Power distribution network frame risk assessment method

Abstract

The invention provides a power distribution network frame risk assessment method, and relates to the technical field of power distribution network frame risk assessment. In order to solve the risk factors of unstable power supply, reverse power flow, islanding effect and the like in a distributed new energy power supply mode in the prior art, the method is based on big data information of various new energy power supplies. Power supply structure information composed of generating capacity information, generating capacity proportion information, power supply stability information and the like is obtained through analysis of a machine learning model, and then influence factor information corresponding to various new energy sources is obtained according to the power supply structure information obtained through analysis. Therefore, the generating capacity of the power distribution network frame in the future preset time is accurately predicted, and the risk of the power distribution network frame is evaluated from the three aspects of power supply stability, reverse power flow and islanding effect based on the residual power value.

Description

Technical Field

[0001] The present invention belongs to the technical field of risk assessment, and in particular relates to a technical method for risk assessment of a distribution network. Background Art

[0002] With the large-scale access of new grid-connected entities such as distributed renewable energy, additional security risks and management challenges may be brought to the distribution network. For example, the output stability of renewable energy types such as solar and wind power is closely related to factors such as weather, and when the supply and demand of power resources do not match, the reverse transmission of surplus power will also bring risk factors such as flow reverse transmission and island effect. Summary of the invention

[0003] The purpose of the present invention is to provide a distribution network risk assessment method to solve the technical problems in the prior art such as low power supply stability, power flow reversal and islanding effect caused by the grid connection of distributed renewable energy.

[0004] The present invention proposes a distribution network risk assessment method, which includes: S1: Determine first power supply structure information according to first power supply information of a first distribution network framework within a first preset time interval; The first power supply structure information includes the total power generation information of the first distribution network in the first preset time interval, the power generation proportion information of various types of new energy sources, and the power supply stability information of various types of new energy sources; S2: determining a plurality of first influencing factor type information according to the first power supply structure information, and acquiring first influencing factor information within a second preset time interval according to the plurality of first influencing factor type information; S3: obtaining first power supply amount prediction information according to the first power supply structure information and the first influencing factor information; S4: acquiring first remaining power information according to the first power supply prediction information and the first power demand information; S5: Obtain a first risk assessment result according to the first power supply structure information and the first remaining power information.

[0005] Preferably, S1 comprises the following sub-steps: S11: Acquire first power supply information of the first distribution network in a first preset time interval; wherein the first power supply information is in the form of a data sequence corresponding to a time sequence; S12: determining first total power generation information of the first distribution network framework within the first preset time interval and first sub-power generation information corresponding to each type of new energy according to the first power supply information; S13: Acquire first power supply structure information according to the first power supply information, the first total power generation information and the first sub-power generation information.

[0006] Preferably, the S13 comprises the following sub-steps: S131: taking the ratio of each of the first sub-power generation information to the first total power generation information as first power generation proportion information; S132: acquiring a plurality of first power supply amount change curves corresponding to various types of new energy sources according to the first power supply information; S133: inputting the plurality of first power supply amount variation curves into a power supply stability determination model respectively to determine a plurality of first power supply stability information; S134: Obtain the first power supply structure information according to the first total power generation information, the first power generation proportion information and the first power supply stability information.

[0007] Preferably, S2 comprises the following sub-steps: S21: Determine multiple first new energy power supply types according to the first power supply structure information; S22: determining a plurality of first influencing factor type information according to a plurality of the first new energy power supply types; S23: Acquire first influencing factor information corresponding to a plurality of first influencing factor type information within a second preset time interval.

[0008] Preferably, S3 includes the following sub-steps: S31: Acquire a plurality of first historical power supply information from the first power supply structure information; S32: Acquire a plurality of second influencing factor information corresponding to each of the first historical power supply information from the first influencing factor information, thereby obtaining a plurality of first power supply information sets; S33: Inputting a plurality of the first power supply information sets into the power supply amount information determination model to obtain the first power supply amount prediction information.

