New energy power grid power transmission reliability margin probability assessment method and system

Abstract

The invention discloses a new energy power grid power transmission reliability margin probability assessment method and system. Under the uncertainty caused by a large number of renewable energy source grid connection and load changes, an uncertainty quantification method of orthogonal polynomial approximation is combined, an innovative solution is provided for probability power transmission reliability margin assessment in an electric network, the power transmission reliability margin can be accurately and efficiently assessed, and the probability power transmission reliability margin can be accurately and efficiently assessed. The method shows remarkable effects in the aspects of improving the calculation efficiency, improving the evaluation precision, supporting decision making, promoting renewable energy source integration and the like, and has important practical application value for planning and operation of a modern power system.

Description

Technical Field

[0001] The present invention relates to a new energy power grid transmission reliability margin probability assessment method and system, belonging to the field of power system planning and operation optimization. Background Art

[0002] With the widespread access to renewable energy (such as wind power and solar photovoltaic) and new loads (such as electric vehicles), more and more uncertainties have been brought to the power grid, which has brought new challenges to the assessment of transmission reliability margin of renewable energy power grids. These challenges mainly stem from the high uncertainty of renewable energy, such as wind and solar energy, and the volatility of their output, which increase the complexity of the power network in maintaining stable and reliable power transmission capabilities. Existing probabilistic assessment methods, such as Monte Carlo simulation, although they can provide certain assessment results, are computationally very time-consuming and inefficient because a large number of samples are required to obtain statistical accuracy. Therefore, developing a new method that can effectively handle uncertainty and significantly improve computational efficiency and assessment accuracy is crucial for the planning and optimization of renewable energy in power networks. Summary of the invention

[0003] Purpose of the invention: In view of the shortcomings of the prior art, the present invention proposes a new energy power grid transmission reliability margin probabilistic assessment method and system, introduces a calculation algorithm combined with orthogonal polynomials, which can accurately assess the transmission reliability margin while significantly reducing the calculation workload.

[0004] Technical solution: In the first aspect, a new energy power grid transmission reliability margin probability assessment method comprises the following steps:

[0005] Step S1: inputting data of the power network, including probability distribution and parameters of renewable energy and load;

[0006] Step S2: For each random input, select an independent standard variable and its corresponding orthogonal polynomial;

[0007] Step S3: Generate an experimental design, generate samples in the standard normal space using the Latin hypercube sampling method, then transform the random variables in the standard normal space into the original input variable space, and use the probability continuous power flow equation PCPF to evaluate the transformed samples to obtain deterministic response values;

[0008] Step S4: determining the best orthogonal polynomial approximation PCE for each response through an adaptive sparse process;

[0009] Step S5: If the accuracy of the orthogonal polynomial approximation of all responses meets the specified requirements, go to step S6, otherwise go to step S3 to generate additional samples, and then go to step S4;

[0010] Step S6: evaluating a specified large number of samples using the obtained best orthogonal polynomial approximation to determine the response value;

[0011] Step S7: Calculate the statistics of each response and generate a result report.

[0012] Furthermore, in step S1, renewable energy includes wind power generation and solar power generation, and each uncertain input model is represented as a probability density function X~f X (x) is a random variable related to wind power generation. For wind power generation, the statistical characteristics of wind speed v in the short term are fitted by normal distribution, and the corresponding probability density function is: Where v is the wind speed and μ is the expected wind speed. v and variance Obtained from historical data;

[0013] For solar power generation, the statistical characteristics of solar radiation r in the short term also adopt normal distribution, and the corresponding probability density function is: Where r is the actual solar radiation and μ is the expected r and variance Obtained from historical data;

[0014] Normal distribution is used to describe the uncertainty of load forecasting. The load active power P L The average of the predicted results Provided by the load forecaster, represents the prediction variance.

[0015] Furthermore, for each realization of wind speed v, the corresponding wind power injected into the terminal bus is calculated by the wind speed-power output relationship:

[0016]

[0017] Where v in , v out and v rated are cut-in, cut-out and rated wind speed, P r is the rated wind power, P w Output active power for wind power;

[0018] The solar radiation-power output relationship is expressed as:

[0019]

[0020] Where P pv Represents the power output of solar power generation, r c is the specified radiation point, r std is the solar radiation under standard environment, P rs is the rated capacity of solar photovoltaic.

