Power system probabilistic load flow calculation method, device and equipment and storage medium

Abstract

The invention discloses a power system probabilistic load flow calculation method, device and equipment and a storage medium. Dividing the historical data of the photovoltaic power station and the historical data of the wind power station according to a set time period to obtain the historical data of the photovoltaic power station and the historical data of the wind power station in each time period, and removing the historical data of the photovoltaic power station in the time period when the sunlight radiation intensity is lower than a preset value; constructing a first output model of the photovoltaic power station and a second output model of the wind power station in each time period, extracting a plurality of first sample points from the first output model and extracting a plurality of second sample points from the second output model based on the spatial correlation of the photovoltaic power station and the wind power station, the probabilistic load flow of the power system in each time period is calculated based on the first sample point and the second sample point, the influence of the spatial-temporal correlation on the probabilistic load flow of the power system is comprehensively considered, and the accuracy of probabilistic load flow calculation is improved.

Description

Technical Field

[0001] The present invention relates to electric power technology, and in particular to a method, device, equipment and storage medium for calculating probabilistic power flow in an electric power system. Background Art

[0002] The probabilistic power flow calculation of the power system is an analytical method based on statistical principles to study the power flow distribution of the power system. The probability distribution of the node's injected power, the node's output power, etc. is considered during the calculation, so as to obtain the statistical data of the power flow distribution of the power system, which can provide a more comprehensive understanding of the grid behavior when renewable energy is connected to the grid.

[0003] With the large-scale development and construction of renewable energy, the capacity of renewable energy grid connection has increased rapidly. Therefore, the high proportion of renewable energy grid connection operation will become the development trend and important feature of the power network in the future. Compared with traditional power generation technology, the controllability of renewable energy power generation technology is significantly lower. For example, wind power generation is affected by the instantaneous wind speed, and photovoltaic power generation is affected by the intensity of light radiation. Under this influence, renewable energy power generation technology shows the characteristics of intermittency, uncertainty, and volatility. According to the statistics of the International Energy Agency, the output of renewable energy power generation technology shows greater volatility. In the second level analysis, the maximum fluctuation can reach 2%; at the minute level and hour level, the fluctuation can be as high as 30% and 40% respectively.

[0004] Existing probabilistic power flow calculation methods for power systems often ignore the impact of time and space on renewable energy, resulting in large errors in probabilistic power flow calculations. Summary of the invention

[0005] The present invention provides a method, device, equipment and storage medium for calculating probabilistic power flow of an electric power system, so as to improve the accuracy of probabilistic power flow calculation.

[0006] In a first aspect, the present invention provides a method for calculating a probabilistic power flow in a power system, comprising:

[0007] Obtain historical data of photovoltaic power plants and wind power plants;

[0008] Dividing the historical data of the photovoltaic power station and the historical data of the wind power station according to a set time period to obtain the historical data of the photovoltaic power station and the historical data of the wind power station in each time period;

[0009] The historical data of the photovoltaic power station in each time period is screened based on the sunshine radiation intensity, and the historical data of the photovoltaic power station in the time period in which the sunshine radiation intensity is lower than a preset value is eliminated;

[0010] Modeling is performed based on the historical data of each time period after the photovoltaic power station is eliminated, so as to obtain a first output model that characterizes the relationship between the output power of the photovoltaic power station and the intensity of solar radiation in each time period, and modeling is performed based on the historical data of the wind power station, so as to obtain a second output model that characterizes the relationship between the output power of the wind power station and the wind speed in each time period;

[0011] Based on the spatial correlation between the photovoltaic power station and the wind power station, extracting a plurality of first sample points from the first output model and extracting a plurality of second sample points from the second output model;

[0012] The probabilistic power flow of the power system in each time period is calculated based on the first sample point and the second sample point.

