Method and device for analyzing influence of photovoltaic access to power distribution area on line voltage

Abstract

The invention discloses a method and equipment for analyzing the influence of photovoltaic access to a power distribution area on line voltage, belongs to the technical field of line voltage influence analysis, and aims to analyze the influence of photovoltaic access to the voltage of the area only by using an area topological graph of an operation and inspection system of a power system and user data in a centralized reading system so as to realize convenient data and data collection. And large-scale batch analysis is facilitated. By adopting a clustering analysis mode, discrete data extracted from each user meter is calculated, the voltage and load corresponding relation of typical conditions of all users under the conditions of various moments is determined, and the problem of inaccurate load prediction is avoided. According to the method, the voltage change conditions of the users close to the photovoltaic access position are analyzed one by one, the voltage value change conditions of the photovoltaic access point and the influence user range are finally determined, user or position analysis calculation which is not influenced by photovoltaic is avoided, the calculation amount is reduced, and the actual user rather than the fuzzy electrical position is accurately determined.

Description

Technical Field

[0001] The present invention belongs to the technical field of line voltage impact analysis, and in particular relates to a method and device for analyzing the impact of photovoltaic access to a distribution station area on line voltage. Background Art

[0002] In the prior art, the academic papers "Analysis of the Impact of Distributed Photovoltaic Power Generation Grid Connection on Distribution Network Voltage" published on July 4, 2023 by Luo Riteng, the author of Meizhou Pingyuan Power Supply Bureau of Guangdong Power Grid Co., Ltd., and "The Impact of Photovoltaic Access on Distribution Network Voltage Distribution" published in the first issue of "Ningxia Electric Power" journal in 2024, both studied the impact of different access methods, different access capacities and access locations of photovoltaic systems on the voltage distribution of the distribution network; they are mainly static studies, without considering the dynamic relationship between the outlet voltage of the substation, user load and photovoltaic output, and cannot analyze the dynamic results of the range of voltage impact.

[0003] The article “The Impact and Control of Distributed Photovoltaic Sources Connected to Distribution Networks on Voltage” published in the journal China High-Tech 2023 (21) determined the voltage fluctuations caused by the integration of distributed photovoltaic sources through power flow calculations, and proposed a control method for installing reactive power compensation devices and regulating the reactive power of photovoltaic inverters. However, it did not determine the voltage fluctuations at various locations on the conductors, and only analyzed and addressed the voltage problems at the photovoltaic access location.

[0004] In the “Study on the Impact of Distributed Photovoltaic Grid Connection on Line Voltage” published in the journal “Electrical Engineering” with the issue number 2023 (10), the distribution change curve of line voltage due to different photovoltaic capacity, load capacity and other parameters was determined, but the degree of impact on users and the power grid was not analyzed.

[0005] In summary, the above existing technical solutions all analyze the voltage distribution on the basis of power flow calculation, but the impedance parameters of the line grid are required in the power flow calculation. In practical applications, the low-voltage distribution network is difficult to have a complete grid topology diagram and single-line parameters, and is not available; at the same time, in the case of absorption, it is necessary to dynamically consider the user load situation in the substation area. The load of a single residential user is uncertain, and it is difficult to accurately predict the compliance curve, and it is difficult to guarantee the accuracy of the final voltage impact result.

[0006] In addition, power flow calculation or probabilistic power flow calculation is to calculate the voltage conditions of the entire substation. The actual impact of a single photovoltaic access location and access capacity on the substation is within a certain range, which adds extra workload. Summary of the invention

[0007] In order to solve the defects in the prior art, the present invention proposes a photovoltaic access distribution station area to line voltage analysis device and method, which can only use the power system operation and inspection system area topology map and the user data in the collection system to analyze the photovoltaic influence on the area voltage, realize the convenience of data and information collection, and improve feasibility; adopt the cluster analysis method to calculate the discrete data extracted from each household meter, and determine the typical voltage and load correspondence of all users under the table at various times. The voltage changes of users near the photovoltaic access location are analyzed one by one, and finally the voltage value changes of the photovoltaic access point and the scope of users affected are determined; effectively avoiding the defects of the prior art that the dynamic results of the range of voltage impact cannot be analyzed, the voltage fluctuations of each position of the conductor are not determined, only the voltage problems of the photovoltaic access location are analyzed and managed, and the degree of impact on users and the power grid is not analyzed. In actual applications, the low-voltage distribution network is difficult to have a complete grid topology map and single-line parameters, and it is not available. There is uncertainty in the load of a single residential user, it is difficult to accurately predict the compliance curve, and the accuracy of the final voltage impact result is difficult to guarantee, which increases the additional workload.