[0021] Furthermore, the step S2 includes: selecting a suitable orthogonal polynomial as a basis function according to the probability distribution type of the random variable to construct a proxy model: Where the random vector ξ=(ξ1,ξ2,...,ξ n ) has n independent components; Ψ k (ξ) is related to f ξ Orthogonal multivariate polynomials; c k are the coefficients of the orthogonal polynomials; The subscript i j represents the jth degree of the i-th univariate polynomial basis, f ξ is the joint probability density function of random vectors, About An orthogonal basis.

[0022] Furthermore, step S3 includes: generating M in the standard normal space using the Latin hypercube sampling method C Samples

[0023] Using the variable transformation principle, the random variable ξ in the standard normal space is transformed into C Convert to the original input variable space u C : Here you C Represents wind speed v, solar radiation r and load active power P L , is u C The inverse cumulative distribution function of C The cumulative distribution function of

[0024] Use PCPF to evaluate the transformed sample u C , to obtain a deterministic response value

[0025] Furthermore, with v, r and P L are random vectors representing wind speed, solar radiation and load changes respectively. For PV type nodes, the three-phase probabilistic continuous power flow PCPF equation corresponding to the N-node system is expressed as:

[0026]

[0027] In the formula, is the node i in phase The voltage amplitude of , M is the number of phases; and is the distance between nodes i and j at phase k and The conductance and susceptance of the admittance matrix; and are the phases k and The cosine and sine of the voltage phase angle difference; λ is the Lagrange multiplier, which is used to deal with the constraints in the optimization problem; and Wind power, solar photovoltaic, load and other types of distributed generators on bus phase i The actual power change; and are the reactive power changes caused by wind speed changes and load changes respectively; is the node i in phase Reactive power generation; Q min,i and Q max,i are the minimum and maximum values ​​of reactive power respectively; the above equation is expressed in a compact form:

[0028] f(x,μ,λ,U)=f(x,μ)-λb(U)=0

[0029] Where x is the state vector, μ is the control parameter vector, and U = [v, r, P L ] is a random vector describing wind speed, solar radiation, and load active power; the system load variation vector b is:

[0030]

[0031] Then the optimization problem of transmission reliability margin probability can be expressed as:

[0032] maxλ

[0033] stf(x,μ)-λb(U)=0

[0034] V min ≤V i (x,μ,λ,U)≤V max

[0035]

[0036] Where V min and V max are the lower and upper limits of the node voltage amplitude respectively; I ij,max is the specified capacity of the line or transformer between node i and node j; λ is the normalized load margin under a given load generation change vector. When the constraints of the above formula are met, the maximum value of λ corresponds to the transmission reliability margin.

[0037] Furthermore, in step S4, the response Y i The k-th orthogonal polynomial approximation of is constructed by the following steps:

[0038] S41, generate k candidate multi-index sets, and use the formula Truncating them, where n is the number of random variable inputs and p is the total order of the orthogonal polynomials; the k-term multivariate polynomials obtained by evaluation form the design matrix H k =[H k-1 , ΔH k ], ΔH k is the matrix corresponding to the newly added k-th polynomial;

[0039] S42, apply the least angle regression LAR algorithm to select the best retained design matrix H k The column set uses the least squares method to estimate the corresponding coefficients. For random variables The corresponding random response calculated by the deterministic method The coefficients are calculated by solving a least squares minimization problem:

[0040] S43, using the formula ε cloo =ε loo ×T(M C ,P) Calculate the leave-one-out error index In the formula h=diag[H(H T H) -1 H T ], is the response vector y C If the error reaches the specified accuracy, the best orthogonal polynomial approximation is returned; otherwise, the degree of the polynomial is increased and S41 is returned.

[0041] In a second aspect, a new energy power grid transmission reliability margin probability assessment system includes:

[0042] A data input module, used for inputting data of the electric network, including probability distribution and parameters of renewable energy sources and loads;

[0043] An orthogonal polynomial selection module is used to select an independent standard variable and its corresponding orthogonal polynomial for each random input;

[0044] Experimental design module, used to generate experimental design, generate samples in the standard normal space using the Latin hypercube sampling method, then transform the random variables in the standard normal space into the original input variable space, and use the probability continuous power flow equation PCPF to evaluate the transformed samples to obtain deterministic response values;

[0045] A PCE determination module, for determining the best orthogonal polynomial approximation PCE for each response through an adaptive sparse process;

[0046] The response value determination module is used to determine whether the accuracy of the orthogonal polynomial approximation of all responses meets the specified requirements. If so, it calls the best orthogonal polynomial approximation obtained to evaluate a large number of specified samples to determine the response value; if not, it calls the experimental design module to generate additional samples, and then transfers to the PCE determination module for processing;

[0047] The result output module is used to calculate the statistical data of each response according to the determined response value and generate a result report.