[0013] Optionally, the first output model is:

[0014]

[0015] Among them, r c is the set solar radiation intensity; r std is the rated solar radiation intensity; P d is the rated power of the PV power station, and r is the solar radiation intensity received by the PV power station.

[0016] Optionally, the second output model is:

[0017]

[0018] Among them, v ci is the cut-in wind speed, v r is the rated wind speed, v co is the cut-out wind speed, P r is the rated active power of the wind power station, C1 is the slope, and v is the wind speed received by the wind power station.

[0019] Optionally, based on the spatial correlation between the photovoltaic power station and the wind power station, extracting a plurality of first sample points from the first output model and extracting a plurality of second sample points from the second output model comprises:

[0020] A probability density function of the sunshine radiation intensity in each time period is constructed based on the sunshine radiation intensity in each time period, and a probability density function of the wind speed in each time period is constructed based on the wind speed in each time period;

[0021] Construct the Copra function that represents the correlation between the probability density function of solar radiation intensity and the probability density function of wind speed;

[0022] The first sample point and the second sample point satisfying the Coppola function are screened out from the first output model and the second processing model by using an unscented transformation.

[0023] Optionally, the probability density function of solar radiation intensity is:

[0024]

[0025] Among them, α and β are the shape parameters of the Beta distribution, γ max is the maximum acceptable radiation intensity, μ represents the mathematical expectation value of the radiation intensity during the time period, and σ 2 represents the variance of radiation intensity during a time period, Γ() represents the gamma function;

[0026] The probability density function of wind speed is:

[0027]

[0028] Among them, v represents the wind speed, k is the shape parameter, which affects the width of the single peak image in the density curve of the instantaneous wind speed, and c is the scale parameter, which affects the position of the entire curve in the distribution curve.

[0029] Optionally, calculating the probabilistic power flow of the power system in each time period based on the first sample point and the second sample point includes:

[0030] The Newton-Raphson method is used to solve the data of the first sample point and the second sample point of a given node in the power grid to obtain the mean and covariance of the voltage, phase, and output power of the given node.

[0031] Optionally, the Newton-Raphson method is used to solve the data of the first sample point and the second sample point of a given node in the power grid to obtain the mean and covariance of the voltage, phase, and output power of the given node, including:

[0032] For a given node in the power grid, calculating the active power and reactive power of the given node based on the data of the first sample point and the second sample point;

[0033] The active power and the reactive power are substituted into the power equation of the given node, and solved using Newton-Raphson to obtain the mean and covariance of the voltage, phase, and output power of the given node.

[0034] In a second aspect, the present invention further provides a power system probabilistic power flow calculation device, comprising:

[0035] A data acquisition module, used to acquire historical data of photovoltaic power plants and historical data of wind power plants;

[0036] A data partitioning module, used to partition the historical data of the photovoltaic power station and the historical data of the wind power station according to a set time period, and obtain the historical data of the photovoltaic power station and the historical data of the wind power station in each time period;

[0037] A data screening module, used to screen the historical data of the photovoltaic power station in each time period based on the sunshine radiation intensity, and remove the historical data of the photovoltaic power station in the time period where the sunshine radiation intensity is lower than a preset value;

[0038] An output model building module is used to perform modeling based on the historical data of each time period after the photovoltaic power station is eliminated, so as to obtain a first output model representing the relationship between the output power of the photovoltaic power station and the sunshine radiation intensity in each time period, and to perform modeling based on the historical data of the wind power station, so as to obtain a second output model representing the relationship between the output power of the wind power station and the wind speed in each time period;

[0039] A sample point extraction module, configured to extract a plurality of first sample points from the first output model and a plurality of second sample points from the second output model based on the spatial correlation between the photovoltaic power station and the wind power station;

[0040] The probabilistic power flow calculation module is used to calculate the probabilistic power flow of the power system in each time period based on the first sample point and the second sample point.

