Typical scene generation method and device, flexible direct transmission curve generation method and device, terminal equipment and storage medium

Abstract

The invention discloses a typical scene generation method and device, a flexible direct transmission curve generation method and device, terminal equipment and a storage medium, and relates to the technical field of electric power systems.The typical scene generation method comprises the steps that historical scene set data of a plurality of new energy bases are obtained, and marginal probability distribution functions of the new energy bases are obtained; a target Copula function is determined; constructing a log-likelihood function according to the density function and the marginal probability distribution function of the target Copula function, and solving the log-likelihood function to obtain a correlation coefficient; according to the correlation coefficient and the target Copula function, constructing a Copula model; a joint output scene set of the new energy base is generated based on a Copula model, and a typical scene set is extracted from the joint output scene set based on a K-means clustering method. According to the method, the problem that the generated scene deviates from reality due to ignoring of tail characteristics can be avoided, and the accuracy of the generated typical scene is effectively improved.

Description

Technical Field

[0001] This invention relates to the field of power system technology, and in particular to a method for generating typical scenarios, a method for generating flexible DC transmission curves, an apparatus, a terminal device, and a storage medium. Background Technology

[0002] The output of new energy sources is characterized by strong randomness, intermittency, and spatiotemporal coupling. Its combined fluctuations directly determine the power regulation range, valve group switching frequency, and reserve capacity configuration of the flexible DC transmission channel. Therefore, generating output scenarios that accurately reflect the combined uncertainties of multiple bases during the planning stage is a prerequisite for formulating flexible DC transmission curves, optimizing the power supply capacity at both the sending and receiving ends, and reducing system operating costs.

[0003] Existing typical scenario generation methods typically use a "single-site probability model + linear correlation matrix" approach to generate scenarios. This involves independently fitting Weibull, beta, and other parameter distributions to each wind power and photovoltaic power station, then performing linear transformations using Pearson or rank correlation matrices, and finally weighting and superimposing the results by correlation coefficients after independent sampling to form typical scenarios. However, this approach does not consider the tail characteristics of the joint distribution of new energy bases when generating typical scenarios, resulting in low accuracy of the generated typical scenarios. Summary of the Invention

[0004] This invention provides a method for generating typical scenarios, a method for generating flexible direct transmission curves, an apparatus, a terminal device, and a storage medium, which can solve the technical problem that the generation of typical scenarios in the prior art does not take into account the tail characteristics of the joint distribution of new energy bases, resulting in low accuracy of the generated typical scenarios.

[0005] This invention provides a method for generating a typical scene, including:

[0006] Acquire historical scene set data from multiple new energy bases, and determine the marginal probability distribution function for each new energy base based on the historical scene set data;

[0007] Based on the marginal probability distribution function, a multivariate frequency histogram of the historical scene set data is plotted, and the target Copula function is determined based on the tail characteristics of the multivariate frequency histogram.

[0008] A log-likelihood function is constructed based on the density function of the target Copula function and the marginal probability distribution function. The log-likelihood function is then solved to obtain the correlation coefficient of the target Copula function.

[0009] Based on the correlation coefficient, the historical output datasets of multiple new energy bases, and the target Copula function, a Copula model is constructed;

[0010] Based on the Copula model, a set of joint power output scenarios for new energy bases is generated, and a set of typical scenarios is extracted from the set of joint power output scenarios using the K-means clustering method.

[0011] Furthermore, determining the target Copula function based on the tail characteristics of the multivariate frequency histogram includes:

[0012] If the tail characteristic is a symmetrical tail and a gradually independent tail characteristic, then the Gaussian Copula function is selected as the target Copula function;

[0013] If the tail characteristic is a symmetrical tail and asymptotically correlated tail, then the t-Copula function is selected as the target Copula function;

[0014] If the tail characteristic is an asymmetric tail and is sensitive to the upper tail, then the Gumbel Copula function is selected as the target Copula function;

[0015] If the tail characteristic is an asymmetric tail and a characteristic sensitive to the lower tail, then the Clayton Copula function is selected as the target Copula function;

[0016] If the tail characteristics are symmetrical and the upper and lower tails are moderately correlated, then the Frank Copula function is selected as the target Copula function.

[0017] Furthermore, the process involves generating a set of joint output scenarios for new energy bases based on the Copula model, and then extracting a set of typical scenarios from the set of joint output scenarios using the K-means clustering method.

[0018] Random sampling is performed using the Copula model to generate a set of joint output scenarios;

[0019] K-means clustering was used to extract a set of typical scenarios from the set of joint output scenarios.

[0020] Furthermore, the expression for the log-likelihood function is as follows:

[0021] L(ρ)=∑lnc(f(x1),f(x2),…,f(x n ))

[0022]

[0023] Where c(·) is the Copula density function, f(x) n Let be the marginal density function of the nth new energy base. Let F(x) be the correlation coefficient. nLet be the marginal probability distribution function of the nth new energy base, and C(·) be the Copula function.

[0024] Furthermore, the step of constructing a Copula model based on the correlation coefficient, the historical output datasets of multiple new energy bases, and the target Copula function includes:

[0025] The correlation coefficient and the historical power output datasets of the multiple new energy bases are substituted into the target Copula function to obtain the Copula model.