[0009] Preferably, S5 comprises the following sub-steps: S51: Determine power supply stability risk information according to the first power supply structure information; S52: determining power flow reverse transmission risk information according to the first remaining power information; S53: determining island risk information according to the first remaining power information and the first maintenance plan; S54: Obtain the first risk assessment result according to the power supply stability risk information, the power flow reverse risk information and the island risk information.

[0010] The distribution network risk assessment method proposed in the present invention is to solve the risk factors such as unstable power supply, power flow reverse and islanding effect faced by the distributed renewable energy power supply mode in the prior art. Based on the big data information of various renewable energy power supplies, the power supply structure information composed of power generation information, power generation proportion information and power supply stability information is analyzed through a machine learning model. Then, according to the power supply structure information obtained by the analysis, the influencing factor information corresponding to each type of renewable energy is obtained, so as to accurately predict the power generation of the distribution network within a preset time in the future. Then, based on the remaining power value, the risk of the distribution network is assessed from three aspects: power supply stability, power flow reverse and islanding effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the implementation methods or the description of the prior art. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived from the provided drawings without creative work.

[0012] Figure 1 It is an execution flow chart of the distribution network risk assessment method of the present invention; Figure 2 It is an execution flow chart of step S1 in the distribution network risk assessment method of the present invention. DETAILED DESCRIPTION

[0013] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0014] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments, wherein the illustrative embodiments and descriptions are only used to explain the present invention but are not intended to limit the present invention.

[0015] The distribution network risk assessment method of the present invention is described in detail below.

[0016] This embodiment proposes a distribution network risk assessment method, the method flow is as follows: Figure 1 As shown, the specific steps include: S1: Determine first power supply structure information according to multiple first new energy historical information of the first distribution network grid.

[0017] With the diversification of power generation energy types, more and more new energy types are used to generate electricity. In this step, it is necessary to determine the first power supply structure information corresponding to the first distribution network grid based on the new energy historical information of the first distribution network grid within the first preset time interval.

[0018] The first power supply structure information includes information such as the new energy power supply type and power supply proportion corresponding to the first distribution network grid.

[0019] The S1 includes the following steps, the specific steps are as follows: Figure 2 As shown: S11: Obtain first power supply information of the first distribution network within a first preset time interval.

[0020] The first power supply information is in the form of a data sequence corresponding to a time sequence. The data sequence is power supply information corresponding to various types of new energy sources within the first preset time interval and with a preset time interval as a step length.

[0021] S12: Determine first total power generation information of the first distribution network framework within the first preset time interval and first sub-power generation information corresponding to each type of new energy according to the first power supply information.

[0022] The calculation method of the first total power generation information is specifically to add the result of the data sequence of the first distribution network grid in the first preset time interval, that is, to represent the total power generation of the first distribution network grid in the first preset time interval.

[0023] The calculation method of the first sub-generation capacity information is specifically to add the data sequence of each type of renewable energy in the first preset time interval with the first distribution network, that is, each of the first sub-generation capacity information represents the total generation of each type of renewable energy in the first preset time interval. The sum of each of the first sub-generation capacity information should be equal to the first total generation capacity information.

[0024] S13: Acquire first power supply structure information according to the first power supply information, the first total power generation information and the first sub-power generation information.

[0025] The first power supply structure information includes the total power generation information of the first distribution network framework within the first preset time interval, the power generation proportion information of various types of renewable energy sources, and the power supply stability information of various types of renewable energy sources.

[0026] The S13 comprises the following sub-steps: S131: taking the ratio of each of the first sub-power generation information to the first total power generation information as first power generation proportion information.

[0027] Since the first total power generation information is the sum of multiple first sub-power generation information, multiple first power generation proportion information can be obtained by calculating the ratio information of the two.

[0028] S132: Acquire a first power supply amount variation curve corresponding to each type of new energy source according to the first power supply information.

[0029] Since the first power supply information includes a power supply data change sequence corresponding to each type of new energy, the first power supply change curve can be obtained by fitting the power supply data corresponding to each time point in the change sequence.

