CN105449672A - Method for estimating total supply capability of 220KV loop ring network - Google Patents

Abstract

The invention discloses a method for estimating total supply capability of a 220kV looped ring network. The method comprises the following steps of extracting a grid structure of the 220kV looped ring network, determining network node numbers and branch numbers of the grid structure, and numbering nodes and branches of the grid structure; reading a grid structure parameter, a load parameter and a voltage amplitude and a phase angle of a balance node; carrying out group expansion operation on a predetermined load population to obtain an initial load population, and carrying out variation and cross evolution operations on the load population; carrying out power flow calculation and verification on the 220kV looped ring network in a normal running mode and a (N-1) running of each branch; and updating the population according to self-adaptive degree, and outputting a load scheme of the total supply capability. According to the method, the total supply capability is estimated by taking a district as a minimum unit, and the total supply capability of the 220kV looped ring network is rapidly and accurately figured out when the (N-1) condition is met.

Description

Method for evaluating maximum power supply capacity of 220KV loop ring network

Technical Field

The invention relates to the field of power supply capability evaluation of a power grid, in particular to a method for evaluating the maximum power supply capability of a 220kV loop ring network.

Background

Along with the economic development of China, the increase of power load is obviously accelerated, so that the power quality, the power supply capacity and the power supply reliability cannot meet the power requirements of users, and a plurality of power supply bottlenecks are formed. However, the urban power grid is basically built at present, and it is very difficult to obtain the site of a new substation and the underground passage of a new feeder from the planning and reconstruction of the system. Therefore, it becomes important to study the maximum power supply capacity of the grid without constructing underground tunnels for new power stations and feeders.

The maximum power supply capacity (TSC) of a power grid means that the power grid in a certain power supply area meets the N-1 safety criterion, and the maximum load supply capacity under the actual operation condition of the network is considered. The TSC reflects the safety margin of the load supply of the power grid to a certain extent, provides certain reference for operation and planning personnel of the power grid, and particularly after the introduction of the power market, the power grid personnel urgently need to utilize the existing power transmission network to transmit more electric energy, so that the cost is reduced to the maximum extent, and the benefit of the power grid is improved.

At present, the maximum power supply capacity is solved mainly through three stages: the first stage is mainly an evaluation stage of the maximum power supply capacity, a lot of assumptions are made in the first stage for modeling and calculation convenience, load flow calculation is directly ignored, accurate calculation cannot be performed, and the method is generally used for roughly evaluating the TSC of the power grid, such as a capacity-load ratio method. And in the second stage, the maximum power supply capacity of the power grid is solved by adopting a direct current power flow model or a linear programming method, and the defect that the influence of voltage and network loss on the TSC is ignored, so that a large error is generated. The third stage is a nonlinear model based on the power flow, which can accurately solve the power flow of the network and can consider the influence of voltage and network loss on the TSC, so that the calculation result is more reliable.

In addition, when the maximum power supply capacity of a power grid is solved by the conventional method, a complete power grid model or a simplified model is generally adopted for calculation, and if the complete power grid model and an accurate calculation method are adopted and N-1 static safety constraint calculation is considered, the calculation is slow, and even the problem of numerical calculation can occur; the simplified model causes the error of the result to be overlarge, and the application value is greatly reduced.

Disclosure of Invention

The invention aims to overcome the defects of the prior art, provides a method for evaluating the maximum power supply capacity of a 220kV loop network, establishes a nonlinear model based on alternating current power flow, considers the influence of voltage drop and network loss on the maximum power supply capacity of the 220kV loop network, and considers the static safety constraint of an electric power system N-1; the method has the advantages that the fragment area can be used as the minimum unit, the calculation scale is simplified, the calculation dimensionality is reduced, and the calculation precision is guaranteed while calculation is accelerated. In addition, the invention adopts the self-adaptive differential evolution algorithm embedded with the Newton Raphson method to solve the 220kV ring network, and has the advantages of high convergence speed and strong robustness.