[0026] This invention also provides a method for generating a flexible straight-line transmission curve, comprising:

[0027] Based on the typical scenario set generated by the above-mentioned typical scenario generation method, a flexible DC transmission operation model is established. The flexible DC transmission operation model includes a new energy base output model, a flexible DC channel model, and a receiving-end power grid operation model.

[0028] The regulator is invoked to solve the flexible DC transmission operation model, and the flexible DC transmission curve of the new energy base is obtained.

[0029] The present invention also provides a typical scene generation apparatus, comprising:

[0030] The edge probability distribution function determination module is used to acquire historical scenario set data of multiple new energy bases and determine the edge probability distribution function of each new energy base based on the historical scenario set data.

[0031] The target Copula function determination module is used to draw a multivariate frequency histogram of the historical scene set data based on the marginal probability distribution function, and determine the target Copula function based on the tail characteristics of the multivariate frequency histogram;

[0032] The correlation coefficient calculation module is used to construct a log-likelihood function based on the density function of the target Copula function and the marginal probability distribution function, solve the log-likelihood function, and obtain the target...

[0033] The correlation coefficient of the Copula function;

[0034] The Copula model construction module is used to generate a set of joint output scenarios for new energy bases based on the Copula model, and to extract a set of typical scenarios from the set of joint output scenarios based on the K-means clustering method.

[0035] The typical scene set generation module is used to generate a typical scene set based on the Copula model.

[0036] Furthermore, determining the target Copula function based on the tail characteristics of the multivariate frequency histogram includes:

[0037] If the tail characteristic is a symmetrical tail and a gradually independent tail characteristic, then the Gaussian Copula function is selected as the target Copula function;

[0038] If the tail characteristic is a symmetrical tail and asymptotically correlated tail, then the t-Copula function is selected as the target Copula function;

[0039] If the tail characteristic is an asymmetric tail and is sensitive to the upper tail, then the Gumbel Copula function is selected as the target Copula function;

[0040] If the tail characteristic is an asymmetric tail and a characteristic sensitive to the lower tail, then the Clayton Copula function is selected as the target Copula function;

[0041] If the tail characteristics are symmetrical and the upper and lower tails are moderately correlated, then the Frank Copula function is selected as the target Copula function.

[0042] The present invention also provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements the typical scene generation method as described above.

[0043] The present invention also provides a computer-readable storage medium, comprising: a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the typical scene generation method as described above.

[0044] The following benefits can be obtained by implementing the present invention:

[0045] This invention analyzes the tail characteristics of the multivariate frequency histogram of historical data from new energy bases, enabling the selection of a suitable target Copula function to generate typical scenarios. This avoids the problem of generated scenarios deviating from reality due to ignoring tail characteristics, effectively improving the accuracy of generated typical scenarios.

[0046] Furthermore, based on the correlation coefficient, the historical output datasets of multiple new energy bases, and the target Copula function, the present invention constructs a Copula model, which enables the Copula model to cover the spatiotemporal coupling characteristics between multiple new energy bases, thereby further improving the accuracy of the generated typical scenarios. Attached Figure Description

[0047] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0048] Figure 1 This is a flowchart illustrating a typical scene generation method provided in an embodiment of the present invention;

[0049] Figure 2 This is a comparison diagram of a marginal probability distribution function and an empirical distribution function provided in an embodiment of the present invention;

[0050] Figure 3 This is a stacking diagram of the power output of each power source in the receiving-end power grid under typical scenario 1 provided by the embodiments of the present invention;

[0051] Figure 4 This is a stacking diagram of the power output of each power source in the receiving-end power grid under typical scenario 2 provided in the embodiments of the present invention;

[0052] Figure 5 This is a stacking diagram of the power output of each power source in the receiving-end power grid under typical scenario 3 provided in the embodiments of the present invention;

[0053] Figure 6 This is a stacking diagram of the power output of each power source in the receiving-end power grid under typical scenario 4 provided in the embodiments of the present invention;

[0054] Figure 7 This is a stacking diagram of the power output of each power source in the receiving-end power grid under typical scenario 5 provided in the embodiments of the present invention;

[0055] Figure 8 This is a stacking diagram of the power output of each power source in the receiving-end power grid under typical scenario 6 provided in the embodiments of the present invention;

[0056] Figure 9 This is a flowchart illustrating a method for generating a flexible direct current transmission curve according to an embodiment of the present invention.

[0057] Figure 10 This is a schematic diagram of a flexible straight-line delivery curve provided in an embodiment of the present invention;

[0058] Figure 11 This is a schematic diagram of a typical scene generation device provided in an embodiment of the present invention. Detailed Implementation

[0059] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0060] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0061] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0062] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0063] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0064] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0065] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0066] See Figure 1 To address the technical problem that existing technologies fail to consider the tail characteristics of the joint distribution of new energy bases when generating typical scenarios, resulting in low accuracy of the generated typical scenarios, an embodiment of the present invention provides a method for generating typical scenarios, comprising:

[0067] S1. Obtain historical scene set data from multiple new energy bases, and determine the marginal probability distribution function for each new energy base based on the historical scene set data;

[0068] Please see Figure 2 This is a comparison diagram of marginal probability distribution functions and empirical distribution functions provided by an embodiment of the present invention. The embodiment of the present invention can use a non-parametric estimation method to determine the marginal probability distribution function of the power output of each new energy base. Figure 2 In this context, the difference between the marginal probability distribution function and the empirical distribution function is relatively small. When the accuracy allows, the empirical distribution function can be used as an approximation of the marginal probability distribution function.

