessing 220KV handle net net capability, can be that least unit is assessed net capability with section, meet under N-1 condition, determine the net capability of 220KV handle net fast and accurately.

Assess a method for 220KV handle net net capability, comprise the steps:

Obtain the grid structure of 220KV handle net, determine number of network node and the circuitry number of described grid structure, and the node of described grid structure and branch road are numbered; Wherein, the balance node of 220kV handle net is the 220kV side of 500kV transforming plant main transformer, and PQ node is the load bus in 220kV handle net;

To the grid structure of the 220KV handle net after numbering, read the voltage magnitude V of grid structure parameter, load parameter and balance node _nand phase angle theta _n;

According to the node voltage V preset _iwith phase angle difference δ _ijconstraint, and described PQ node and balance node, obtain the Newton-Laphson method after initialization;

According to the initialization of each node load, the initial population preset carried out to population X, default differential evolution algorithm maximum iteration time Gm, self adaptive pantographic factor minimum value F0 that group expanding process obtains _minwith maximal value F0 _max, adaptive crossover mutation factor minimum value CR _minwith maximal value CR _max, obtain the adaptive differential evolution algorithm after initialization; Wherein, according to following formula, the mean value initialization of node load being got to each load upper lower limit value generates initial population: in formula s _i be respectively maximal value and the minimum value of the nominal load of node i;

Solve the trend of described 220KV handle net according to described Newton-Laphson method, by described adaptive differential evolution algorithm, described trend is processed, obtain the net capability that the described 220KV handle under N-1 constraint is netted.

The method of above-mentioned assessment 220KV handle net net capability, embedded Newton-Laphson method in adaptive differential evolvement method, the effect of embedded Newton-Laphson method is the trend of Exact Solution handle net, the effect of adaptive differential evolution algorithm processes the trend that Newton-Laphson method is obtained fast, to reach under N-1 constraint, obtain the net capability of handle net fast and accurately; Can be that least unit calculates net capability with section, the method has the features such as good stability, precision is high, ability of searching optimum is strong.

Accompanying drawing explanation

Fig. 1 is the method schematic flow sheet in one embodiment that the present invention assesses 220KV handle net net capability.

Fig. 2 is a kind of typical handle net schematic diagram.

Embodiment

Below in conjunction with embodiment and accompanying drawing, the present invention is described in further detail, but embodiments of the present invention are not limited thereto.

As shown in Figure 1, be a kind of method assessing 220KV handle net net capability of the present invention, comprise the steps:

S11, obtain the grid structure of 220KV handle net, determine number of network node and the circuitry number of described grid structure, and the node of described grid structure and branch road are numbered; Wherein, the balance node of 220kV handle net is the 220kV side of 500kV transforming plant main transformer, and PQ node is the load bus in 220kV handle net;

S12, the grid structure of netting the 220KV handle after numbering, read the voltage magnitude V of grid structure parameter, load parameter and balance node _nand phase angle theta _n;

The node voltage V that S13, basis are preset _iwith phase angle difference δ _ijconstraint, and described PQ node and balance node, obtain the Newton-Laphson method after initialization;

S14, according to the initialization of each node load, the initial population preset carried out to population X, default differential evolution algorithm maximum iteration time Gm, self adaptive pantographic factor minimum value F0 that group expanding process obtains _minwith maximal value F0 _max, adaptive crossover mutation factor minimum value CR _minwith maximal value CR _max, obtain the adaptive differential evolution algorithm after initialization; Wherein, according to following formula, the mean value initialization of node load being got to each load upper lower limit value generates initial population: in formula s _i be respectively maximal value and the minimum value of the nominal load of node i;

S15, to solve described 220KV according to described Newton-Laphson method and to shake hands the trend of net, by described adaptive differential evolution algorithm, described trend is processed, obtain the net capability that the lower described 220KV handle of N-1 constraint is netted;

Net capability, refers to that in certain power supply area, electrical network meets N-1 safety criterion, and considers the peak load deliverability under network practical operation situation.The calculating 220KV handle net net capability method of the present embodiment, it is the adaptive differential evolvement method of embedded Newton-Laphson method, the effect of embedded Newton-Laphson method is the trend of Exact Solution handle net, the effect of adaptive differential evolvement method processes the trend that Newton-Laphson method is obtained fast, to reach under N-1 constraint, obtain the net capability of handle net fast and accurately.