[0008] The present invention uses the following technical solutions.

[0009] A method for analyzing the impact of photovoltaic access to a distribution station area on line voltage is applied to a low-voltage distribution station area with distributed photovoltaic access. The method comprises: S1: Establish the location model of all users in the area through the target area topology structure and user account information; S2: Establish original data sets, original data clusters, and original voltage databases based on the original data of export voltage U and power P of all users in the target substation and the substation at each time during the previous operation days; S3: Output to photovoltaic stations Make a prediction and determine the user i closest to the proposed photovoltaic access location; S4: performing cluster analysis on the voltage database to establish a voltage fluctuation model; S5: Calculate and absorb the photovoltaic output at each moment and the typical situation at the corresponding moment in the voltage fluctuation model to determine the impact degree and voltage impact range of photovoltaics on the line voltage at each moment.

[0010] Furthermore, the low-voltage distribution station area includes multiple electricity users and a distributed photovoltaic system to be connected to the station area, and the low-voltage distribution station area is the target station area.

[0011] Furthermore, the user location model is a model formed by a user in the target station area ranking other users in terms of line distance.

[0012] Furthermore, the original data set is the target area outlet voltage value and user voltage value, the target area outlet power and user power and the corresponding time at a certain time, which is expressed as ;in For a time of day, is the target area outlet voltage value at the corresponding time, is the voltage value of the ith user at the corresponding time, is the target area outlet power value at the corresponding time, is the electric power value of the ith user at the corresponding time, is the total number of users; The time mentioned is a certain time of the day, including all previous operating days.

[0013] Furthermore, the t is the hour of the day.

[0014] Furthermore, the original data cluster is a new set of original data sets with the same time t in the original data sets, which is expressed as ,in is the original data cluster at time t, and the superscripts 1, 2, ... are used to mark the time t on different dates; The raw voltage database contains raw data clusters of all time, expressed as [ … … ].

[0015] Furthermore, the cluster analysis is performed on the raw data clusters at each time of the raw data voltage database. Perform clustering calculations separately to obtain cluster data clusters at each time, expressed as ,in is the cluster data cluster at time t, Clustering data One of the clustering datasets.

[0016] Furthermore, the clustering calculation adopts K-means algorithm; K-means The number of clusters k in the algorithm is 10, that is, the cluster data cluster is , The voltage fluctuation model contains the original data clusters of all time periods, expressed as [ … … ].

[0017] Furthermore, the voltage impact degree is the user voltage change before photovoltaic access and the user voltage change after access; The above-mentioned consumption calculation is to calculate the photovoltaic output power at time t after the photovoltaic power is connected. In the typical case The load of user i is reached when the consumption is carried out; before the photovoltaic connection, the voltage of user i is Clustering dataset Corresponding voltage ; After photovoltaic connection, the voltage of user i is a cluster data cluster Zhongyu The cluster data set with the closest user load value The corresponding voltage in In the above-mentioned absorption calculation process, when If it is less than zero, rolling consumption calculation is required; in this case, the voltage of user i after photovoltaic connection is a cluster data cluster The user load value is the smallest Clustering dataset The corresponding voltage in The rolling absorption calculation is to select the i-1 or i+1 user closest to the i user by the user location model, and calculate the typical situation of the i-1 or i+1 user at the corresponding time in the voltage fluctuation model according to the absorption calculation process to continue absorption. The remaining amount, and whether to calculate again the rolling consumption, as well as the voltage change of the user i-1 or i+1 user before and after the photovoltaic access; The voltage impact range is all user areas that participate in the rolling absorption calculation.

[0018] A device for analyzing the impact of photovoltaic access to a distribution station area on line voltage, comprising: The import module is used to import the target area topology structure and user account information, as well as the voltage U, power P data and photovoltaic output characteristics of all users in the target area and the area outlet at all times; The processing module establishes the location model of all users in the substation area according to the data in the import module, and establishes the original data set, original data cluster, and original voltage database; The prediction module is used to predict the output of the photovoltaic station according to the photovoltaic output characteristics of the station area in the import module ; Analysis module, used to perform cluster analysis on the original voltage database and establish a voltage fluctuation model; The judgment module is used for absorption calculation and judging the influence degree and voltage influence range of photovoltaic on line voltage at each moment.