[0069] S2. Based on the marginal probability distribution function, draw a multivariate frequency histogram of the historical scene set data. Based on the tail characteristics of the multivariate frequency histogram, determine the target Copula function (connection function).

[0070] In this embodiment of the invention, historical scene set data can be mapped to the Uniform[0,1] space according to the marginal probability distribution function to obtain a set of pseudo-observations that follow a standard uniform distribution; a multivariate frequency histogram is then plotted in this standardized space. Based on the shape of the multivariate frequency histogram, the tail characteristics of the new energy base can be determined. For example, a new energy base includes wind power energy bases, photovoltaic energy bases, and hydropower energy bases. Based on the plotted multivariate frequency histogram, it is determined that wind power and photovoltaic power often occur together at high output (e.g., sunny days + strong winds), while hydropower output is relatively independent. Therefore, the current tail characteristic can be determined to be an asymmetric tail and a tail-sensitive characteristic.

[0071] In this embodiment of the invention, the target Copula function can be determined from the candidate Copula functions based on the tail characteristics of the elementary frequency histogram.

[0072] The candidate Copula functions include: Gaussian Copula (symmetric tail with progressively independent tails); t-Copula (symmetric tail with progressively correlated tails); Gumbel Copula (asymmetric tail with sensitivity to the upper tail); Clayton Copula (asymmetric tail with sensitivity to the lower tail); and Frank Copula (symmetric tail with moderate correlation between the upper and lower tails).

[0073] S3. Construct a log-likelihood function based on the density function and marginal probability distribution function of the target Copula function, solve the log-likelihood function, and obtain the correlation coefficient of the target Copula function;

[0074] In this embodiment of the invention, the expression for the log-likelihood function in step S3 is as follows:

[0075] L(ρ)=∑lnc(f(x1),f(x2),…,f(x n ))

[0076]

[0077] Where c(·) is the Copula density function, f(x) n Let be the marginal density function of the nth new energy base. Let F(x) be the correlation coefficient. n Let be the marginal probability distribution function of the nth new energy base, and C(·) be the Copula function.

[0078] S4. Construct a Copula model based on the correlation coefficient, historical power output datasets from multiple new energy bases, and the objective Copula function;

[0079] In this embodiment of the invention, the correlation coefficient and the historical power output datasets of multiple new energy bases are substituted into the target Copula function to obtain the Copula model.

[0080] S5. Generate a set of joint output scenarios for new energy bases based on the Copula model, and extract a set of typical scenarios from the set of joint output scenarios based on the K-means clustering method.

[0081] In this embodiment of the invention, the typical scenario set is the typical output scenario set of the new energy base. This embodiment analyzes the tail characteristics of the multivariate frequency histogram of historical data from the new energy base, thereby selecting a suitable target Copula function to generate typical scenarios. This avoids the problem of generated scenarios deviating from reality due to ignoring tail characteristics, effectively improving the accuracy of the generated typical scenarios.

[0082] Furthermore, in this embodiment of the invention, a Copula model is constructed based on the correlation coefficient, the historical output datasets of multiple new energy bases, and the target Copula function. This allows the Copula model to encompass the spatiotemporal coupling characteristics between multiple new energy bases, thereby further improving the accuracy of the generated typical scenarios.

[0083] In one embodiment, the typical scenario set generated in step S5 includes six typical scenarios. (See also...) Figure 3-8 This is a stacking diagram of the power output of each power source in the receiving-end power grid under six typical scenarios.

[0084] In one embodiment, step S2, determining the target Copula function based on the tail characteristics of the multivariate frequency histogram, includes:

[0085] S21. If the tail characteristic is a symmetrical tail and the tail is asymptotically independent, then the Gaussian Copula function is selected as the target Copula function.

[0086] S22. If the tail characteristics are symmetrical tails and the tails are asymptotically correlated, then the t-Copula function is selected as the target Copula function.

[0087] S23. If the tail characteristic is an asymmetric tail and is sensitive to the upper tail, then the Gumbel Copula function is selected as the target Copula function.

[0088] S24. If the tail characteristic is an asymmetric tail and is sensitive to the lower tail, then the Clayton Copula function is selected as the target Copula function.

[0089] S25. If the tail characteristics are symmetrical tails and moderately correlated upper and lower tails, then the Frank Copula function is selected as the target Copula function.

[0090] This invention analyzes the tail characteristics of the power output data of new energy bases and adaptively selects the optimal Copula function to ensure that the model can accurately characterize the joint distribution characteristics under different scenarios, thereby improving the reliability of the generated typical scenarios.

[0091] In one embodiment, step S5, generating a set of joint output scenarios for new energy bases based on the Copula model, and extracting a set of typical scenarios from the set of joint output scenarios using K-means clustering, includes:

[0092] S51. Use the Copula model to perform random sampling to generate a set of joint output scenarios;

[0093] In this embodiment of the invention, the historical power output datasets of various new energy bases can be fitted with Copula and transformed into the format required by the Copula distribution. The fitted data can then be randomly sampled using the Copula model to generate a certain number of joint power output scenario sets.

[0094] S52. K-means clustering is used to extract a typical scenario set from the joint output scenario set.

