Method and system for analyzing influence factors of distributed resource bearing capacity of power distribution network

Abstract

The invention discloses a distribution network distributed resource bearing capacity influence factor analysis method and system, and relates to the technical field of distribution network bearing capacity assessment, and the method comprises the steps: carrying out the first preprocessing of first target data of a distribution network, and obtaining second target data; performing first screening on the second target data to obtain a first influence factor of the bearing capacity of the power distribution network; presetting a first causal graph structure of the first influence factor, and analyzing a causal effect of the first influence factor on the bearing capacity of the power distribution network; and constructing a structural causal model based on the causal effect and the first causal graph structure to evaluate the influence of different intervention strategies on the bearing capacity of the power distribution network. According to the method, deep understanding of the power distribution network bearing capacity influence mechanism is realized, powerful support can be provided for power distribution network planning and operation decision making, and improvement of the distributed resource bearing capacity of the power distribution network is facilitated.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network carrying capacity evaluation, and in particular to a method and system for analyzing factors affecting the carrying capacity of a distribution network for distributed resources. Background Art

[0002] With the large-scale access of new loads such as distributed energy and electric vehicles, the carrying capacity of distribution networks faces severe challenges. Traditional carrying capacity assessment methods are mainly based on static power flow analysis and probability statistical models, which are difficult to fully capture the complex interactive relationship between influencing factors, and cannot accurately quantify the impact of each factor on carrying capacity. In addition, existing methods often ignore the characteristics of time series and dynamic changes, and are difficult to cope with the rapid changes in the operating status of distribution networks. Summary of the invention

[0003] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a method and system for analyzing factors affecting the ability of a distribution network to carry distributed resources to solve the problems mentioned in the background technology.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides a method for analyzing factors affecting the ability of a distribution network to carry distributed resources, including: performing first preprocessing on first target data of the distribution network to obtain second target data;

[0008] Performing a first screening on the second target data to obtain a first influencing factor of the distribution network carrying capacity;

[0009] Presetting a first causal graph structure of the first influencing factor, and analyzing the causal effect of the first influencing factor on the carrying capacity of the distribution network;

[0010] A structural causal model is constructed based on the causal effect and the first causal graph structure to evaluate the impact of different intervention strategies on the carrying capacity of the distribution network.

[0011] As a preferred solution of the method for analyzing factors affecting the ability of a distribution network to carry distributed resources according to the present invention, wherein: the first causal graph structure includes: a first graph structure;

[0012] The first influencing factor is used to test the first graph structure to delete redundant edges;

[0013] Orient the remaining edges to obtain the second graph structure.

[0014] As a preferred solution of the method for analyzing factors affecting the ability of a distribution network to carry distributed resources according to the present invention, wherein: analyzing the causal effect of a first influencing factor on the carrying capacity of the distribution network comprises: constructing a first prediction model and a second prediction model for any influencing factor;

[0015] The first prediction model is used to predict key influencing factors;

[0016] The second prediction model is used to predict the carrying capacity of the distribution network.

[0017] As a preferred solution of the method for analyzing factors affecting the ability of a distribution network to carry distributed resources described in the present invention, it also includes: the first prediction model and the second prediction model are result predictions based on other influencing factors other than any one influencing factor.

[0018] As a preferred solution of the method for analyzing factors affecting the distribution network's ability to carry distributed resources described in the present invention, it also includes: calculating causal effect estimates based on the predicted key influencing factors and the distribution network's carrying capacity.

[0019] As a preferred solution of the method for analyzing factors affecting the ability of a distribution network to carry distributed resources according to the present invention, a structural causal model is constructed based on the causal effect and the first causal graph structure to evaluate the impact of different intervention strategies on the carrying capacity of the distribution network, including:

[0020] Presetting a target intervention strategy for the structural causal model, and setting key influencing factors in the structural causal model as target influencing factors;

[0021] Calculate the intervention effect and evaluate the impact of the target intervention strategy on the carrying capacity of the distribution network.

[0022] As a preferred solution of the method for analyzing factors affecting the ability of a distribution network to carry distributed resources described in the present invention, wherein: the calculation of the intervention effect and the evaluation of the impact of the target intervention strategy on the carrying capacity of the distribution network further include: the intervention effect is the difference between the expected value of the target influencing factor under the target intervention strategy and the expected value under the condition of no intervention;

[0023] The structural causal model updates the target influencing factors according to the intervention strategy to calculate the distribution network carrying capacity;

[0024] Traverse different intervention strategies, obtain the distribution of distribution network carrying capacity after intervention, and calculate the average or expected value of the distribution as an estimate of the intervention effect to analyze the influencing mechanism of distribution network carrying capacity.