[0048] In a third aspect, a computer device comprises one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and when the programs are executed by the processors, the steps of the new energy power grid transmission reliability margin probabilistic assessment method as described in the first aspect of the present invention are implemented.

[0049] In a fourth aspect, a computer storage medium stores a computer program thereon, which, when executed by a processor, implements the steps of the new energy power grid transmission reliability margin probabilistic assessment method as described in the first aspect of the present invention.

[0050] Beneficial effects: The present invention provides an innovative solution for the probabilistic transmission reliability margin assessment in power networks by combining the uncertainty quantification method of orthogonal polynomial approximation. This method provides an accurate and efficient assessment method for the uncertainties caused by renewable energy grid connection and load changes. Compared with traditional methods such as Monte Carlo simulation, the present invention has significant advantages in the following aspects:

[0051] (1) Significantly improve computational efficiency: By using the adaptive sparse method to construct an orthogonal polynomial approximation, the present invention greatly reduces the number of samples required to evaluate the probabilistic transmission reliability margin, thereby significantly improving computational efficiency.

[0052] (2) Improved evaluation accuracy: Combining the adaptive sparse method with the orthogonal polynomial approximation not only optimizes the calculation process, but also improves the evaluation accuracy. This method can more accurately capture and handle uncertainty, ensuring that the evaluation results are closer to the actual network operation.

[0053] (3) Supporting decision making: By providing more accurate and efficient probabilistic transmission reliability margin assessment, the present invention supports grid planners and operators to make more scientific and reasonable decisions in the face of uncertainty. Especially in the context of increasing renewable energy, the present invention provides strong technical support for the reliability and stability management of the grid.

[0054] (4) Promoting the integration of renewable energy: The present invention helps optimize the access and use of renewable energy by accurately assessing the probabilistic transmission reliability margin of the power network. This not only improves the operating efficiency of the power grid, but also promotes the wider application of renewable energy and supports the green transformation of the energy structure. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is a flow chart of the probability assessment method of the reliability margin of new energy power grid transmission.

[0056] Figure 2 β is the PV curve and the transmission reliability margin for deterministic evaluation of the transmission reliability margin of the renewable energy grid in the embodiment.

[0057] Figure 3 3 is a schematic diagram of the cumulative distribution function (CDF) of the voltage over-limit reliability margin calculated by using the MCS and orthogonal polynomial approximation method in the embodiment. DETAILED DESCRIPTION

[0058] The technical solutions in the embodiments of the present invention will be described clearly and completely below with reference to the accompanying drawings.

[0059] The present invention proposes a method for evaluating the reliability margin of power grid transmission under the uncertainty caused by large-scale grid connection of renewable energy and load changes. Figure 1 , the method comprises the following steps:

[0060] Step S1: inputting data of the power network, including probability distribution and parameters of renewable energy and load;

[0061] Renewable energy Considering wind power and solar power, each uncertain input model can be expressed as a probability density function (PDF) X~f X (x) is a random variable related to wind power generation. For wind power generation, the statistical characteristics of wind speed v in the short term can be fitted by normal distribution, and the corresponding probability density function is:

[0062]

[0063] Where v is the wind speed and μ is the expected wind speed. v and variance It can be obtained from historical data. For each realization of wind speed v, the corresponding wind power injected into the terminal bus can be calculated by the wind speed-power output relationship:

[0064]

[0065] Where v in , v out and v rated are cut-in, cut-out and rated wind speed (m / s), Pr is the rated wind power, P w Output active power for wind power.