[0095] In this embodiment of the invention, N×M dimensional data samples satisfying the Copula function distribution are determined from the joint output scenario set, where N is the total number of samples and M is the dimension of the random variable;

[0096] The K-means clustering method is used to divide the N×M dimensional data samples into S classes, and the centers of each class are... As the quantile of the scene; the proportion of samples falling into this class out of the total number of samples is counted, and this proportion is used as the probability value p of each class. s (s = 1, 2, ..., S);

[0097] Adopted Will By converting the scenario to the original joint distribution function, the quantiles of the desired scenario can be obtained, and the probability corresponding to each quantile is p. s (s=1,2,…,S). By inversely transforming the generated joint scenario output data, the Copula-distributed data can be converted back to the original output data, thus generating S typical scenarios.

[0098] The embodiments of the present invention are based on historical data-driven modeling, which ensures that the Copula model can adapt to the output correlation of different new energy bases, effectively reducing the errors caused by human assumptions, thereby generating an accurate and reliable set of typical scenarios.

[0099] In one embodiment, the joint distribution function of an N-dimensional component can be described by the marginal distributions of these N variables and a Copula function. That is, the Copula function can connect the joint distribution of the multivariables with the marginal distributions of these N variables. The theorem expression is as follows:

[0100] F(x 1, x 2, ,…,x n,)=C(F(x1),F(x2),…,F(x n ))

[0101]

[0102] In the formula, F(x) n f(x) n ) are the distribution function and density function of the marginal distribution of the variable, respectively.

[0103] Copula distribution function C(F(x1),F(x2),…,F(x) n )) and Copula density function c(f(x1),f(x2),…,f(x) n The relationship between )) is:

[0104]

[0105] Definition 1: An N-ary copula function is a function C(u1, u2, ..., u...) that has the following properties. N );

[0106] Definition I N [0,1] N ;

[0107] C(u1,u2,…,u N It has a zero basal plane and is N-dimensionally increasing;

[0108] C(u1,u2,…,u N The marginal distribution of ) is C n (u n ), n = 1, 2, ..., N, and satisfy:

[0109] C n (u n )=C(1,…1,u n ,1,…,1)=u n

[0110] Among them, u n ∈[0,1], n=1,2,…N. Clearly, if the marginal distribution functions F1(x1), F2(x2), …, F… N (x N Both are continuous univariate distribution functions, let u n =F n (x n ), n=1,2,…,N, then C(u1,u2,…,u N ) is a multivariate distribution function that follows a uniform distribution along the margin [0,1].

[0111] Definition 2: Contextualization:

[0112] Using a set of discrete probability distributions The continuous probability distribution function F(x1,x2,…,x) is approximated by s = 1,2,…,S. N The process of contextualization is called scenario-based approach. For the quantile points of the scene, Let S be the corresponding probability, and S be the total number of scenarios.

[0113] Definition 3: Discretized Copula function:

[0114] From F(x1,x2,…,x) N )=C(F1(x1),F2(x2),…,F N (x N )), let u i =F i (x i The above formula becomes C(F1(x1),F2(x2),…,F…). N (x N ))=C(u1,u2,…,u N Assume that discretizing the Copula function yields the quantiles for a given scene as follows: Due to u i =F i (x i Then, the inverse operation x of the marginal distribution function can be used. i =F i -1 (u i The scenario corresponding to the original joint distribution function is obtained.

[0115] Implementing the embodiments of the present invention has the following beneficial effects:

[0116] This invention, through analyzing the tail characteristics of the multivariate frequency histogram of historical data from new energy bases, can select a suitable target Copula function to generate typical scenarios, avoiding the problem of generated scenarios deviating from reality due to ignoring tail characteristics, and effectively improving the accuracy of generated typical scenarios.

[0117] Furthermore, in this embodiment of the invention, a Copula model is constructed based on the correlation coefficient, the historical output datasets of multiple new energy bases, and the target Copula function. This allows the Copula model to encompass the spatiotemporal coupling characteristics between multiple new energy bases, thereby further improving the accuracy of the generated typical scenarios.

[0118] Please see Figure 9 An embodiment of the present invention also provides a method for generating a flexible direct current transmission curve, comprising:

[0119] S10. Based on the typical scenario set generated by the above typical scenario generation method, establish a flexible DC transmission operation model. The flexible DC transmission operation model includes a new energy base output model, a flexible DC channel model, and a receiving-end power grid operation model.

[0120] In this embodiment of the invention, the power output model of the new energy base includes a hydropower unit processing model, a wind power and photovoltaic unit power output model, a thermal power unit power output model, and an energy storage model.

[0121] 1) Hydropower unit output model:

[0122] To simplify calculations, the inflow from the upper reservoir and the power generation from the lower reservoir are not considered in the hydropower unit output model. The entire hydropower station is self-sufficient and does not share the same reservoir with other cascade hydropower stations. The sum of the upper and lower reservoir capacities of a pumped-storage hydropower station remains constant at all times; therefore, only the upper reservoir capacity limitation needs to be considered. Pumping and power generation cannot occur simultaneously. The simplified constraints are as follows:

[0123]