[0025] In a second aspect, the present invention provides a system for analyzing factors affecting the ability of a distribution network to carry distributed resources, comprising:

[0026] A data acquisition unit, used for performing a first preprocessing on the first target data of the distribution network to acquire second target data;

[0027] A screening module, configured to perform a first screening on the second target data to obtain a first influencing factor of the distribution network carrying capacity;

[0028] An analysis module, configured to preset a first causal graph structure of the first influencing factor and analyze the causal effect of the first influencing factor on the carrying capacity of the distribution network;

[0029] An evaluation module is used to construct a structural causal model based on the causal effect and the first causal graph structure to evaluate the impact of different intervention strategies on the carrying capacity of the distribution network.

[0030] In a third aspect, the present invention provides an electronic device, comprising:

[0031] Memory and processor;

[0032] The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the method for analyzing factors affecting the ability of the distribution network to carry distributed resources are implemented.

[0033] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the method for analyzing factors affecting the ability of a distribution network to carry distributed resources.

[0034] Compared with the prior art, the present invention has the following beneficial effects: the present invention obtains second target data by performing a first preprocessing on first target data of the distribution network; obtains a first influencing factor of the distribution network's carrying capacity by performing a first screening on the second target data; presets a first causal graph structure of the first influencing factor, and analyzes the causal effect of the first influencing factor on the distribution network's carrying capacity; constructs a structural causal model based on the causal effect and the first causal graph structure to evaluate the impact of different intervention strategies on the distribution network's carrying capacity; achieves an in-depth understanding of the influencing mechanism of the distribution network's carrying capacity, can provide strong support for distribution network planning and operation decisions, and helps to improve the distribution network's ability to carry distributed resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0036] Figure 1 A method flow chart of a method and system for analyzing factors affecting the ability of a distribution network to carry distributed resources according to an embodiment of the present invention. DETAILED DESCRIPTION

[0037] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0038] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0039] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0040] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0041] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0042] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0043] Example 1

[0044] Reference Figure 1 , is an embodiment of the present invention, which provides a method for analyzing factors affecting the ability of a distribution network to carry distributed resources, including:

[0045] S100: performing first preprocessing on first target data of the distribution network to obtain second target data;

[0046] S200: Performing a first screening on the second target data to obtain a first influencing factor of the distribution network carrying capacity;

[0047] S300: Preset a first causal graph structure of a first influencing factor, and analyze the causal effect of the first influencing factor on the carrying capacity of the distribution network;

[0048] S400: Construct a structural causal model based on the causal effect and the first causal graph structure to evaluate the impact of different intervention strategies on the carrying capacity of the distribution network.

[0049] It should be noted that with the large-scale access of new loads such as distributed energy and electric vehicles, the carrying capacity of the distribution network faces severe challenges. Since traditional carrying capacity assessment methods are difficult to fully capture the complex interactive relationship between influencing factors, it is also impossible to accurately quantify the degree of influence of each factor on the carrying capacity. In addition, the existing methods also ignore the characteristics of time series and the laws of dynamic changes, and it is difficult to cope with the rapid changes in the operating status of the distribution network. The embodiment of the present application, through the above steps S100-S400, constructs a causal relationship network covering multi-dimensional factors such as the physical characteristics, operating status and external environment of the distribution network, and accurately quantifies the causal effect of each influencing factor on the carrying capacity of the distribution network. By integrating multi-source heterogeneous data and combining machine learning and statistical inference techniques, an in-depth analysis of the influencing mechanism of the distribution network carrying capacity is achieved.

[0050] Example 2

[0051] Reference Figure 1 , which is an embodiment of the present invention, provides a method for analyzing factors affecting the ability of a distribution network to carry distributed resources based on the previous embodiment, including:

[0052] In the embodiment of the present application, the first causal graph structure includes: a first graph structure;

[0053] The first influencing factor is used to test the first graph structure to delete redundant edges;

[0054] Orient the remaining edges to obtain the second graph structure.