The purpose of the invention is realized by the following technical scheme:

a method for evaluating the maximum power supply capacity of a 220kV loop network comprises the following steps:

s1, extracting a grid structure of the 220kV loop ring network, determining the number of network nodes and the number of branches of the grid structure, and numbering the nodes and the branches of the grid structure; the balance node of the 220kV ring-sleeved ring network is a node at the 220kV side of a main transformer of a 500kV transformer substation, and the PQ node is a load node in the 220kV ring-sleeved ring network;

s2, numberedGrid structure of 220kV ring-sleeved ring network, reading grid structure parameters, load parameters and voltage amplitude V of balance node_nAnd phase angle theta_n；

S3, carrying out group expanding operation on the preset load population to obtain an initialized load population X, and carrying out variation and cross evolution operation on the initialized load population X through a self-adaptive differential evolution algorithm to respectively obtain variation populations X_mutaAnd cross population X_cros；

S4, performing load flow calculation and check on the 220kV ring-sleeved ring network in a normal operation mode and in an N-1 operation mode of each branch by a Newton Raphson method;

s5, according to the self-adaptive degree F_itUpdating the population X and outputting a maximum power supply capacity load scheme S_di。

The step S3 specifically includes the following steps:

s301, judging whether the current iteration time T reaches the maximum value, if not, executing the step S302, and if the iteration time T reaches the set maximum iteration time T or the calculation precision meets the requirement, jumping to the step S309;

s302, carrying out group expanding operation on the population to obtain an initialized load species group, carrying out variation processing on the population X after the group expanding operation to obtain a variation population X_muta: randomly selecting two individual vectors to generate a difference vector, adding the generated difference vector to another randomly selected vector to generate a variation vector X_muta；

S303, carrying out mutation on the population X_mutaPerforming cross treatment to obtain a cross population X_cros: the variation vector X_mutaCrossing with the target vector X to generate a crossing vector X_cros；

S304, judging whether the traversal of the population is finished, if not, performing the step S305, otherwise, performing the processing of adding 1 to the iteration times, and jumping to the step S301;

S305. for individuals in the initial population X and the cross population X_crosThe individuals in the population X and the population X are calculated simultaneously_crosRespectively substituting the individuals into the Newton Raphson method to carry out load flow calculation under normal operation and under each N-1 operation mode;

s306, checking the power flows in the normal operation mode and each N-1 operation mode, if all the checks are passed, performing the step S307, otherwise, adding 1 to the number of variables traversed by the population, and skipping to the step S304;

s307, taking a preset maximum power supply capacity mathematical model as a self-adaptive function, wherein the calculation formula is as follows:whereinRepresenting the active load of node i, i.e. group X or X_crosThe ith number in one dimension of (1);

s308, updating the population X: updating the individual with high self-adaptive degree into the population X;

and S309, outputting the maximum power supply capacity and the load scheme thereof.

The step S4 specifically includes the following steps:

s401, inputting original data: voltage V of 220kV side for inputting main transformer of 500kV transformer substation_nAnd phase angle theta_nParameters of the grid frame, voltage constraint and phase angle difference constraint;

s402, forming a node admittance matrix: forming a node admittance matrix under the normal operation mode and modifying the node admittance matrix under the normal operation mode according to each N-1 fault to form a node admittance matrix under each N-1 fault;

s403, calculating the power unbalance amount of each node $\left[\begin{array}{c}\mathrm{\Δ}P\\ \mathrm{\Δ}Q\end{array}\right],$ Wherein, the delta P and the delta Q respectively refer to the deviation of the active power and the reactive power of the node, and whether the maximum power flow deviation meets the convergence condition is judged; if yes, jumping to step S406, and if not, performing step S404; the calculation formula of the power flow deviation is as follows:

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

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

wherein, P_is、Q_isGiving power to the ith node; v_i、V_jThe voltages of the ith node and the jth node respectively; g_ij、B_ijRespectively the conductance and susceptance of the branch from the node i to the node j; theta_ijIs the phase angle difference between the node i and the node j;

s404, generating a power flow by the input variables and the existing node admittance matrix, and calculating a Jacobian matrix J:

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

wherein H is an n-1 order square matrix having elements ofN is an (N-1) × mth order matrix having elements ofK is an m × (n-1) order matrix with elements ofL is an m-th order square matrix having elements of $L_{ij}=V_j\frac{\∂{\mathrm{\ΔQ}}_i}{\∂V_j};$

S405, solving a linear correction equation set $\left[\begin{array}{c}\mathrm{\Δ}P\\ \mathrm{\Δ}Q\end{array}\right]=-\left[\begin{array}{cc}H& N\\ K& L\end{array}\right]\left[\begin{array}{c}\mathrm{\Δ}\mathrm{\θ}\\ V_D^{-1}\mathrm{\Δ}V\end{array}\right],$ WhereinObtaining correction quantities delta theta and delta V of the voltage amplitude and the phase angle of each node, obtaining the voltage and the phase angle of each node, and jumping to the step S403;

s406, calculating the power of all branches according to the following formula:

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

wherein i is the first node of the branch, j is the last node of the branch, the wave number represents the conjugate value of the complex number,is the conjugate value of the admittance value to ground to node i,is the conjugate of the admittance values between line node i to node j.