[0124] Equations (1)-(3) represent the equality and inequality constraints for the number of start-ups and shutdowns of the hydroelectric generator unit; equation (4) represents the constraints for the power generation and pumping power of the pumped storage unit; and equation (5) represents the constraints for the reservoir capacity of the upper and lower reservoirs of the pumped storage unit. Where, and These represent the on / off status of pumped storage hydropower station unit p during time period t, indicating whether it is in pumping or generating power. "1" indicates that the unit is on and "0" indicates that it is off. and These represent the number of times the pumped storage hydropower station unit p is started and stopped during time period t, respectively. and These represent the number of times the pumped storage hydropower station unit p is started and stopped during time period t, respectively, when unit p is in a discharge state. and These represent the number of times the pumped storage unit charges upwards and downwards during charging, respectively; MT represents the total number of time periods in the system. N represents the number of times a pumped-storage unit charges and discharges in a single time period. p The maximum total number of times a pumped storage hydropower station unit p is allowed to be switched on and off within a unit scheduling time. and Let p be the power generation and pumping power of pumped storage hydropower station unit p at time t; and Let p be the minimum and maximum generating power of the pumped storage hydropower station unit; and Let p be the minimum and maximum pumping power of the pumped storage hydropower station unit. and These represent the upper and lower reservoir capacities of unit p in the t-section pumped storage hydropower station, respectively; η g and η p These are the water conversion coefficient and the electricity conversion coefficient of pumped storage hydropower station unit p in power generation mode and pumping mode, respectively, which represent the ratio of electricity and water conversion between pumped storage hydropower station unit p in power generation and pumping modes.

[0125] 2) Output models for wind and solar power units:

[0126]

[0127] Equation (6) represents the actual power output constraint of new energy units, Equation (7) represents the power curtailment equation for new energy, Equation (8) represents the quota system constraint for new energy units, Equation (9) represents the installed capacity constraint for new energy, and Equation (10) represents the power generation constraint for new energy units. Where... This refers to the actual power output of the renewable energy unit n during time period t under scenario s; The renewable energy unit n can absorb power during time period t under scenario s; P represents the curtailed power of renewable energy unit n during time period t in scenario s; N represents the total number of renewable energy units; T represents the total time period; P w (n,t) and P pv (n,t) represent the power output of wind power and photovoltaic unit n at time t, respectively; Q rps For new energy quota requirements; and These represent the installed capacity of photovoltaic and wind power that have been connected to the grid, respectively. and P represents the upper limit of new energy installed capacity in region n; w,cur (n,t) and P pv.cur (n,t) represent the amount of abandoned power from wind power and photovoltaic units n during time period t, respectively; β w (n,t) and β pv (n,t) represent the per-unit output of new energy sources for wind power and photovoltaic unit n during time period t, respectively.

[0128] 3) Thermal power unit output model:

[0129]

[0130] Equation (11) represents the upper and lower limits of the output of the thermal power unit, Equations (12) and (13) represent the ramp-up constraints of the thermal power unit, Equations (14)-(19) represent the minimum start-up and shutdown time constraints of the thermal power unit, and Equations (20) and (21) represent the operating status constraints of the thermal power unit. P i max and P i minThese represent the upper and lower limits of the output of thermal power unit i, respectively; This represents the output of thermal power unit i during time period t in scenario s; These represent the operating status and start / stop status of thermal power unit i during time period t under scenario s, respectively; T represents the maximum uphill and downhill ramp rates of thermal power unit i, respectively; UTi T DTi T represents the number of hours that thermal power unit i must start or stop at the beginning of the dispatch cycle; ON,i T OFF,i Represent the minimum start-up and shutdown time of thermal power unit i; T represents the dispatching cycle; X ON,i0 X OFF,i0 Let represent the start-up and shutdown times of thermal power unit i at the initial moment, then:

[0131] T UTi =max{0,min[T,(T)} ON,i -X ON,i0 )I i0 ]} (twenty two)

[0132] T DTi =max{0,min[T,(T)} ON,i -X ON,i0 )I i0 ]} (twenty three)

[0133] 4) Energy storage model:

[0134] The constraints that energy storage units must meet during operation include:

[0135]

[0136] Equations (24) and (25) represent the charging and discharging power constraints of energy storage, respectively. Equation (26) represents the capacity constraint of energy storage at the beginning and end of the scheduling period. Equation (27) represents the remaining capacity constraint of energy storage in each time period. The purpose of this constraint is to prevent overcharging / over-discharging of energy storage and extend its service life. This represents the discharge power output from the AC side of the energy storage inverter during time period t. U represents the charging power input to the AC side of the energy storage inverter during time period t; s (t) represents the charging and discharging state of energy storage. A value of 1 indicates discharging, and a value of 0 indicates charging. η represents the maximum allowable charge and discharge power of the energy storage unit, primarily limited by the capacity of the grid-connected inverter; N represents the charge and discharge efficiency of the energy storage unit. T This refers to the scheduling cycle; if it's a daily schedule, it's 24 hours. s (0) represents the capacity of the energy storage at the initial moment of scheduling. and This refers to the maximum / minimum remaining capacity of energy storage during the scheduling process.

[0137] In this embodiment of the invention, based on the modeling approach of equivalent virtual machine groups and considering the characteristics of flexible DC power transmission, the constraints of the flexible DC channel model are divided into two categories: normal operation constraints and power transmission mode related constraints.

[0138] 1) Normal operation constraints:

[0139] Normal operation constraints mainly consider upper and lower limits of transmission power, power regulation rate, regulation direction and number of regulation constraints, power reverse regulation restriction constraints between adjacent time periods, power reversal constraints, and minimum holding time constraints when power is 0.

