CN105354628A - Robust available power transmission capacity evaluation method for power transmission system - Google Patents

Abstract

The present invention discloses a robust available power transmission capacity evaluation method for a power transmission system. Under the condition that wind power probability distribution is not fully known, the method can maximize available transmission capacity under constraint precondition of ensuring the system to operate safely. The method comprises: firstly, in the context of no determined probability distribution function, describing an available transmission capacity evaluation problem by using a probability distribution robust opportunity constraint optimization model; secondly, eliminating a random variable in the optimization model with a robust optimization method, and converting the optimization model into a deterministic model only containing second-order moment information of the random variable; and thirdly, performing computation on the deterministic model by using an immune particle swarm algorithm based on linear matrix inequality optimization. The method provided by the present invention meets the situation that a wind power prediction technique is limited in an actual power transmission system and an accurate wind power probability distribution function cannot be obtained; and a robust available power transmission capacity evaluation policy meets actual demands.

Description

Robust available transmission capacity evaluation method for transmission system

Technical Field

The invention relates to the technical field of power transmission network reliability, in particular to a robust available power transmission capacity evaluation method for a power transmission system.

Background

The available transmission capacity of the grid has a large impact on the safety and reliability of the overall power system. Under the power market environment, the power transmission capacity of a power grid is based on the existing power transmission contract, and the maximum power for transmission can be increased between regions or points under the condition of ensuring the safe and reliable operation of the system. Uncertainty factors have a significant impact on the available transmission capacity of the transmission system, e.g. line and generator faults may cause a drop in the deliverable power of the transmission network. With the wide access of the distributed generator, how to process the influence of uncertain factors in the power transmission network, and efficiently and accurately calculating the available power transmission capacity is a key problem in the calculation of the available power transmission capacity and a difficulty in the calculation of the current available power transmission capacity.

As the penetration of wind power in a power grid increases, the intermittency, randomness, and unpredictability of the wind power increases the uncertainty of the power grid. As the amount of uncertainty in the system increases, it will have a significant impact on the Available Transmission Capacity (ATC) of the grid. In order to improve the safety and reliability of the power transmission network, the uncertainty of wind power probability distribution needs to be considered in a traditional ATC evaluation model, the influence of the uncertainty of the wind power probability distribution on the ATC of the system is analyzed and processed in a more comprehensive way, and a stable and economic ATC evaluation strategy is formulated. Therefore, the method for calculating the ATC of the power transmission network considering the uncertainty of the probability distribution of the wind power adopts a probability distribution robust opportunity constraint optimization model to describe the calculation problem of the ATC. And then eliminating random variables in the optimization model by integrating the property of worst-caseCVarapproxication, converting the random variables into a deterministic model containing a matrix inequality, and further providing an immune particle swarm optimization algorithm based on linear matrix inequality for solving. The method can be used for converting the probability distribution uncertain model into the deterministic model convenient to solve according to the specific parameters of the probability distribution function which cannot be obtained in the actual power system and considering the uncertainty of the wind power probability distribution, and selecting the ATC optimization scheme which meets the safe operation requirement of the power transmission network in any possible probability distribution realization scene of the wind power and simultaneously maximizes the transmission power of the tie line.

At present, probability statistics and scene analysis methods are mostly adopted in available transmission capacity evaluation methods including wind power, and it is assumed that a probability distribution function of wind speed or wind power output is constant. However, in an actual power grid, the probability distribution function of wind power cannot be accurately obtained, and the prior art cannot provide a practical and effective evaluation method for available transmission capacity considering uncertainty of wind power probability distribution, so that the available transmission capacity of the transmission grid is optimized on the basis of only knowing the second moment of the wind power probability distribution function.

Disclosure of Invention

The invention aims to solve the technical problem of providing an available transmission capacity estimation method considering wind power probability distribution uncertainty, converting the uncertainty problem containing wind power into a certainty problem by adopting a probability distribution robust optimization method under the condition of only knowing a secondary moment of the wind power probability distribution and not knowing an accurate form of a wind power probability distribution function, and then solving by using an immune particle swarm optimization to optimize safe and reliable operation of a power transmission network.