In step S307, the state variables of the mathematical model of the maximum power supply capacity include: apparent power S of each load point_diAngle of power factorVoltage amplitude V of upper node_nAnd phase angle difference_n；

The objective function of the mathematical model of the maximum power supply capacity is the maximum power supply capacity, and the sum of the active powers of the load nodes is calculated as the objective function according to the following formula:

the constraint conditions of the maximum power supply capacity mathematical model are as follows: and (3) load restraint:constraint of line transmission power:and (3) limiting the upper and lower limits of the node voltage:and about the phase angleBundling: non-viable cells_i-_j|<|_i-_j|_maxWhereinRespectively, a lower limit value and an upper limit value of the apparent power of the node i;respectively is the lower limit value and the upper limit value of the transmission power of the line from the node i to the node j branch;respectively, the lower and upper limit values of the voltage at node i.

In step S303, the interleaving process includes: randomly selecting two individual vectors to generate a difference vector, and adding the generated difference vector to another vector randomly selected to generate a variation vector according to the following formula:

$X_{muta,ij}^{t+1}=x_{r3}^t+F_0(x_{r1}^t-x_{r2}^t)$

wherein x is_r1、x_r2、x_r3In a presentation population3 different individuals, said F₀The scaling factor employs an adaptive strategy:F_0u、F_0lare respectively F₀Upper and lower limits of f_t1、f_t2、f_t3Are respectively asThe fitness of (2).

In step S302, the mutation process includes:

the variation vector is calculated according to the following formulaCrossing the target vectorGenerating cross vectors

$X_{cros,ij}^{t+1}=\{\begin{array}{cc}{X}_{muta,ij}^{t+1},& rand\left(j\right)\&le;C_R\\ X_{ij}^t,& otherwise\end{array},$ Wherein rand (j) ∈ [0,1 ]]Is a random function of C_RIs a cross probability factor;

the cross probability factor adopts a self-adaptive strategy:in the formula C_Rmin、C_RmaxThe minimum cross probability factor and the maximum cross probability factor are respectively used, T is the current iteration frequency, and T is the maximum iteration frequency.

Compared with the prior art, the invention has the following advantages and beneficial effects:

the method for evaluating the maximum power supply capacity of the 220kV loop ring network establishes a nonlinear model based on alternating current power flow, and considers the influence of voltage drop and network loss on the maximum power supply capacity of the 220kV loop ring network; the maximum power supply capacity of the 220kV ring network is calculated by taking the zone as the minimum unit, a simplified wiring model of the maximum power supply capacity of the 220kV ring network is established, a large amount of redundant data is effectively simplified, the dimensionality of solution is reduced, the calculation speed is accelerated, and therefore the possibility is provided for more precise modeling and solution accuracy; the static safety constraint of the power system N-1 is considered, so that the maximum power supply capacity of the 220kV loop ring network can better meet the safety and reliability of the actual power grid in operation, and the calculation has practical significance; the self-adaptive strategy of differential evolution effectively accelerates the convergence of calculation, ensures the diversity of population and the robustness of calculation, and the Newton-Raphson method solution based on the alternating current model can accurately solve the load flow of the network, thereby ensuring the accuracy of the calculation result.

Drawings

Fig. 1 is a flowchart of a method for evaluating the maximum power supply capability of a 220kV loop ring network according to the present invention.

Fig. 2 is a schematic diagram of a typical looped network.

Detailed Description

The present invention will be described in further detail with reference to examples and drawings, but the present invention is not limited thereto.

As shown in fig. 1, a method for evaluating the maximum power supply capacity of a 220kV loop ring network includes the following steps:

s11, extracting the grid structure of the 220kV ring-sleeved ring network, determining the number of network nodes and the number of branches of the grid structure, and numbering the nodes and the branches of the grid structure.

S12, reading grid structure parameters, load parameters and voltage amplitude V of balance node_nAnd phase angle theta_n。

S13, presetting population scale N_pThe maximum iteration number T of the preset differential evolution algorithm and the minimum value F of the self-adaptive scaling factor_0lAnd maximum value F_0uMinimum value of adaptive cross probability factor C_RminAnd maximum value C_Rmax(ii) a And carrying out group expanding operation on a preset load population to obtain an initialized load population X, and carrying out variation and cross evolution operation on the load population through the self-adaptive differential evolution algorithm to obtain a variation load population X_mutaAnd cross population X_cros。

And S14, performing load flow calculation and check on the 220kV ring-sleeved ring network in a normal operation mode and in an N-1 operation mode of each branch by the Newton Raphson method.

