A Dynamic Evaluation Method, Device, Equipment and Medium for Open Capacity of Distribution Network

Abstract

The present invention relates to a method, device, equipment and medium for dynamically evaluating the open capacity of a distribution network. The offline calculation steps include: expanding the information model of the electricity meter to obtain an extended information model of the electricity meter; extracting the attribute parameters of the electricity meter within a preset time, and clustering and classifying the electricity meters according to the attribute parameters; estimating the developable area and installable capacity corresponding to each type of electricity meter based on the installed capacity of distributed photovoltaics and the developable area of distributed photovoltaics of the users to which the electricity meters in the extended information model of the electricity meter belong. The online calculation steps include: updating the photovoltaic access information according to the information of the distributed photovoltaic control system, updating the information on the increase and decrease of user electricity meters according to the data of the marketing system, determining new users based on the above information, estimating the installable capacity of distributed photovoltaics of the new users, and evaluating the open capacity of each area and each layer based on the estimation results. The present invention can achieve accurate calculation of the bearing capacity of each area and each layer, and support the dynamic measurement of the open capacity of different regions.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network assessment, and particularly to a method, device, equipment and medium for dynamically assessing the open capacity of a distribution network. Background Technique

[0002] The dynamic assessment of the open capacity of a distribution network is a process of dynamically calculating the scale of distributed photovoltaics that the distribution network can carry based on dynamic information such as the distribution network grid structure, the access situation of distributed power sources (including energy storage), and load changes. The core technology is the calculation of the bearing capacity of different regions at all levels considering grid constraints and the association between the information of existing or planned newly built distributed photovoltaics and the power grid topology model.

[0003] Existing scientific papers on the assessment of the bearing capacity of distributed rooftop photovoltaics in distribution networks based on high-definition satellite map images (Wang Haifeng et al., "Guangdong Electric Power", Vol. 36, No. 10, 2023) have given a method for accurately assessing the developable area of rooftops in the whole county. The developable area of each block obtained through high-definition satellite map images only accounts for the building rooftop area that can be recognized in the image. Therefore, it is difficult to identify the photovoltaic installable areas such as courtyards and open spaces in rural areas, and it is difficult to support the assessment of the bearing capacity and operation analysis of different regions and layers in dimensions such as low-voltage lines, substations, feeders, and transformers. The number of photovoltaic grid-connected nodes and the grid-connected positions of photovoltaics in this method are random, making it difficult to conduct the bearing capacity assessment considering the actual existing photovoltaics, and it is difficult to predict the impact of newly added photovoltaic installations on the distribution network. As a result, it is difficult to carry out the dynamic assessment of the open capacity, and it lacks a refined guiding role for the development plan of distributed photovoltaics.

[0004] The existing patent document CN113963055A discloses a method for calculating the theoretical installed capacity of rooftop photovoltaics based on multi-spectral map data. This method uses high-precision multi-spectral map data to extract the spectral characteristics of building rooftops, thereby accurately distinguishing the rooftop area and the non-rooftop area and obtaining the area of the photovoltaic installation area. However, this method does not consider the situation of the association between the information of existing or planned newly built distributed photovoltaics and the power grid topology model, which will lead to a large estimation error. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a method, device, equipment and medium for dynamically assessing the open capacity of a distribution network, which can accurately calculate the bearing capacity of different regions and layers in dimensions such as low-voltage lines, substations, feeders, and transformers, and support the dynamic measurement of the open capacity of different regions.

[0006] The technical solution adopted by the present invention to solve its technical problems is: to provide a method for dynamically assessing the open capacity of a distribution network, including an offline calculation step and an online calculation step:

[0007] The offline calculation step includes:

[0008] Add the distributed PV installed capacity and the developable area of distributed PV of the user to which the electricity meter belongs to the electricity meter information model to obtain an extended electricity meter information model;

[0009] Extract the attribute parameters of the electricity meter within a preset time, and cluster and classify the electricity meters according to the attribute parameters;

[0010] Based on the distributed PV installed capacity and the developable area of distributed PV of the user to which the electricity meter belongs in the extended electricity meter information model, estimate the developable area and installable capacity corresponding to each type of electricity meter;

[0011] The online calculation steps include:

[0012] Update the PV access information according to the distributed PV control system information, update the information on the increase and decrease of user electricity meters according to the marketing system data, determine new users, and estimate the distributed PV installable capacity of the new users;

[0013] Evaluate the openable capacity of each area and each layer based on the developable area and installable capacity corresponding to each type of electricity meter, and the distributed PV installable capacity of new users.

