Modeling method and device for high-temperature scene simulation model of electric power system, terminal and medium

Abstract

The invention discloses an electric power system high temperature scene simulation model modeling method and device, a terminal and a medium, and relates to the technical field of electric power system operation simulation. According to the scheme provided by the invention, related characteristics among multi-dimensional meteorological factors are described through an R-vine copula function, and a Markov multi-dimensional transfer kernel is generated; a double-layer Markov chain weather model is established, an upper-layer model describes transition of typical weather states, a lower-layer model divides 24 hours in a day into time periods, a state transition matrix in each time period is established, a non-stationary change time sequence of meteorological factors in the day is generated based on a multi-dimensional transition kernel, and the double-layer model is integrated to obtain a complete high-temperature scene. The nonlinear dependency relationship among multiple variables in the high-temperature scene is described more accurately, so that a more accurate extreme high-temperature weather sequence is obtained, and the accuracy of high-temperature scene reliability evaluation of a power system is improved.

Description

Technical Field

[0001] The present application relates to the technical field of power system operation simulation, and in particular to a modeling method, device, terminal and medium for high temperature scenario simulation modeling of a power system. Background Art

[0002] Under the new power system paradigm, the supply side will be dominated by wind power / photovoltaic and other new energy sources with significant uncertainty; the proportion of responsive loads on the demand side will increase significantly, and the uncertainty on the demand side will increase significantly due to factors such as user response behavior. In extreme natural disasters, the risk of power supply interruption increases sharply. In particular, the occurrence of high temperature weather is becoming more and more frequent, which poses a great challenge to the safe and stable operation of the power system.

[0003] In order to improve the reliability of new power systems to cope with increasingly frequent extreme high temperature events, the commonly used method is to construct an operation simulation model of the power system in a high temperature scenario, evaluate the reliability of the power system in a high temperature scenario through simulation, and optimize the design or transformation plan of the power system based on the simulation results. However, in actual scenarios, there are also complex interactions between various meteorological factors in high temperature weather, and the existing power system high temperature scenario simulation model is difficult to accurately describe the nonlinear dependency between multiple variables in the high temperature scenario, resulting in the technical problem of inaccurate reliability assessment of the existing power system high temperature scenario. Summary of the invention

[0004] The present application provides a method, device, terminal and medium for modeling a high temperature scenario simulation model of an electric power system, which are used to solve the technical problem of inaccurate reliability assessment of high temperature scenarios in existing electric power systems.

[0005] In order to solve the above technical problems, the first aspect of the present application provides a method for modeling a high temperature scenario simulation model of a power system, comprising:

[0006] Based on several meteorological factors corresponding to the high temperature scene, the marginal distribution of the meteorological factors is calculated by kernel density estimation;

[0007] Based on the marginal distribution, constructing an R-vine copula maximum spanning tree by improving Prim's algorithm, so as to determine the multi-dimensional joint probability distribution of the meteorological factors based on the R-vine copula maximum spanning tree;

[0008] Based on the historical meteorological data, by clustering the historical meteorological data, the typical weather state corresponding to the historical meteorological data is determined, and then based on the historical weather state sequence, combined with the Markov architecture logic and the random generation algorithm, a typical weather state transition submodel is constructed, wherein the historical weather state sequence is a typical weather state sequence generated according to the time sequence of the historical meteorological data, and the typical weather state transition submodel is used to simulate the typical weather state transition process;

[0009] Based on the historical meteorological data and in combination with the Markov multidimensional transfer kernel, a sub-model of intraday meteorological factor changes is constructed, wherein the Markov multidimensional transfer kernel is obtained based on the multidimensional joint probability distribution, and the sub-model of intraday meteorological factor changes is used to simulate the non-stationary change process of intraday meteorological factors corresponding to the typical weather state based on the typical weather state sequence output by the typical weather state transfer sub-model;

[0010] The typical weather state transfer sub-model and the intraday meteorological factor change sub-model are integrated to obtain a high temperature scenario simulation model of the power system based on a double-layer Markov architecture.

[0011] Preferably, based on the edge distribution, constructing the R-vine copula maximum spanning tree by improving Prim's algorithm includes:

[0012] Based on the edge distribution of each meteorological factor, combined with the Kendall correlation coefficient calculation method, the correlation coefficient between each meteorological factor is calculated, and the edge weight between each meteorological factor is determined by the correlation coefficient;

[0013] Taking each meteorological factor as a node and combining the edge weights between them, the R-vine copula maximum spanning tree is constructed by improving Prim's algorithm.

[0014] Preferably, taking each meteorological factor as a node and combining the edge weights between each meteorological factor, constructing the R-vine copula maximum spanning tree by improving Prim's algorithm includes:

[0015] Taking each meteorological factor as a node, one is selected from all nodes as the starting node, and the nodes are associated according to the connection relationship between the nodes and the edge weight according to Prim's algorithm to determine the maximum spanning tree, and then the optimal binary copula function is selected by using the Akaike information criterion to connect the nodes of the first layer of the tree to construct edges and determine the maximum spanning tree of the first layer;

[0016] The maximum spanning tree of the previous layer is looped, the correlation coefficient between each pair of conditional variables is calculated, the connectivity relationship of the conditional variables is determined, and then the maximum spanning tree of the current layer is determined according to the Prim's algorithm and the Akaike information criterion, until the maximum spanning trees of all layers and the optimal binary copula of the edges in each layer of the tree are determined, and the R-vine copula maximum spanning tree is obtained.