[0019] A device for analyzing the impact of photovoltaic access to a distribution station area on line voltage, comprising: at least one processor, a data interface, and a memory; The memory stores computer-executable instructions; The data interface imports external data; The at least one processor executes the computer-executable instructions stored in the memory, so that the at least one processor executes the method for analyzing the impact of photovoltaic access distribution station area on line voltage.

[0020] A computer-readable storage medium stores computer-executable instructions. When a processor executes the computer-executable instructions, the method for analyzing the impact of photovoltaic access to a distribution station area on line voltage is implemented.

[0021] A computer program product includes a computer program. When the computer program is executed by a processor, the method for analyzing the impact of photovoltaic access to a distribution station area on line voltage is implemented.

[0022] The beneficial effects of the present invention are that, compared with the prior art, the technical effects of the present invention include: The impact of photovoltaic power on the voltage of the power grid can be analyzed by using only the topological diagram of the power grid operation and inspection system and the user data in the centralized reading system, which makes data and information collection convenient and facilitates large-scale batch analysis. Cluster analysis is used to calculate the discrete data extracted from each household meter to determine the typical voltage and load correspondence of all users under the grid at various times, avoiding inaccurate load forecasting. The voltage changes of users near the photovoltaic access location are analyzed one by one, and finally the voltage value changes of the photovoltaic access point and the range of affected users are determined, avoiding the analysis and calculation of users or locations not affected by photovoltaic power, reducing the amount of calculation, and accurately determining the actual user instead of the vague electrical location. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a partial flow chart of the method for analyzing the impact of photovoltaic access to distribution station area on line voltage described in the present invention; Figure 2 It is a partial structural schematic diagram of the device for analyzing the impact of photovoltaic access to a distribution station area on line voltage in the present invention; Figure 3 It is a partial structural schematic diagram of the device for analyzing the impact of photovoltaic access to a distribution station area on line voltage in the present invention; Figure 4 It is the target area grid topology diagram in the present invention, where 1 is the distribution transformer, 2 is the area outlet (also the starting point of all branches), 11~17 are 1#~7# users, and 21 is distributed photovoltaic. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely expressed in combination with the drawings in the embodiments of the present invention. The embodiments expressed in this application are only some embodiments of the present invention, not all embodiments. According to the spirit of the present invention, other embodiments obtained by ordinary technicians in this field without creative work are all within the protection scope of the present invention.

[0025] like Figure 1 As shown, the method for analyzing the impact of photovoltaic access to a distribution station area on line voltage according to the present invention comprises: Applied to a low-voltage distribution station area with distributed photovoltaic access, the method includes: S1: Establish the location model of all users in the area through the target area topology structure and user account information; In a preferred but non-limiting embodiment of the present invention, the low-voltage distribution station area includes multiple electricity users and a distributed photovoltaic to be connected to the station area, and the low-voltage distribution station area is the target station area.

[0026] In a preferred but non-limiting embodiment of the present invention, the user location model is a model formed by sorting other users in terms of line distances by a certain user in the target station area.

[0027] S2: Establish original data sets, original data clusters, and original voltage databases based on the original data of export voltage U and power P of all users in the target substation and the substation at each time during the previous operation days; In a preferred but non-limiting embodiment of the present invention, the original data set is the target area outlet voltage value and user voltage value at a certain time, the target area outlet power and user power and the corresponding time, which is expressed as ;in For a time of day, is the target area outlet voltage value at the corresponding time, is the voltage value of the ith user at the corresponding time, is the target area outlet power value at the corresponding time, is the electric power value of the ith user at the corresponding time, is the total number of users; The time mentioned is a certain time of the day, including all previous operating days.

[0028] In a preferred but non-limiting embodiment of the present invention, the t is an hour in a day.

[0029] In a preferred but non-limiting embodiment of the present invention, the original data cluster is constructed by constructing the original data sets with the same time t in the original data sets into a new set, which is expressed as ,in is the original data cluster at time t, and the superscripts 1, 2, ... are used to mark the time t on different dates; The raw voltage database contains raw data clusters of all time, expressed as [ … … ].

[0030] S3: Output to photovoltaic stations Make a prediction and determine the user i closest to the proposed photovoltaic access location; Output of photovoltaic stations BP neural network can be used for prediction.