[0055] In an optional embodiment, the first causal graph structure may be constructed by a fast causal inference method;

[0056] In another optional embodiment, the first causal graph structure may also be constructed using a Bayesian network structure learning method;

[0057] In a preferred embodiment, the present application uses the Peter-Clark algorithm to construct a preliminary causal graph structure. The algorithm belongs to a constraint-based method, which mainly infers the causal relationship between variables by statistically testing marginal and conditional independence. The basic principle of the Peter-Clark algorithm is to gradually delete the edges between variables through conditional independence tests, and finally obtain a directed acyclic graph.

[0058] In an optional embodiment, the first graph structure is a completely undirected graph, and the second graph structure is a directed acyclic graph.

[0059] In an optional embodiment, the edges between variables are gradually deleted through the conditional independence test, and finally a directed acyclic graph is obtained, which specifically includes steps A1-A3:

[0060] A1: Construct a completely undirected graph, with edges connecting all variables.

[0061] A2: For any two connected variables X and Y, test whether variables X and Y are independent given a subset of other variables S. If they are independent, delete the edge between X and Y.

[0062] A3: Orient the remaining edges to obtain a directed acyclic graph.

[0063] For example, the partial correlation coefficient is used as a statistic for the conditional independence test. Assuming that we want to test whether X and Y are independent under the condition of given Z, the calculation formula of the partial correlation coefficient is:

[0064]

[0065] Among them, ρ XY ,ρ XZ ,ρ YZ They represent the correlation coefficients between X and Y, X and Z, and Y and Z respectively.

[0066] It should be noted that, in the embodiment of the present application, the first influencing factor is obtained by performing a first screening on the second target data, wherein the second target data is obtained by performing a first preprocessing on the first target data of the distribution network.

[0067] In an embodiment of the present application, the first target data is the original data collected to analyze the factors affecting the distribution network's ability to carry distributed resources, and these original data can come from multiple aspects. The present application integrates multi-source heterogeneous data and combines machine learning and statistical inference techniques to achieve an in-depth analysis of the mechanism affecting the distribution network's carrying capacity.

[0068] In an optional embodiment, performing a first preprocessing on the first target data of the power distribution network may include a preprocessing method of outlier detection and missing value filling;

[0069] In another optional embodiment, the first preprocessing of the first target data of the distribution network may also include preprocessing methods such as data standardization, data cleaning, and data integration to obtain a clean, complete and uniformly formatted second target data to facilitate subsequent analysis and modeling, which is not specifically limited in this application.

[0070] For example, the missing load data can be filled by time series interpolation method; the abnormal voltage data can be corrected by combining expert knowledge and statistical methods.

[0071] In the embodiment of the present application, the first influencing factor obtained by the first screening of the second target data is based on expert knowledge and statistical analysis, and is a preliminary screening of factors that may affect the carrying capacity of the distribution network.

[0072] In an optional embodiment, the first influencing factor may be obtained from the distribution network topology structure and equipment parameters and operation status data;

[0073] In another optional embodiment, the first influencing factor may also be obtained from distributed resource data and environmental factor data.

[0074] Exemplarily, the distribution network topology and equipment parameters include data such as transformer capacity, line impedance, and switch status;

[0075] Exemplarily, the operating status data includes data such as node voltage, line power flow, load demand, etc.;

[0076] Exemplarily, the distributed resource data includes access capacity and output characteristics of distributed power sources such as photovoltaic and wind power;

[0077] Exemplarily, the environmental factor data includes meteorological information data such as temperature, humidity, and sunshine intensity.

[0078] In an optional embodiment, the variables that can be selected as the first influencing factor include:

[0079] X1: Transformer capacity utilization

[0080] X2: Line load factor

[0081] X3: Photovoltaic penetration

[0082] X4: Wind power access capacity

[0083] X5: Electric vehicle charging load ratio

[0084] X6: Ambient temperature

[0085] X7: Power supply radius

[0086] X8: Load density

[0087] Y: Distribution network carrying capacity (characterized by the maximum accessible distributed resource capacity)

[0088] In an embodiment of the present application, analyzing the causal effect of the first influencing factor on the carrying capacity of the distribution network includes: constructing a first prediction model and a second prediction model for any influencing factor;

[0089] The first prediction model is used to predict key influencing factors;

[0090] The second prediction model is used to predict the distribution network carrying capacity.