[0017] Preferably, based on historical meteorological data, by clustering the historical meteorological data, determining the typical weather state corresponding to the historical meteorological data, and then based on the historical weather state sequence, combining Markov architecture logic and random generation algorithm, constructing a typical weather state transition sub-model includes:

[0018] Based on the preset historical meteorological data, a historical meteorological data set matrix is ​​constructed according to the data date and the number of meteorological factors;

[0019] According to the historical meteorological data set matrix, the weather similarity between meteorological data of different dates is calculated according to a preset similarity calculation formula, and then the historical meteorological data is clustered according to the weather similarity to determine the typical weather state corresponding to each date in the historical meteorological data, and a historical weather state sequence is generated according to the time sequence of the historical meteorological data;

[0020] Based on the historical weather state sequence, combined with Markov architecture logic and random generation algorithm, a typical weather state transfer matrix and a cumulative probability transfer matrix are constructed in sequence;

[0021] According to the cumulative probability transfer matrix and the preset random generation algorithm, a typical weather state transfer sub-model is constructed.

[0022] Preferably, based on the historical meteorological data and combined with the Markov multidimensional transfer kernel, constructing a sub-model of intraday meteorological factor changes includes:

[0023] Based on the historical meteorological data, merging the intraday meteorological factor sequences in the historical meteorological data with the same weather conditions;

[0024] According to the preset intra-day time period division information, the meteorological factor sequence is divided into a plurality of state intervals;

[0025] The typical weather state transfer sub-model is used to determine the weather state transfer matrix and cumulative probability transfer matrix of each state interval respectively, and then combined with the Markov multidimensional transfer kernel to construct a sub-model of intraday meteorological factor changes.

[0026] A second aspect of the present application provides a modeling device for a high temperature scenario simulation model of an electric power system, comprising:

[0027] A meteorological factor distribution calculation unit, used to calculate the marginal distribution of the meteorological factors based on a number of meteorological factors corresponding to the high temperature scene by a kernel density estimation method;

[0028] A multi-dimensional joint probability distribution determining unit, configured to construct an R-vine copula maximum spanning tree based on the marginal distribution by improving Prim's algorithm, so as to determine the multi-dimensional joint probability distribution of the meteorological factors based on the R-vine copula maximum spanning tree;

[0029] The upper sub-model construction unit is used to determine the typical weather state corresponding to the historical meteorological data by clustering the historical meteorological data based on the historical meteorological data, and then construct a typical weather state transfer sub-model based on the historical weather state sequence in combination with the Markov architecture logic and the random generation algorithm, wherein the historical weather state sequence is a typical weather state sequence generated according to the time sequence of the historical meteorological data, and the typical weather state transfer sub-model is used to simulate the typical weather state transition process;

[0030] A lower-level sub-model construction unit is used to construct a sub-model of intraday meteorological factor changes based on the historical meteorological data and in combination with a Markov multidimensional transfer kernel, wherein the Markov multidimensional transfer kernel is obtained based on the multidimensional joint probability distribution, and the sub-model of intraday meteorological factor changes is used to simulate the non-stationary change process of intraday meteorological factors corresponding to the typical weather state based on the typical weather state sequence output by the typical weather state transfer sub-model;

[0031] The high temperature scenario simulation model construction unit is used to integrate the typical weather state transfer sub-model and the intraday meteorological factor change sub-model to obtain a high temperature scenario simulation model of the power system based on a double-layer Markov architecture.

[0032] Preferably, the multidimensional joint probability distribution determining unit is specifically used for:

[0033] Based on the edge distribution of each meteorological factor, combined with the Kendall correlation coefficient calculation method, the correlation coefficient between each meteorological factor is calculated, and the edge weight between each meteorological factor is determined by the correlation coefficient;

[0034] Taking each meteorological factor as a node and combining the edge weights between them, the R-vine copula maximum spanning tree is constructed by improving Prim's algorithm.

[0035] Preferably, the upper sub-model construction unit is specifically used for:

[0036] Based on the preset historical meteorological data, a historical meteorological data set matrix is ​​constructed according to the data date and the number of meteorological factors;

[0037] According to the historical meteorological data set matrix, the weather similarity between meteorological data of different dates is calculated according to a preset similarity calculation formula, and then the historical meteorological data is clustered according to the weather similarity to determine the typical weather state corresponding to each date in the historical meteorological data, and a historical weather state sequence is generated according to the time sequence of the historical meteorological data;

[0038] Based on the historical weather state sequence, combined with Markov architecture logic and random generation algorithm, a typical weather state transfer matrix and a cumulative probability transfer matrix are constructed in sequence;

[0039] According to the cumulative probability transfer matrix and the preset random generation algorithm, a typical weather state transfer sub-model is constructed;

[0040] The lower layer sub-model construction unit is specifically used for:

[0041] Based on the historical meteorological data, merging the intraday meteorological factor sequences in the historical meteorological data with the same weather conditions;

[0042] According to the preset intra-day time period division information, the meteorological factor sequence is divided into a plurality of state intervals;

[0043] The typical weather state transfer sub-model is used to determine the weather state transfer matrix and cumulative probability transfer matrix of each state interval respectively, and then combined with the Markov multidimensional transfer kernel to construct a sub-model of intraday meteorological factor changes.