And S15, updating the population according to the self-adaptive degree, and outputting a maximum power supply capacity load scheme.

The maximum power supply capacity in this embodiment means that a power grid in a certain power supply area meets the N-1 safety criterion, and the maximum load supply capacity under the actual operation condition of the network is considered. The maximum power supply capacity of the 220kV ring-sleeved ring network is calculated by adopting a self-adaptive differential evolution method embedded with a Newton Raphson method, the embedded Newton Raphson method is used for accurately solving the alternating current power flow of the ring-sleeved ring network, the self-adaptive differential evolution method is used for providing a large number of solutions for the Newton Raphson method, and the solutions solved by the Newton Raphson method are selected, so that the maximum power supply capacity of the ring-sleeved ring network can be quickly and accurately solved under the N-1 constraint.

Specifically, the method comprises the following steps:

firstly, a 220kV loop ring network structure is extracted. In the 220kV ring-sleeved ring network, a power supply point of an upper-level power grid is a node on the 220kV side of a main transformer of a 500kV transformer substation, and the node is regarded as a balance node in the 220kV ring-sleeved ring network; the other load nodes are generally referred to as PQ nodes. Determining the number of network nodes and the number of branches in the net rack, and numbering from left to right in sequence;

the 220kV ring-sleeved ring network is a first ring network composed of a 500kV substation and a plurality of 220kV substations, and a second ring network composed of some 220kV substations, or even more ring networks, as shown in fig. 2, a typical ring-sleeved ring network schematic diagram is shown.

And secondly, inputting simplified net rack data. Reading structural parameters of 220kV loop ring network frame (namely, each branch resistance R_liReactance X_liAnd a ground susceptance value B_liAnd maximum ampacity I_max,li) Load parameter (maximum load value at each load point)And minimum load valueSd _i ) And a voltage amplitude V given by the balancing node_nAnd phase angle theta_n。

And thirdly, initializing the self-adaptive differential algorithm. The main steps include setting of parameters, e.g. population size N_pMaximum iteration number T of differential evolution algorithm and minimum value F of self-adaptive scaling factor_0lAnd maximum value F_0uMinimum value of adaptive cross probability factor C_RminAnd maximum value C_RmaxSetting of (2); and carrying out group expanding operation on a preset load population to obtain an initialized load population X, and carrying out variation and cross evolution operation on the load population through the self-adaptive differential evolution algorithm to obtain a variation load population X_mutaAnd cross population X_cros. The initialization of the node load randomly generates an initial population X according to the upper and lower limit values of the load, and the specific formula is as follows:in the formulaRespectively the maximum value and the minimum value of the rated load of the node j in the ith dimension;

the key steps of the self-adaptive differential evolution algorithm comprise:

1. cross-handling

Two individual vectors are randomly selected to generate a difference vector, and the generated difference vector is added to another vector randomly selected to generate a variation vector. The concrete formula is as follows:in the formula x_r1、x_r2、x_r3Representing 3 different individuals in the population.

Wherein, the scaling factor of the mutation operation adopts a self-adaptive strategy, and the specific formula is as follows:F_0u、F_0lare respectively F₀Upper and lower limits of f_t1、f_t2、f_t3Are respectively asThe fitness of (2);

2. mutation treatment

The variation vectorCrossing the target vectorGenerating cross vectorsThe concrete formula is as follows: $X_{cros,ij}^{t+1}=\{\begin{array}{cc}{X}_{muta,ij}^{t+1},& rand\left(j\right)\&le;CR\\ X_{ij}^t,& otherwise\end{array},$ wherein rand (j) ∈ [0,1 ]]Random function of C_RIs a cross probability factor.

The cross probability factor adopts a self-adaptive strategy, and the specific formula is as follows:

in the formula C_Rmin、C_RmaxRespectively a minimum cross probability factor and a maximum cross probability factor, wherein T is the current iteration frequency, and T is the maximum iteration frequency;

3. selection process

If it is notIs adapted toIs superior toIs adapted toThen useInstead of the formerAnd is selected as the next generation; otherwiseAs the next generation. And adopting a greedy search strategy, and carrying out selection operation by taking the maximum load level as an objective function.