[0041] In a third aspect, the present invention further provides an electronic device, comprising:

[0042] one or more processors;

[0043] A storage device for storing one or more programs;

[0044] When the one or more programs are executed by the one or more processors, the one or more processors implement the power system probabilistic power flow calculation method provided in the first aspect of the present invention.

[0045] In a fourth aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for calculating the probabilistic power flow of a power system as provided in the first aspect of the present invention.

[0046] The method for calculating the probability flow of a power system provided by the present invention divides the historical data of a photovoltaic power station and the historical data of a wind power station according to a set time period to obtain the historical data of the photovoltaic power station and the historical data of the wind power station in each time period, screens the historical data of the photovoltaic power station in each time period based on the sunshine radiation intensity, removes the historical data of the photovoltaic power station in the time period in which the sunshine radiation intensity is lower than a preset value, models the historical data of each time period after the photovoltaic power station is removed, obtains a first output model characterizing the relationship between the output power of the photovoltaic power station and the sunshine radiation intensity in each time period, and models the historical data of the wind power station to obtain a second output model characterizing the relationship between the output power of the wind power station and the wind speed in each time period, extracts a plurality of first sample points from the first output model based on the spatial correlation of the photovoltaic power station and the wind power station, and extracts a plurality of second sample points from the second output model, calculates the probability flow of the power system in each time period based on the first sample points and the second sample points, comprehensively considers the influence of the spatiotemporal correlation on the probability flow of the power system, and improves the accuracy of the probability flow calculation.

[0047] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0049] Figure 1 A flow chart of a method for calculating a probabilistic power flow in a power system provided by the present invention;

[0050] Figure 2 A schematic diagram of the structure of a power system probabilistic power flow calculation device provided by the present invention;

[0051] Figure 3 The present invention provides a schematic structural diagram of an electronic device.

[0052] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0053] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0054] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0055] Figure 1 This is a flow chart of a method for calculating a probabilistic power flow of a power system provided by the present invention. This embodiment considers the influence of time and space on the output of renewable energy to calculate the probabilistic power flow. The method can be executed by a probabilistic power flow calculation device for a power system provided by an embodiment of the present invention. The device can be implemented by software and / or hardware, and is usually configured in an electronic device, such as Figure 1 As shown, the power system probabilistic flow calculation method includes the following steps:

[0056] S101. Acquire historical data of a photovoltaic power station and historical data of a wind power station.

[0057] In the embodiment of the present invention, for each photovoltaic power station and wind power station in the power grid, historical data of the photovoltaic power station and historical data of the wind power station are acquired.

[0058] S102 , dividing the historical data of the photovoltaic power station and the historical data of the wind power station according to the set time period, and obtaining the historical data of the photovoltaic power station and the historical data of the wind power station in each time period.

[0059] In the embodiment of the present invention, the historical data of the photovoltaic power station and the historical data of the wind power station are divided according to the set time period to obtain the historical data of the photovoltaic power station and the historical data of the wind power station in each time period. For example, the time period can be 1 hour or a set time length, which is not limited in the embodiment of the present invention.

[0060] S103: Filter the historical data of the photovoltaic power station in each time period based on the sunshine radiation intensity, and remove the historical data of the photovoltaic power station in the time period when the sunshine radiation intensity is lower than a preset value.

[0061] In the embodiment of the present invention, the historical data of the photovoltaic power station in each time period is screened based on the sunlight radiation intensity, and the historical data of the photovoltaic power station in the time period when the sunlight radiation intensity is lower than the preset value is eliminated, and the valid historical data of the photovoltaic power station is retained. Exemplarily, the preset value can be set to 1000 Lux.

[0062] S104. Modeling is performed based on the historical data of each time period after the photovoltaic power station is eliminated to obtain a first output model that characterizes the relationship between the output power of the photovoltaic power station and the solar radiation intensity in each time period, and modeling is performed based on the historical data of the wind power station to obtain a second output model that characterizes the relationship between the output power of the wind power station and the wind speed in each time period.