[0044] A third aspect of the present application provides a modeling terminal for a high temperature scenario simulation model of an electric power system, comprising: a memory and a processor;

[0045] The memory is used to store program codes, and the program codes are used to implement the modeling method of high temperature scenario simulation model of power system provided in the first aspect of the present application;

[0046] The processor is used for reading and executing the program code.

[0047] The fourth aspect of the present application provides a computer-readable storage medium, in which program code is stored, and the program code is used to be read and executed by a processor to implement the method for modeling a high temperature scenario simulation model of a power system as provided in the first aspect of the present application.

[0048] It can be seen from the above technical solutions that this application has the following advantages:

[0049] The solution provided in this application uses the R-vine copula function to characterize the correlation characteristics between multidimensional meteorological factors and generate a Markov multidimensional transfer kernel; a double-layer Markov chain weather model is established, in which the upper model describes the transition of typical weather conditions, and the lower model divides the 24 hours of the day into time periods, and establishes a state transfer matrix within each time period, and generates a non-stationary change time series of meteorological factors within the day based on the multidimensional transfer kernel, and integrates the double-layer model to obtain a complete high temperature scenario, so as to more accurately characterize the nonlinear dependency relationship between multiple variables in the high temperature scenario, thereby obtaining a more accurate extreme high temperature weather sequence, which helps to improve the accuracy of reliability assessment of high temperature scenarios of power systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0051] Figure 1 A schematic flow chart of an embodiment of a method for modeling a high temperature scenario simulation model for a power system provided in the present application.

[0052] Figure 2 The topology diagram of the first-layer tree of R-vine.

[0053] Figure 3 The topology diagram of the second layer tree of R-vine.

[0054] Figure 4 The topology diagram of the complete R-vine copula maximum spanning tree.

[0055] Figure 5 It is a comparison chart of the solar radiation sequence generated based on the solution of the present application, historical data and the output sequence of the traditional Markov model.

[0056] Figure 6 It is a comparison chart of the temperature series generated based on the solution of the present application, historical data and the output series of the traditional Markov model.

[0057] Figure 7 This is a comparison chart of the wind speed sequence generated based on the solution of the present application, historical data, and the output sequence of the traditional Markov model.

[0058] Figure 8 It is the autocorrelation characteristic curve of the irradiation sequence.

[0059] Fig. 9 It is the temperature series autocorrelation characteristic curve.

[0060] Fig.10 This is the autocorrelation characteristic curve of wind speed series.

[0061] Fig.11 A schematic diagram of the structure of an embodiment of a modeling device for simulating a high-temperature scenario of an electric power system provided in the present application.

[0062] Fig.12 A schematic diagram of the structure of an embodiment of a modeling terminal for simulating a high temperature scenario of an electric power system provided in the present application. DETAILED DESCRIPTION

[0063] The embodiments of the present application provide a method, device, terminal and medium for modeling a high-temperature scenario simulation model of an electric power system, which are used to solve the technical problem of inaccurate reliability assessment of high-temperature scenarios in existing electric power systems.

[0064] In order to make the purpose, features, and advantages of the invention of this application more obvious and easy to understand, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the embodiments described below are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0065] First, a detailed description of an embodiment of a method for modeling a high temperature scenario simulation model for a power system provided by the present application is as follows:

[0066] See also Figure 1 The method for modeling a high temperature scenario simulation model of a power system provided in an embodiment of the present application includes:

[0067] Step 101, based on a number of meteorological factors corresponding to the high temperature scene, calculate the marginal distribution of the meteorological factors by kernel density estimation;

[0068] It should be noted that the marginal distribution of each meteorological factor (such as temperature, wind speed, solar radiation, etc.) is first established by the Gaussian kernel density estimation method, specifically: the Gaussian kernel function of the non-parametric kernel density estimation is fitted to each meteorological factor. This method does not require the assumption of the theoretical probability distribution of the variable, and its probability distribution can be directly obtained from historical data.

[0069] For a meteorological factor with L data samples, its PDF (Probability Density Function) can be estimated by the following kernel density function:

[0070] (1)

[0071] In the formula is the bandwidth, m l Represents the lth meteorological factor. Gaussian kernel function It can be expressed as:

[0072] (2)

[0073] Step 102: Based on the marginal distribution, an R-vine copula maximum spanning tree is constructed by improving Prim's algorithm to determine the multi-dimensional joint probability distribution of meteorological factors based on the R-vine copula maximum spanning tree;

[0074] It should be noted that the R-vine copula of the L-dimensional meteorological factor is Trees and The edges are constructed by specific conditional and unconditional copulas, and each edge corresponds to two nodes. Representation Tree The edge set of , then each edge of R-vine can be expressed as “ ",in and Represents a conditional set, and the elements in the set are all nodes. It should be noted that in the first tree is an empty set, in the tree An edge in Can be tree The two edges in and OK, then the edge The corresponding copula density function can be expressed as At this point, the joint probability density function of the L-dimensional variables can be obtained from its marginal distribution and the specific R-vine structure:

[0075] (3)

[0076] Wakabe and share the same node, then , The conditional CDF in formula (3) The Cumulative Distribution Function can be calculated as follows:

[0077] (4)

[0078] The conditional CDF in formula (3) The same method can also be used to obtain Represents the conditional copula CDF of edge a.