And fourthly, initializing the Newton Raphson method. The method mainly comprises the steps of setting the maximum iteration number T and the convergence progress of the Newton-Raphson method, and setting the node voltage V_iPhase angle difference_ijAnd the identification of PQ nodes and balancing nodes, etc.

The Newton Raphson method comprises the following steps of:

step 31, inputting original data: voltage V of 220kV side for inputting main transformer of 500kV transformer substation_nAnd phase angle theta_nParameters of the grid frame, voltage constraint and phase angle difference constraint;

step 32, forming a node admittance matrix: forming a node admittance matrix under the normal operation mode and modifying the node admittance matrix under the normal operation mode according to each N-1 fault to form a node admittance matrix under each N-1 fault;

step 33, calculating the power unbalance amount of each node $\left[\begin{array}{c}\mathrm{\&Delta;}P\\ \mathrm{\&Delta;}Q\end{array}\right]$ (in the formula, delta P and delta Q respectively refer to the deviation of the active power and the reactive power of the node), and whether the maximum power flow deviation meets the convergence condition is judged; if yes, jumping to step 36, if not, proceeding to step 34; the calculation formula of the power flow deviation is as follows:

$\left\{\begin{array}{c}{\mathrm{\&Delta;P}}_i=P_{is}-V_i\underset{j=1}{\overset{n}{\&Sigma;}}{V}_j(G_{ij}{\mathrm{cos\&theta;}}_{ij}+B_{ij}{\mathrm{sin\&theta;}}_{ij}),i=1,2,\mathrm{...},n-1\\ {\mathrm{\&Delta;Q}}_i=Q_{is}-V_i\underset{j=1}{\overset{n}{\&Sigma;}}{V}_j(G_{ij}{\mathrm{sin\&theta;}}_{ij}-B_{ij}{\mathrm{cos\&theta;}}_{ij}),i=1,2,...,m\end{array}\right.$

wherein, P_is、Q_isGiving power to the ith node; v_i、V_jThe voltages of the ith node and the jth node respectively; g_ij、B_ijRespectively the conductance and susceptance of the branch from the node i to the node j; theta_ijIs the phase angle difference between node i and node j)

Step 34, generating a power flow by the input variables and the existing node admittance matrix, and calculating a Jacobian matrix J:

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

wherein H is an n-1 order square matrix having elements ofN is an (N-1) × mth order matrix having elements ofK is an m × (n-1) order matrix with elements ofL is an m-th order square matrix having elements of $L_{ij}=V_j\frac{\&part;{\mathrm{\&Delta;Q}}_i}{\&part;V_j};$

Step 35, solving the linear correction equation set $\left[\begin{array}{c}\mathrm{\&Delta;}P\\ \mathrm{\&Delta;}Q\end{array}\right]=-\left[\begin{array}{cc}H& N\\ K& L\end{array}\right]\left[\begin{array}{c}\mathrm{\&Delta;}\mathrm{\&theta;}\\ V_D^{-1}\mathrm{\&Delta;}V\end{array}\right],$ WhereinObtaining the correction quantities delta theta and delta V of the voltage amplitude and the phase angle of each node, obtaining the voltage and the phase angle of each node, and skipping to the step 33;

step 36, calculating the power of all branches according to the following formula:

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

wherein i is the first node of the branch, j is the last node of the branch, the wave number represents the conjugate value of the complex number,is the conjugate value of the admittance value to ground to node i,is the conjugate of the admittance values between line node i to node j.

And fifthly, optimizing and realizing.

Step 21, judging whether the current iteration time T reaches the maximum value, if not, performing step 22, and if the iteration time T reaches the set maximum iteration time T or the calculation precision meets the requirement, skipping to step 29;

step 22, performing population expansion operation on the population to obtain an initialized load population, and performing variation processing on the population X after population expansion to obtain a variation population X_muta: randomly selecting two individual vectors to generate a difference vector, adding the generated difference vector to another randomly selected vector to generate a variation vector X_mutaThe concrete formula isIn the formula x_r1、x_r2、x_r3Representing 3 different individuals in the population, F₀The scaling factor of the mutation operation adopts an adaptive strategy, and the specific formula is as follows:F_0u、F_0lare respectively F₀Upper and lower limits of f_t1、f_t2、f_t3Are respectively asThe fitness of (2);