[0014] The developable area of distributed PV includes: building roofs, courtyards and open spaces owned by the user to which the electricity meter belongs.

[0015] The installation location of the electricity meter in the basic information of the electricity meter in the extended electricity meter information model is extended to: the low-voltage line to which it belongs, the transformer substation area to which it belongs, and the medium-voltage line to which it belongs.

[0016] When clustering and classifying the electricity meters according to the attribute parameters, the K-means clustering method, the hierarchical clustering method, the density clustering method, or the grid-based clustering method can be used.

[0017] The estimation of the developable area and installable capacity corresponding to each type of electricity meter includes the following steps:

[0018] Judge whether the built PV in each type of electricity meter is less than the threshold;

[0019] If the proportion of built PV in each type of electricity meter is less than the threshold, randomly sample the electricity meter users at the threshold for actual measurement, and use the actual measurement results as the developable area and installable capacity corresponding to this type of electricity meter;

[0020] If the proportion of built PV in each type of electricity meter is equal to or greater than the threshold, count the area and installed capacity of the built distributed PV in each type of electricity meter, calculate the average value, and use the obtained average value as the developable area and installable capacity corresponding to this type of electricity meter.

[0021] Estimating the installable capacity of distributed photovoltaics for the newly added users specifically includes: when the newly added user is a user planning to build a distributed photovoltaic power station, the developable area and installable capacity of the distributed photovoltaics of the newly added user are the area and installable capacity filed in the distributed photovoltaic management and control system; when the newly added user is a newly added electricity meter user, classify the newly added electricity meter users based on the clustering classification results, re-estimate the developable area and installable capacity corresponding to the electricity meters to which the classification results belong, and use the re-estimated developable area and installable capacity as the developable area and installable capacity of the distributed photovoltaics of the newly added user.

[0022] When the newly added user is a user planning to build a distributed photovoltaic power station, it further includes classifying the users planning to build a distributed photovoltaic power station based on the clustering classification results, and re-estimating the developable area and installable capacity corresponding to the electricity meters of this category based on the classification results.

[0023] Evaluating the openable capacity of each area and each layer based on the developable area and installable capacity corresponding to each type of electricity meter and the installable capacity of distributed photovoltaics for the newly added users specifically includes: based on the public information model of the distribution network, integrating the equipment change information of the PMS system, the data of the distributed photovoltaic management and control system, and the data of the marketing system, associating the distributed photovoltaic filing information in the distributed photovoltaic management and control system with the user electricity meter data, and updating the grid topology; according to the updated grid topology, combining the developable area and installable capacity corresponding to each type of electricity meter and the installable capacity of distributed photovoltaics for the newly added users, calculating the bearing capacity of each area and each layer according to the power supply area and voltage level, and determining the bearing capacity level and the capacity of newly added distributed power sources within the power supply area.

[0024] The online calculation steps further include:

[0025] Judging whether there is a power supply area where the newly added capacity is less than or equal to zero;

[0026] If there is a power supply area where the newly added capacity is less than or equal to zero, mark it as a weak area under the current penetration rate, and judge whether the installation ratio of distributed photovoltaics reaches 100%;

[0027] If the installation ratio of distributed photovoltaics does not reach 100%, add distributed photovoltaics with a preset ratio according to the user electricity meter data, add newly added users based on the average distribution construction time sequence, and classify the newly added users based on the clustering classification results.

[0028] The technical solution adopted by the present invention to solve its technical problems is to provide a device for dynamically evaluating the openable capacity of a distribution network, including an offline calculation part and an online calculation part;

[0029] The offline calculation part includes:

[0030] An expansion module for adding the distributed photovoltaic installed capacity and the distributed photovoltaic developable area of the user to which the electricity meter belongs to the electricity meter information model to obtain an extended electricity meter information model;

[0031] A clustering module for extracting the attribute parameters of the electricity meter within a preset time and clustering and classifying the electricity meters according to the attribute parameters;

[0032] An estimation module for estimating the developable area and installable capacity corresponding to each type of electricity meter based on the distributed photovoltaic installed capacity and the distributed photovoltaic developable area of the user to which the electricity meter belongs in the extended electricity meter information model;

[0033] The online calculation part includes:

[0034] A new user estimation module for updating the photovoltaic access information according to the distributed photovoltaic control system information, updating the electricity meter addition and subtraction information of the user according to the marketing system data, determining new users, and estimating the distributed photovoltaic installable capacity of the new users;

[0035] An evaluation module for evaluating the openable capacity of each area and each layer based on the developable area and installable capacity corresponding to each type of electricity meter and the distributed photovoltaic installable capacity of new users.