[0079] For edge construction, firstly, the Kendall correlation coefficient Calculate the correlation between all meteorological factors and use it as the weight value of each edge:

[0080] (5)

[0081] Where C and D are the number of consistent pairs and the number of inconsistent pairs, respectively.

[0082] The improved Prim's algorithm is executed to select one of the nodes as the starting node from all the nodes. Then, among the nodes connected to it but not selected, an edge with the largest weight is selected, and then a new node is added. This process is repeated to connect the edge with the largest weight and add new nodes until all nodes are selected to determine the maximum spanning tree. Then, the Akaike Information Criterion (AIC) is used to select the optimal binary copula function to connect the nodes of the first layer of the tree to construct edges and determine the maximum spanning tree.

[0083] (6)

[0084] In the formula is the number of parameters in the copula function, is the likelihood function. The selected bivariate copula families include Gaussian copula, t copula, Frank copula, Joe copula, Gumbel copula, Clayton copula, and the survival forms of Joe, Gumbel and Clayton are also introduced to capture the dependencies of variables under extreme conditions.

[0085] More specifically, there are five meteorological factor variables: , then the R-vine model construction process of multivariate variables is as follows:

[0086] 1) Build the first layer tree

[0087] Enter variable data , calculate the empirical Kendall coefficient between variables , thus obtaining a complete connected graph. Run the adjusted Prim's algorithm to determine the maximum spanning tree of the first layer. Determine the optimal binary copula for each edge and estimate its parameters by AIC. Then The conditional variable is calculated by equation (4) and , the first-level tree structure constructed is as follows Figure 2shown.

[0088] 2) Construct the second layer tree

[0089] Based on the constructed first-level tree, calculate all conditional variable pairs The empirical Kendall coefficient between , and obtain the connectivity graph of the conditional variables. Run the adjusted Prim's algorithm to determine the maximum spanning tree of the conditional variables and the edges in the tree The optimal bivariate copula for each edge is determined by AIC Then by The conditional variable is calculated by equation (4) and , the second-level tree structure constructed is as follows Figure 3 shown.

[0090] 3) Construct the remaining layers of the tree

[0091] According to the construction process of the previous step, continue to calculate the correlation coefficient between each pair of conditional variables based on the maximum spanning tree of the previous layer, determine the connectivity relationship of the conditional variables, and then determine the maximum spanning tree of the current layer according to Prim's algorithm and Akaike information criterion until the maximum spanning trees of all layers and the optimal binary copula of the edges in each layer of the tree are determined. , then the R-vine of multidimensional variables is constructed. Substituting the marginal distribution of all variables and the binary copula of the unconditional or conditional variables in each layer of the tree into formula (3) can obtain the joint probability density function of the multivariate variables. The structure of the maximum spanning tree of each layer is constructed as follows: Figure 4 shown.

[0092] Step 103: Based on the historical meteorological data, the typical weather state corresponding to the historical meteorological data is determined by clustering the historical meteorological data, and then based on the historical weather state sequence, a typical weather state transition sub-model is constructed in combination with Markov architecture logic and random generation algorithm, wherein the historical weather state sequence is a typical weather state sequence generated according to the time sequence of the historical meteorological data, and the typical weather state transition sub-model is used to simulate the typical weather state transition process;

[0093] It should be noted that the L-dimensional historical meteorological data within N days is used to construct the data set matrix Z according to the daily average value:

[0094] (7)

[0095] The data set is normalized to eliminate differences between units, and the typical weather conditions are determined by fuzzy clustering. The angle cosine method is used to calculate the weather similarity between any two days:

[0096] (8)

[0097] Where r cd represents the similarity between day c and day d, , represents the normalized value of the lth meteorological factor on day c and day d. Then the fuzzy similarity matrix between all days R=[r cd ] can be constructed. Through the fuzzy similarity matrix R=[r cd The self-multiplication process of ] can cluster the data according to the similarity between any two days to obtain the typical weather conditions. The self-multiplication process of the fuzzy similarity matrix obtains the fuzzy equivalent matrix t(R), in:

[0098] (9)

[0099] (10)

[0100] Where t(R) represents the fuzzy equivalence matrix. Mapping each day in the data set Z to a typical state with good clustering, we can get the historical weather state sequence: , K is the number of states obtained by clustering.

[0101] Next, based on the weather status data, establish a The state transition matrix P of order is used to record the probability of each state reaching another state. Specifically, the frequency of transitioning from state m to any other possible state k is counted , and then each possible state frequency Divide by the total state frequency Get the transition probability corresponding to state m.

[0102] (11)

[0103] By analyzing all states in the original sequence, the state transition matrix P will eventually summarize the probability of all states transitioning to another state.