[0066] For solar power generation, the statistical characteristics of solar radiation r in the short term can also adopt normal distribution, and the corresponding probability density function is:

[0067]

[0068] Where r is the actual solar radiation and μ is the expected r and variance It can be obtained from historical data; the solar radiation-power output relationship is expressed as:

[0069]

[0070] Where P pv Represents the power output of solar power generation, r c For a certain radiation point, usually set to 150W / m 2 , r std is the solar radiation under standard environment, P rs is the rated capacity of solar photovoltaic.

[0071] In order to describe the uncertainty of load forecasting, the most common approach is to use normal distribution. Load active power P L The average of the predicted results Usually provided by a load forecaster, Represents the prediction variance. The load forecaster generally only provides active power, while the reactive power is determined under the assumption of a constant power factor. The load forecaster can use common load forecasting methods and can achieve high-resolution load forecasting based on a large amount of real-time load data provided by smart meters.

[0072] Step S2: For each random input, select an independent standard variable and its corresponding orthogonal polynomial;

[0073] According to the probability distribution type of the random variable, select the appropriate orthogonal polynomial as the basis function to construct the proxy model: Where the random vector ξ=(ξ1,ξ2,...,ξ n ), has n independent components; Ψ k (ξ) is related to f ξ Orthogonal multivariate polynomials ( The subscript i j represents the jth degree of the i-th univariate polynomial basis, f ξ is the joint probability density function of random vectors, About orthogonal basis); c kare the coefficients of the orthogonal polynomial. In practical applications, the orthogonal polynomial approximation of the random response will be truncated so that the total order is no higher than p (p is the total order of the orthogonal polynomial: ), so the truncated proxy model is Where P is the number of reserved items.

[0074] The common continuous probability distribution correspondence is shown in Table 1.

[0075] Table 1 Standard forms of classical continuous distributions and their corresponding orthogonal polynomials

[0076]

[0077] Through step 2, a multivariate orthogonal polynomial of polynomial chaos expansion is constructed, which is composed of the tensor product of univariate orthogonal polynomials. To illustrate the construction of mixed multivariate orthogonal polynomials, take a two-dimensional input random vector ξ=(ξ1,ξ2) as an example, the distribution functions of its independent variables are ξ1~N(0,1) and ξ2~N(0,1), respectively, then the multivariate polynomial corresponding to p≤2 is:

[0078]

[0079] Where φ1 and φ2 are Hermite polynomials, and thus we get the proxy model Y=c0Ψ0(ξ)+…+c5Ψ5(ξ) based on orthogonal polynomial expansion. k The subscript k and index a of (ξ) k =(a k1 ,...,a kn ) there is an implicit one-to-one mapping between ki Defines the order j of the i-th univariate polynomial basis, i.e. j = a ki .

[0080] Step S3: Generate the experimental design, generate samples using Latin hypercube sampling, and then transform the random variables in the standard normal space into the original input variable space, and use the probability continuous power flow equation (PCPF) to obtain the deterministic response value;

[0081] First, the Latin hypercube sampling method is used to generate M in the standard normal space. C Samples

[0082] Secondly, the variable transformation principle is used to transform the random variable ξ in the standard normal space C Convert to the original input variable space u C : Here you C is wind speed v, solar radiation r and load active power P L , is u C The inverse cumulative distribution function of C The cumulative distribution function of ξ can be obtained by this inverse transformation, which can then be used to convert the sample set of ξ back to the sample of U. The PCPF can then be used to evaluate the converted sample u. C , to obtain a deterministic response value

[0083] The present invention uses wind speed, solar radiation and load change as random input variables, and then uses the power flow equation to perform a deterministic assessment of the transmission reliability margin of the renewable energy power grid. The power flow equation can be expressed as: where x = [θ a ,θ b ,θ c ,V a ,V b ,V c ] T , represents the voltage phase angle and amplitude of each phase; and Represents the node i in phase Initial values ​​of active power and reactive power (e.g. phases a, b, c); and They represent the phase of node i in f(x). The calculated values ​​of active power and reactive power. Let v, r and P L are random vectors representing wind speed, solar radiation and load changes respectively. For PV type nodes, the three-phase probabilistic continuous power flow (PCPF) equation corresponding to the N-node system can be expressed as:

[0084]