[0036] The technical solution adopted by the present invention to solve its technical problems is: to provide an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the computer program, the steps of the above-mentioned dynamic evaluation method for the openable capacity of the distribution network are implemented.

[0037] The technical solution adopted by the present invention to solve its technical problems is: to provide a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above-mentioned dynamic evaluation method for the openable capacity of the distribution network are implemented.

[0038] Beneficial effects

[0039] Due to the adoption of the above technical solution, compared with the prior art, the present invention has the following advantages and positive effects: By establishing an extended electricity meter model, increasing the photovoltaic construction situation and the developable area, the present invention realizes the association between distributed photovoltaic management data and network topology data. Through the classification and clustering method, the developable area information corresponding to each type of electricity meter is obtained. On this basis, accurate information on the dynamic changes of the distributed photovoltaic installed capacity and location is obtained, so as to realize the accurate calculation of the bearing capacity of low-voltage lines, substations, feeders, transformers, etc. in different regions and different layers, and support the dynamic measurement of the openable capacity of different regions. Description of the drawings

[0040] Figure 1It is the flowchart of the method for dynamically evaluating the open capacity of the distribution network in the first embodiment of the present invention;

[0041] Figure 2 It is the flowchart of estimating the developable area and installable capacity corresponding to the electric energy meter in the first embodiment of the present invention;

[0042] Figure 3 It is the flowchart of evaluating the open capacity of each layer in each area in the first embodiment of the present invention;

[0043] Figure 4 It is the flowchart of evaluating the weak links of the power grid in the first embodiment of the present invention. Specific Embodiments

[0044] The following further elaborates the present invention in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and not to limit the scope of the present invention. In addition, it should be understood that after reading the content taught by the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms also fall within the scope defined by the appended claims of this application.

[0045] The first embodiment of the present invention relates to a method for dynamically evaluating the open capacity of a distribution network, as Figure 1 shown, including an offline calculation step and an online calculation step.

[0046] Among them, the offline calculation step includes:

[0047] Step 1, adding the distributed photovoltaic installed capacity and the distributed photovoltaic developable area of the users to which the electric energy meter belongs to the electric energy meter information model to obtain an extended electric energy meter information model.

[0048] The traditional electric energy meter information model includes multiple contents, mainly including basic electric meter information, electric meter measurement data, electric meter event records, electric meter operating status, electric meter environmental parameters, electric meter communication status, etc.

[0049] Basic electric meter information: including electric meter type, electric meter number, electric meter installation location, electric meter manufacturer, etc.

[0050] Electric meter measurement data: including parameters such as electric energy, voltage, current, power factor, frequency, etc., which can reflect the working status and power consumption of the electric meter.

[0051] Electric meter event records: including power-off events, overload events, short-circuit events, etc., which can record abnormal and fault conditions of the electric meter.

[0052] Electric meter operating status: including the switch status and working mode of the electric meter, which can reflect the operating situation and power consumption of the electric meter.

[0053] Environmental parameters of the electricity meter: including environmental parameters such as temperature, humidity, and air pressure, which can reflect the working environment and safety conditions of the electricity meter.

[0054] Communication status of the electricity meter: including signal strength of the communication module, connection status of the communication module, etc., which can reflect the communication situation and data transmission situation of the electricity meter.

[0055] This step is an extension based on the traditional electricity meter information model, mainly expanding the content of the distributed photovoltaic installed capacity of the users to which the electricity meter belongs and the content of the developable area of distributed photovoltaics, which can reflect the built photovoltaic situation and installable situation of the users belonging to the power grid. Among them, the developable area of distributed photovoltaics includes the building roof, and also includes the courtyard, open space, etc. that can be built with photovoltaics owned by the users to which the electricity meter belongs. In addition, in the electricity meter information extension model of this step, the installation location of the electricity meter in the basic information of the electricity meter is extended to the low-voltage line to which it belongs, the transformer substation area to which it belongs, and the medium-voltage line to which it belongs.