Concrete:

One, 220kV typical case handle web frame is refined.In 220kV handle net, its higher level's mains supply point is generally two, is generally the 220kV side of 500kV transforming plant main transformer, is generally regarded as balance node; Other load buses generally regard PQ node as.Determine number of network node and circuitry number in rack, and from left to right number successively;

Wherein, described handle net, refers to a kind of topological structure of the electrical network formed in hand-in-hand mode, as shown in Figure 2, shows a kind of typical handle net schematic diagram.

Two, the rack data of input simplification.Read 220kV handle grid structure parameter (i.e. each branch resistance R _i, reactance X _i, susceptance value B over the ground _iand maximum carrying capacity I _max), load parameter (the peak load value of each load point and minimal negative charge values sd _i ) and the given voltage magnitude V of balance node _nand phase angle theta _n.

Three, Newton-Laphson method initialization.Key step comprises node voltage V _iwith phase angle difference δ _ijthe setting of constraint and the identification etc. to PQ node and balance node.

At handle net owing to there are two balance node, simultaneously in order to ensure the accuracy calculated, Newton-Laphson method is adopted to calculate trend.In computation process, the load iteration of 3 220KV just can restrain for 10 times, and the error calculated is almost nil.Use the step of Newton-Laphson method calculating trend as follows:

Step 21, input raw data: as inputted voltage Vn and phase angle theta n, the parameter of rack and the constraint of voltage constraint and phase angle difference of the 220kV side of two 500KV transforming plant main transformers;

Step 22, formation bus admittance matrix, and according to N-1 fault criteria, bus admittance matrix is modified;

Step 23, calculate each node power amount of unbalance $\left[\begin{array}{c}\mathrm{\ΔP}\\ \mathrm{\ΔQ}\end{array}\right]$ (in formula, Δ P, Δ Q distinguish active power deviation and the reactive power deviation of dactylus point), judges whether strength of current deviation meets the condition of convergence; As met, then jumping to step 26, if do not met, then carry out step 24; Wherein, the computing formula of trend deviation is as follows:

$\begin{array}{c}{\mathrm{\ΔP}}_i=P_{\mathrm{is}}-V_i\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{V}_j(G_{\mathrm{ij}}\cos {\mathrm{\θ}}_{\mathrm{ij}}+B_{\mathrm{ij}}\sin {\mathrm{\θ}}_{\mathrm{ij}}),i=\mathrm{1,2},...,n-1\\ {\mathrm{\ΔQ}}_i=Q_{\mathrm{is}}-V_i\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{V}_j(G_{\mathrm{ij}}\sin {\mathrm{\θ}}_{\mathrm{ij}}-B_{\mathrm{ij}}\cos {\mathrm{\θ}}_{\mathrm{ij}}),i=\mathrm{1,2},...,m\end{array}$ (P _is, Q _isbe respectively the given active power of i-th node and reactive power; V _i, V _jbe respectively the voltage of i-th node and a jth node; G _ij, B _ijbe respectively from node i to the conductance of node j branch road and susceptance; θ _ijphase angle difference for node i and node j);

Step 24, generate Load flow calculation Jacobi matrix J by the variable inputted and existing bus admittance matrix:

$J=\left[\begin{array}{cc}H& N\\ K& L\end{array}\right];$

Wherein, H is (n-1) rank square formations, and its element is n is (n-1) × m rank matrixes, and its element is k is m × (n-1) rank matrix, and its element is l is m rank square formations, and its element is $L_{\mathrm{ij}}=V_j\frac{\∂\mathrm{\Δ}{Q}_i}{\∂V_j};$