[0031] S4: performing cluster analysis on the voltage database to establish a voltage fluctuation model; In a preferred but non-limiting embodiment of the present invention, the cluster analysis is performed on the raw data clusters at each time of the raw data voltage database. Perform clustering calculations separately to obtain cluster data clusters at each time, expressed as ,in is the cluster data cluster at time t, Clustering data One of the clustering datasets.

[0032] In a preferred but non-limiting embodiment of the present invention, the clustering calculation adopts K-means algorithm; K- means The number of clusters k in the algorithm is 10, that is, the cluster data cluster is , The voltage fluctuation model contains the original data clusters of all time periods, expressed as [ … … ].

[0033] S5: Calculate the output of photovoltaic power at each moment and the typical situation at the corresponding moment in the voltage fluctuation model to determine the influence degree and voltage influence range of photovoltaic power on the line voltage at each moment; Absorption means that the photovoltaic power generation power is offset by the user load; when the two are relative, the power is balanced; if the power generation power is less than the load, it will be completely absorbed. If it is greater than this and cannot be absorbed, the regional load on the line will need to absorb the remaining photovoltaic power generation power.

[0034] In a preferred but non-limiting embodiment of the present invention, the voltage impact degree is the user voltage change before photovoltaic access and the user voltage change after access; The above-mentioned consumption calculation is to calculate the photovoltaic output power at time t after the photovoltaic power is connected. In the typical case The load of user i is reached when the consumption is carried out; before the photovoltaic connection, the voltage of user i is Clustering dataset Corresponding voltage ; After photovoltaic connection, the voltage of user i is a cluster data cluster Zhongyu The cluster data set with the closest user load value The corresponding voltage in In the above-mentioned absorption calculation process, when If it is less than zero, rolling consumption calculation is required; in this case, the voltage of user i after photovoltaic connection is a cluster data cluster The user load value is the smallest Clustering dataset The corresponding voltage in The rolling absorption calculation is to select the i-1 or i+1 user closest to the i user by the user location model, and calculate the typical situation of the i-1 or i+1 user at the corresponding time in the voltage fluctuation model according to the absorption calculation process to continue absorption. The remaining amount, and whether to calculate again the rolling consumption, as well as the voltage change of the user i-1 or i+1 user before and after the photovoltaic access; The voltage impact range is all user areas that participate in the rolling absorption calculation.

[0035] The present invention is a static planning analysis, which is based on the data that can be collected to determine the maximum impact on the user voltage under typical circumstances after planning a distributed photovoltaic power station area in the future, and the photovoltaic data is predicted based on its characteristics; it is not a set of real-time operation to calculate the voltage change, so the limited known historical data is clustered to facilitate the typical operation of the station area, so as to reduce extreme and individual cases; and this process does not directly calculate the relationship between current, resistance, power and voltage, but directly relies on historical data to find a group of data that is the same as the load after photovoltaic absorption and other load conditions in the historical data, and determines the voltage of each node at this time in turn; considering the maximum impact of the study, it is the most typical two scenarios at any time voltage; one is that when there is no photovoltaic, the user voltage at the intended access location is the minimum (usually the load is the maximum); the other is that the photovoltaic output will be the maximum in the future, which will make the surrounding users consume the photovoltaic output and the equivalent load will be the minimum, or even 0. In the historical data, the voltage distributed data when the situation is closest to the above two situations is found, and the comparison is made to obtain the change after photovoltaic access, that is, the impact of photovoltaic access on the user voltage. Rolling calculation is an "absorption process". Absorption is the process of balancing the power distribution of the line. The power distribution and balance will affect the current distribution in the actual operation in the future, and then affect the voltage distribution. That is, when the photovoltaic power generation power is greater than the load of the nearest user, the user can only absorb the power generation power equal to his own load, which has affected the voltage of this user. The remaining photovoltaic output needs to be absorbed by the next nearby user, and this absorption has an impact on the voltage of this user.