The invention adopts the following technical scheme for solving the technical problems:

a robust available transmission capacity evaluation method for a transmission system comprises the following steps:

step 1), describing an ATC problem by adopting a probability distribution robust opportunity constraint model, wherein an objective function is an accumulated value of output power of all connecting lines of a maximized power supply area a to a load area b, and constraint conditions comprise a power flow balance equation, a power generation capacity constraint, a node voltage opportunity constraint and a branch power opportunity constraint:

in the formula, P_a→bOutput power, P, for all links from zone a to zone b_G，Q_G，P_D，Q_DRespectively the active power and the reactive power of a conventional generator and a load; p_W，Q_WRespectively the active and reactive power, P, output by the wind turbine_L，Q_LRespectively the active power flow and the reactive power flow of the system, S is a node branch incidence matrix, P_Gi,max，P_Gi,minRespectively the upper limit and the lower limit of the active output of the conventional generator i; q_Gi,max，Q_Gi,minRespectively the upper and lower limit of i reactive power output, V of the conventional generator_i，V_i,max，V_i,minVoltage amplitude and voltage amplitude upper and lower limits, N, of node i_nIs the total number of system nodes, Pr_φRepresenting the probability that the wind power probability distribution φ holds, β is a set confidence level, P_l,maxFor the upper limit of the active transmission power of the branch,represents the minimum probability that event a holds under all possible probability distributions;

step 2), the node voltage inequality is constrained to be:

wherein,j is a contracted V-Q Jacobian matrix, the ith diagonal element corresponds to the V-Q sensitivity of a node i, and a V-Q sensitivity table of one nodeShowing the slope of the Q-V curve, V, at a given operating point_NIs a reference voltage;

according to the direct current power flow equation, the power flow equation of the system can be expressed as a function of the wind power:

in the formula, F₂＝[(S^T)^-1(S^T)^-1·(P_G-P_D)]，z₂＝[P_W ^T1]^T。

Eliminating random variables in the optimization model by using worst condition risk estimation properties, and obtaining a feasible solution of node voltage constraint:

in the formula, Tr (-) is trace operation, M_k1Is a symmetric matrix containing all dual variables,α_1iis a strictly positive proportionality parameter, gamma₁Is a real number in n dimensions;

step 3), obtaining a feasible solution of branch power constraint:

in the formula, Tr (-) is trace operation, M_k2Is a symmetric matrix containing all dual variables,γ₂is a vector of n-dimensional real numbers, α_2iIs a strictly positive proportionality parameter;

step 4), substituting feasible solutions of the feasible node voltage and the branch power into the probability distribution robust opportunity constraint model in the step 1):

and 5) solving the model in the step 4) by adopting an immune particle swarm algorithm to obtain an optimal solution in the available transmission capacity evaluation scheme considering the uncertainty of the wind power probability distribution.

As a further optimization method of the robust available transmission capacity evaluation method of the power transmission system, the power transmission system adopts an IEEE30 node.

As a further optimization method of the robust available transmission capacity evaluation method of the transmission system, the feasible solutions of the feasible node voltage and the branch power are substituted into the probability distribution robust opportunity constraint model in the step 1) by adopting a power flow calculation method in the step 4).

As a further optimization method of the robust available transmission capacity evaluation method of the transmission system, the load flow calculation method is carried out by adopting a Newton method.

Compared with the prior art, the invention adopting the technical scheme has the following technical effects:

the available transmission capacity evaluation strategy designed by the invention in consideration of the uncertainty of the wind power probability distribution accords with the condition that the wind power probability distribution function in an actual power system cannot be accurately predicted, is only the second moment of the wind power probability distribution function, and provides the ATC optimization method of the power transmission network in consideration of the uncertainty of the wind power probability distribution. And setting all the expected values and the variance matrixes as an uncertain set, and meeting the requirement of safe and economic operation of the system for any situation in the uncertain set. According to a power flow balance equation, separating out random variables, eliminating the random variables in the power flow balance equation by comprehensively using a probability distribution robust optimization method, converting uncertain problems into a deterministic model containing a random variable second moment, and solving by adopting an immune particle swarm optimization based on linear matrix inequality optimization to obtain an optimal available transmission capacity evaluation scheme.