[0091] In the embodiment of the present application, it also includes: the first prediction model and the second prediction model are result predictions based on other influencing factors other than any one influencing factor.

[0092] In an embodiment of the present application, it also includes: calculating an estimated value of the causal effect based on the predicted key influencing factors and the distribution network carrying capacity.

[0093] It should be noted that in the embodiment of the present application, the causal effect estimation problem is decomposed into two prediction problems to reduce the impact of model errors on the estimation results.

[0094] In a preferred embodiment, a dual machine learning method is used to estimate the causal effect of each factor on the carrying capacity, specifically including steps B1-B3:

[0095] B1: For each treatment variable X i , build two prediction models:

[0096] First prediction model: predict Xi using other variables

[0097] Second prediction model: predicting the outcome variable Y using other variables

[0098] B2: Calculate the residual:

[0099]

[0100] ε Y =Y-Ε[YX -i ]

[0101] Among them, X -i Indicates the division by X i All variables except .

[0102] B3: Use residual ε Y , Estimating the causal effect:

[0103]

[0104] Among them, Cov represents covariance and Var represents variance.

[0105] In an optional embodiment, the prediction model may adopt a variety of other machine learning and statistical models that can be used for prediction tasks, such as support vector machines, convolutional neural networks, gradient boosting trees, etc.

[0106] In a preferred embodiment, random forest may be used as a prediction model to estimate the causal effect of the first influencing factor on the carrying capacity of the distribution network.

[0107] For example, taking the causal effect estimation of photovoltaic penetration rate (X3) on carrying capacity (Y) as an example, the method specifically includes steps C1-C5:

[0108] C1: Build a random forest model RF1 and use X1, X2, X4, X5, X6, X7, and X8 to predict X3;

[0109] C2: Build a random forest model RF2 and use X1, X2, X4, X5, X6, X7, and X8 to predict Y;

[0110] C3: Calculate the residual:

[0111]

[0112] ε Y =Y-RF2*predict(X1,X2,X4,X5,X6,X7,X8)

[0113] C4: Estimation of causal effects:

[0114]

[0115] C5: Repeat steps C1-C4 to obtain the estimated causal effects of all factors on carrying capacity.

[0116] In an embodiment of the present application, a structural causal model is constructed based on the causal effect and the first causal graph structure to evaluate the impact of different intervention strategies on the carrying capacity of the distribution network, including:

[0117] Preset the target intervention strategy of the structural causal model and set the key influencing factors in the structural causal model as the target influencing factors;

[0118] Calculate the intervention effect and evaluate the impact of the target intervention strategy on the carrying capacity of the distribution network.

[0119] In the embodiment of the present application, calculating the intervention effect and evaluating the impact of the target intervention strategy on the carrying capacity of the distribution network further includes: the intervention effect is the difference between the expected value of the target influencing factor under the target intervention strategy and the expected value under the non-intervention condition;

[0120] The structural causal model updates the target influencing factors according to the intervention strategy to calculate the distribution network carrying capacity;

[0121] Traverse different intervention strategies, obtain the distribution of distribution network carrying capacity after intervention, and calculate the average or expected value of the distribution as an estimate of the intervention effect to analyze the influencing mechanism of distribution network carrying capacity.

[0122] It should be noted that this application reveals the essential mechanism that affects the carrying capacity of the distribution network through causal reasoning, overcoming the limitations of traditional correlation analysis methods.

[0123] In an optional embodiment, the structural causal model used to evaluate the impact of different intervention strategies on the distribution network carrying capacity can estimate the causal effects through a Bayesian framework, which can naturally handle uncertainty and perform inference through methods such as Markov Chain Monte Carlo.

[0124] In another optional embodiment, the structural causal model used to evaluate the impact of different intervention strategies on the carrying capacity of the distribution network can assign a weight to each observation through inverse probability weighting so that the distributions of the treatment group and the control group on the confounding factors become similar, thereby estimating the causal effect.

[0125] In a preferred embodiment, the potential impact of different intervention strategies on carrying capacity is evaluated through counterfactual reasoning, which specifically includes steps D1-D3:

[0126] D1: Constructing a structural causal model:

[0127] X i =f i (PA i ,U i ),i=1,...,8

[0128] Y=f(X1,...X8,UY )

[0129] Among them, PA i Represents X i The parent node set, U i and U Y represents an exogenous random variable.