[0063] In an embodiment of the present invention, modeling is performed based on the historical data of each time period after the photovoltaic power station is eliminated, so as to obtain a first output model that characterizes the relationship between the output power of the photovoltaic power station and the intensity of solar radiation in each time period, and modeling is performed based on the historical data of the wind power station, so as to obtain a second output model that characterizes the relationship between the output power of the wind power station and the wind speed in each time period.

[0064] The first output model is:

[0065]

[0066] Among them, r c is the set solar radiation intensity; r std is the rated solar radiation intensity; P d is the rated power of the PV power station, and r is the solar radiation intensity received by the PV power station.

[0067] The second output model is:

[0068]

[0069] Among them, v ci is the cut-in wind speed, v r is the rated wind speed, v co is the cut-out wind speed, P r is the rated active power of the wind power station, C1 is the slope, and v is the wind speed received by the wind power station.

[0070] S105 . Based on the spatial correlation between the photovoltaic power station and the wind power station, extract a plurality of first sample points from the first output model, and extract a plurality of second sample points from the second output model.

[0071] In the embodiment of the present invention, based on the spatial correlation of the photovoltaic power station and the wind power station, multiple first sample points are extracted from the first output model, and multiple second sample points are extracted from the second output model. The spatial correlation represents the correlation between wind speed and solar radiation intensity in different spatial regions.

[0072] In some embodiments of the present invention, in order to find the spatial correlation between the data of the photovoltaic power station and the data of the wind power station, the untraceable transformation method can be used for extraction. The basic idea of ​​the untraceable transformation is to approximate the probability distribution of the original state through a carefully selected set of points (called Sigma points), and then map these points to the new state space through a nonlinear function, and approximate the probability distribution of the new state through the mapped points. The specific extraction process is as follows:

[0073] 1. Based on the sunshine radiation intensity in each time period, a probability density function of the sunshine radiation intensity in each time period is constructed, and based on the wind speed in each time period, a probability density function of the wind speed in each time period is constructed.

[0074] For example, the solar radiation intensity is usually fitted using the Beta distribution, which has the best effect. The probability density function of the solar radiation intensity is:

[0075]

[0076] Among them, α and β are the shape parameters of the Beta distribution, γ max is the maximum acceptable radiation intensity, μ represents the mathematical expectation value of the radiation intensity during the time period, and σ 2 represents the variance of the radiation intensity within a time period, Γ() represents the gamma function, and its function expression is as follows:

[0077]

[0078] The two shape parameters can be calculated from the mathematical expectation and variance of the historical data of the solar radiation intensity of the photovoltaic power station:

[0079]

[0080] Among them, μ is the mathematical expectation of the historical data of solar radiation intensity, and σ is the variance of the historical data of solar radiation intensity.

[0081] The probability density function of wind speed is:

[0082]

[0083] Among them, v represents the wind speed, k is the shape parameter, which affects the width of the single peak image in the density curve of the instantaneous wind speed, and c is the scale parameter, which affects the position of the entire curve in the distribution curve.

[0084] In actual fitting, both k and c can be calculated from sample data:

[0085]

[0086]

[0087] Among them, μ is the mathematical expectation of the historical data of wind speed, σ is the variance of the historical data of wind speed, and h is the number of sample data.

[0088] 2. Construct the Coppola function that represents the correlation between the probability density function of solar radiation intensity and the probability density function of wind speed.

[0089] Since the required unscented transform (UT) is based on the Gaussian assumption, there are large errors when dealing with non-Gaussian variables. Therefore, it is necessary to perform a Copula transformation on the probability density function to convert the non-Gaussian probability density function into a Gaussian distribution. The Copula transformation itself is an important tool in statistics and finance, and is usually used to describe the correlation structure between random variables. The Copula function separates the marginal distribution of multiple random variables from the correlation between them. Therefore, choosing different Copula functions to simulate different types of correlation does not have to be limited to specific marginal distributions.