Drawings

FIG. 1 is a flow chart of an available transmission capacity assessment scheme designed in accordance with the present invention that takes into account uncertainty in wind power probability distribution functions;

FIG. 2 is an IEEE-30 node system;

FIG. 3 is a comparison of probability distribution robust opportunity constraint ATC and traditional opportunity constraint ATC optimization;

fig. 4 shows the transmission network ATC [ MW ], ═ 10 at different mean values;

fig. 5 shows the transmission network ATC [ MW ], μ ═ 0.6MW under different variances.

Detailed Description

The technical scheme of the invention is further explained in detail by combining the attached drawings:

as shown in figure 1, the invention discloses a method for evaluating the robust available transmission capacity of a transmission system, aiming at the condition that the probability distribution function of wind power in an actual power system is unknown, firstly, under the background that no determined probability distribution function exists, a probability distribution robust opportunity constraint optimization model is used for describing a target function, then, a robust optimization method is used for eliminating random variables in the optimization model, the random variables are converted into a deterministic model only containing random variable second moment information, and then, an immune particle swarm optimization based on linear matrix inequality optimization is used for solving the deterministic model. The specific steps of the immune particle swarm distribution are that initial values of a conventional unit, a transformer and a compensation capacitor of a power transmission network system are initialized, available power transmission capacity of the power transmission network under the second moment information of a given wind power probability distribution function is obtained, iteration is carried out according to each available power transmission capacity scheme, the maximum possible power transmission capacity of each iteration is obtained, and finally values of the conventional unit, the transformer and the compensation capacitor corresponding to the maximum available power transmission capacity are obtained in the maximum available power transmission capacity corresponding to all iteration processes.

The invention designs a robust available transmission capacity evaluation method for a transmission system, which is based on the technical scheme of the design and specifically adopts the following technical scheme: MATLABR2010a is adopted in simulation, a computer is Corei53.20Ghz, 4GRAM, and in the actual application process, the following steps are adopted in specific design:

step 1), describing an ATC problem by adopting a probability distribution robust opportunity constraint model, wherein an objective function is an accumulated value of output power of all connecting lines of a maximized power supply region a to a load region b, and constraint conditions comprise a power flow balance equation, a power generation capacity constraint, a node voltage opportunity constraint and a node power amplitude opportunity constraint:

in the ATC optimization model, the known quantity is the expectation and variance set phi_Ξ(μ,) hence, the introduction of probability distribution robust opportunistic constraint (1)Representing the minimum probability that event a holds under all possible probability distributions. In the absence of a defined probability distribution function, it is necessary to ensure that the set of probability distribution functions Φ_Ξ(μ,) the ATC evaluation schemes both meet the pre-set constraints of no-over-voltage and no-over-branch power.

Step 2), node voltage inequality constraint is as follows:

according to the node voltage and the reactive power model,order toWhere J is the shrunk V-Q Jacobian matrix, and the ith diagonal element corresponds to the V-Q sensitivity of node i. Therefore V_i＝J(Q_Gi+Q_Wi-Q_Di)，i＝1,2,...N_nFormula (2) is

So node voltage constraint can be as

Wherein,z₁＝[Q_W ^T1]^T。

according to the direct current power flow equation, the power flow equation of the system can be expressed as a function of the wind power:

in the formula, F₂＝[(S^T)^-1(S^T)^-1·(P_G-P_D)]，z₂＝[P_W ^T1]^T。

Constraining problems for joint robust opportunities

For functionIn thatProbability distribution ofAnd confidence level β∈ (0,1) for conditional risk J (x, α) at confidence level β in equation (6) is defined as follows:

wherein the EP () probability distributionThe expected values of. Then a feasible solution Z of equation (7)^ICC(α) is equivalent to J (x, α). ltoreq.0.