[0056] It is worth mentioning that the electricity meters mentioned in this embodiment only include the electricity meters directly trading with the power grid company, and do not include the subordinate electricity meters in industrial and commercial buildings.

[0057] Step 2: Extract the attribute parameters of the electricity meter within a preset time, and perform clustering classification on the electricity meter according to the attribute parameters.

[0058] The electricity meters can be divided into three categories according to the attribute values of the user's basic information: household users, industrial users, and commercial users. In this step, the statistical characteristic values of electricity, voltage, current, power factor, and frequency, the maximum load utilization hours, the annual maximum power and annual minimum power and the time when they appear (including hours, dates, months, quarters), the maximum single-day maximum and minimum power difference and the time when it appears (including dates, months, quarters), etc. of the electricity meter within one year are extracted as the attribute parameters of each electricity meter for clustering.

[0059] For the electricity meter data points of various users, clustering is performed according to the following sub-steps respectively:

[0060] Sub-step (1): First, randomly select K objects {x1, x2, …, x k} from all n data points as the initial clustering centers to form the initial clustering center set S (0) = {x1, x2, …, x k} = {s1 (0) , s2 (0) , …, s k (0)};

[0061] For each data point x, calculate its Euclidean distance d(x, x i (0)), if d(x, s i (0) ) ≤ d(x, s j (0) ), , then assign the data point x to the C i (0) -th class to form the initial classification C i (0) = {C1 (0) , C2 (0) , …, C k (0)};

[0062] Sub-step (2): Recalculate each cluster center s i (1) using Equation (1) to obtain the new cluster centers S (1) = {s1 (1) , s2 (1) , …, s k (1)}, calculate the Euclidean distance d(x, s i (1) ) between each data point x and each new cluster center s i (1) ). If d(x, s i (1) ) ≤ d(x, s j (1) ), , then classify the data point x into the C i (1) -th class to form the new classification C i (1) = {C1 (1) , C2 (1) , …, C k (1)};

[0063] (1)

[0064] where n i is the number of samples in class C i (0) .

[0065] Sub-step (3): Repeat step (2) m times to obtain the new cluster centers S (m) = {s1 (m) , s2 (m) , …, s k (m)} and the classification C i (m) = {C1 (m) , C2 (m),…,C k (m)}, if the class centers no longer change or the maximum number of iterations M is reached, i.e., S (m) ≈S (m+1) or C (m) ≈C (m+1) or , enter sub-step (4), otherwise return to sub-step (2);

[0066] Sub-step (4): Calculate the clustering validity evaluation function f(x) using Equation (2), determine the optimal number of clusters k, and output the classification result.

[0067] (2)

[0068] Among them, D b (k) is the sum of the between-class centroid distances of all n data points, and the calculation formula is as shown in Equation (3):

[0069] (3)

[0070] D w (k) is the sum of the within-class distances of all n data points, and the calculation formula is as shown in Equation (4):

[0071] (4)

[0072] is the mean of all n data points, called the centroid of all data points, and the calculation formula is as shown in Equation (5):

[0073] (5)

[0074] is the cluster center of the i-th cluster C i , n i is the number of data objects in the i-th cluster C i , and the calculation formula is as shown in Equation (6):

[0075] (6)

[0076] When f(x) takes the minimum value, the corresponding number of clusters k is the optimal number of clusters, and the corresponding classification result C = {C1, C2, …, C k} is the optimal classification.

[0077] It is worth mentioning that in addition to using the above K-means clustering method in this step, other common data mining and machine learning methods such as hierarchical clustering method, density clustering method, and grid-based clustering method can also be used to complete the clustering classification of electric energy meters.

[0078] Step 3: Based on the installed capacity of distributed PV and the developable area of distributed PV of the users to which the electricity meters belong in the electricity meter information expansion model, estimate the developable area and installable capacity corresponding to each type of electricity meter.

[0079] In this step, as Figure 2 shown, determine whether the proportion of the built PV in each type of electricity meter is less than 10%. If the proportion of the built PV in each type of electricity meter is less than 10%, randomly sample 10% of the electricity meter users for actual measurement, and use the actual measurement results as the developable area and installable capacity corresponding to this type of electricity meter. Among them, if the proportion of the built PV is zero, it means that there is no built PV project in each type of electricity meter. If the proportion of the built PV in each type of electricity meter is equal to or greater than 10%, then count the area and installed capacity of the built distributed PV in each type of electricity meter, calculate the average value, and use the obtained average value as the developable area and installable capacity corresponding to this type of electricity meter. It is worth mentioning that during the construction process, this implementation method will continuously update the estimation results with new data dynamically.