Step 25, solve linear revise system of equations $-\left[\begin{array}{c}\mathrm{\ΔP}\\ \mathrm{\ΔQ}\end{array}\right]=\left[\begin{array}{cc}H& N\\ K& L\end{array}\right]\left[\begin{array}{c}\mathrm{\Δ\θ}\\ V_D^{-1}\mathrm{\ΔV}\end{array}\right],$ Wherein obtain the correction amount θ of each node voltage amplitude and phase angle, Δ V, upgrade node voltage, jump to step 23;

Step 26, be calculated as follows all branch powers:

$S_{\mathrm{ij}}=V_i^2{\tilde{y}}_{i0}+{\stackrel{\·}{V}}_i({\tilde{V}}_i-{\tilde{V}}_j){\tilde{y}}_{\mathrm{ij}}$

Wherein, i is the first node of branch road, and j is branch road end-node, and tilde represents the conjugate getting plural number.

Four, the initialization of adaptive differential algorithm (DE).Main step comprises the initialization of each node load and carries out group expanding process to initial population and obtain population X, the setting of differential evolution algorithm maximum iteration time Gm, self adaptive pantographic factor minimum value F0 _minwith maximal value F0 _maxsetting, adaptive crossover mutation factor minimum value CR _minwith maximal value CR _maxisoparametric.Wherein, the mean value generation initial population of each load upper lower limit value is got in the initialization of node load, and concrete formula is: in formula s _i be respectively maximal value and the minimum value of the nominal load of node i;

To the population at individual X preset _i={ S _i1, S _i2..., S _imcarry out group expanding operation, obtain initialization population Ω={ X ₁, X ₂..., X _np; Wherein np is default group expanding parameter, each individual X in population _iin the generation rule of each variable as follows: $S_{\mathrm{ij}}^0=S_{\mathrm{ij}}^{\min }+\mathrm{rand}\left(\mathrm{0,1}\right)\·(S_{\mathrm{ij}}^{\max }-S_{\mathrm{ij}}^{\min }),$ In formula, with be respectively X _ithe minimum value of a middle jth component and maximal value, rand (0,1) is the uniform random number between (0,1);

Wherein, adaptive differential evolution algorithm committed step comprises:

1, cross processing

The individual vector of Stochastic choice two generates difference vector, by the difference vector of generation and another addition of vectors of Stochastic choice, generates variation vector.Concrete formula is: x in formula _r1, x _r2, x _r3to represent in population 3 different individualities.T represents current state, and t+1 represents state of future generation.

Wherein, the zoom factor of mutation operation adopts adaptive strategy, and concrete formula is:

f0 in formula _l, F0 _ube respectively the bound of F0, f _t1, f _t2, f _t3be respectively fitness;

2, variation process

To be made a variation vector intersect with object vector generate intersection vector concrete formula is: ${\mathrm{ui}}_j^{t+1}=\left\{\begin{array}{c}{v}_{\mathrm{ij}}^{t+1},\mathrm{rand}\left(j\right)\≤\mathrm{CR}\\ x_{\mathrm{ij}}^{t+1},\mathrm{otherwise}\end{array}\right.,$ In formula, rand (j) ∈ [0,1] is equally distributed random function, and CR is the crossover probability factor.

Wherein, the crossover probability factor adopts adaptive strategy, and concrete formula is:

in formula, CRmin, CRmax are respectively the maximum crossover probability factor of minimum crossover probability Summing Factor, and T is maximum iteration time;

3, process is selected

If fitness be better than fitness then use replace and be selected as the next generation; Otherwise as the next generation.Adopt the greedy search strategy preset, with maximum load level for objective function carries out selection operation.

Five, realization is optimized.

Step 31, judge whether current iterations reaches maximal value, if do not reach, carry out step 32, if the precision of the iterations iterations or calculating that reach setting reaches requirement, jump procedure 39;

Step 32, the population X after group expanding carried out to variation process, obtain variation population Xvar: the individual vector of Stochastic choice two generates difference vector, by difference vector and another addition of vectors of Stochastic choice of generating, generate variation vectorial; Wherein, the zoom factor of mutation operation adopts adaptive strategy, and concrete formula is: f0 in formula _min, F0 _maxbe respectively the bound of default F0, f _t1, f _t2, f _t3be respectively fitness;