[0104] The cumulative probability transfer matrix P mentioned in this embodiment is cum The calculation is as follows:

[0105] (12)

[0106] The state transition matrix P is a The matrix of order, the cumulative probability transfer matrix P cum is a The first column of elements are all 0, and starting from the second column, each element p cum,mk The values ​​of are the sum of the elements before the mth row and kth column in the matrix P.

[0107] Next, the obtained weather state transfer matrix and cumulative probability transfer matrix are used as the Markov architecture logic, combined with the random generation algorithm, to construct a typical weather state transfer sub-model as the upper model. This typical weather state transfer sub-model can be used to generate a typical weather state sequence based on a preset random generation algorithm, such as the Monte Carlo method. First, set the typical weather initial state m, and then continuously generate uniform random numbers With the cumulative transfer matrix P cum The elements of the mth row are compared. If , then k is determined as the next state. Repeat this process until the typical weather state sequence within N days is completely generated. .

[0108] Step 104: Based on the historical meteorological data and in combination with the Markov multidimensional transfer kernel, a sub-model of intraday meteorological factor changes is constructed, wherein the Markov multidimensional transfer kernel is obtained based on the multidimensional joint probability distribution, and the sub-model of intraday meteorological factor changes is used to simulate the non-stationary change process of intraday meteorological factors corresponding to the typical weather state sequence output by the typical weather state transfer sub-model under the typical weather state;

[0109] It should be noted that the historical weather state sequence The 24-hour meteorological factor sequences contained in the historical days with the same status are merged together, and then the meteorological factor sequences are divided into Time period. The meteorological factors in the data are divided into num state intervals according to their size, where num is the number of states that need to be discretized. Each interval contains a certain range of values ​​of the meteorological factors, and the time points where the values ​​fall within the interval are considered to be in the same state, thereby converting the original data into discrete state points. The conversion process is as follows:

[0110] (13)

[0111] In the formula , Indicates The maximum and minimum values ​​of the lth meteorological factor in a time period.

[0112] Then execute the upper model in conjunction to establish each time period The state transition matrix within And the cumulative probability transfer matrix For a historical series with K typical weather states and L meteorological factors, a total of state transfer matrix and cumulative probability transfer matrix.

[0113] Then, the Markov transition kernel is constructed by the multi-dimensional meteorological factor joint distribution established by the R-vine copula function, and the cumulative joint distribution is obtained by integrating equation (3):

[0114] (14)

[0115] Next, the Markov multidimensional transfer kernel is used to construct a sub-model of intraday meteorological factors changes as a lower-level model. The sub-model of intraday meteorological factors changes can generate a daily meteorological factor sequence corresponding to a typical weather state k according to the typical weather state sequence generated by the upper-level model. The Markov model can generate a daily meteorological factor sequence corresponding to a typical weather state k.

[0116] Step 105: Integrate the typical weather state transfer sub-model and the intraday meteorological factor change sub-model to obtain a power system high temperature scenario simulation model based on a double-layer Markov architecture.

[0117] Finally, based on the typical weather state transition sub-model and the intraday meteorological factor change sub-model constructed in the previous step, the typical weather state sequence output by the typical weather state transition sub-model and the intraday meteorological factor sequence output by the intraday meteorological factor change sub-model are integrated. The intraday meteorological factor sequences of each day are spliced ​​together in order to obtain a complete meteorological factor sequence.

[0118] The specific process example is as follows:

[0119] 1) Establish the joint distribution of the historical data of multi-dimensional meteorological factors at the first moment of each day under state k, and sample the values ​​at the first moment. Compare these values ​​with the state intervals in the first time period under state k. Compare and obtain the initial state of meteorological factors, so as to determine the row number of the state in the cumulative transfer matrix. Then retrieve the typical weather state sequence generated by the upper model The number of days N in state k k .

[0120] 2) Randomly generate a uniform random number between 0 and 1 , compare it with the first column of the cumulative joint distribution, and find The closest number is in row number r, and all elements in row r are used as the Markov transition kernel MTK:

[0121] (15)

[0122] (16)

[0123] Compare each element of row r with the cumulative transfer matrix determined by the initial state in the first period Compare the elements in the corresponding row to get the next state. Then repeat this step until N is fully generated. k All time periods during the day 3) After the intraday meteorological factor sequences under K typical states are established, the non-stationary change process of meteorological factors under state k is established. At the location of each day in the , the sequence of meteorological factors within the day is spliced ​​together to obtain a complete sequence.

[0124] In summary, the solution provided by this application uses the vine copula function to describe the correlation between multidimensional meteorological factors, generates a multidimensional joint probability distribution, and converts it into a Markov transition kernel after integration; secondly, a double-layer Markov model is constructed considering the non-stationary change process of meteorological factors, and a meteorological factor time series is generated based on the Markov transition kernel. The generated meteorological factor time series can better fit the actual data, making the established high temperature scene more accurate.