Solving the formula (7) step by step according to the definition of the Worst-caseCVaRapproximation, firstly solving the formula (7)

Order toFormula (8) can be expressed as follows:

in the formula:is composed ofTop non-negative Borel measure cones. The optimization variable of the problem (9) is a non-negative measure f, and it is noted that the first constraint in (9) makes f a probability measure, while the other two constraints make f satisfy the information of the known first moment (i.e., the expected value vector) and second moment (i.e., the covariance matrix), respectively.

The following problem formula (10) and problem formula (9) are known to be a mutual dual problem from the dual theory, and satisfy the strong dual theorem: z^P＝Z^D。

In the formula: y is₀And Y, Y are dual variables corresponding to the first, second and third constraints of the problem (9), respectively. Thus theta_wcCorresponding to the optimal value of the dual problem (10). The following variables are defined as follows,

the dual problem (10) can be written as

For any determined x ∈ χ, ψ ∈ R, α ∈ A, formula (11) is written as LMI, formula (12) shows

By substituting formula (12) for formula (7) to give

s.t.M∈S^k+1,ψ∈R(13)

A feasible solution of the formula (6) is

Similarly, a feasible solution to the branch power constraint is

In the formula: tr (-) is a trace operation, M_k2Is a symmetric matrix containing all dual variables,

the formula (14) and the formula (15) are substituted into the formula (1), so that the probability distribution robust optimization problem can be converted into a deterministic problem:

and 3) starting to perform immune particle swarm optimization iteration aiming at the deterministic model. Based on immune particle swarm optimization, initially generating a two-dimensional array (L, T), wherein L is 1, k is 1, L is the size of the population, T represents the number of conventional units, transformers and compensation capacitors in the power transmission network, and aiming at the conventional units, the transformers and the compensation capacitors in the power transmission network, obtaining an initial value in a given range according to an equation (17);

x＝(x_(max)-x_(min))*rand(L,T)+x_(min)(17)

obtaining preset values x, T ∈ {1, …, T } corresponding to each conventional unit, each reactive compensation capacitor bank and each transformer in the l available transmission capacity evaluation scheme in the kth iteration process, wherein x represents the value of the tth element (a conventional generator, a transformer and a compensation capacitor), and corresponds to the position of each particle one by one, and x is one of the positions of each particle_(max)Denotes the maximum value of the t-th element, x_(min)Represents the minimum value of the t element, wherein, an IEEE30 node power transmission system is modified, the system comprises 6 conventional generator nodes, and specific parameters are shown in a table 2, wherein 1 is a balance node, and the rest are PV nodes; 22 PQ nodes (where nodes 11 and 24 are reactive compensation points and the step size is 0.048), the upper and lower limits of the node voltage are 1.06 and 0.94, respectively; 41 branches, wherein the branches 11,12,15 and 36 are transformer branches, the step length is 0.025, and the upper limit and the lower limit of the transformer transformation ratio are 1.1 and 0.9 respectively; the number of the transformer and the parallel capacitor is 10, the transformer and the parallel capacitor are respectively connected to nodes 10,12,15,19,21,24,26,27 and 30, the system comprises 3 fans, the fans are respectively connected to a power grid at nodes 16,23 and 26, and the expected value, the variance and the value range of the active power output of the wind power plant access node are assumed to be known. The parameters of the immune particle swarm algorithm are shown in table 2, wherein to increase the diversity of the particles, new particles are generated during each iteration by the following 2 aspects: generating N particles by an updating formula of a particle swarm algorithm, and randomly generating M particles; the vaccine selection refers to the global optimal position G in the particle swarm optimization process_bestClosest to the global optimal solution, and G_bestAs effective characteristic information, i.e. vaccine; r is aIn the process of vaccine inoculation, the number of particles extracted from a particle swarm is randomly selected, and the particles are inoculated by using the vaccine extracted previously to form a new generation of N particles; c1 and C2 are learning factors, w_minw_maxRespectively an initial value and an end value of the inertial weight, i_itermaxIs the maximum iteration number; immune selection means that if the fitness of the particles after vaccination is not better than that of the parent, the vaccination is cancelled; otherwise, keeping the particles to form a new generation of particle swarm.