Step 33, the population Xvar of variation is carried out cross processing, obtain cross-species Xcros: the vector that will make a variation intersects with object vector, generates intersection vectorial; Wherein, the crossover probability factor adopts adaptive strategy, and concrete formula is: in formula, CRmin, CRmax are respectively the maximum crossover probability factor of minimum crossover probability Summing Factor, and T is maximum iteration time;

Step 34, judge whether the traversal of population completes, if do not complete, carry out step 35, otherwise 1 process will be added to iterations, jump to step 31;

Step 35, initial population X and cross-species Xcros to be calculated simultaneously, be substituting in the inferior method of described newton's pressgang by population X and population Xcros respectively according to dimension (representative of every one dimension be the load value of each node), Load flow calculation when ground state and N-1 fault carried out to it; Wherein, the load value of every each node of one-dimensional representation of described dimension;

Step 36, trend when ground state and N-1 fault to be verified, if all verifications by; carry out step 37, otherwise adds 1 process, jump procedure 34 by the variable number of traversal;

Step 37, with preset net capability mathematical model be adaptive response function, computing formula is: wherein the active load of representation node i, the i-th number in the one dimension namely in population X or Xcros; The numerical value that fitness is larger will be saved, and this is exactly likely the net capability of trying to achieve;

Step 38, Population Regeneration X: dimension value fitness being greater than preset value is updated in the same one dimension of population X;

Step 39, exit iteration, export the numerical value of net capability.

Wherein, described net capability mathematical model can be described as: take net capability as objective function, according to the definition of net capability, take into account N-1 safety criterion, and consider the actual conditions of the network operation, comprise the constraints such as main transformer capacity, network topology structure, circuit overload ability, the net capability obtained.Concrete mathematical model is as follows:

State variable: the applied power S comprising each load point _diand power-factor angle (angle of impedance) the voltage magnitude V of node _i, phase angle difference δ _ij.Consider the ruuning situation of 220kV electrical network reality, herein by the power factor of all load point all be set to 0.98.

Objective function: with maximum power supply capacity for objective function, namely with the active power sum of each load bus for objective function

Constraint condition comprises:

1) load constraint

${\underset{\‾}{S}}_{\mathrm{di}}\≤S_{\mathrm{di}}\≤{\stackrel{\‾}{S}}_{\mathrm{di}}---\left(2\right)$

2) line transmission power constraint

${\underset{\‾}{S}}_{\mathrm{ij}}\≤S_{\mathrm{ij}}\≤{\stackrel{\‾}{S}}_{\mathrm{ij}}---\left(3\right)$

3) node voltage bound constraint

${\underset{\‾}{V}}_i\≤V_i\≤{\stackrel{\‾}{V}}_i---\left(4\right)$

4) phase angle retrains up and down

|δ _i-δ _j|＜|δ _i-δ _j| _max(5)

In formula, s _di, be respectively lower limit and the higher limit of the applied power of node i; s _ij, be respectively from node i to the lower limit of the line transmission power of node j branch road and higher limit; v _i, be respectively lower limit and the higher limit of the voltage of node i.

The present invention assesses the method for 220KV handle net net capability, embedded Newton-Laphson method in adaptive differential evolvement method, the effect of embedded Newton-Laphson method is the trend of Exact Solution handle net, the effect of adaptive differential evolvement method processes the trend that Newton-Laphson method is obtained fast, to reach under N-1 constraint, obtain the net capability of handle net fast and accurately; Can be that least unit calculates net capability with section, the method has the features such as good stability, precision is high, ability of searching optimum is strong.

The above embodiment only have expressed several embodiment of the present invention, and it describes comparatively concrete and detailed, but therefore can not be interpreted as the restriction to the scope of the claims of the present invention.It should be pointed out that for the person of ordinary skill of the art, without departing from the inventive concept of the premise, can also make some distortion and improvement, these all belong to protection scope of the present invention.Therefore, the protection domain of patent of the present invention should be as the criterion with claims.