[0140]

[0141]

[0142] Equation (28) is the power relationship constraint between the sending and receiving ends; Equations (29) and (30) are the upper and lower limits of power constraints; Equations (31)-(34) are the power adjustment rate, adjustment direction and adjustment number constraints; Equations (35) and (36) are the power adjustment direction constraints between adjacent time periods; Equations (37) and (38) are the minimum holding time constraints when the power is 0; Equations (39)-(41) are the power reversal constraints. Wherein, d represents the flexible DC unit, D is the set of flexible DC units; T is the time set, superscript 1 represents the sending end of the flexible DC system, and superscript 2 represents the receiving end of the flexible DC system; and P represents the power at the sending and receiving ends of the flexible DC system at time t, respectively. d,min ,P d,max These represent the upper and lower limits of the regulating power of a single valve group in the flexible direct current unit; f d,t α represents the minimum number of valve groups operating at the sending and receiving ends of the flexible DC unit at time t; d,t ,β d,t It is a 0-1 variable, and a value of 0 indicates that the power transmission capacity is 0. These represent the upper and lower limits of the downward adjustment rate of the flexible straight-line unit, respectively. These represent the upper and lower limits of the upward adjustment rate of the flexible straight-line unit, respectively; The maximum number of times the flexible DC unit can adjust its power throughout the day; U up,t U d,t U represents the 0-1 state variables for upward and downward adjustment of the power of the flexible DC unit, respectively. d,t =1 indicates that the power in time period t+1 is adjusted downwards compared to time period t, U up,t =1 indicates that the power level in time period t+1 is adjusted upwards compared to time period t, while a value of 0 indicates that the power level does not change between adjacent time periods; M is a large number; z d,tThis is a state variable indicating whether the power of the flexible DC unit is 0. A value of 0 indicates that the power of the flexible DC unit is 0, and a value of 1 indicates that the power of the flexible DC unit is 1. Y represents the minimum dwell time required when the power of the flexible DC unit is 0; d,t It is a 0-1 variable, which is 1 when the power of the flexible DC unit flips at time t, and 0 under other conditions; These represent the values ​​that the flexible DC unit needs to operate with the minimum power when the power is switched, and they are opposites of each other; This represents the maximum number of power reversals allowed for the flexible DC unit throughout the day.

[0143] 2) Power transmission mode association constraints:

[0144] This constraint modeling mainly considers the different situations that may exist between two AC power transmission stations when the topology of the flexible DC transmission system is adjusted. Specifically, it can be divided into "full transmission mode" and "partial transmission mode". The two modes are modeled separately, and the specific constraints are as follows:

[0145]

[0146] Equations (43) and (44) represent the constraints under the distribution mode, and equation (45) represents the constraints under the full distribution mode. These represent the 0-1 variables for forward power transmission in sending unit 1 and unit 2, respectively. These represent the 0-1 variables representing the reverse power supply of unit 1 and unit 2 at time t, respectively; These represent the equivalent output power of the sending end unit 1 and unit 2, respectively.

[0147] Additionally, it should be noted that if either of the two units of the flexible DC transmission line is out of service for any reason, then the above-mentioned power transmission mode constraints need not be met, and the relevant constraints are masked in the optimization calculation model.

[0148] In this embodiment of the invention, the operational constraints of the receiving-end power grid operation model include node power balance constraints, generator output constraints, line power flow constraints, system reserve capacity constraints, and node voltage phase angle constraints. Specifically:

[0149]

[0150] Equation (46) represents the node power balance constraint, and equations (47) and (48) represent the line power flow constraints. Equations (49) and (50) represent the node voltage phase angle constraints, where the unit output constraints are the same as those for the sending-end renewable energy base, as shown in equations (1)-(27), and will not be listed here again. Inject the active power flow of node n into line l during time period t under scenario s; Let n be the load demand of node n in time period t, where n∈Ω N ,Ω N A set of nodes; A 0-1 variable representing whether the flexible DC channel is connected to node n; These are the thermal power unit, the new energy unit, and the energy storage unit located at node n, respectively. b is the set of lines connecting node n; l The susceptance of the receiving-end power grid line l; These represent the voltage phase angles at nodes i and j at both ends of line l during time period t in scenario s; P l max This represents the maximum transmission capacity of line l; Let n be the voltage phase angle of node n during time period t in scenario s. These are the upper and lower limits of the voltage phase angle at node n, respectively; This represents the voltage phase angle at the balancing node of the receiving-end power grid.

[0151] In addition to the constraints of the above-mentioned new energy base output model, flexible DC channel model, and receiving-end grid operation model, the flexible DC transmission operation model of this invention also includes other constraints:

[0152] 1) Carbon emission constraints:

[0153]

[0154] In the formula, e i Q is the carbon emission factor of unit i; e This refers to the carbon emission limit.

[0155] 2) Constraints on the penetration rate of new energy electricity:

[0156]

[0157] In the formula, σ s Let be the probability of scenario s occurring.