[0085] In the formula, is the node i in phase The voltage amplitude of , M is the number of phases; and is the distance between nodes i and j at phase k and The conductance (real part) and susceptance (imaginary part) of the admittance matrix; and are the phases k and The cosine and sine of the voltage phase angle difference; λ is the Lagrange multiplier, which is used to deal with the constraints in the optimization problem; and Wind power, solar photovoltaic, load and other types of distributed generators on bus phase i The actual power change; and are the reactive power changes caused by wind speed changes and load changes respectively; is the node i in phase Reactive power generation; Q min,i and Q max,i are the minimum and maximum values ​​of reactive power respectively. The above equation can be expressed in a compact form:

[0086] f(x,μ,λ,U)=f(x,μ)-λb(U)=0

[0087] Where x is the state vector, μ is the control parameter vector such as transformer tapping ratio, and U = [v, r, P L ] is a random vector describing wind speed, solar radiation, and load active power. The system load produces a change vector b:

[0088]

[0089] Therefore, the following probabilistic transmission reliability margin formula can be proposed:

[0090] maxλ

[0091] stf(x,μ)-λb(U)=0

[0092] V min ≤V i (x,μ,λ,U)≤V max

[0093]

[0094] Where V min and V max are the lower and upper limits of the node voltage amplitude respectively; I ij,max is the specified capacity of the line or transformer between node i and node j; λ is the normalized load margin under a given load generation change vector. The maximum value of λ under the above constraint conditions corresponds to the transmission reliability margin.

[0095] Step S4: determining the best orthogonal polynomial approximation (PCE) for each response through an adaptive sparse process;

[0096] Response Y i The k orthogonal polynomial approximations are constructed by the following steps:

[0097] a) Generate k candidate multi-index sets and use the formula Truncating them, where n is the number of random variable inputs and p is the total order of the orthogonal polynomials, is the definition of the hyperbolic (q-norm) truncation scheme. k It is a multiple indicator. is the order of the univariate orthogonal polynomial under the q-norm cutoff. The k-term multivariate polynomials obtained by evaluation form the design matrix H k =[H k-1 , ΔH k ], H k-1 Contains the H matrix corresponding to the first k-1 polynomials; ΔH k is the matrix corresponding to the newly added k-th polynomial, by replacing ΔH k Add to H k-1 In the design matrix H k It contains all the truncated polynomial basis function values ​​from the 0th term to the kth term.

[0098] b) Apply the least angle regression (LAR) algorithm to select the best retained design matrix H k The least squares method is used to estimate the corresponding coefficients. The corresponding random response calculated by the deterministic method The coefficients are calculated by solving the following least squares minimization problem: Establish the vector that minimizes the objective function The calculation formula for the fitted value is: Then use the ordinary least squares method to get the solution to the objective function of the above formula,

[0099] c) Using the formula ε cloo =ε loo ×T(M C ,P) Calculate the leave-one-out error index In the formula

[0100] h=diag[H(H T H)- 1 H T ], is the response vector y C The empirical variance of is calculated using the surrogate model in the variable ξ (i) The response value obtained under i is the i-th component of h; diag means extracting the diagonal elements of the matrix; tr means the trace of the matrix, that is, the sum of the diagonal elements of the matrix. If the specified accuracy is achieved, that is or Then the best orthogonal polynomial approximation is returned, where ∈ is the specified accuracy threshold. Otherwise, increase the degree of the polynomial and return a).

[0101] Step S5: If all the PCEs of the responses meet the required accuracy, go to step S6; otherwise, go to step S3 to generate additional ΔMC New sample, then return to step S4.

[0102] Step S6: Once the orthogonal polynomial approximation for all responses is obtained, it can be used to evaluate a large number of samples to determine the response value; the calculation method is as follows:

[0103]

[0104] Step S7: Calculate the statistics of each response and generate a result report.

[0105] In step S6, the output responses of a large number of samples are calculated by the polynomial expansion model, and these responses are the available transmission capacity of the power grid. In step S7, the output data of these samples are statistically analyzed to obtain the statistical indicators and probability distribution curve of the available transmission capacity of the power grid, so as to calculate the system reliability margin under a certain confidence level, providing a reference for power grid performance evaluation and planning.

[0106] In order to verify the performance of the method of the present invention, the proposed method is applied to study the improved IEEE 13 node, and the accuracy and performance of the method are verified by using Monte Carlo simulation (MCS) based on Latin hypercube sampling. It is assumed that wind speed, solar radiation, and load power all obey normal distribution. Here, it is assumed that the mean of the load power is equal to the original bus load power, and the standard deviation is equal to 5% of its mean.