[0080] The online calculation steps include:

[0081] Step 4: Update the PV access information according to the information of the distributed PV control system, update the increase and decrease information of the user electricity meters according to the marketing system data, determine the new users, and estimate the installable capacity of the distributed PV of the new users.

[0082] In this step, first update the PV access information according to the information of the distributed PV control system, and update the increase and decrease information of the user electricity meters according to the marketing system data, so as to determine the new users. The new users in this implementation method include two types: users planning to build new distributed PV and users with new electricity meters. For users planning to build new distributed PV, the developable area and installable capacity of the distributed PV of the new users are the area and installed capacity filed in the distributed PV control system, and update the power grid topology according to the relevant information for subsequent analysis and calculation. At the same time, based on the classification and clustering method and results in Step 2, classify the users planning to build new distributed PV, and use them as new samples. Regularly update the developable area and installable capacity corresponding to the electricity meters of their respective types through Step 3. For users with new electricity meters, based on the classification and clustering method and results in Step 2, classify the users of the new electricity meter, and re - estimate the developable area and installable capacity corresponding to this type of electricity meter in the way of Step 3, and use the re - estimated results as the developable area and installable capacity of the distributed PV of the new users.

[0083] It can be seen that the distributed photovoltaic developable area estimation method based on classification and clustering technology in this embodiment has the similarity among similar buildings in the same area. By using the classification and clustering distributed photovoltaic developable area estimation method, while ensuring the accuracy, the workload of individual measurement can be greatly reduced.

[0084] Step 5: Evaluate the openable capacity of each area and each layer based on the developable area and installable capacity corresponding to each type of electricity meter, as well as the distributed photovoltaic installable capacity of new users.

[0085] As Figure 3 shown, this step is based on the public information model of the distribution network, integrates the device change information of the PMS system, the data of the distributed photovoltaic control system, and the data of the marketing system, associates the distributed photovoltaic filing information in the distributed photovoltaic control system with the user electricity meter data, and updates the power grid topology; then, according to the updated power grid topology, combined with the developable area and installable capacity corresponding to each type of electricity meter, as well as the distributed photovoltaic installable capacity of new users, in accordance with the "Guidelines for Assessing the Carrying Capacity of Distributed Power Sources Connected to the Grid" (DL / T 2041-2019), in the order from overall to local and from high voltage to low voltage, calculate the carrying capacity of each area and each layer by power supply area and voltage level, determine the carrying capacity level and the additional distributed power source capacity within the power supply area, and release the openable capacity of each area and each layer in the current state.

[0086] It is not difficult to find that the online and offline combined openable capacity calculation in this embodiment can ensure the accuracy of the model and the result and the analysis speed, and at the same time can be dynamically updated following the change of the network topology to realize the dynamic assessment of weak areas under different development speeds. In addition, the topology update method based on multi-source information fusion in this embodiment can reflect the actual access position and capacity, so as to accurately calculate the impact of photovoltaic access on the power grid. This embodiment expands the electricity meter model, can realize the real-time association of the grid connection position and installable capacity information of new distributed photovoltaic project approval with the power grid topology information, and through the data fusion of the distributed photovoltaic control system, PMS system, marketing system, power consumption information collection system, etc., can respectively estimate the additional photovoltaic installed capacity and the total developable amount of each voltage level line and each power supply area, so as to realize the rapid and accurate calculation of the openable capacity of each layer and each area.