[0036] like Figure 2 As shown, the device for analyzing the influence of photovoltaic access to a distribution station area on line voltage described in the present invention is applied to a low-voltage distribution station area with distributed photovoltaic access, wherein the low-voltage distribution station area has multiple electricity users and a distributed photovoltaic to be connected to the station area; comprising: The import module is used to import the target area topology structure and user account information, as well as the voltage U, power P data and photovoltaic output characteristics of all users in the target area and the area outlet at all times; The processing module establishes the location model of all users in the substation area according to the data in the import module, and establishes the original data set, original data cluster, and original voltage database; The prediction module is used to predict the output of the photovoltaic station according to the photovoltaic output characteristics of the station area in the import module ; Analysis module, used to perform cluster analysis on the original voltage database and establish a voltage fluctuation model; The judgment module is used for absorption calculation and judging the influence degree and voltage influence range of photovoltaic on line voltage at each moment.

[0037] like Figure 3As shown, the device for analyzing the impact of photovoltaic access to a distribution station area on line voltage according to the present invention comprises: at least one processor, a data interface, and a memory; The memory stores computer-executable instructions; The data interface imports external data; The at least one processor executes the computer-executable instructions stored in the memory, so that the at least one processor executes the method for analyzing the impact of photovoltaic access distribution station area on line voltage.

[0038] The computer-readable storage medium described in the present invention stores computer-executable instructions. When a processor executes the computer-executable instructions, the method for analyzing the impact of photovoltaic access to a distribution station area on line voltage is implemented.

[0039] A computer program product described in the present invention includes a computer program. When the computer program is executed by a processor, the method for analyzing the impact of photovoltaic access to a distribution station area on line voltage is implemented.

[0040] The beneficial effects of the present invention are that, compared with the prior art, the technical effects of the present invention include: The impact of photovoltaic power on the voltage of the power grid can be analyzed by using only the topological diagram of the power grid operation and inspection system and the user data in the centralized reading system, which makes data and information collection convenient and facilitates large-scale batch analysis. Cluster analysis is used to calculate the discrete data extracted from each household meter to determine the typical voltage and load correspondence of all users under the grid at various times, avoiding inaccurate load forecasting. The voltage changes of users near the photovoltaic access location are analyzed one by one, and finally the voltage value changes of the photovoltaic access point and the range of affected users are determined, avoiding the analysis and calculation of users or locations not affected by photovoltaic power, reducing the amount of calculation, and accurately determining the actual user instead of the vague electrical location.

[0041] Embodiments of the present invention are as follows: like Figure 4 As shown, the present invention is applied to a low-voltage distribution substation with distributed photovoltaic access, including: a distribution transformer 1, an area outlet 2, 1#~7# users 11~17, and a distributed photovoltaic 21.

[0042] like Figure 1 As shown, S1 is a QS file or a model file for collecting the topological structure of the area; the user account information includes the user name and location, and the distance between two users is calculated. Taking each user as the starting point, the other users are sorted from near to far according to the distance from the starting point to form a model of all user locations; Figure 4 The user location model for example can be: User 1 [2#, area exit, 3#, 4#, 5#, 6#, 7#] User 2 [3#, 4#, 5#, 1#, area exit, 6#, 7#] User 3 [4#, 5#, 2#, 1#, 6#, 7#, area exit] User 4 [3#, 5#, 2#, 6#, 7#, 1#, area exit] User 5 [3#, 4#, 2#, 6#, 1#, 7#, area exit] User 6 [4#, 7#, 3#, 2#, 5#, 1#, area exit] User 7 [6#, 4#, 3#, 5#, 2#, 1#, area exit] S2: Establish original data sets, original data clusters, and original voltage databases based on the original data of export voltage U and power P of all users in the target substation and the substation at each time during the previous operation days; The original data set is the target area outlet voltage value and user voltage value at a certain time, the target area outlet power and user power and the corresponding time, expressed as ;in For a time of day, is the target area outlet voltage value at the corresponding time, is the voltage value of the i-th user at the corresponding time; The time is a certain time of the day, including all previous operating days; Preferably, the t is the hour of the day; The original data cluster is a new set of original data sets with the same time t in the original data, which is expressed as ,in is the original data cluster at time t; The raw voltage database contains raw data clusters of all time periods, indicating [ … … ].

[0043] Can be based on Figure 4 Taking the substation as an example, the original voltage database [ … … ]Some voltage database information is as follows: Table 1

[0044] This is the original data set with 11002 columns; The original data sets from columns 11001 to 11100 and from columns 13001 to 13100 in Table 1 respectively form the original data clusters.