[0030] S133: Inputting the plurality of first power supply amount variation curves into a power supply stability determination model respectively to determine a plurality of first power supply stability information.

[0031] The power supply stability determination model is obtained by training a convolutional neural network model. The specific training process is as follows: First, power supply sample data is obtained, each of which includes a plurality of standard power supply amount change curves corresponding to the specific type of new energy under a plurality of power supply stability information.

[0032] For example, for solar energy new energy, a power supply sample data includes a standard power supply change curve corresponding to the solar power supply stability of the distribution network at values ​​such as 50%, 60%, and 90% within a preset time interval.

[0033] Then, the power supply sample data is used to train a convolutional neural network model to obtain the power supply stability determination model. In the power supply sample data, the historical power supply change curve of the distribution network is used as input, and the power supply stability is used as output.

[0034] Through this step, a plurality of first power supply stability information corresponding to various types of new energy sources can be obtained.

[0035] S134: Obtain the first power supply structure information according to the first total power generation information, the first power generation proportion information and the first power supply stability information.

[0036] The first total power generation information and the first power generation proportion information can display the total power generation of the first distribution network and each type of new energy within the first preset time interval, and can also obtain the power supply stability of each type of new energy within the first preset time interval.

[0037] S2: determining a plurality of first influencing factor type information according to the first power supply structure information, and acquiring the first influencing factor information within a second preset time interval according to the plurality of first influencing factor type information.

[0038] The first influencing factor type information includes the main influencing factors corresponding to each type of new energy power supply, including meteorological factors, hydrological factors, etc.

[0039] The S2 comprises the following sub-steps: S21: Determine a plurality of first new energy power supply types according to the first power supply structure information.

[0040] The first power supply structure information includes first power generation proportion information and first power supply stability information corresponding to various types of new energy sources.

[0041] In order to assess the risk of the distribution network, it is necessary to find out the types of new energy sources that have a greater impact on power generation and power generation stability. Therefore, in this step, it is mainly necessary to determine the types of new energy sources corresponding to the first distribution network grid, which have a large proportion of power generation and poor power supply stability, through the first power supply structure information. The main reason for using the above standards to determine the types of new energy sources is that: for new energy types with large power generation and good power supply stability, the probability of risk is often small; for new energy types with small power generation, they usually do not bring too many risks due to unstable power supply.

[0042] Taking the above factors into consideration, we can identify several first-generation renewable energy power supply types that have a greater impact on the risk of distribution network structure.

[0043] Preferably, the multiple new energy power supply types corresponding to the first power supply structure information may be sorted according to the probability of causing power supply risk, and the new energy power supply type with a probability greater than a preset value may be used as the first new energy power supply type.

[0044] S22: Determine multiple first influencing factor type information according to multiple first new energy power supply types.

[0045] Among them, each of the first new energy power supply types has a corresponding relationship with several influencing factors, for example, solar energy has a direct influencing relationship with sunlight exposure.

[0046] Preferably, after determining multiple influencing factor type information based on multiple first renewable energy power supply types, multiple influencing factor type information can be weighted according to the probability that each first renewable energy power supply type can cause distribution network grid risk, thereby obtaining multiple first influencing factor type information.

[0047] S23: Acquire first influencing factor information corresponding to a plurality of first influencing factor type information within a second preset time interval.

[0048] In order to predict the power generation of the first distribution network in the future second preset time interval, in this step, it is necessary to obtain data corresponding to the determined multiple first influencing factor type information, and then form the first influencing factor information.

[0049] S3: Obtain first power supply prediction information according to the first power supply structure information and the first influencing factor information.

[0050] In order to calculate the remaining power information that will cause power flow reversal and island effect in subsequent steps, in this step, it is necessary to determine the first power supply forecast information corresponding to the first distribution network grid in the future period based on the first power supply structure information and the first influencing factor information.

[0051] The S3 comprises the following sub-steps: S31: Acquire a plurality of first historical power supply information from the first power supply structure information.