[0090] 3. Use untraceable transformation to select the first sample point and the second sample point that satisfy the Coppola function from the first output model and the second processing model.

[0091] In the embodiment of the present invention, unscented transformation is used to screen out the first sample point and the second sample point satisfying the Coppola function from the first output model and the second processing model.

[0092] The basic idea of ​​the unscented transformation (UT) is to generate some appropriate sample points of the input variables (for deterministic power flow) that can maintain significant information (especially the mean and covariance) on their probability density functions. Let X represent the n-dimensional random variables of wind speed and solar radiation intensity, and the mean is The covariance is P XX Since the diagonal elements in the covariance matrix are the variances of the corresponding variables, and the relevant information between the variables is stored in the off-diagonal elements:

[0093] P XX (i,h)=cov(X i ,X j )

[0094] The element in the i-th row and j-th column of the covariance matrix is ​​the variable X i and X jWhen the input variables are photovoltaic and wind power output, the covariance can be considered to be the spatial correlation of wind speed and solar radiation intensity in different spatial regions. Therefore, compared with the traditional power flow algorithm, this algorithm can take into account the spatial correlation between different variables.

[0095] S106: Calculate the probability flow of the power system in each time period based on the first sample point and the second sample point.

[0096] In the embodiment of the present invention, the existing probability power flow calculation method can be used to calculate the probability power flow of the power system in each time period based on the first sample point and the second sample point.

[0097] Exemplarily, in an embodiment of the present invention, the Newton-Raphson method is used to solve the data of the first sample point and the second sample point of a given node in the power grid to obtain the mean and covariance of the voltage, phase, and output power of the given node. Specifically, for a given node in the power grid, the active power and reactive power of the given node are calculated based on the data of the first sample point and the second sample point, and the active power and reactive power are substituted into the power equation of the given node, and Newton-Raphson is used to solve it to obtain the mean and covariance of the voltage, phase, and output power of the given node.

[0098] Exemplarily, the mean of the unscented transformed target random output variable is and its corresponding covariance P YY It can be calculated by the following steps:

[0099] (1) Obtain 2n+1 sample points of input variables:

[0100] X 0 =m;

[0101]

[0102] in is a matrix The kth row of This means that there exists a solution that satisfies P XX =AA T The matrix It can be calculated using Cholesky decomposition; K∈R represents the degree of dispersion of sample points relative to the mean. When n+K=3, the fourth-order moment mismatch can be minimized and the effect is best.

[0103] (2) Calculate the weight of each X:

[0104]

[0105] Among them, the corresponding weights satisfy the following formula:

[0106]

[0107] The sample points extracted in the aforementioned steps can be used as input quantities in the power system for power flow calculation.

[0108] For the injected power at a given node and the parameters of the power network, the power equation can be solved by the Newton-Raphson method. The power equation is as follows:

[0109]

[0110] When using the Newton-Raphson method for power flow calculation, the node voltage is expressed in polar coordinates, that is,

[0111] U i =U i ∠δ i =U i (cosδ i +j sinδ i )

[0112] Among them, δ i is the phase of node i.

[0113] The real and imaginary parts of the power equation can be separated and rewritten as:

[0114]

[0115] Among them U i and U j Represent the voltage amplitude of nodes i and j respectively; P i and Q i Represents the active and reactive power injection amount of node i respectively; G ij and B ij They represent the admittance between node i and node j in the node admittance matrix respectively. In this way, the voltage and phase of the node can be calculated.

[0116] In the power flow calculation model, the power equation can also be written in general matrix form:

[0117] Y=f(X)

[0118] Among them, X represents the active and reactive injected power of the node; f represents the power equation function; and Y represents the state variable consisting of the node voltage and phase.