Table 1: generator parameters and upper and lower limits

Table 2: parameters of IAPSO algorithm

And carrying out load flow calculation according to the initialized power transmission network parameters to obtain the available power transmission capacity of the power transmission system.

Modifying an IEEE30 node power transmission system, as shown in fig. 2, the system is now divided into 3 regions, the modified system includes 6 conventional generator nodes, the specific parameters are shown in table 1, wherein 1 is a balance node, and the rest are PV nodes; 22 PQ nodes (wherein, the nodes 11 and 24 are reactive compensation points, the step length is 0.048), and the upper limit and the lower limit of the node voltage are 1.1pu and 0.97pu respectively; 41 branches, the transformer parameters are shown in table 2. The system comprises 3 fans which are respectively connected to a power grid at nodes 16,23 and 26, and the expected value, variance and value range of the active power output of the wind power plant access node are assumed to be known. The parameters of the IAPSO algorithm are shown in table 3.

Table 3: transformer parameter and upper and lower limit values

In order to illustrate the effectiveness of the method provided by the text, an optimal scheme of probability distribution robust opportunity constraint ATC is adopted, and the optimal scheme obtained by solving by adopting the traditional opportunity constraint ATC is contrastively analyzed when the wind power is assumed to obey normal distribution; in order to analyze the influence of the wind power access scale on the system reliability, the influence of the confidence level, the wind power mean value and the second moment on the ATC of the system is tested.

1) Comparing probability distribution robust opportunity constraint ATC with traditional opportunity constraint ATC optimal scheme

When the mean value of the wind power is 0.6MW, the covariance is 10, and different confidence levels are set, the optimal scheme of the probability distribution robust opportunity constraint ATC and the optimal scheme of the traditional opportunity constraint ATC correspond to those shown in fig. 3.

As can be seen from fig. 3, with the increase of the set confidence level, the ATCs obtained by the two methods are both gradually reduced, and the main reason is that when the confidence level of the safety constraint of the power transmission network is increased, the ATCs are reduced in order to avoid the safety constraint being out of range due to the randomness of wind power.

The probability distribution robust opportunity constraint ATC is smaller than the traditional opportunity constraint ATC within a given confidence level. The main reason is that the probability distribution robust opportunity constraint ATC requires that the system requires that the node voltage and branch power of the power transmission network meet a given confidence level in all possible wind power distribution, so that the ATC of the power transmission network under the probability distribution robust opportunity constraint is small.

When the confidence level is 0.95, under almost all wind power probability distribution conditions of the two models, each branch is not overloaded, and the node voltage load is required. Thus, at this point, the optimal results for the probability distribution robust opportunity constraint and the conventional opportunity constraint ATC are the same, and the results in fig. 3 also demonstrate the effectiveness of the probability distribution robust opportunity constraint ATC presented herein.

2) Grid ATC at different confidence levels 10.

When the variance is the same, 10, and different confidence levels β are set, the ATC under the probability distribution robust opportunity constraint evaluation strategy is shown in fig. 4. When the mean value and the second moment are fixed, the ATC of the system is gradually reduced along with the increase of the confidence level, because when the confidence level of the safety constraint of the power transmission network is increased, the ATC is reduced in order to avoid the safety constraint from being out of range due to the randomness of wind power.

3) Transmission network ATC, mu 0.6MW under different variance

As can be seen from fig. 5, compared with the same mean value and confidence level, the larger the variance is, the smaller the corresponding ATC is. This is because the larger the variance is, the larger the fluctuation range of the wind power is, the larger the influence on the line power flow in the system is, and the ATC is reduced under the strict safety constraint condition according to the direct current power flow equation.

In actual operation, a reasonable confidence coefficient is formulated by the power transmission network according to actual conditions, and the safety and the economy of the system are considered, so that the ATC of the power transmission network is effectively evaluated.