Claims (6)

1. assess a method for 220KV handle net net capability, it is characterized in that, comprise the steps:

Obtain the grid structure of 220KV handle net, determine number of network node and the circuitry number of described grid structure, and the node of described grid structure and branch road are numbered; Wherein, the balance node of 220kV handle net is the 220kV side of 500kV transforming plant main transformer, and PQ node is the load bus in 220kV handle net;

To the grid structure of the 220KV handle net after numbering, read the voltage magnitude V of grid structure parameter, load parameter and balance node _nand phase angle theta _n;

According to the node voltage V preset _iwith phase angle difference δ _ijconstraint, and described PQ node and balance node, obtain the Newton-Laphson method after initialization;

According to the initialization of each node load, the initial population preset carried out to population X, default differential evolution algorithm maximum iteration time Gm, self adaptive pantographic factor minimum value F0 that group expanding process obtains _minwith maximal value F0 _max, adaptive crossover mutation factor minimum value CR _minwith maximal value CR _max, obtain the adaptive differential evolution algorithm after initialization; Wherein, according to following formula, the mean value initialization of node load being got to each load upper lower limit value generates initial population: in formula be respectively maximal value and the minimum value of the nominal load of node i;

Solve the trend of described 220KV handle net according to described Newton-Laphson method, by described adaptive differential evolution algorithm, described trend is processed, obtain the net capability that the described 220KV handle under N-1 constraint is netted.

2. assess the method for 220KV handle net net capability according to claim 1, it is characterized in that, the described step solving the trend of described 220KV handle net according to described Newton-Laphson method comprises:

Step 21, input raw data: the constraint of the parameter of the voltage Vn of the 220kV side of two 500KV transforming plant main transformers and phase angle theta n, rack and voltage constraint and phase angle difference;

Step 22, formation bus admittance matrix, and according to N-1 fault, bus admittance matrix is modified;

Step 23, calculate each node power amount of unbalance $\left[\begin{array}{c}\mathrm{\ΔP}\\ \mathrm{\ΔQ}\end{array}\right],$ Judge whether strength of current deviation meets the condition of convergence; As met, then jumping to step 26, if do not met, then carry out step 24; Wherein, the computing formula of trend deviation is as follows:

$\begin{array}{c}{\mathrm{\ΔP}}_i=P_{\mathrm{is}}-V_i\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{V}_j(G_{\mathrm{ij}}\cos {\mathrm{\θ}}_{\mathrm{ij}}+B_{\mathrm{ij}}\sin {\mathrm{\θ}}_{\mathrm{ij}}),i=\mathrm{1,2},...,n-1\\ {\mathrm{\ΔQ}}_i=Q_{\mathrm{is}}-V_i\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{V}_j(G_{\mathrm{ij}}\sin {\mathrm{\θ}}_{\mathrm{ij}}-B_{\mathrm{ij}}\cos {\mathrm{\θ}}_{\mathrm{ij}}),i=\mathrm{1,2},...,m\end{array};$

Wherein, Δ P, Δ Q are respectively active power deviation and the reactive power deviation of node, P _is, Q _isbe the active power and reactive power that i-th node is given; V _i, V _jbe respectively the voltage of i-th node and a jth node; G _ij, B _ijbe respectively from node i to the conductance of node j branch road and susceptance; θ _ijfor the phase angle difference of node i and node j;

Step 24, generate trend by the variable inputted and existing bus admittance matrix, calculate Jacobi matrix J:

$J=\left[\begin{array}{cc}H& N\\ K& L\end{array}\right];$

Wherein, H is (n-1) rank square formations, and its element is n is (n-1) × m rank matrixes, and its element is k is m × (n-1) rank matrix, and its element is l is m rank square formations, and its element is $L_{\mathrm{ij}}=V_j\frac{{\∂\mathrm{\ΔQ}}_i}{{\∂V}_j};$

Step 25, solve linear revise system of equations $-\left[\begin{array}{c}\mathrm{\ΔP}\\ \mathrm{\ΔQ}\end{array}\right]=\left[\begin{array}{cc}H& N\\ K& L\end{array}\right]\left[\begin{array}{c}\mathrm{\Δ\θ}\\ V_D^{-1}\mathrm{\ΔV}\end{array}\right],$ Wherein obtain the correction amount θ of each node voltage amplitude and phase angle, Δ V, upgrade node voltage, jump to step 23;

Step 26, be calculated as follows all branch powers:

$S_{\mathrm{ij}}=V_i^2{\tilde{y}}_{i0}+{\stackrel{\·}{V}}_i({\tilde{V}}_i-{\tilde{V}}_j){\tilde{y}}_{\mathrm{ij}}$

Wherein, i is the first node of branch road, and j is branch road end-node, and tilde represents the conjugate getting plural number.