[0087] The online calculation step in this embodiment also includes the assessment of power grid weak links, and its process is as Figure 4As shown in the figure, the specific process is as follows: Determine whether there is an area where the additional capacity is less than or equal to 0. If so, mark it as a weak area under the current penetration rate, and then determine whether the distributed photovoltaic installation ratio has reached 100%. If the distributed photovoltaic installation ratio reaches 100%, the process ends. If the distributed photovoltaic installation ratio has not reached 100%, a certain proportion of distributed photovoltaic system quantities will be newly added according to the user's electricity meter data. At the same time, based on the principle of fairness and justice, distributed photovoltaic system nodes (i.e., new users) will be newly added according to the construction time sequence of uniform distribution. According to the classification results in step 2, the newly added users will be classified and matched, and combined with the estimation results of the distributed photovoltaic access capacity estimated in step 3, the newly added distributed photovoltaic installed capacity of each node will be estimated. Integrate the electricity load data in the electricity information acquisition system and the distributed photovoltaic installed capacity information data in the distributed photovoltaic control system, and update the grid topological structure and distributed photovoltaic penetration rate through the associated location information of the electricity meter. Then calculate the grid sub-region and layer bearing capacity and the additional capacity after adding a certain proportion of photovoltaic systems, and then determine whether there is an area where the additional capacity is less than or equal to 0. If so, mark it as a weak area under the current penetration rate until the distributed photovoltaic installation ratio reaches 100%.

[0088] The dynamic evaluation method of the distributed photovoltaic bearing capacity of the distribution network in this embodiment adds a certain proportion of distributed photovoltaic system quantities according to the number of household meters, and based on the principle of fairness and justice, adds distributed photovoltaic system nodes according to the construction time sequence of uniform distribution, considering the actual developable scale and the fairness and justice in the construction and development process.

[0089] The second embodiment of the present invention relates to a dynamic evaluation device for the openable capacity of a distribution network, including an offline calculation part and an online calculation part;

[0090] The offline calculation part includes:

[0091] An expansion module for adding the distributed photovoltaic installed capacity of the users to which the electricity meters belong and the distributed photovoltaic developable area to the electricity meter information model to obtain an expanded electricity meter information model;

[0092] A clustering module for extracting the attribute parameters of the electricity meters within a preset time and clustering and classifying the electricity meters according to the attribute parameters;

[0093] An estimation module for estimating the developable area and installable capacity corresponding to each type of electricity meter based on the distributed photovoltaic installed capacity of the users to which the electricity meters belong and the distributed photovoltaic developable area in the expanded electricity meter information model;

[0094] The online calculation part includes:

[0095] A new user estimation module is used to update the photovoltaic access information according to the distributed photovoltaic control system information, update the information on the increase and decrease of user electricity meters according to the marketing system data, determine new users, and estimate the installable capacity of distributed photovoltaics for the new users.

[0096] An evaluation module is used to evaluate the openable capacity of each area and each layer based on the developable area and installable capacity corresponding to each type of electricity meter, as well as the installable capacity of distributed photovoltaics for new users.

[0097] The distributed photovoltaic developable area includes: building roofs, courtyards and open spaces owned by the users to which the electricity meters belong.

[0098] In the electricity meter information extension model, the installation location of the basic information of the electricity meter is extended to: the low-voltage line to which it belongs, the transformer substation area to which it belongs, and the medium-voltage line to which it belongs.

[0099] When the clustering module clusters and classifies the electricity meters according to the attribute parameters, the K-means clustering method, the available hierarchical clustering method, the density clustering method, or the grid-based clustering method is adopted.

[0100] The estimation module includes:

[0101] A judgment unit is used to judge whether the proportion of built photovoltaic in each type of electricity meter is less than the threshold;

[0102] A first estimation unit is used to, when the proportion of built photovoltaic in each type of electricity meter is less than the threshold, randomly sample the electricity meter users at the threshold for actual measurement, and use the actual measurement results as the developable area and installable capacity corresponding to this type of electricity meter;

[0103] A second estimation unit is used to, when the proportion of built photovoltaic in each type of electricity meter is equal to or greater than the threshold, count the area and installed capacity of the built distributed photovoltaics in each type of electricity meter, calculate the average value, and use the obtained average value as the developable area and installable capacity corresponding to this type of electricity meter.

[0104] When the new user estimation module estimates the installable capacity of distributed photovoltaics for the new users, when the new user is a user planning to build a new distributed photovoltaic, the developable area and installable capacity of the distributed photovoltaics for the new user are the area and installed capacity filed in the distributed photovoltaic control system; when the new user is a new electricity meter user, the new electricity meter user is classified based on the clustering classification result, and the developable area and installable capacity corresponding to the electricity meter to which the classification result belongs are re-estimated, and the re-estimated developable area and installable capacity are used as the developable area and installable capacity of the distributed photovoltaics for the new user.

[0105] When the new user is a user planning to build a distributed photovoltaic system, it further includes classifying the users planning to build a distributed photovoltaic system based on the clustering classification results, and re-estimating the developable area and installable capacity corresponding to the electricity meters of this category based on the classification results.