[0125] In order to further demonstrate the technical effect of this solution, this application also provides an experimental example based on this method, as follows:

[0126] This embodiment uses the vine copula function to characterize the correlation between meteorological factors under high temperature weather, and establishes a time series of meteorological factors based on a double-layer Markov model, which can generate a wind and solar power generation sequence more accurately. The high temperature historical meteorological data of a certain region from 2019 to August 2023 is used to generate a sequence of meteorological factors under high temperature weather. The time series of meteorological factors mentioned in this example include solar radiation series, temperature series, and wind speed series. The results are compared with the data in August 2024 and the traditional Markov model. The comparison results are as follows: Figures 5 to 7 As shown in the figure. The lower model of this model divides each day into 4 time periods, and the number of discrete states in each time period is 10. The traditional Markov model is discretized into 10 typical states. As can be seen from the figure, the proposed method can well capture the daily periodic characteristics of meteorological factors, that is, the non-stationary change characteristics of meteorological factors, especially for irradiation sequences, the traditional Markov model is completely unable to simulate the periodic characteristics of irradiation.

[0127] As shown in Table 1, Table 1 is a comparison of probability distribution parameters of meteorological factors. The probability distribution parameters of the method proposed in this application are closer to the actual data in 2024 and can better capture the distribution characteristics of historical data.

[0128]

[0129] like Figures 8 to 10 , Figures 8 to 10The following are comparison diagrams of the 24-hour autocorrelation characteristics of three meteorological factors obtained based on the scheme of the present application and the traditional Markov model. From the autocorrelation characteristics, it can be seen that the method proposed in the present application is closer to the autocorrelation curve of the historical data in 2024, and can better retain the periodic characteristics of meteorological factors.

[0130] From the experimental results, we can know that the inventive method uses the vine copula function to describe the correlation between multidimensional meteorological factors, generates a multidimensional joint probability distribution, and converts it into a Markov transfer kernel after integration; secondly, the non-stationary change process of meteorological factors is considered to construct a double-layer Markov model, and the meteorological factor time series is generated based on the Markov transfer kernel. The generated meteorological factor time series can better fit the actual data, making the established high temperature scene more accurate.

[0131] The above is a detailed description of an embodiment of a method for modeling a high-temperature scenario simulation model for a power system provided in the present application. The following is a detailed description of an embodiment of a device for modeling a high-temperature scenario simulation model for a power system provided in the present application.

[0132] See also Fig.11 , an embodiment of the present application provides a modeling device for simulating a high temperature scenario of an electric power system, comprising:

[0133] The meteorological factor distribution calculation unit 201 is used to calculate the marginal distribution of the meteorological factors by kernel density estimation based on a number of meteorological factors corresponding to the high temperature scene;

[0134] The multi-dimensional joint probability distribution determining unit 202 is used to construct an R-vine copula maximum spanning tree based on the marginal distribution by improving Prim's algorithm, so as to determine the multi-dimensional joint probability distribution of meteorological factors based on the R-vine copula maximum spanning tree;

[0135] The upper sub-model construction unit 203 is used to determine the typical weather state corresponding to the historical meteorological data by clustering the historical meteorological data based on the historical meteorological data, and then construct a typical weather state transfer sub-model based on the historical weather state sequence, combined with the Markov architecture logic and the random generation algorithm, wherein the historical weather state sequence is a typical weather state sequence generated according to the time sequence of the historical meteorological data, and the typical weather state transfer sub-model is used to simulate the typical weather state transition process;

[0136] The lower sub-model construction unit 204 is used to construct a sub-model of intraday meteorological factor changes based on historical meteorological data in combination with a Markov multidimensional transfer kernel, wherein the Markov multidimensional transfer kernel is obtained based on a multidimensional joint probability distribution, and the sub-model of intraday meteorological factor changes is used to simulate the non-stationary change process of intraday meteorological factors corresponding to the typical weather state sequence output by the typical weather state transfer sub-model under the typical weather state;

[0137] The high temperature scenario simulation model construction unit 205 is used to integrate the typical weather state transfer sub-model and the intraday meteorological factor change sub-model to obtain a high temperature scenario simulation model of the power system based on a double-layer Markov architecture.

[0138] Further, the multi-dimensional joint probability distribution determining unit 202 is specifically used for:

[0139] Based on the edge distribution of each meteorological factor, combined with the Kendall correlation coefficient calculation method, the correlation coefficient between each meteorological factor is calculated, and the edge weight between each meteorological factor is determined by the correlation coefficient;

[0140] Taking each meteorological factor as a node and combining the edge weights between them, the R-vine copula maximum spanning tree is constructed by improving Prim's algorithm.

[0141] Furthermore, the upper sub-model construction unit 203 is specifically used for:

[0142] Based on the preset historical meteorological data, a historical meteorological data set matrix is ​​constructed according to the data date and the number of meteorological factors;

[0143] According to the historical meteorological data set matrix, the weather similarity between meteorological data of different dates is calculated according to the preset similarity calculation formula, and then the historical meteorological data is clustered according to the weather similarity to determine the typical weather state corresponding to each date in the historical meteorological data, and a historical weather state sequence is generated according to the time sequence of the historical meteorological data;

[0144] Based on the historical weather state sequence, combined with Markov architecture logic and random generation algorithm, the typical weather state transfer matrix and cumulative probability transfer matrix are constructed in turn;

[0145] According to the cumulative probability transfer matrix and the preset random generation algorithm, a typical weather state transfer sub-model is constructed;

[0146] The lower layer sub-model construction unit 204 is specifically used for:

[0147] Based on historical meteorological data, the intraday meteorological factor sequences in historical meteorological data with the same weather conditions are merged;

[0148] According to the preset intra-day time period division information, the meteorological factor sequence is divided into multiple state intervals;

[0149] Through the typical weather state transfer sub-model, the weather state transfer matrix and cumulative probability transfer matrix of each state interval are determined respectively, and then combined with the Markov multidimensional transfer kernel, the sub-model of intraday meteorological factors change is constructed.