[0045] S3: The output of the photovoltaic station connected to user i Assume that the proposed distributed photovoltaic 21 access location is as follows Figure 4 As shown, in the area close to user 5, based on the photovoltaic access capacity and photovoltaic output characteristics, the output curve of distributed photovoltaic 21 is predicted as shown in the following table: Table 2

[0046] S4: Taking the original voltage database in Table 1 as an example, the cluster analysis is performed on the original data voltage data at each time point. Perform clustering calculations separately to obtain cluster data clusters at each time.

[0047] The clustering calculation uses K-means algorithm; K-means The number of clusters k in the algorithm is 10, which is to cluster the data at each time The 100 original data sets are clustered into 10 original data sets based on the similarity between the original data sets. Each original data set belongs to and only belongs to one class original data set with the smallest distance to the center of the class original data set, forming .

[0048] This is the clustering data set of 1101 columns in Table 3; The clustering data sets in columns 1101 to 1110 and columns 1301 to 1310 in Table 3 are respectively formed into clustering data clusters.

[0049] Table 3 shows the voltage fluctuation model [ … … ]Partial data.

[0050] Table 3

[0051] S5: Take 11:00 in Table 3 as an example to calculate the typical situation and analyze the voltage impact degree and voltage impact range. The proposed access location of distributed photovoltaic 21 is close to user 5. First, calculate the voltage of user 5 before photovoltaic access and the voltage change of user 5 after access; the voltage change is divided into 24 hour moments and calculated separately. First, take 11:00 as an example to calculate the voltage impact on user 5 at 11 o'clock after photovoltaic access. The typical load situation at this moment is 11:00. The maximum value of , the voltage of user 5 is 218.5V, that is, the voltage of user 5 is 218.5V 11 o'clock before the photovoltaic connection; assuming that the photovoltaic output at this time 4.3kW, ; and each group of the voltage fluctuation model middle The value is closest to 1.85, that is, 218.77V in the 1104th column of Table 3 is the voltage of user 5 at 11 o'clock after the photovoltaic connection. Therefore, the voltage change degree of user 5 caused by the power generation at 11:00 after the photovoltaic connection is 218.5-218.77=-0.27V; and because If it is greater than zero, there is no need to perform rolling absorption calculation, and the voltage impact range is the area where user 5 is located.

[0052] After the calculation at 11 o'clock is completed, the data at other times are analyzed in turn. The analysis process at other times will not be described in detail. The following takes the data at 13 o'clock as an example to introduce the rolling absorption calculation process. Similarly, the typical absorption calculation at 13:00 is performed, as well as the voltage impact degree and voltage impact range analysis. The typical load situation at this moment is 13:00. , corresponding to 224.5V is the voltage of user 5 at 13 o'clock before photovoltaic access; Photovoltaic output at this time 5.1kW, , rolling consumption calculation is required, then at 13:00 after photovoltaic access, the voltage of user 5 is the minimum household load value In column 1301 of Table 3, the corresponding voltage U5=222.5V, so after the photovoltaic connection, the power generation at 13:00 causes the voltage change of user 5 to be 224.5.5-222.5=2V.

[0053] When calculating rolling consumption, according to the user location model, user 3 is close to user 5, so user 3 is selected to continue to consume the load. Remaining amount; According to the previous consumption calculation process, the typical voltage of household 3 is the maximum In column 1309 of Table 3, that is In the figure, P3=4.19, and U3's 225.5V is the voltage of user 3 at 13:00 before photovoltaic connection; ; and each group of the voltage fluctuation model middle The value is closest to 1.09, that is, U3=223V in the data of column 1302 of Table 3 is the voltage of user 1 at 13:00 after photovoltaic connection. Therefore, the voltage change degree of user 3 caused by power generation at 13:00 after photovoltaic connection is 225.5-223=2.5V; and because If it is greater than zero, there is no need to perform rolling absorption calculation, and the voltage impact range is between user 5 and user 3.

[0054] According to the above method, the influence degree and voltage influence range of the line voltage after the photovoltaic access to the user 5 at other times are calculated. It is assumed that the results in Table 4 are obtained. Table 4

[0055] According to Table 4, after the photovoltaic power plant is connected to a location close to user 5, the voltage impact range throughout the day is the maximum value of the voltage impact range at all times, which should be the three user areas 5#, 3#, and 4# at 14:00; the voltage impact levels of the three users throughout the day are 3.2V, 3.5V, and 1.6V.