step 23, changing the population X_mutaPerforming cross treatment to obtain a cross population X_cros: the variation vector X_mutaCrossing with the target vector X to generate a crossing vector X_crosThe concrete formula is $X_{cros,ij}^{t+1}=\{\begin{array}{cc}{X}_{muta,ij}^{t+1},& rand\left(j\right)\&le;CR\\ X_{ij}^t,& otherwise\end{array},$ Wherein rand (j) ∈ [0,1 ]]Random function of C_RThe method adopts a self-adaptive strategy as a cross probability factor, and the specific formula is as follows:in the formula C_Rmin、C_RmaxRespectively a minimum cross probability factor and a maximum cross probability factor, wherein T is the current iteration frequency, and T is the maximum iteration frequency;

step 24, judging whether the traversal of the population is finished, if not, performing step 25, otherwise, performing 1 addition processing on the iteration times, and jumping to the step 21;

step 25, individual in the initial population X and the cross population X_crosThe individuals in the population X and the population X are calculated simultaneously_crosRespectively substituting the individuals into the Newton Raphson method to carry out load flow calculation under normal operation and under each N-1 operation mode;

step 26, checking the power flow in the normal operation mode and each N-1 operation mode, if all checks pass, such as voltage constraint, phase angle constraint, constraint of line transmission power and the like, performing step 27, otherwise, adding 1 to the number of variables traversed by the population, and skipping to step 24;

step 27, taking a preset maximum power supply capacity mathematical model as a self-adaptive function, and calculating a formula as follows:whereinRepresenting the active load of node i, i.e. group X or X_crosThe ith number in one dimension of (1);

step 28, updating the population X: updating the individual with high self-adaptive degree into the population X;

and step 29, outputting the maximum power supply capacity and the load scheme thereof.

Wherein the maximum power supply capability mathematical model may be described as: and taking the maximum power supply capacity as a target function, taking the N-1 safety criterion into consideration according to the definition of the maximum power supply capacity, and taking the actual conditions of network operation into consideration, wherein the actual conditions comprise the constraints of main transformer capacity, voltage and phase angle constraints, line overload capacity and the like, so as to obtain the maximum power supply capacity. The specific mathematical model is as follows:

the state variables are as follows: including apparent power S of each load point_di(ii) a Voltage amplitude V of upper node_nPhase angle difference_n。

An objective function: taking the maximum power supply capacity as an objective function, namely taking the sum of the active power of each load node as the objective function

Considering the actual operation condition of a 220kV power grid, the power factors of all load points are consideredAre all set to 0.98.

The constraint conditions include:

A. load restraint

${\underset{\&OverBar;}{S}}_{di}\&le;S_{di}\&le;{\stackrel{\&OverBar;}{S}}_{di}---\left(2\right)$

In the formula,S _dithe value is 30 percent of the sum of the rated capacities of all main transformers in the transformer substation,the value is that when one main transformer with the maximum capacity in the transformer substation quits operation, the other main transformers are overloaded by 20 percent.

B. Line transmission power constraint

$\left|S_{ij}^k\right|\&le;{\stackrel{\&OverBar;}{S}}_{ij}^k---\left(3\right)$

Wherein, when k is 0, it represents normal operation mode; when k is>When 0, the fault of the k line N-1 is shown;indicating the maximum transmission capacity of the node i to node j line in the k-th state.

C. Upper and lower limit constraints of node voltage

${\underset{\&OverBar;}{V}}_i^k\&le;V_i^k\&le;{\stackrel{\&OverBar;}{V}}_i^k---\left(4\right)$

In the formula,the minimum voltage of the node i in the kth state is generally 0.95;the maximum voltage of the node i in the kth state is generally 1.05.

D. Upper and lower constraint of phase angle

$\left|{{\mathrm{\&delta;}}_{ij}}^k\right|<{{\stackrel{\&OverBar;}{\mathrm{\&delta;}}}_{ij}}^k---\left(5\right)$

In the formula,which represents the maximum value of the phase angle difference between the node i and the node j in the k-th state.

The invention relates to a method for evaluating the maximum power supply capacity of a 220kV loop ring network, which establishes a nonlinear model based on alternating current power flow and considers the influence of voltage drop and network loss on the maximum power supply capacity of the 220kV loop ring network; the maximum power supply capacity of the 220kV ring network is calculated by taking the zone as the minimum unit, a simplified wiring model of the maximum power supply capacity of the 220kV ring network is established, a large amount of redundant data is effectively simplified, the dimensionality of solution is reduced, the calculation speed is accelerated, and therefore the possibility is provided for more precise modeling and solution accuracy; the static safety constraint of the power system N-1 is considered, so that the maximum power supply capacity of the 220kV loop ring network can better meet the safety and reliability of the actual power grid in operation, and the calculation has practical significance; the self-adaptive strategy of differential evolution effectively accelerates the convergence of calculation, ensures the diversity of population and the robustness of calculation, and the Newton-Raphson method solution based on the alternating current model can accurately solve the load flow of the network, thereby ensuring the accuracy of the calculation result.