[0052] Each of the first historical power supply information corresponds to a certain new energy type. For example, a plurality of the first historical power supply information may include solar power generation, wind power generation, etc. within a first historical time interval.

[0053] S32: Acquire a plurality of second influencing factor information corresponding to each of the first historical power supply information from the first influencing factor information, and thereby obtain a plurality of first power supply information sets.

[0054] Since each of the first historical power supply information corresponds to a certain new energy type, the corresponding multiple second influencing factor information can be determined in the first influencing factor information according to the new energy type, and each of the first historical power supply information and the corresponding multiple second influencing factor information can be combined into the first power supply information set, thereby obtaining multiple first power supply information sets, and each of the first power supply information sets corresponds to a certain type of new energy.

[0055] For example, the first influencing factor information may include sunlight illumination, wind conditions, etc. For the new energy type of solar energy, sunlight illumination can be determined from the first influencing information as the second influencing factor information.

[0056] S33: Inputting a plurality of the first power supply information sets into the power supply amount information determination model to obtain the first power supply amount prediction information.

[0057] The power supply information determination model is obtained by training a convolutional neural network model, and the training process is as follows: First, sample data is obtained. Each piece of sample data is composed of multiple sets of historical power supply information and corresponding power supply amounts. For example, if a piece of sample data includes solar energy, wind energy, and nuclear power generation types, then the sample data includes three power supply information sets corresponding to solar energy, wind energy, and nuclear energy, respectively. Each of the power supply information sets includes the power supply amount corresponding to the type of new energy within a specified historical time range and the corresponding influencing factor information, and includes the total power generation within a preset time range in the future.

[0058] Secondly, the sample data is used to train a convolutional neural network model to obtain the power supply information determination model, wherein multiple power supply information sets in the sample data are used as input, and the total power generation within a future preset time range is used as output to complete the above training process.

[0059] S4: Acquire first remaining power information according to the first power supply prediction information and the first power demand information.

[0060] In this step, it is necessary to predict the first power demand information corresponding to the first distribution network framework within a preset time range in the future, and use the difference between the first power supply prediction information and the first power demand information as the first remaining power information.

[0061] Preferably, the ratio of the first remaining power information to the first power supply amount prediction information may be used as additional information of the first remaining power information.

[0062] S5: Obtain a first risk assessment result according to the first power supply structure information and the first remaining power information.

[0063] In this step, the first power supply structure information and the first remaining power information obtained in the previous step are used to evaluate and obtain the first risk assessment result from three aspects: power supply stability, power flow reversal and islanding effect.

[0064] The S5 comprises the following sub-steps: S51: Determine power supply stability risk information according to the first power supply structure information.

[0065] Since the first power supply structure information includes a plurality of first power supply stability information corresponding to each new energy type, appropriate calculations can be performed on the plurality of first power supply stability information to obtain the power supply stability information. The power supply stability information is a probability value of a risk.

[0066] S52: Determine power flow reverse risk information according to the first surplus power information.

[0067] In this step, the probability value of power flow reverse transmission is determined according to the size relationship information between the first remaining power information and the first threshold value, and is used as the power flow reverse transmission risk information. The first threshold value refers to the critical value of remaining power that will cause the power flow reverse transmission risk, and the probability of power flow reverse transmission risk can be determined according to the size relationship between the first remaining power information and the first threshold value.

[0068] S53: Determine island risk information according to the first remaining power information and the first maintenance plan.

[0069] In this step, the degree of matching between the first remaining power information and the first maintenance plan within a preset time interval is used as islanding risk information.

[0070] Among them, the first maintenance plan refers to a plan to arrange maintenance personnel to carry out distribution network grid maintenance within a preset time interval. If the maintenance plan and the remaining power information have a large overlap within the same time period, the probability of island risk will be greater.

[0071] The reason for obtaining the above-mentioned power flow reverse transmission risk information and island risk information is that when the surplus power value is large, the backflow will cause an adverse impact on the distribution network grid, thereby causing the distribution network to overload the threshold or bring safety hazards to maintenance personnel.