[0130] D2: Define intervention operation do(X i = x), which means to convert X i Forces a setting to a specific value x.

[0131] D3: Calculate the intervention effect E[Ydo(X i =x)]-E[Y], which means that X i The average causal effect of a change in x from its current value on Y.

[0132] For example, taking the evaluation of the impact of increasing the transformer capacity on the load-bearing capacity as an example, the steps E1-E3 are specifically included:

[0133] E1: Construct SCM based on the obtained causal diagram and estimated causal effects:

[0134] X1=f1(U1)

[0135] X1=f2(X1,U2) ...

[0137] Y=f(X1,...,X8,U Y )

[0138] E2: Define the intervention operation do(X i =x1′), where x1′ represents the improved transformer capacity utilization.

[0139] E3: Calculate intervention effect:

[0140] ΔY=E[Ydo(X i =x1′)]-E[Y]

[0141] The above expected values ​​were estimated by Monte Carlo simulation method, and the experiment was repeated many times to obtain stable results. Based on the above analysis, the following main conclusions were obtained:

[0142] Transformer capacity utilization (X1), line load rate (X2) and photovoltaic penetration rate (X3) are the three most important factors affecting the carrying capacity of the distribution network, and their causal effect estimates are θ1=-0.42, θ2=-0.38, and θ3=0.31, respectively.

[0143] Through counterfactual analysis, it was found that reducing the transformer capacity utilization from the current 75% to 65% can increase the distribution network's carrying capacity by about 12%.

[0144] The negative impact of the power supply radius (X7) on the carrying capacity (θ7 = -0.25) is greater than expected, indicating that optimizing the network topology is of great significance to improving the carrying capacity.

[0145] Based on the above analysis results, the following suggestions can be put forward to improve the carrying capacity of the distribution network:

[0146] Priority should be given to increasing transformer capacity or reducing transformer load factor, such as by adding new transformers or load transfer.

[0147] Strengthen the transformation of distribution network lines, improve line current-carrying capacity, and reduce line load rate.

[0148] Rationally plan the access location and capacity of photovoltaic power generation to fully utilize the positive impact of photovoltaic power generation.

[0149] Optimize the distribution network structure, appropriately shorten the power supply radius, and improve the network's carrying capacity.

[0150] It should be noted that this application combines machine learning and statistical inference techniques to improve the accuracy and reliability of causal effect estimation. By introducing counterfactual analysis, the potential effects of different intervention strategies can be evaluated, providing strong support for decision-making.

[0151] It should also be noted that this application first collects and preprocesses multi-source heterogeneous data, determines potential influencing factors through variable selection, uses the Peter-Clark algorithm for causal discovery, and constructs a preliminary causal graph structure. On this basis, a dual machine learning method is used to estimate the causal effect of each factor. Finally, the potential impact of different intervention strategies is evaluated through counterfactual analysis, which achieves an in-depth understanding of the mechanism affecting the carrying capacity of the distribution network, can provide strong support for distribution network planning and operation decisions, and help improve the distribution network's ability to carry distributed resources.

[0152] Example 3

[0153] The above is a schematic scheme of a method for analyzing factors affecting the ability of a distribution network to carry distributed resources in this embodiment. It should be noted that the technical scheme of the system for analyzing factors affecting the ability of a distribution network to carry distributed resources and the technical scheme of the method for analyzing factors affecting the ability of a distribution network to carry distributed resources are of the same concept. For details not described in detail in the technical scheme of the system for analyzing factors affecting the ability of a distribution network to carry distributed resources in this embodiment, please refer to the description of the technical scheme of the method for analyzing factors affecting the ability of a distribution network to carry distributed resources.

[0154] In this embodiment, a system for analyzing factors affecting the ability of a distribution network to carry distributed resources includes:

[0155] A data acquisition unit, used for performing a first preprocessing on the first target data of the distribution network to acquire second target data;

[0156] A screening module, used for performing a first screening on the second target data to obtain a first influencing factor of the distribution network carrying capacity;

[0157] An analysis module, used for presetting a first causal graph structure of a first influencing factor and analyzing a causal effect of the first influencing factor on the carrying capacity of the distribution network;

[0158] The evaluation module is used to construct a structural causal model based on the causal effect and the first causal graph structure to evaluate the impact of different intervention strategies on the carrying capacity of the distribution network.