[0056] Based on the contents described in the above embodiments, the present application also provides an apparatus for analyzing the impact of photovoltaic access to distribution stations on line voltage, which can be applied to the method for analyzing the impact of photovoltaic access to distribution stations on line voltage described in the above embodiments. Figure 2 , Figure 2 This is a schematic diagram of a program module of a device for analyzing the impact of photovoltaic access to a distribution station area on line voltage provided in an embodiment of the present application. The device includes: The import module is used to import the target area topology and user account information, as well as the voltage U, power P data and photovoltaic output characteristics of all users in the target area and the area outlet at all times; The processing module establishes the location model of all users in the substation area according to the data in the import module, and establishes the original data set, original data cluster, and original voltage database; The prediction module is used to predict the output of the photovoltaic station according to the photovoltaic output characteristics of the station area in the import module ; Analysis module, used to perform cluster analysis on the original voltage database and establish a voltage fluctuation model; The judgment module is used for the absorption calculation in S5 and for judging the influence degree and voltage influence range of photovoltaic power on the line voltage at each moment.

[0057] It should be noted that the specific implementation contents of the import module, processing module, prediction module, analysis module, and judgment module in the embodiment of the present application can be referred to in Figure 4 , Figure 1 and Figure 2 The relevant contents in the illustrated embodiment will not be described in detail here.

[0058] The present disclosure can be a system, method and / or computer program product. The computer program product can include a computer-readable backup medium carrying computer-readable program instructions for causing a processor to implement various aspects of the disclosure.

[0059] The computer readable backup medium can be a tangible network capable of retaining and backing up the instructions used by the instruction execution network. The computer readable backup medium can be, but is not limited to, an electrical backup network, a magnetic backup network, an optical backup network, an electromagnetic backup network, a semiconductor backup network, or any suitable combination of the above. Further examples of computer readable backup media (non-exhaustive list) include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disk read-only memory (HD-ROM), digital versatile disk (DXD), memory stick, floppy disk, mechanical encoding network, such as punch cards or protrusions in grooves with instructions backed up thereon, and any suitable combination of the above. The computer readable backup medium used herein is not to be construed as a transient signal itself, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a power line cable), or an electrical signal transmitted through wires.

[0060] The computer readable program instructions expressed herein can be downloaded from the computer readable backup medium to each inference / processing power grid line, or downloaded to an external computer or external backup power grid line through a wireless network, such as the Internet, a local area network, a wide area network and / or a wireless network. The wireless network can include copper transmission cables, power transmission lines, wireless transmission, routers, firewalls, switches, gateway computers and / or edge service devices. The wireless network adapter card or wireless network interface in each inference / processing power grid line receives the computer readable program instructions from the wireless network and forwards the computer readable program instructions for storage in the computer readable backup medium in each inference / processing power grid line.

[0061] The computer program instructions for executing the operations of the present disclosure can be assembler instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, conditional setting values, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Sdalltal A, H++, etc., and ordinary procedural programming languages, such as "H" language or similar programming languages. The computer-readable program instructions can be executed completely on the client computer, partially on the client computer, as a separate software package, partially on the client computer and partially on the remote computer, or completely on the remote computer or server. In the case of a remote computer, the remote computer can be connected to the client computer through any wireless network, including a local area network (LAb) or a wide area network (WAb), or can be connected to an external computer (just like using an Internet service provider to connect through the Internet). In some embodiments, the electronic circuit is customized by using the state values ​​of computer-readable program instructions, such as a programmable logic circuit, a field programmable gate array (FPGA) or a programmable logic array (PLA), and the electronic circuit can execute computer-readable program instructions to achieve various aspects of the overhead disclosure.

[0062] Finally, 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 above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not deviate from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A method for analyzing the impact of photovoltaic access to a distribution station area on line voltage, applied to a low-voltage distribution station area with distributed photovoltaic access, characterized in that: The method comprises: S1: Establish the location model of all users in the area through the target area topology structure and user account information; S2: Establish original data sets, original data clusters, and original voltage databases based on the original data of export voltage U and power P of all users in the target substation and the substation at each time during the previous operation days; S3: Output to photovoltaic stations Make a prediction and determine the user i closest to the proposed photovoltaic access location; S4: performing cluster analysis on the voltage database to establish a voltage fluctuation model; S5: Calculate and absorb the photovoltaic output at each moment and the typical situation at the corresponding moment in the voltage fluctuation model to determine the impact degree and voltage impact range of photovoltaics on the line voltage at each moment.