[0150] In addition, if Fig.12 As shown, an embodiment of the present application provides a modeling terminal for a high temperature scenario simulation model of a power system, the implementation types of the terminal include but are not limited to: a personal computer, a server and an embedded intelligent device, and the main components of the terminal include: a memory 33 and a processor 31, and the memory 33 and the processor 31 can be connected via a communication bus 34;

[0151] The memory 33 is used to store program codes, and the program codes are used to implement the modeling method of the high temperature scenario simulation model of the power system provided in the above embodiment;

[0152] The processor 31 is used to read and execute program codes.

[0153] The present application provides a computer-readable storage medium in which program code is stored. The program code is used to be read and executed by a processor to implement a method for modeling a high-temperature scenario simulation model of a power system as provided in the above-mentioned embodiment.

[0154] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the terminals, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0155] In the several embodiments provided in the present application, it should be understood that the disclosed terminals, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0156] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application 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 application described herein can, for example, be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations 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.

[0157] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0158] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0159] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0160] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0161] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for modeling a high temperature scenario simulation model for a power system, characterized in that: include: Based on several meteorological factors corresponding to the high temperature scene, the marginal distribution of the meteorological factors is calculated by kernel density estimation; Based on the marginal distribution, constructing an R-vine copula maximum spanning tree by improving Prim's algorithm, so as to determine the multi-dimensional joint probability distribution of the meteorological factors based on the R-vine copula maximum spanning tree; Based on the historical meteorological data, by clustering the historical meteorological data, the typical weather state corresponding to the historical meteorological data is determined, and then based on the historical weather state sequence, combined with the Markov architecture logic and the random generation algorithm, a typical weather state transition submodel is constructed, wherein the historical weather state sequence is a typical weather state sequence generated according to the time sequence of the historical meteorological data, and the typical weather state transition submodel is used to simulate the typical weather state transition process; Based on the historical meteorological data and in combination with the Markov multidimensional transfer kernel, a sub-model of intraday meteorological factor changes is constructed, wherein the Markov multidimensional transfer kernel is obtained based on the multidimensional joint probability distribution, and the sub-model of intraday meteorological factor changes is used to simulate the non-stationary change process of intraday meteorological factors corresponding to the typical weather state based on the typical weather state sequence output by the typical weather state transfer sub-model; The typical weather state transfer sub-model and the intraday meteorological factor change sub-model are integrated to obtain a high temperature scenario simulation model of the power system based on a double-layer Markov architecture.

2. A method for modeling a high temperature scenario simulation model of a power system according to claim 1, characterized in that: Based on the edge distribution, by improving Prim's algorithm, constructing the R-vine copula maximum spanning tree includes: Based on the edge distribution of each meteorological factor, combined with the Kendall correlation coefficient calculation method, the correlation coefficient between each meteorological factor is calculated, and the edge weight between each meteorological factor is determined by the correlation coefficient; Taking each meteorological factor as a node and combining the edge weights between them, the R-vine copula maximum spanning tree is constructed by improving Prim's algorithm.

3. A method for modeling a high temperature scenario simulation model of a power system according to claim 2, characterized in that: Taking each meteorological factor as a node and combining the edge weights between each meteorological factor, the R-vinecopula maximum spanning tree is constructed by improving Prim's algorithm, including: Taking each meteorological factor as a node, one is selected from all nodes as the starting node, and the nodes are associated according to the connection relationship between the nodes and the edge weight according to Prim's algorithm to determine the maximum spanning tree, and then the optimal binary copula function is selected by using the Akaike information criterion to connect the nodes of the first layer of the tree to construct edges and determine the maximum spanning tree of the first layer; The maximum spanning tree of the previous layer is looped, the correlation coefficient between each pair of conditional variables is calculated, the connectivity relationship of the conditional variables is determined, and then the maximum spanning tree of the current layer is determined according to the Prim's algorithm and the Akaike information criterion, until the maximum spanning trees of all layers and the optimal binary copula of the edges in each layer of the tree are determined, and the R-vine copula maximum spanning tree is obtained.