[0159] This embodiment further provides an electronic device, which is applicable to the method for analyzing factors affecting the ability of a distribution network to carry distributed resources, and includes:

[0160] Memory and processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the method for analyzing factors affecting the ability of a distribution network to carry distributed resources as proposed in the above embodiment.

[0161] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for analyzing factors affecting the ability of a distribution network to carry distributed resources as proposed in the above embodiment is implemented.

[0162] The storage medium proposed in this embodiment and the method for analyzing factors affecting the ability of a distribution network to carry distributed resources proposed in the above embodiment belong to the same inventive concept. For technical details not described in detail in this embodiment, please refer to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0163] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ReadOnly, Memory, ROM), random access memory (RandomAccess Memory, RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform the methods of various embodiments of the present invention.

[0164] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for analyzing factors affecting the ability of a distribution network to carry distributed resources, characterized in that: include: Performing a first preprocessing on the first target data of the distribution network to obtain second target data; Performing a first screening on the second target data to obtain a first influencing factor of the distribution network carrying capacity; Presetting a first causal graph structure of the first influencing factor, and analyzing the causal effect of the first influencing factor on the carrying capacity of the distribution network; A structural causal model is constructed based on the causal effect and the first causal graph structure to evaluate the impact of different intervention strategies on the carrying capacity of the distribution network.

2. The method for analyzing factors affecting the ability of a distribution network to carry distributed resources according to claim 1, characterized in that: The first causal graph structure includes: a first graph structure; The first influencing factor is used to test the first graph structure to delete redundant edges; Orient the remaining edges to obtain the second graph structure.

3. The method for analyzing factors affecting the ability of a distribution network to carry distributed resources according to claim 2, characterized in that: Analyzing the causal effect of the first influencing factor on the carrying capacity of the distribution network includes: constructing a first prediction model and a second prediction model for any influencing factor; The first prediction model is used to predict key influencing factors; The second prediction model is used to predict the carrying capacity of the distribution network.

4. The method for analyzing factors affecting the ability of a distribution network to carry distributed resources according to claim 3, characterized in that: Also includes: The first prediction model and the second prediction model are result predictions based on other influencing factors other than any one influencing factor.

5. The method for analyzing factors affecting the ability of a distribution network to carry distributed resources according to claim 4, characterized in that: Also includes: The estimated value of the causal effect is calculated based on the key influencing factors obtained from the prediction and the carrying capacity of the distribution network.

6. The method for analyzing factors affecting the ability of a distribution network to carry distributed resources according to claim 5, characterized in that: A structural causal model is constructed based on the causal effect and the first causal graph structure to evaluate the impact of different intervention strategies on the carrying capacity of the distribution network, including: Presetting a target intervention strategy for the structural causal model, and setting key influencing factors in the structural causal model as target influencing factors; Calculate the intervention effect and evaluate the impact of the target intervention strategy on the carrying capacity of the distribution network.

7. The method for analyzing factors affecting the ability of a distribution network to carry distributed resources according to claim 6, characterized in that: The calculating of the intervention effect and evaluating the impact of the target intervention strategy on the carrying capacity of the distribution network further includes: the intervention effect is the difference between the expected value of the target influencing factor under the target intervention strategy and the expected value under the non-intervention condition; The structural causal model updates the target influencing factors according to the intervention strategy to calculate the distribution network carrying capacity; Traverse different intervention strategies, obtain the distribution of distribution network carrying capacity after intervention, and calculate the average or expected value of the distribution as an estimate of the intervention effect to analyze the influencing mechanism of distribution network carrying capacity.

8. A system for analyzing factors affecting the ability of a distribution network to carry distributed resources, characterized in that: include: A data acquisition unit, used for performing a first preprocessing on the first target data of the distribution network to acquire second target data; A screening module, configured to perform a first screening on the second target data to obtain a first influencing factor of the distribution network carrying capacity; An analysis module, configured to preset a first causal graph structure of the first influencing factor and analyze the causal effect of the first influencing factor on the carrying capacity of the distribution network; An evaluation module is used to construct a structural causal model based on the causal effect and the first causal graph structure to evaluate the impact of different intervention strategies on the carrying capacity of the distribution network.

9. An electronic device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the method for analyzing factors affecting the ability of a distribution network to carry distributed resources as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the method for analyzing factors affecting the ability of a distribution network to carry distributed resources as described in any one of claims 1 to 7.

Images