[0106] Based on the common information model of the distribution network, the evaluation module integrates the equipment change information of the PMS system, the data of the distributed photovoltaic control system, and the data of the marketing system, associates the distributed photovoltaic filing information in the distributed photovoltaic control system with the user electricity meter data, and updates the grid topology; according to the updated grid topology, combined with the developable area and installable capacity corresponding to each type of electricity meter, as well as the installable capacity of the distributed photovoltaic of the new user, calculate the sub-region and hierarchical bearing capacity according to the power supply area and voltage level, and determine the bearing capacity level and the additional installable distributed power capacity within the power supply area.

[0107] The online calculation part further includes:

[0108] A new capacity judgment unit, used to judge whether there is a power supply area where the additional installable capacity is less than or equal to zero;

[0109] An identification judgment unit, used to identify as a weak area under the current penetration rate and judge whether the installation ratio of distributed photovoltaics reaches 100% when there is a power supply area where the additional installable capacity is less than or equal to zero;

[0110] A new addition unit, used to add a preset proportion of distributed photovoltaics according to the user electricity meter data when the installation ratio of distributed photovoltaics does not reach 100%, add new users based on the construction time sequence of uniform distribution, and classify the new users based on the clustering classification results.

[0111] The third embodiment of the present invention relates to an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned method for dynamically evaluating the open capacity of the distribution network are implemented.

[0112] The fourth embodiment of the present invention relates to a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned method for dynamically evaluating the open capacity of the distribution network are implemented.

[0113] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer-usable program code.

[0114] This application is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing device produce a means for implementing the specified function in one or more flows Figure 1 one or more flows and / or blocks Figure 1 or a means for implementing the specified function in one or more blocks.

[0115] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including the instruction method, and the instruction method implements the specified function in one or more flows Figure 1 one or more flows and / or blocks Figure 1 or a means for implementing the specified function in one or more blocks.

[0116] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the specified function in one or more flows Figure 1 one or more flows and / or blocks Figure 1 or a means for implementing the specified function in one or more blocks.

[0117] As described above, the above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims described.

Claims

1. A dynamic evaluation method for the open capacity of a distribution network, characterized in that It includes an offline calculation step and an online calculation step; The offline calculation step includes: Adding the distributed PV installed capacity and the distributed PV developable area of the user to which the electricity meter belongs to the electricity meter information model to obtain an extended electricity meter information model; Extracting the attribute parameters of the electricity meter within a preset time and classifying the electricity meters by clustering according to the attribute parameters; Based on the distributed PV installed capacity and the distributed PV developable area of the user to which the electricity meter belongs in the extended electricity meter information model, estimating the developable area and installable capacity corresponding to each type of electricity meter; The online calculation step includes: Updating the PV access information according to the distributed PV control system information, updating the electricity meter addition and subtraction information of users according to the marketing system data, and determining new users based on the PV access information and the electricity meter addition and subtraction information of users, and estimating the distributed PV installable capacity of the new users; Evaluating the openable capacity of each area and each layer based on the developable area and installable capacity corresponding to each type of electricity meter and the distributed PV installable capacity of new users. Specifically: Based on the common information model of the distribution network, integrating the equipment change information of the PMS system, the data of the distributed PV control system, and the data of the marketing system, associating the distributed PV filing information in the distributed PV control system with the electricity meter data of users, and updating the grid topology; According to the updated grid topology, combining the developable area and installable capacity corresponding to each type of electricity meter and the distributed PV installable capacity of new users, calculating the bearing capacity of each area and each layer according to the power supply area and voltage level, and determining the bearing capacity level and the installable capacity of new distributed power sources within the power supply area.

2. The dynamic evaluation method for the open capacity of the distribution network according to claim 1, wherein The distributed PV developable area includes: the roof of the building, the courtyard and the open space owned by the user to which the electricity meter belongs.

3. The method for dynamically evaluating the open capacity of a distribution network according to claim 1, characterized in that The extended electricity meter information model includes the basic information of the electricity meter, and the installation location of the electricity meter in the basic information of the electricity meter includes: the low-voltage line to which it belongs, the substation area to which it belongs, and the medium-voltage line to which it belongs.

4. The dynamic evaluation method for the open capacity of a distribution network according to claim 1, wherein When classifying the electricity meters by clustering according to the attribute parameters, the K-means clustering method, the hierarchical clustering method, the density clustering method or the grid-based clustering method is adopted.