In actual operation, a reasonable confidence coefficient needs to be formulated by the power transmission network according to actual conditions, the safety and the economy of the system are considered, and the ATC of the power transmission network is increased.

The available transmission capacity evaluation method considering the uncertainty of the wind power probability distribution designed by the technical scheme provides the transmission grid ATC optimization method considering the uncertainty of the wind power probability distribution for the second moment information of the wind power acquired in the actual wind power prediction. And setting all the expected values and the variance matrixes as an uncertain set, and meeting the requirement of safe and economic operation of the system for any situation in the uncertain set. According to a power flow balance equation, separating out random variables, eliminating the random variables in the power flow balance equation by comprehensively using a probability distribution robust optimization method, converting the uncertain problems into a deterministic model containing a random variable second moment, and solving by adopting an immune particle swarm optimization based on linear matrix inequality optimization to obtain the final optimal available transmission capacity evaluation.

The finally obtained available transmission capacity evaluation strategy considering the wind power distribution uncertainty can effectively improve the speed of optimizing the available transmission capacity of the transmission network, accords with the condition that the wind power probability distribution function in an actual power system cannot be accurately obtained, and only needs the second moment of the probability distribution function. The effectiveness and feasibility of the ATC optimization strategy provided by the embodiment simulation analysis result are proved, and the condition that the wind power distribution probability function in the actual power grid is not completely known is met. The simulation result proves part of general rules, and when the expectation of wind power is larger, the ATC in the power grid is increased; when the variance of wind power is larger, the ATC of the system is smaller, and when the confidence level set by the system is higher, the ATC of the system is smaller.

It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.

The above-mentioned embodiments, objects, technical solutions and advantages of the present invention are further described in detail, it should be understood that the above-mentioned embodiments are only illustrative of the present invention and are not intended to limit the present invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (4)

1. A robust available transmission capacity evaluation method for a transmission system is characterized by comprising the following steps:

step 1), describing an ATC problem by adopting a probability distribution robust opportunity constraint model, wherein an objective function is an accumulated value of output power of all connecting lines of a maximized power supply area a to a load area b, and constraint conditions comprise a power flow balance equation, a power generation capacity constraint, a node voltage opportunity constraint and a branch power opportunity constraint:

$\left\{\begin{array}{c}T={\mathrm{max\ΣP}}_{a\→b}\\ \begin{array}{cc}s.t.& P_W+P_G-P_D-S^T{P}_L=0\\ & Q_W+Q_G-Q_D-S^T{Q}_L=0\\ & P_{Gi,\min }\≤P_{Gi}\≤P_{Gi,\max }\\ & Q_{Gi,\min }\≤Q_{Gi}\≤Q_{Gi,\max }\\ & {\inf }_{\mathrm{\φ}\∈{\mathrm{\Φ}}_{\mathrm{\Ξ}}\left(\mathrm{\μ},L\right)}{\mathrm{Pr}}_{\mathrm{\φ}}\left\{V_{i,\min }\≤V_i\≤V_{i,\max }\right\}\≥\mathrm{\β}\\ & i=1,2,...N_n\\ & {\inf }_{\mathrm{\φ}\∈{\mathrm{\Φ}}_{\mathrm{\Ξ}}\left(\mathrm{\μ},\mathrm{\Γ}\right)}{\mathrm{Pr}}_{\mathrm{\φ}}\left\{\left|P_L\right|\≤P_{l,\max }\right\}\≥\mathrm{\β}\end{array}\end{array}\right.$

in the formula, P_a→bOutput power, P, for all links from zone a to zone b_G，Q_G，P_D，Q_DRespectively the active power and the reactive power of a conventional generator and a load; p_W，Q_WRespectively the active and reactive power, P, output by the wind turbine_L，Q_LRespectively the active power flow and the reactive power flow of the system, S is a node branch incidence matrix, P_Gi,max，P_Gi,minRespectively the upper limit and the lower limit of the active output of the conventional generator i; q_Gi,max，Q_Gi,minRespectively the upper and lower limit of i reactive power output, V of the conventional generator_i，V_i,max，V_i,minVoltage amplitude and voltage amplitude upper and lower limits, N, of node i_nIs the total number of system nodes, Pr_φRepresenting the probability that the wind power probability distribution φ holds, β is a set confidence level, P_l,maxFor the upper limit of the active transmission power of the branch,represents the minimum probability that event a holds under all possible probability distributions;

step 2), the node voltage inequality is constrained to be:

$\begin{array}{c}{\inf }_{\mathrm{\φ}\∈{\mathrm{\Φ}}_{\mathrm{\Ξ}}\left(\mathrm{\μ},\mathrm{\Γ}\right)}{\mathrm{Pr}}_{\mathrm{\φ}}\left\{\left|\left[\begin{array}{cc}\frac{J}{V_N}& \frac{J\left(Q_G-Q_D\right)}{V_N}-1\end{array}\right]\left[\begin{array}{c}{Q}_W\\ 1\end{array}\right]\right|\≤\left|\left(\frac{V_{\max }-V_{\min }}{2}\right)\right|\right\}\≥\mathrm{\β}\\ \⇔{\inf }_{\mathrm{\φ}\∈{\mathrm{\Φ}}_{\mathrm{\Ξ}}\left(\mathrm{\μ},\mathrm{\Γ}\right)}{\mathrm{Pr}}_{\mathrm{\φ}}\left\{{\[F_1{z}_1\]}^2\≤{\left(\frac{V_{\max }-V_{\min }}{2}\right)}^2\right\}\≥\mathrm{\β}\end{array}$

wherein, $\left[\begin{array}{cc}\frac{J}{V_N}& \frac{J(Q_G-Q_D)}{V_N}-1\end{array}\right]=F_1,z_1={\left[\begin{array}{cc}{Q_W}^T& 1\end{array}\right]}^T,$ j is the contracted V-Q Jacobian matrix, the ith diagonal element corresponds to the V-Q sensitivity of node i, the V-Q sensitivity of a node represents the slope of the Q-V curve at a given operating point, V_NIs a reference voltage;

according to the direct current power flow equation, the power flow equation of the system can be expressed as a function of the wind power:

$\begin{array}{c}{P}_L={\left(S^T\right)}^{-1}\·\left(P_W+P_G-P_D\right)\\ =\left[\begin{array}{cc}{\left(S^T\right)}^{-1}& {\left(S^T\right)}^{-1}\·\left(P_G-P_D\right)\end{array}\right]\·\left[\begin{array}{c}{P}_W\\ 1\end{array}\right]\\ =F_2{z}_2\end{array}$

in the formula, $F_2=\left[\begin{array}{cc}{\left(S^T\right)}^{-1}& {\left(S^T\right)}^{-1}\·(P_G-P_D)\end{array}\right],z_2={\left[\begin{array}{cc}{P_W}^T& 1\end{array}\right]}^T.$

eliminating random variables in the optimization model by using worst condition risk estimation properties, and obtaining a feasible solution of node voltage constraint:

$\left\{\begin{array}{c}\∃M_{k1}\∈S^{k+1},{\mathrm{\γ}}_1\∈R,M_{k1}\≥0,{\mathrm{\γ}}_1+\frac{1}{1-\mathrm{\β}}Tr\left(Q.M_{k1}\right)\≤0\\ Q_G\∈R^n:M_{k1}-\left[\begin{array}{cc}{\mathrm{\α}}_{1i}{F}_1^T\left(Q_G\right)F_1\left(Q_G\right)& 0\\ 0& -{\mathrm{\α}}_{1i}{\left(\frac{V_{i,\max }-V_{i,\min }}{2}\right)}^2-{\mathrm{\γ}}_1\end{array}\right]\≥0\end{array}\right.$

in the formula, Tr (-) is trace operation, M_k1Is a symmetric matrix containing all dual variables, $Q=\left[\begin{array}{cc}\mathrm{\Γ}+{\mathrm{\μ\μ}}^T& \mathrm{\μ}\\ {\mathrm{\μ}}^T& 1\end{array}\right],$ α_1iis a strictly positive proportionality parameter, gamma₁Is a real number in n dimensions;

step 3), obtaining a feasible solution of branch power constraint:

$\left\{\begin{array}{c}\∃M_{k2}\∈S^{k+1},{\mathrm{\γ}}_1\∈R,M_{k2}\≥0,{\mathrm{\γ}}_2+\frac{1}{1-\mathrm{\β}}Tr\left(Q.M_{k2}\right)\≤0\\ P_G\∈R^n:M_{k2}-\left[\begin{array}{cc}{\mathrm{\α}}_{2i}{F}_2^T\left(P_G\right)F_2\left(P_G\right)& 0\\ 0& -{\mathrm{\α}}_{2i}{\left(P_{j,\max }\right)}^2-{\mathrm{\γ}}_2\end{array}\right]\≥0\end{array}\right.$

in the formula, Tr (-) is trace operation, M_k2Is a symmetric matrix containing all dual variables, $Q=\left[\begin{array}{cc}\mathrm{\Γ}+{\mathrm{\μ\μ}}^T& \mathrm{\μ}\\ {\mathrm{\μ}}^T& 1\end{array}\right],$ γ₂is a vector of n-dimensional real numbers, α_2iIs a strictly positive proportionality parameter;

step 4), substituting feasible solutions of the feasible node voltage and the branch power into the probability distribution robust opportunity constraint model in the step 1):

$\left\{\begin{array}{c}{\mathrm{max\ΣP}}_{a\→b}\\ \begin{array}{cc}s.t.& \∃\left({\mathrm{\γ}}_1,M_{k1}\right)\∈R\×S^{k+1},\∃\left({\mathrm{\γ}}_2,M_{k2}\right)\∈R\×S^{k+1}\\ & M_{k1}\≥0,M_{k2}\≥0,{\mathrm{\α}}_1,{\mathrm{\α}}_2\∈R^n,{\mathrm{\γ}}_1+\frac{1}{1-\mathrm{\β}}Tr\left(Q.M_{k1}\right)\≤0,\\ & {\mathrm{\γ}}_2+\frac{1}{1-\mathrm{\β}}Tr\left(Q.M_{k2}\right)\≤0,Q_G,P_G\∈R^n:\\ & M_{k1}-\left[\begin{array}{cc}{\mathrm{\α}}_{1i}{F}_1^T\left(Q_G\right)F_1\left(Q_G\right)& 0\\ 0& -{\mathrm{\α}}_{1i}{\left(\frac{V_{i,\max }-V_{i,\min }}{2}\right)}^2-{\mathrm{\γ}}_1\end{array}\right]\≥0\\ & M_{k2}-\left[\begin{array}{cc}{\mathrm{\α}}_{2i}{F}_2^T\left(P_G\right)F_2\left(P_G\right)& 0\\ 0& -{\mathrm{\α}}_{2i}{\left(P_{j,\max }\right)}^2-{\mathrm{\γ}}_2\end{array}\right]\≥0\\ & P_{Gi,\min }\≤P_{Gi}\≤P_{Gi,\max }\\ & Q_{Gi,\min }\≤Q_{Gi}\≤Q_{Gi,\max }\end{array}\end{array}\right.$

and 5) solving the model in the step 4) by adopting an immune particle swarm algorithm to obtain an optimal solution in the available transmission capacity evaluation scheme considering the uncertainty of the wind power probability distribution.

2. The method of power transmission system robust available power transmission capability assessment according to claim 1, characterized in that said power transmission system employs an IEEE30 node.

3. The method for evaluating robust available transmission capacity of a transmission system according to claim 1, wherein feasible solutions of feasible node voltages and branch powers are substituted into the probability distribution robust opportunity constraint model in step 1) by adopting a power flow calculation method in step 4).

4. The method of claim 3, wherein the power flow calculation is performed by Newton method.