[0158] 3) Average utilization hours constraint for inter-provincial and inter-regional flexible direct channels:

[0159]

[0160] In this embodiment of the invention, the objective function of the flexible DC power transmission operation model is an objective function that aims to minimize the annual power generation cost:

[0161]

[0162] C p =C vu n vu +C vd n vd (57)

[0163] In the formula, β is the conversion factor between daily operating costs and annual costs; C SU (i,t), CSD (i,t) represent the start-up and shutdown costs of thermal power unit i during time period t, respectively. G Ω is the set of all generator sets. RES It is a collection of all renewable energy units; Let C be the power generation cost function of thermal power unit i; curt C represents the cost coefficient for the curtailment penalty of renewable energy units. s (t) is the average charging and discharging cost function of the energy storage unit during time period t; C p The cost of a pumped storage unit during time period t; a i b i c i K is the unit power generation cost coefficient for thermal power units. s The unit charge / discharge cost coefficient is the converted value; Δt is the scheduling step size; C vu C vd These represent the start-up and shutdown costs of a single pumped storage unit; n vu n vd These represent the number of pumped storage units that are started and stopped.

[0164] S20. Call the regulator to solve the flexible DC transmission operation model and obtain the flexible DC transmission curve of the new energy base.

[0165] In this embodiment of the invention, the objective function of equation (54) is used to solve the problem under the premise of satisfying the constraints of equations (1)-(53) by calling the Gurobi toolkit, and the flexible direct transmission curve of the new energy base is obtained.

[0166] Please see Figure 10 This is a schematic diagram of a flexible straight-line delivery curve provided in an embodiment of the present invention.

[0167] Implementing the embodiments of the present invention has the following beneficial effects:

[0168] This invention constructs a flexible DC transmission operation model based on a set of generated typical scenarios, and generates a flexible DC transmission curve by comprehensively considering the regulation capability of the receiving-end power grid and the dynamic response characteristics of flexible DC. This enables it to adapt to different scales of new energy bases and scenarios where the receiving-end power grid transmits electricity via flexible DC channels.

[0169] Please see Figure 11 Based on the same inventive concept as the above embodiments, the present invention also provides a typical scene generation apparatus, comprising:

[0170] The edge probability distribution function determination module 10 is used to acquire historical scenario set data of multiple new energy bases and determine the edge probability distribution function of each new energy base based on the historical scenario set data.

[0171] The target Copula function determination module 20 is used to draw a multivariate frequency histogram of historical scene set data based on the marginal probability distribution function, and determine the target Copula function based on the tail characteristics of the multivariate frequency histogram.

[0172] The correlation coefficient solving module 30 is used to construct the log-likelihood function based on the density function and marginal probability distribution function of the target Copula function, solve the log-likelihood function, and obtain the correlation coefficient of the target Copula function.

[0173] Copula model building module 40 is used to build a Copula model based on correlation coefficients, historical power output datasets from multiple new energy bases, and the objective Copula function.

[0174] The typical scenario set generation module 50 is used to generate a joint output scenario set of new energy bases based on the Copula model, and extract the typical scenario set from the joint output scenario set based on the K-means clustering method.

[0175] In one embodiment, determining the target Copula function based on the tail characteristics of the multivariate frequency histogram includes:

[0176] If the tail characteristics are symmetrical tails and the tails are asymptotically independent, then the Gaussian Copula function is selected as the target Copula function.

[0177] If the tail characteristics are symmetrical and the tail is asymptotically correlated, then the t-Copula function is selected as the target Copula function.

[0178] If the tail feature is an asymmetric tail and is sensitive to the upper tail, then the Gumbel Copula function is selected as the target Copula function.

[0179] If the tail feature is an asymmetric tail and is sensitive to the lower tail, then the Clayton Copula function is selected as the target Copula function.

[0180] If the tail characteristics are symmetrical and the upper and lower tails are moderately correlated, then the Frank Copula function is selected as the target Copula function.

[0181] In one embodiment, a set of joint power output scenarios for new energy bases is generated based on the Copula model, and a set of typical scenarios is extracted from the set of joint power output scenarios using the K-means clustering method, including:

[0182] Random sampling is performed using the Copula model to generate a set of joint output scenarios;

[0183] K-means clustering was used to extract a set of typical scenarios from the set of joint force scenarios.

[0184] In one embodiment, the log-likelihood function is expressed as follows:

[0185] L(ρ)=∑lnc(f(x1),f(x2),…,f(x n ))

[0186]

[0187] Where c(·) is the Copula density function, f(x) n Let be the marginal density function of the nth new energy base. Let F(x) be the correlation coefficient. n Let be the marginal probability distribution function of the nth new energy base, and C(·) be the Copula function.

[0188] In one embodiment, a Copula model is constructed based on correlation coefficients, historical power output datasets from multiple new energy bases, and a target Copula function, including:

[0189] By substituting the correlation coefficients and historical power output datasets from multiple new energy bases into the objective Copula function, a Copula model is obtained.

[0190] It is understood that the above-described device embodiments correspond to the method embodiments of the present invention, and can implement the typical scene generation method provided by any of the above-described method embodiments of the present invention.

[0191] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can specifically be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0192] Based on the above-described typical scene generation method embodiments, another embodiment of the present invention provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the typical scene generation method of any embodiment of the present invention.

[0193] For example, in this embodiment, the computer program can be divided into one or more modules, one or more modules are stored in memory and executed by a processor to complete the present invention. One or more module elements can be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in a terminal device.

[0194] Terminal devices can be computing devices such as desktop computers, laptops, handheld computers, and cloud servers. Terminal devices may include, but are not limited to, processors and memory.

[0195] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device through various interfaces and lines.