[0107] For simplicity, all wind turbines are equipped with the same v rate 、v in and v out , 15.0, 4.0 and 25.0 (m / s) respectively. Similarly, assuming that all solar generators are assigned the same r C and r std , respectively 150.0 and 1000.0 (W / m 2 ).

[0108] Add 2 solar photovoltaic generators and 2 wind turbines to the IEEE 13-bus system, with a total load of 1.733MW and 1.051Mvar respectively. The solar photovoltaic rated power P of busbars 675 and 692 is r The rated power P of wind turbines with busbars 680 and 634 are 180kW and 240kW respectively. r 450kW and 300kW respectively. Since there are 8 single phase loads in this feeder, the total random input is 12.

[0109] In order to study the impact of uncertainty on the system transmission reliability margin, the power flow equation is used to track the PV curve and transmission reliability margin of the renewable energy grid (i.e., without uncertainty). Figure 2 As shown, Figure 2 The three-phase PV curves of the 675 bus at the sampling point where all random inputs are equal to their average values ​​(i.e., deterministic systems) are shown. Due to the imbalance of network parameters and loads, the PV curves of different phases are quite different. As the load increases in the predetermined direction, the voltage value of phase C decreases the fastest. Among the three transmission reliability margins (voltage over-limit reliability margin, thermal limit reliability margin, and voltage collapse reliability margin), the voltage over-limit reliability margin is 0.894MW, which is the smallest reliability margin.

[0110] Table 2 Comparison of the statistics of reliability margin under the two calculation methods

[0111]

[0112] Table 2 gives the estimated mean and variance of the system transmission reliability margin using MCS and orthogonal polynomial approximation method (subscript pce). It can be seen that the orthogonal polynomial approximation method can accurately estimate the probability and statistical characteristics of the system transmission reliability margin.

[0113] Table 3 Comparison of calculation time under two calculation methods

[0114] method <![CDATA[t ed (s)]]> <![CDATA[t sc (s)]]> <![CDATA[t es (s)]]> <![CDATA[t total (s)]]> PCE 8.713 0.934 0.213 9.860 MCS - - 2810.654 2810.654

[0115] Table 3 lists the experimental design time t ed , solve the coefficient time t sc , Evaluation statistics sample time t es and the total time t total Compared with MCS, the orthogonal polynomial approximation method (PCE) spends negligible time on solving coefficients and evaluating statistical samples. Obviously, the orthogonal polynomial approximation method is more efficient than MCS.

[0116] Once the statistical data of the probabilistic transmission reliability margin are available, the next direction is to study how uncertainty affects the transmission reliability margin of the power grid.

[0117] like Figure 2 As shown in the figure, the voltage over-limit reliability margin without considering uncertainty is 0.894MW, while in the CDF curve ( Figure 3 ) is 0.518, indicating that there is a 51.8% probability that the transmission reliability margin is less than or equal to 0.894MW. In practical applications, such a low safety probability is usually unacceptable. Therefore, in order to ensure a 95% confidence level that the system will not encounter voltage limit violations, the voltage limit reliability margin must be reduced from 0.894MW to 0.868MW, a corresponding reduction of 2.91%.

[0118] Compared with traditional Monte Carlo simulation, the method of the present invention greatly reduces the computational workload, provides insights into how uncertainty affects the reliability margin of power grid transmission, and provides important information and new perspectives for the planning and operation of power systems containing renewable energy.

[0119] Based on the same technical concept as the method embodiment, the present invention also provides a new energy power grid transmission reliability margin probability assessment system, including:

[0120] A data input module, used for inputting data of the electric network, including probability distribution and parameters of renewable energy sources and loads;

[0121] An orthogonal polynomial selection module is used to select an independent standard variable and its corresponding orthogonal polynomial for each random input;

[0122] Experimental design module, used to generate experimental design, generate samples in the standard normal space using the Latin hypercube sampling method, then transform the random variables in the standard normal space into the original input variable space, and use the probability continuous power flow equation PCPF to evaluate the transformed samples to obtain deterministic response values;

[0123] A PCE determination module, for determining the best orthogonal polynomial approximation PCE for each response through an adaptive sparse process;

[0124] The response value determination module is used to determine whether the accuracy of the orthogonal polynomial approximation of all responses meets the specified requirements. If so, it calls the best orthogonal polynomial approximation obtained to evaluate a large number of specified samples to determine the response value; if not, it calls the experimental design module to generate additional samples, and then transfers to the PCE determination module for processing;

[0125] The result output module is used to calculate the statistical data of each response according to the determined response value and generate a result report.