4. The method for modeling a high temperature scenario simulation model of a power system according to claim 1, characterized in that: Based on the historical meteorological data, by clustering the historical meteorological data, the typical weather state corresponding to the historical meteorological data is determined, and then based on the historical weather state sequence, combined with the Markov architecture logic and random generation algorithm, a typical weather state transition sub-model is constructed, including: Based on the preset historical meteorological data, a historical meteorological data set matrix is ​​constructed according to the data date and the number of meteorological factors; According to the historical meteorological data set matrix, the weather similarity between meteorological data of different dates is calculated according to a preset similarity calculation formula, and then the historical meteorological data is clustered according to the weather similarity to determine the typical weather state corresponding to each date in the historical meteorological data, and a historical weather state sequence is generated according to the time sequence of the historical meteorological data; Based on the historical weather state sequence, combined with Markov architecture logic and random generation algorithm, a typical weather state transfer matrix and a cumulative probability transfer matrix are constructed in sequence; According to the cumulative probability transfer matrix and the preset random generation algorithm, a typical weather state transfer sub-model is constructed.

5. The method for modeling a high temperature scenario simulation model of a power system according to claim 1, characterized in that: Based on the historical meteorological data and combined with the Markov multidimensional transfer kernel, a sub-model of intraday meteorological factor changes is constructed, including: Based on the historical meteorological data, merging the intraday meteorological factor sequences in the historical meteorological data with the same weather conditions; According to the preset intra-day time period division information, the meteorological factor sequence is divided into multiple state intervals; The typical weather state transfer sub-model is used to determine the weather state transfer matrix and cumulative probability transfer matrix of each state interval respectively, and then combined with the Markov multidimensional transfer kernel to construct a sub-model of intraday meteorological factor changes.

6. A modeling device for simulating a high temperature scenario of an electric power system, characterized in that: include: A meteorological factor distribution calculation unit, used to calculate the marginal distribution of the meteorological factors based on a number of meteorological factors corresponding to the high temperature scene by a kernel density estimation method; A multi-dimensional joint probability distribution determining unit, configured to construct an R-vine copula maximum spanning tree based on the marginal distribution by improving Prim's algorithm, so as to determine the multi-dimensional joint probability distribution of the meteorological factors based on the R-vine copula maximum spanning tree; The upper sub-model construction unit is used to determine the typical weather state corresponding to the historical meteorological data by clustering the historical meteorological data based on the historical meteorological data, and then construct a typical weather state transfer sub-model based on the historical weather state sequence in combination with the Markov architecture logic and the random generation algorithm, wherein the historical weather state sequence is a typical weather state sequence generated according to the time sequence of the historical meteorological data, and the typical weather state transfer sub-model is used to simulate the typical weather state transition process; A lower-level sub-model construction unit is used to construct a sub-model of intraday meteorological factor changes based on the historical meteorological data and in combination with a Markov multidimensional transfer kernel, wherein the Markov multidimensional transfer kernel is obtained based on the multidimensional joint probability distribution, and the sub-model of intraday meteorological factor changes is used to simulate the non-stationary change process of intraday meteorological factors corresponding to the typical weather state based on the typical weather state sequence output by the typical weather state transfer sub-model; The high temperature scenario simulation model construction unit is used to integrate the typical weather state transfer sub-model and the intraday meteorological factor change sub-model to obtain a high temperature scenario simulation model of the power system based on a double-layer Markov architecture.

7. A modeling device for a high temperature scenario simulation model of a power system according to claim 6, characterized in that: The multidimensional joint probability distribution determination unit is specifically used for: Based on the edge distribution of each meteorological factor, combined with the Kendall correlation coefficient calculation method, the correlation coefficient between each meteorological factor is calculated, and the edge weight between each meteorological factor is determined by the correlation coefficient; Taking each meteorological factor as a node and combining the edge weights between them, the R-vine copula maximum spanning tree is constructed by improving Prim's algorithm.

8. The device for modeling a high temperature scenario simulation model of a power system according to claim 6, characterized in that: The upper sub-model construction unit is specifically used for: Based on the preset historical meteorological data, a historical meteorological data set matrix is ​​constructed according to the data date and the number of meteorological factors; According to the historical meteorological data set matrix, the weather similarity between meteorological data of different dates is calculated according to a preset similarity calculation formula, and then the historical meteorological data is clustered according to the weather similarity to determine the typical weather state corresponding to each date in the historical meteorological data, and a historical weather state sequence is generated according to the time sequence of the historical meteorological data; Based on the historical weather state sequence, combined with Markov architecture logic and random generation algorithm, a typical weather state transfer matrix and a cumulative probability transfer matrix are constructed in sequence; According to the cumulative probability transfer matrix and the preset random generation algorithm, a typical weather state transfer sub-model is constructed; The lower layer sub-model construction unit is specifically used for: Based on the historical meteorological data, merging the intraday meteorological factor sequences in the historical meteorological data with the same weather conditions; According to the preset intra-day time period division information, the meteorological factor sequence is divided into multiple state intervals; The typical weather state transfer sub-model is used to determine the weather state transfer matrix and cumulative probability transfer matrix of each state interval respectively, and then combined with the Markov multidimensional transfer kernel to construct a sub-model of intraday meteorological factor changes.

9. A modeling terminal for simulating a high temperature scenario of an electric power system, characterized in that: include: Memory and processor; The memory is used to store program codes, and the program codes are used to implement the method for modeling a high temperature scenario simulation model of a power system according to any one of claims 1 to 5; The processor is used for reading and executing the program code.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program code, and the program code is used to be read and executed by a processor to implement the method for modeling a high temperature scenario simulation model of a power system as provided in any one of claims 1 to 5.

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