[0196] Based on the above-described method embodiments, another embodiment of the present invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is running, it controls the device where the computer-readable storage medium is located to execute the typical scene generation method of any of the above-described method embodiments of the present invention.

[0197] The modules / units integrated into the device / terminal equipment, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0198] The above are preferred embodiments of the present invention. It should be noted that, for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for generating a typical scene, characterized in that, include: Acquire historical scene set data from multiple new energy bases, and determine the marginal probability distribution function for each new energy base based on the historical scene set data; Based on the marginal probability distribution function, a multivariate frequency histogram of the historical scene set data is plotted, and the target Copula function is determined based on the tail characteristics of the multivariate frequency histogram. A log-likelihood function is constructed based on the density function of the target Copula function and the marginal probability distribution function. The log-likelihood function is then solved to obtain the correlation coefficient of the target Copula function. Based on the correlation coefficient, the historical output datasets of multiple new energy bases, and the target Copula function, a Copula model is constructed; Based on the Copula model, a set of joint power output scenarios for new energy bases is generated, and a set of typical scenarios is extracted from the set of joint power output scenarios using the K-means clustering method.

2. The typical scene generation method as described in claim 1, characterized in that, Determining the target Copula function based on the tail characteristics of the multivariate frequency histogram includes: If the tail characteristic is a symmetrical tail and a gradually independent tail characteristic, then the Gaussian Copula function is selected as the target Copula function; If the tail characteristic is a symmetrical tail and asymptotically correlated tail, then the t-Copula function is selected as the target Copula function; If the tail characteristic is an asymmetric tail and is sensitive to the upper tail, then the Gumbel Copula function is selected as the target Copula function; If the tail characteristic is an asymmetric tail and a characteristic sensitive to the lower tail, then the Clayton Copula function is selected as the target Copula function; If the tail characteristics are symmetrical and the upper and lower tails are moderately correlated, then the Frank Copula function is selected as the target Copula function.

3. The typical scene generation method as described in claim 1, characterized in that, The process involves generating a set of joint output scenarios for new energy bases based on the Copula model, and extracting a set of typical scenarios from the set of joint output scenarios using K-means clustering, including: Random sampling is performed using the Copula model to generate a set of joint output scenarios; K-means clustering was used to extract a set of typical scenarios from the set of joint output scenarios.

4. The typical scene generation method as described in claim 1, characterized in that, The expression for the log-likelihood function is as follows: L(ρ)=∑lnc(f(x1),f(x2),…,f(x n )) Where c(·) is the Copula density function, f(x) n Let be the marginal density function of the nth new energy base. Let F(x) be the correlation coefficient. n Let be the marginal probability distribution function of the nth new energy base, and C(·) be the Copula function.

5. The typical scene generation method as described in claim 1, characterized in that, The step of constructing a Copula model based on the correlation coefficient, historical power output datasets from multiple new energy bases, and the target Copula function includes: The correlation coefficient and the historical power output datasets of the multiple new energy bases are substituted into the target Copula function to obtain the Copula model.

6. A method for generating a flexible straight-line transmission curve, characterized in that, include: Based on the typical scenario set generated by the typical scenario generation method as described in any one of claims 1-5, a flexible DC transmission operation model is established, which includes a new energy base output model, a flexible DC channel model, and a receiving-end power grid operation model. The regulator is invoked to solve the flexible DC transmission operation model, and the flexible DC transmission curve of the new energy base is obtained.

7. A typical scene generation device, characterized in that, include: The edge probability distribution function determination module is used to acquire historical scenario set data of multiple new energy bases and determine the edge probability distribution function of each new energy base based on the historical scenario set data. The target Copula function determination module is used to draw a multivariate frequency histogram of the historical scene set data based on the marginal probability distribution function, and determine the target Copula function based on the tail characteristics of the multivariate frequency histogram; The correlation coefficient solving module is used to construct a log-likelihood function based on the density function of the target Copula function and the marginal probability distribution function, solve the log-likelihood function, and obtain the correlation coefficient of the target Copula function. The Copula model building module is used to construct a Copula model based on the correlation coefficient, the historical output datasets of multiple new energy bases, and the target Copula function. The typical scenario set generation module is used to generate a joint output scenario set of new energy bases based on the Copula model, and extract typical scenario sets from the joint output scenario set based on the K-means clustering method.

8. The typical scene generation apparatus as described in claim 7, characterized in that, Determining the target Copula function based on the tail characteristics of the multivariate frequency histogram includes: If the tail characteristic is a symmetrical tail and a gradually independent tail characteristic, then the Gaussian Copula function is selected as the target Copula function; If the tail characteristic is a symmetrical tail and asymptotically correlated tail, then the t-Copula function is selected as the target Copula function; If the tail characteristic is an asymmetric tail and is sensitive to the upper tail, then the Gumbel Copula function is selected as the target Copula function; If the tail characteristic is an asymmetric tail and a characteristic sensitive to the lower tail, then the Clayton Copula function is selected as the target Copula function; If the tail characteristics are symmetrical and the upper and lower tails are moderately correlated, then the Frank Copula function is selected as the target Copula function.

9. A terminal device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements the typical scene generation method as described in any one of claims 1-5.

10. A computer-readable storage medium, characterized in that, include: A stored computer program, wherein, when the computer program is executed, it controls the device containing the computer-readable storage medium to perform the typical scene generation method as described in any one of claims 1-5.

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