[0126] It should be understood that the new energy power grid transmission reliability margin probabilistic assessment system in the embodiment of the present invention can implement all the technical solutions in the above-mentioned method embodiment, and the functions of its various functional modules can be specifically implemented according to the methods in the above-mentioned method embodiments. The specific implementation process can refer to the relevant description in the above-mentioned embodiments, which will not be repeated here.

[0127] The present invention also provides a computer device, comprising: one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and when the programs are executed by the processors, the steps of the new energy power grid transmission reliability margin probabilistic assessment method as described above are implemented.

[0128] The present invention also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the above-mentioned new energy power grid transmission reliability margin probability assessment method are implemented.

[0129] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, devices (systems), computer equipment or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0130] The present invention is described with reference to a flowchart of a method according to an embodiment of the present invention. It should be understood that each process in the flowchart and a combination of processes in the flowchart can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the process in the flowchart. Figure 1 A device that specifies functions in a process or multiple processes.

[0131] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A function specified in a process or multiple processes.

[0132] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 The steps of a specified function in a process or multiple processes.

Claims

1. A new energy power grid transmission reliability margin probability assessment method, characterized in that: The method comprises the following steps: Step S1: inputting data of the power network, including probability distribution and parameters of renewable energy and load; Step S2: For each random input, select an independent standard variable and its corresponding orthogonal polynomial; Step S3: Generate an experimental design, generate samples in the standard normal space using the Latin hypercube sampling method, then transform the random variables in the standard normal space into the original input variable space, and use the probability continuous power flow equation PCPF to evaluate the transformed samples to obtain deterministic response values; Step S4: determining the best orthogonal polynomial approximation PCE for each response through an adaptive sparse process; Step S5: If the accuracy of the orthogonal polynomial approximation of all responses meets the specified requirements, go to step S6, otherwise go to step S3 to generate additional samples, and then go to step S4; Step S6: evaluating a specified large number of samples using the obtained best orthogonal polynomial approximation to determine the response value; Step S7: Calculate the statistics of each response and generate a result report.

2. The method according to claim 1, characterized in that In step S1, renewable energy includes wind power generation and solar power generation. Each uncertain input model is represented as a probability density function X~f X (x) is a random variable related to wind power generation. For wind power generation, the statistical characteristics of wind speed v in the short term are fitted by normal distribution, and the corresponding probability density function is: Where v is the wind speed and μ is the expected wind speed. v and variance Obtained from historical data; For solar power generation, the statistical characteristics of solar radiation r in the short term also adopt normal distribution, and the corresponding probability density function is: Where r is the actual solar radiation and μ is the expected r and variance Obtained from historical data; Normal distribution is used to describe the uncertainty of load forecasting. The load active power P L The average of the predicted results Provided by the load forecaster, represents the prediction variance.

3. The method according to claim 2, characterized in that For each realization of wind speed v, the corresponding wind power injected into the terminal bus is calculated by the wind speed-power output relationship: Where v in , v out and v rated are cut-in, cut-out and rated wind speed, P r is the rated wind power, P w Output active power for wind power; The solar radiation-power output relationship is expressed as: Where P pv Represents the power output of solar power generation, r c is the specified radiation point, r std is the solar radiation under standard environment, P rs is the rated capacity of solar photovoltaic.

4. The method according to claim 1, characterized in that: The step S2 comprises: selecting a suitable orthogonal polynomial as a basis function according to the probability distribution type of the random variable to construct a proxy model: Where the random vector ξ=(ξ1,ξ2,...,ξ n ) has n independent components; Ψ k (ξ) is related to f ξ Orthogonal multivariate polynomials; c k are the coefficients of the orthogonal polynomials; k=0,...,∞, where the subscript i j represents the jth degree of the i-th univariate polynomial basis, f ξ is the joint probability density function of random vectors, About An orthogonal basis.