3. calculate the method for 220KV handle net net capability according to claim 2, it is characterized in that, describedly processed described trend by described adaptive differential evolution algorithm, the step obtaining the net capability that the lower described 220KV handle of N-1 constraint is netted comprises:

Step 31, judge whether current iterations reaches maximal value, if do not reach, carry out step 32, if the precision of the iterations iterations or calculating that reach setting reaches requirement, jump procedure 39;

Step 32, the population X after group expanding carried out to variation process, obtain variation population Xvar: the individual vector of Stochastic choice two generates difference vector, by difference vector and another addition of vectors of Stochastic choice of generating, generate variation vectorial;

Step 33, the population Xvar of variation is carried out cross processing, obtain cross-species Xcros: the vector that will make a variation intersects with object vector, generates intersection vectorial;

Step 34, judge whether the traversal of population completes, if do not complete, carry out step 35, otherwise 1 process will be added to iterations, jump to step 31;

Step 35, initial population X and cross-species Xcros to be calculated simultaneously, be substituting to respectively in the inferior method of described newton's pressgang according to dimension by population X and population Xcros, carry out Load flow calculation when ground state and N-1 fault; Wherein, the load value of every each node of one-dimensional representation of described dimension;

Step 36, trend when ground state and N-1 fault to be verified, if all verifications by; carry out step 37, otherwise adds 1 process, jump procedure 34 by the variable number of traversal;

Step 37, with preset net capability mathematical model be adaptive response function, computing formula is: wherein the active load of representation node i, the i-th number in the one dimension namely in population X or Xcros;

Step 38, Population Regeneration X: the dimension value being greater than preset value from appropriateness is updated in the same one dimension of population X;

The numerical value of step 39, output net capability.

4. calculate the method for 220KV handle net net capability according to claim 3, it is characterized in that, the state variable of described net capability mathematical model comprises: the applied power S of each load point _di, power-factor angle the voltage magnitude V of node _iand phase angle difference δ _ij;

The objective function of described net capability mathematical model is maximum power supply capacity, calculates the active power sum of each load bus as described objective function by following formula:

The constraint condition of described net capability mathematical model is: load retrains: line transmission power constraint: node voltage bound retrains: and phase angle retrains up and down: | δ _i-δ _j| < | δ _i-δ _j| _max;

Wherein, be respectively lower limit and the higher limit of the applied power of node i; be respectively from node i to the lower limit of the line transmission power of node j branch road and higher limit; be respectively lower limit and the higher limit of the voltage of node i.

5. assess the method for 220KV handle net net capability according to claim 3, it is characterized in that, described cross processing is: generate difference vector according to the individual vector of following formula Stochastic choice two, by the difference vector of generation and another addition of vectors of Stochastic choice, generates variation vector:

x in formula _r1, x _r2, x _r3to represent in population 3 different individualities.

6. assess the method for 220KV handle net net capability according to claim 3, it is characterized in that, described variation process comprises:

To make a variation vector according to following formula intersect with object vector generate intersection vector

${\mathrm{ui}}_j^{t+1}=\left\{\begin{array}{c}{v}_{\mathrm{ij}}^{t+1},\mathrm{rand}\left(j\right)\&le;\mathrm{CR}\\ x_{\mathrm{ij}}^{t+1},\mathrm{otherwise}\end{array}\right.,$ In formula, rand (j) ∈ [0,1] is equally distributed random function, and CR is the crossover probability factor;

The described crossover probability factor adopts adaptive strategy: in formula, CRmin, CRmax are respectively the maximum crossover probability factor of minimum crossover probability Summing Factor, and T is maximum iteration time.