[0119] The mean and covariance of the output Y are calculated using the following formula:

[0120]

[0121] Therefore, for a network with n-dimensional uncertainties, the unscented transformation only needs to perform 2n+1 deterministic power flow calculations during its implementation.

[0122] The method for calculating the probability flow of a power system provided by the present invention divides the historical data of a photovoltaic power station and the historical data of a wind power station according to a set time period to obtain the historical data of the photovoltaic power station and the historical data of the wind power station in each time period, screens the historical data of the photovoltaic power station in each time period based on the sunshine radiation intensity, removes the historical data of the photovoltaic power station in the time period in which the sunshine radiation intensity is lower than a preset value, models the historical data of each time period after the photovoltaic power station is removed, obtains a first output model characterizing the relationship between the output power of the photovoltaic power station and the sunshine radiation intensity in each time period, and models the historical data of the wind power station to obtain a second output model characterizing the relationship between the output power of the wind power station and the wind speed in each time period, extracts a plurality of first sample points from the first output model based on the spatial correlation of the photovoltaic power station and the wind power station, and extracts a plurality of second sample points from the second output model, calculates the probability flow of the power system in each time period based on the first sample points and the second sample points, comprehensively considers the influence of the spatiotemporal correlation on the probability flow of the power system, and improves the accuracy of the probability flow calculation.

[0123] Figure 2 A schematic diagram of a power system probabilistic power flow calculation device provided by the present invention is shown in FIG. Figure 2 As shown, the power system probabilistic power flow calculation device includes:

[0124] The data acquisition module 201 is used to acquire the historical data of the photovoltaic power station and the historical data of the wind power station;

[0125] The data partitioning module 202 is used to partition the historical data of the photovoltaic power station and the historical data of the wind power station according to a set time period, and obtain the historical data of the photovoltaic power station and the historical data of the wind power station in each time period;

[0126] The data screening module 203 is used to screen the historical data of the photovoltaic power station in each time period based on the sunshine radiation intensity, and remove the historical data of the photovoltaic power station in the time period when the sunshine radiation intensity is lower than a preset value;

[0127] The output model building module 204 is used to perform modeling based on the historical data of each time period after the photovoltaic power station is eliminated, so as to obtain a first output model representing the relationship between the output power of the photovoltaic power station and the sunshine radiation intensity in each time period, and to perform modeling based on the historical data of the wind power station, so as to obtain a second output model representing the relationship between the output power of the wind power station and the wind speed in each time period;

[0128] A sample point extraction module 205, configured to extract a plurality of first sample points from the first output model and a plurality of second sample points from the second output model based on the spatial correlation between the photovoltaic power station and the wind power station;

[0129] The probabilistic power flow calculation module 206 is configured to calculate the probabilistic power flow of the power system in each time period based on the first sample point and the second sample point.

[0130] In some embodiments of the present invention, the first output model is:

[0131]

[0132] Among them, r c is the set solar radiation intensity; r std is the rated solar radiation intensity; P d is the rated power of the PV power station, and r is the solar radiation intensity received by the PV power station.

[0133] In some embodiments of the present invention, the second output model is:

[0134]

[0135] Among them, v ci is the cut-in wind speed, v r is the rated wind speed, v co is the cut-out wind speed, P r is the rated active power of the wind power station, C1 is the slope, and v is the wind speed received by the wind power station.

[0136] In some embodiments of the present invention, the sample point extraction module 205 includes:

[0137] A probability density function construction submodule is used to construct a probability density function of the sunshine radiation intensity in each time period based on the sunshine radiation intensity in each time period, and to construct a probability density function of the wind speed in each time period based on the wind speed in each time period;

[0138] A Copra function construction submodule is used to construct a Copra function that characterizes the correlation between the probability density function of the solar radiation intensity and the probability density function of the wind speed;

[0139] The untraceable transformation submodule is used to select the first sample point and the second sample point satisfying the Coppola function from the first output model and the second processing model by using untraceable transformation.