5. The method according to claim 4, characterized in that The step S3 comprises: generating M in the standard normal space by using the Latin hypercube sampling method C Samples Using the variable transformation principle, the random variable ξ in the standard normal space is transformed into C Convert to the original input variable space u C : Here you C Represents wind speed v, solar radiation r and load active power P L , is u C The inverse cumulative distribution function of C The cumulative distribution function of Use PCPF to evaluate the transformed sample u C , to obtain a deterministic response value 6. The method according to claim 5, characterized in that With v, r and P L are random vectors representing wind speed, solar radiation and load changes respectively. For PV type nodes, the three-phase probabilistic continuous power flow PCPF equation corresponding to the N-node system is expressed as: In the formula, is the node i in phase The voltage amplitude of , M is the number of phases; and is the distance between nodes i and j at phase k and The conductance and susceptance of the admittance matrix; and are the phases k and The cosine and sine of the voltage phase angle difference; λ is the Lagrange multiplier, which is used to deal with the constraints in the optimization problem; and Wind power, solar photovoltaic, load and other types of distributed generators on bus phase i The actual power change; and are the reactive power changes caused by wind speed changes and load changes respectively; is the node i in phase Reactive power generation; Q min,i and Q max,i are the minimum and maximum values ​​of reactive power respectively; the above equation is expressed in a compact form: f(x,μ,λ,U)=f(x,μ)-λb(U)=0 Where x is the state vector, μ is the control parameter vector, U = [v, r, P L ] is a random vector describing wind speed, solar radiation, and load active power; the system load variation vector b is: Then the optimization problem of transmission reliability margin probability can be expressed as: maxλ stf(x,μ)-λb(U)=0 V min ≤V i (x,μ,λ,U)≤V max Where V min and V max are the lower and upper limits of the node voltage amplitude respectively; I ij,max is the specified capacity of the line or transformer between node i and node j; λ is the normalized load margin under a given load generation change vector. When the constraints in the above formula are met, the maximum value of λ corresponds to the transmission reliability margin.

7. The method according to claim 5, characterized in that In step S4, the response Y i The k-th orthogonal polynomial approximation of is constructed by the following steps: S41, generate k candidate multi-index sets, and use the formula Truncating them, where n is the number of random variable inputs and p is the total order of the orthogonal polynomials; the k-term multivariate polynomials obtained by evaluation form the design matrix H k =[H k-1 , ΔH k ], ΔH k is the matrix corresponding to the newly added k-th polynomial; S42, apply the least angle regression LAR algorithm to select the best retained design matrix H k The column set uses the least squares method to estimate the corresponding coefficients. For random variables The corresponding random response calculated by the deterministic method The coefficients are calculated by solving a least squares minimization problem: S43, using the formula ε cloo =ε loo ×T(M C ,P) Calculate the leave-one-out error index In the formula h=diag[H(H T H)- 1 H T ], is the response vector y C If the error reaches the specified accuracy, the best orthogonal polynomial approximation is returned; otherwise, the degree of the polynomial is increased and S41 is returned.

8. A new energy power grid transmission reliability margin probability assessment system, characterized in that: include: A data input module, used for inputting data of the electric network, including probability distribution and parameters of renewable energy sources and loads; An orthogonal polynomial selection module is used to select an independent standard variable and its corresponding orthogonal polynomial for each random input; Experimental design module, used to generate experimental design, generate samples in the standard normal space using the Latin hypercube sampling method, then transform the random variables in the standard normal space into the original input variable space, and use the probability continuous power flow equation PCPF to evaluate the transformed samples to obtain deterministic response values; A PCE determination module, for determining the best orthogonal polynomial approximation PCE for each response through an adaptive sparse process; The response value determination module is used to determine whether the accuracy of the orthogonal polynomial approximation of all responses meets the specified requirements. If so, it calls the best orthogonal polynomial approximation obtained to evaluate a large number of specified samples to determine the response value; if not, it calls the experimental design module to generate additional samples, and then transfers to the PCE determination module for processing; The result output module is used to calculate the statistical data of each response according to the determined response value and generate a result report.

9. A computer device, characterized in that: The device includes one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and when the programs are executed by the processors, the steps of the new energy power grid transmission reliability margin probabilistic assessment method as described in any one of claims 1 to 7 are implemented.

10. A computer storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the new energy power grid transmission reliability margin probabilistic assessment method as described in any one of claims 1 to 7 are implemented.

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