The above embodiments are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents thereof, and all such changes, modifications, substitutions, combinations, and simplifications are intended to be included in the scope of the present invention.

Claims (6)

1. A method for evaluating the maximum power supply capacity of a 220kV loop network is characterized by comprising the following steps:

s1, extracting a grid structure of the 220kV loop ring network, determining the number of network nodes and the number of branches of the grid structure, and numbering the nodes and the branches of the grid structure; the balance node of the 220kV ring-sleeved ring network is a node at the 220kV side of a main transformer of a 500kV transformer substation, and the PQ node is a load node in the 220kV ring-sleeved ring network;

s2, reading parameters of the net rack structure of the numbered 220kV ring sleeve ring networkLoad parameter and voltage amplitude V of balance node_nAnd phase angle theta_n；

S3, carrying out group expanding operation on the preset load population to obtain an initialized load population X, and carrying out variation and cross evolution operation on the initialized load population X through a self-adaptive differential evolution algorithm to respectively obtain variation populations X_mutaAnd cross population X_cros；

S4, performing load flow calculation and check on the 220kV ring-sleeved ring network in a normal operation mode and in an N-1 operation mode of each branch by a Newton Raphson method;

s5, according to the self-adaptive degree F_itUpdating the population X and outputting a maximum power supply capacity load scheme S_di。

2. The method for evaluating the maximum power supply capability of the 220kV loop network according to claim 1, wherein the step S3 specifically comprises the following steps:

s301, judging whether the current iteration time T reaches the maximum value, if not, executing the step S302, and if the iteration time T reaches the set maximum iteration time T or the calculation precision meets the requirement, jumping to the step S309;

s302, carrying out group expanding operation on the population to obtain an initialized load species group, carrying out variation processing on the population X after the group expanding operation to obtain a variation population X_muta: randomly selecting two individual vectors to generate a difference vector, adding the generated difference vector to another randomly selected vector to generate a variation vector X_muta；

S303, carrying out mutation on the population X_mutaPerforming cross treatment to obtain a cross population X_cros: the variation vector X_mutaCrossing with the target vector X to generate a crossing vector X_cros；

S304, judging whether the traversal of the population is finished, if not, performing the step S305, otherwise, performing the processing of adding 1 to the iteration times, and jumping to the step S301;

s305, carrying out comparison on individuals in the initial population X and the cross population X_crosI.e. the individuals in the population X are calculated simultaneouslyBody and population X_crosRespectively substituting the individuals into the Newton Raphson method to carry out load flow calculation under normal operation and under each N-1 operation mode;

s306, checking the power flows in the normal operation mode and each N-1 operation mode, if all the checks are passed, performing the step S307, otherwise, adding 1 to the number of variables traversed by the population, and skipping to the step S304;

s307, taking a preset maximum power supply capacity mathematical model as a self-adaptive function, wherein the calculation formula is as follows:whereinRepresenting the active load of node i, i.e. group X or X_crosThe ith number in one dimension of (1);

s308, updating the population X: updating the individual with high self-adaptive degree into the population X;

and S309, outputting the maximum power supply capacity and the load scheme thereof.

3. The method for evaluating the maximum power supply capability of the 220kV loop network according to claim 2, wherein the step S4 specifically comprises the following steps:

s401, inputting original data: voltage V of 220kV side for inputting main transformer of 500kV transformer substation_nAnd phase angle theta_nParameters of the grid frame, voltage constraint and phase angle difference constraint;

s402, forming a node admittance matrix: forming a node admittance matrix under the normal operation mode and modifying the node admittance matrix under the normal operation mode according to each N-1 fault to form a node admittance matrix under each N-1 fault;

s403, calculating the power unbalance amount of each node $\left[\begin{array}{c}\mathrm{\&Delta;}P\\ \mathrm{\&Delta;}Q\end{array}\right],$ Wherein, the delta P and the delta Q respectively refer to the deviation of the active power and the reactive power of the node, and whether the maximum power flow deviation meets the convergence condition is judged; if yes, jumping to step S406, and if not, performing step S404; the calculation formula of the power flow deviation is as follows:

${\mathrm{\&Delta;P}}_i=P_{is}-V_i\underset{j=1}{\overset{n}{\&Sigma;}}{V}_j(G_{ij}{\mathrm{cos\&theta;}}_{ij}+B_{ij}{\mathrm{sin\&theta;}}_{ij}),i=1,2,...,n-1$

${\mathrm{\&Delta;Q}}_i=Q_{is}-V_i\underset{j=1}{\overset{n}{\&Sigma;}}{V}_j(G_{ij}{\mathrm{sin\&theta;}}_{ij}-B_{ij}{\mathrm{cos\&theta;}}_{ij}),i=1,2,...,m$

wherein, P_is、Q_isGiving power to the ith node; v_i、V_jThe voltages of the ith node and the jth node respectively; g_ij、B_ijRespectively the conductance and susceptance of the branch from the node i to the node j; theta_ijIs the phase angle difference between the node i and the node j;

s404, generating a power flow by the input variables and the existing node admittance matrix, and calculating a Jacobian matrix J:

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

whereinH is an n-1 order square matrix with elements ofN is an (N-1) × mth order matrix having elements ofK is an m × (n-1) order matrix with elements ofL is an m-th order square matrix having elements of $L_{ij}=V_j\frac{\&part;{\mathrm{\&Delta;Q}}_i}{\&part;V_j};$

S405, solving a linear correction equation set $\left[\begin{array}{c}\mathrm{\&Delta;}P\\ \mathrm{\&Delta;}Q\end{array}\right]=-\left[\begin{array}{cc}H& N\\ K& L\end{array}\right]\left[\begin{array}{c}\mathrm{\&Delta;}\mathrm{\&theta;}\\ V_D^{-1}\mathrm{\&Delta;}V\end{array}\right],$ WhereinObtaining correction quantities delta theta and delta V of the voltage amplitude and the phase angle of each node, obtaining the voltage and the phase angle of each node, and jumping to the step S403;

s406, calculating the power of all branches according to the following formula:

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

wherein,i is the first node of the branch, j is the last node of the branch, the wave number represents the conjugate value of the complex number,is the conjugate value of the admittance value to ground to node i,is the conjugate of the admittance values between line node i to node j.

4. The method for calculating the maximum power supply capacity of the 220kV loop network according to claim 3, wherein in step S307, the state variables of the mathematical model of the maximum power supply capacity include: apparent power S of each load point_diAngle of power factorVoltage amplitude V of upper node_nAnd phase angle difference_n；

The objective function of the mathematical model of the maximum power supply capacity is the maximum power supply capacity, and the sum of the active powers of the load nodes is calculated as the objective function according to the following formula:

the constraint conditions of the maximum power supply capacity mathematical model are as follows: and (3) load restraint:constraint of line transmission power:and (3) limiting the upper and lower limits of the node voltage:and phase angle upper and lower constraints: non-viable cells_i-_j|<|_i-_j|_maxWhereinS _di、Respectively, a lower limit value and an upper limit value of the apparent power of the node i;S _ij、respectively is the lower limit value and the upper limit value of the transmission power of the line from the node i to the node j branch; v_i、V_iRespectively, the lower and upper limit values of the voltage at node i.

5. The method for detecting the maximum power supply capacity of the 220KV loop ring network according to claim 2, wherein in step S303, the crossing process is: randomly selecting two individual vectors to generate a difference vector, and adding the generated difference vector to another vector randomly selected to generate a variation vector according to the following formula:

$X_{muta,ij}^{t+1}=x_{r3}^t+F_0(x_{r1}^t-x_{r2}^t)$

wherein x is_r1、x_r2、x_r3In a presentation population3 different individuals, said F₀The scaling factor employs an adaptive strategy:F_0u、F_0lare respectively F₀Upper and lower limits of f_t1、f_t2、f_t3Are respectively asThe fitness of (2).

6. The method for evaluating the maximum power supply capability of the 220kV loop network according to claim 2, wherein in step S302, the mutation process comprises:

the variation vector is calculated according to the following formulaCrossing the target vectorGenerating cross vectors

$X_{cros,ij}^{t+1}=\left\{\begin{array}{cc}{X}_{muta,ij}^{t+1},& rand\left(j\right)\&le;C_R\\ X_{ij}^t,& otherwise\end{array}\right.,$ Wherein rand (j) ∈ [0,1 ]]Is a random function of C_RIs a cross probability factor;

the cross probability factor adopts a self-adaptive strategy:in the formula C_Rmin、C_RmaxThe minimum cross probability factor and the maximum cross probability factor are respectively used, T is the current iteration frequency, and T is the maximum iteration frequency.