[0140] In some embodiments of the present invention, the probability density function of the solar radiation intensity is:

[0141]

[0142] Among them, α and β are the shape parameters of the Beta distribution, γ max is the maximum acceptable radiation intensity, μ represents the mathematical expectation value of the radiation intensity during the time period, and σ 2 represents the variance of radiation intensity during a time period, Γ() represents the gamma function;

[0143] The probability density function of wind speed is:

[0144]

[0145] Among them, v represents the wind speed, k is the shape parameter, which affects the width of the single peak image in the density curve of the instantaneous wind speed, and c is the scale parameter, which affects the position of the entire curve in the distribution curve.

[0146] In some embodiments of the present invention, the probabilistic power flow calculation module 206 includes:

[0147] The probabilistic power flow calculation submodule is used to solve the data of the first sample point and the second sample point of a given node in the power grid using the Newton-Raphson method to obtain the mean and covariance of the voltage, phase, and output power of the given node.

[0148] In some embodiments of the present invention, the probabilistic power flow calculation submodule includes:

[0149] A power calculation unit, configured to calculate, for a given node in a power grid, the active power and reactive power of the given node based on the data of the first sample point and the second sample point;

[0150] A solving unit is used to substitute the active power and the reactive power into the power equation of the given node, and solve it using Newton-Raphson to obtain the mean and covariance of the voltage, phase, and output power of the given node.

[0151] The above-mentioned power system probabilistic power flow calculation device can execute the power system probabilistic power flow calculation method provided by the above-mentioned embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the power system probabilistic power flow calculation method.

[0152] Figure 3A schematic diagram of the structure of an electronic device provided by the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0153] like Figure 3 As shown, the electronic device includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0154] A number of components in the electronic device are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0155] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The processor 11 executes the various methods and processes described above, such as a power system probabilistic flow calculation method.

[0156] In some embodiments, the power system probabilistic flow calculation method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on an electronic device via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the power system probabilistic flow calculation method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the power system probabilistic flow calculation method in any other appropriate manner (e.g., by means of firmware).

[0157] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0158] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0159] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0160] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0161] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0162] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0163] An embodiment of the present invention further provides a computer program product, including a computer program, which, when executed by a processor, implements the method for calculating the probabilistic power flow of a power system as provided in any embodiment of the present application.

[0164] In the process of implementation, the computer program product can be written in one or more programming languages ​​or a combination thereof to perform the computer program code of the present invention, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0165] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0166] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for calculating probabilistic power flow in a power system, characterized in that: include: Obtain historical data of photovoltaic power plants and wind power plants; Dividing the historical data of the photovoltaic power station and the historical data of the wind power station according to a set time period to obtain the historical data of the photovoltaic power station and the historical data of the wind power station in each time period; The historical data of the photovoltaic power station in each time period is screened based on the sunshine radiation intensity, and the historical data of the photovoltaic power station in the time period in which the sunshine radiation intensity is lower than a preset value is eliminated; Modeling is performed based on the historical data of each time period after the photovoltaic power station is eliminated, so as to obtain a first output model that characterizes the relationship between the output power of the photovoltaic power station and the intensity of solar radiation in each time period, and modeling is performed based on the historical data of the wind power station, so as to obtain a second output model that characterizes the relationship between the output power of the wind power station and the wind speed in each time period; Based on the spatial correlation between the photovoltaic power station and the wind power station, extracting a plurality of first sample points from the first output model and extracting a plurality of second sample points from the second output model; The probabilistic power flow of the power system in each time period is calculated based on the first sample point and the second sample point.

2. The method for calculating the probabilistic power flow of a power system according to claim 1, characterized in that: The first output model is: Among them, r c is the set solar radiation intensity; r std is the rated solar radiation intensity; P is the rated power of the photovoltaic power station, and r is the solar radiation intensity received by the photovoltaic power station.