[0072] S54: Obtain the first risk assessment result according to the power supply stability risk information, the power flow reverse risk information and the island risk information.

[0073] The distribution network risk assessment method proposed in the present invention is to solve the risk factors such as unstable power supply, power flow reverse and islanding effect faced by the distributed renewable energy power supply mode in the prior art. Based on the big data information of various renewable energy power supplies, the power supply structure information composed of power generation information, power generation proportion information and power supply stability information is analyzed through a machine learning model. Then, according to the power supply structure information obtained by the analysis, the influencing factor information corresponding to each type of renewable energy is obtained, so as to accurately predict the power generation of the distribution network within a preset time in the future. Then, based on the remaining power value, the risk of the distribution network is assessed from three aspects: power supply stability, power flow reverse and islanding effect.

[0074] The above description is only a preferred embodiment of the present invention, so all equivalent changes or modifications made according to the structure, characteristics and principles described in the scope of the patent application of the present invention are included in the scope of the patent application of the present invention.

Claims

1. A distribution network risk assessment method, characterized in that: The method includes: S1: Determine first power supply structure information according to first power supply information of a first distribution network framework within a first preset time interval; The first power supply structure information includes the total power generation information of the first distribution network in the first preset time interval, the power generation proportion information of various types of new energy sources, and the power supply stability information of various types of new energy sources; S2: determining a plurality of first influencing factor type information according to the first power supply structure information, and acquiring first influencing factor information within a second preset time interval according to the plurality of first influencing factor type information; S3: obtaining first power supply amount prediction information according to the first power supply structure information and the first influencing factor information; S4: acquiring first remaining power information according to the first power supply prediction information and the first power demand information; S5: Obtain a first risk assessment result according to the first power supply structure information and the first remaining power information.

2. The distribution network risk assessment method according to claim 1, characterized in that: The S1 comprises the following sub-steps: S11: Acquire first power supply information of the first distribution network in a first preset time interval; wherein the first power supply information is in the form of a data sequence corresponding to a time sequence; S12: determining first total power generation information of the first distribution network framework within the first preset time interval and first sub-power generation information corresponding to each type of new energy according to the first power supply information; S13: Acquire first power supply structure information according to the first power supply information, the first total power generation information and the first sub-power generation information.

3. The distribution network risk assessment method according to claim 2 is characterized in that: The S13 comprises the following sub-steps: S131: taking the ratio of each of the first sub-power generation information to the first total power generation information as first power generation proportion information; S132: acquiring a plurality of first power supply amount change curves corresponding to various types of new energy sources according to the first power supply information; S133: inputting the plurality of first power supply amount variation curves into a power supply stability determination model respectively to determine a plurality of first power supply stability information; S134: Obtain the first power supply structure information according to the first total power generation information, the first power generation proportion information and the first power supply stability information.

4. The distribution network risk assessment method according to claim 3 is characterized in that: The S2 comprises the following sub-steps: S21: Determine multiple first new energy power supply types according to the first power supply structure information; S22: determining a plurality of first influencing factor type information according to a plurality of the first new energy power supply types; S23: Acquire first influencing factor information corresponding to a plurality of first influencing factor type information within a second preset time interval.

5. The distribution network risk assessment method according to claim 4 is characterized in that: The S3 comprises the following sub-steps: S31: Acquire a plurality of first historical power supply information from the first power supply structure information; S32: Acquire a plurality of second influencing factor information corresponding to each of the first historical power supply information from the first influencing factor information, thereby obtaining a plurality of first power supply information sets; S33: Inputting a plurality of the first power supply information sets into a power supply quantity information determination model to obtain the first power supply quantity prediction information.

6. The distribution network risk assessment method according to claim 5 is characterized in that: The S5 comprises the following sub-steps: S51: Determine power supply stability risk information according to the first power supply structure information; S52: determining power flow reverse transmission risk information according to the first remaining power information; S53: determining island risk information according to the first remaining power information and the first maintenance plan; S54: Obtain the first risk assessment result according to the power supply stability risk information, the power flow reverse risk information and the island risk information.

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