2. The method for analyzing the impact of photovoltaic access to distribution stations on line voltage according to claim 1 is characterized in that: The low-voltage distribution substation area includes multiple electricity users and a distributed photovoltaic to be connected to the substation area, and the low-voltage distribution substation area is the target substation area; The user location model is a model formed by sorting other users in terms of line distances by a user in the target station area.

3. The method for analyzing the impact of photovoltaic access to distribution stations on line voltage according to claim 1 is characterized in that: The original data set is the target area outlet voltage value and user voltage value at a certain time, the target area outlet power and user power and the corresponding time, which is expressed as ;in For a time of day, is the target area outlet voltage value at the corresponding time, is the voltage value of the ith user at the corresponding time, is the target area outlet power value at the corresponding time, is the electric power value of the ith user at the corresponding time, is the total number of users; The time mentioned is a certain time of the day, including all previous operating days; The t is the hour of the day.

4. The method for analyzing the impact of photovoltaic access to distribution stations on line voltage according to claim 3 is characterized in that: The original data cluster is a new set of original data sets with the same time t in the original data sets, which is expressed as ,in is the original data cluster at time t, and the superscripts 1, 2, ... are used to mark the time t on different dates; The raw voltage database contains raw data clusters of all time, expressed as [ … … ].

5. The method for analyzing the impact of photovoltaic access to distribution stations on line voltage according to claim 4 is characterized in that: The cluster analysis is performed on the raw data clusters at each time of the raw data voltage database. Perform clustering calculations separately to obtain cluster data clusters at each time, expressed as ,in is the cluster data cluster at time t, Clustering data One of the clustering datasets.

6. The method for analyzing the impact of photovoltaic access to distribution stations on line voltage according to claim 5 is characterized in that: The clustering calculation uses K-means algorithm; K-means The number of clusters k in the algorithm is 10, that is, the cluster data cluster is , The voltage fluctuation model contains the original data clusters of all time periods, expressed as [ … … ]; The voltage impact degree is the user voltage before photovoltaic access and the user voltage change after access; The above-mentioned consumption calculation is to calculate the photovoltaic output power at time t after the photovoltaic power is connected. In the typical case The load of user i is reached when the consumption is carried out; before the photovoltaic connection, the voltage of user i is Clustering dataset Corresponding voltage ; After photovoltaic connection, the voltage of user i is a cluster data cluster Zhongyu The cluster data set with the closest user load value The corresponding voltage in In the above-mentioned absorption calculation process, when If it is less than zero, rolling consumption calculation is required; in this case, the voltage of user i after photovoltaic connection is a cluster data cluster The user load value is the smallest Clustering dataset The corresponding voltage in The rolling absorption calculation is to select the i-1 or i+1 user closest to the i user by the user location model, and calculate the typical situation of the i-1 or i+1 user at the corresponding time in the voltage fluctuation model according to the absorption calculation process to continue absorption. The remaining amount, and whether to calculate again the rolling consumption, as well as the voltage change of the user i-1 or i+1 user before and after the photovoltaic access; The voltage impact range is all user areas that participate in the rolling absorption calculation.

7. A device for analyzing the impact of photovoltaic access to distribution stations on line voltage, characterized in that: include: The import module is used to import the target area topology structure and user account information, as well as the voltage U, power P data and photovoltaic output characteristics of all users in the target area and the area outlet at all times; The processing module establishes the location model of all users in the substation area according to the data in the import module, and establishes the original data set, original data cluster, and original voltage database; The prediction module is used to predict the output of the photovoltaic station according to the photovoltaic output characteristics of the station area in the import module ; Analysis module, used to perform cluster analysis on the original voltage database and establish a voltage fluctuation model; The judgment module is used for absorption calculation and judging the influence degree and voltage influence range of photovoltaic on line voltage at each moment.

8. A device for analyzing the impact of photovoltaic access to distribution stations on line voltage, characterized in that: include: at least one processor, a data interface, and a memory; The memory stores computer-executable instructions; The data interface imports external data; The at least one processor executes the computer-executable instructions stored in the memory, so that the at least one processor performs the method for analyzing the impact of photovoltaic access distribution station area on line voltage according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions. When the processor executes the computer-executable instructions, the method for analyzing the impact of photovoltaic access distribution station area on line voltage according to any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for analyzing the impact of photovoltaic access to a distribution station area on line voltage as described in any one of claims 1 to 6 is implemented.

Images