3. The method for calculating the probabilistic power flow of a power system according to claim 1, characterized in that: The second output model is: Among them, v ci is the cut-in wind speed, v r is the rated wind speed, v co is the cut-out wind speed, P r is the rated active power of the wind power station, C1 is the slope, and v is the wind speed received by the wind power station.

4. The method for calculating the probabilistic power flow of a power system according to claim 1, characterized in that: Based on the spatial correlation between the photovoltaic power station and the wind power station, a plurality of first sample points are extracted from the first output model, and a plurality of second sample points are extracted from the second output model, including: A probability density function of the sunshine radiation intensity in each time period is constructed based on the sunshine radiation intensity in each time period, and a probability density function of the wind speed in each time period is constructed based on the wind speed in each time period; Construct the Copra function that represents the correlation between the probability density function of solar radiation intensity and the probability density function of wind speed; The first sample point and the second sample point satisfying the Coppola function are screened out from the first output model and the second processing model by using an unscented transformation.

5. The method for calculating the probabilistic power flow of a power system according to claim 4, characterized in that: The probability density function of solar radiation intensity is: Among them, α and β are the shape parameters of the Beta distribution, γ max is the maximum acceptable radiation intensity, μ represents the mathematical expectation value of the radiation intensity during the time period, and σ 2 represents the variance of radiation intensity during a time period, Γ() represents the gamma function; The probability density function of wind speed is: Among them, v represents the wind speed, k is the shape parameter, which affects the width of the single peak image in the density curve of the instantaneous wind speed, and c is the scale parameter, which affects the position of the entire curve in the distribution curve.

6. The method for calculating the probabilistic power flow of a power system according to claim 1, characterized in that: Calculating the probabilistic power flow of the power system in each time period based on the first sample point and the second sample point includes: The Newton-Raphson method is used to solve the data of the first sample point and the second sample point of a given node in the power grid to obtain the mean and covariance of the voltage, phase, and output power of the given node.

7. The method for calculating the probabilistic power flow of a power system according to claim 6, characterized in that: The Newton-Raphson method is used to solve the data of the first sample point and the second sample point of a given node in the power grid to obtain the mean and covariance of the voltage, phase, and output power of the given node, including: For a given node in the power grid, calculating the active power and reactive power of the given node based on the data of the first sample point and the second sample point; The active power and the reactive power are substituted into the power equation of the given node, and solved using Newton-Raphson to obtain the mean and covariance of the voltage, phase, and output power of the given node.

8. A power system probabilistic power flow calculation device, characterized in that: include: A data acquisition module, used to acquire historical data of photovoltaic power plants and historical data of wind power plants; A data partitioning module, used to partition the historical data of the photovoltaic power station and the historical data of the wind power station according to a set time period, and obtain the historical data of the photovoltaic power station and the historical data of the wind power station in each time period; A data screening module, used to screen the historical data of the photovoltaic power station in each time period based on the sunshine radiation intensity, and remove the historical data of the photovoltaic power station in the time period where the sunshine radiation intensity is lower than a preset value; An output model building module is used to perform modeling based on the historical data of each time period after the photovoltaic power station is eliminated, so as to obtain a first output model representing the relationship between the output power of the photovoltaic power station and the sunshine radiation intensity in each time period, and to perform modeling based on the historical data of the wind power station, so as to obtain a second output model representing the relationship between the output power of the wind power station and the wind speed in each time period; A sample point extraction module, configured to extract a plurality of first sample points from the first output model and a plurality of second sample points from the second output model based on the spatial correlation between the photovoltaic power station and the wind power station; The probabilistic power flow calculation module is used to calculate the probabilistic power flow of the power system in each time period based on the first sample point and the second sample point.

9. An electronic device, characterized in that: include: one or more processors; A storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the power system probabilistic power flow calculation method as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method for calculating the probabilistic power flow of a power system as described in any one of claims 1 to 7 is implemented.

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