5. The method for dynamically evaluating the open capacity of a distribution network according to claim 1, wherein The estimating the developable area and installable capacity corresponding to each type of electricity meter includes the following steps: Judging whether the proportion of the built PV in each type of electricity meter is less than the threshold; If the proportion of the built PV in each type of electricity meter is less than the threshold, randomly sampling the electricity meter users of the threshold for actual measurement, and taking the actual measurement result as the developable area and installable capacity corresponding to this type of electricity meter; If the proportion of the built PV in each type of electricity meter is equal to or greater than the threshold, then counting the area and installed capacity of the built distributed PV in each type of electricity meter, calculating the average value, and taking the obtained average value as the developable area and installable capacity corresponding to this type of electricity meter.

6. The dynamic evaluation method for the open capacity of a distribution network according to claim 1, characterized in that, The estimation of the installable capacity of distributed photovoltaics for the newly added users is specifically as follows: when the newly added user is a user planning to newly build distributed photovoltaics, the developable area and installable capacity of the distributed photovoltaics of the newly added user are the area and installable capacity filed in the distributed photovoltaic management and control system; when the newly added user is a newly added electricity meter user, the newly added electricity meter users are classified based on the clustering classification result, and the developable area and installable capacity corresponding to the electricity meter to which the classification result belongs are re-estimated, and the re-estimated developable area and installable capacity are used as the developable area and installable capacity of the distributed photovoltaics of the newly added user.

7. The dynamic evaluation method for the open capacity of the distribution network according to claim 6, characterized in that, When the newly added user is a user planning to newly build distributed photovoltaics, it further includes classifying the users planning to newly build distributed photovoltaics based on the clustering classification result, and re-estimating the developable area and installable capacity corresponding to the electricity meter of this class based on the classification result.

8. The dynamic evaluation method for the open capacity of a distribution network according to claim 1, wherein The online calculation step further includes: Judging whether there is a power supply area with an available new capacity less than or equal to zero; If there is a power supply area with an available new capacity less than or equal to zero, it is marked as a weak area under the current penetration rate, and it is judged whether the distributed photovoltaic installation ratio reaches 100%; If the distributed photovoltaic installation ratio does not reach 100%, then a preset proportion of distributed photovoltaics is added according to the user electricity meter data, new users are added based on the construction time sequence of average distribution, and the new users are classified based on the clustering classification result.

9. A dynamic evaluation device for the open capacity of a distribution network, characterized in that, It includes an offline calculation part and an online calculation part; The offline calculation part includes: An expansion module for adding the distributed photovoltaic installation capacity and the developable area of distributed photovoltaics of the users to which the electricity meters belong to the electricity meter information model to obtain an expanded electricity meter information model; A clustering module for extracting the attribute parameters of the electricity meters within a preset time and classifying the electricity meters according to the attribute parameters; An estimation module for estimating the developable area and installable capacity corresponding to each type of electricity meter based on the distributed photovoltaic installation capacity and the developable area of distributed photovoltaics of the users to which the electricity meters belong in the expanded electricity meter information model; The online calculation part includes: A new user estimation module for updating the photovoltaic access information according to the distributed photovoltaic management and control system information, updating the increase and decrease information of the user electricity meters according to the marketing system data, determining the new users, and estimating the installable capacity of the distributed photovoltaics of the new users; An evaluation module, which is used to evaluate the openable capacity of each area and each layer based on the developable area and installable capacity corresponding to various types of electricity meters, as well as the installable capacity of distributed photovoltaics for new users; based on the common information model of the distribution network, the evaluation module integrates the equipment change information of the PMS system, the data of the distributed photovoltaic control system, and the data of the marketing system, associates the distributed photovoltaic filing information in the distributed photovoltaic control system with the user electricity meter data, and updates the power grid topology; according to the updated power grid topology, combining the developable area and installable capacity corresponding to various types of electricity meters, as well as the installable capacity of distributed photovoltaics for new users, calculates the bearing capacity of each area and each layer according to the power supply area and voltage level, and determines the bearing capacity level and the installable capacity of new distributed power sources within the power supply area.

10. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the dynamic evaluation method for the openable capacity of the distribution network as described in any one of claims 1-8.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the dynamic evaluation method for the openable capacity of the distribution network as described in any one of claims 1-8.

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