Method, device and equipment for dynamically evaluating openable capacity of power distribution network and medium

Abstract

The invention relates to a dynamic evaluation method, device and equipment for the openable capacity of a power distribution network, and a medium. The offline calculation step comprises the following steps: expanding an electric energy meter information model to obtain an electric energy meter information expansion model; attribute parameters of the electric energy meters in preset time are extracted, and clustering classification is carried out on the electric energy meters according to the attribute parameters; based on the distributed photovoltaic installed capacity and the distributed photovoltaic developable area of the user to which the electric energy meter belongs in the electric energy meter information expansion model, the developable area and the installed capacity corresponding to each type of electric energy meter are estimated; the online calculation step comprises the steps of updating photovoltaic access information according to distributed photovoltaic management and control system information, updating user electric energy meter increase and decrease information according to marketing system data, determining newly-added users based on the information, estimating distributed photovoltaic installed capacity of the newly-added users, and evaluating openable capacity of each layer of each region based on an estimation result. Partitioned and layered bearing capacity can be accurately calculated, and open capacity of different areas can be dynamically measured and calculated.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network evaluation, and in particular to a method, device, equipment and medium for dynamically evaluating the open capacity of a distribution network. Background Art

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

[0003] The existing scientific paper "Evaluation of the carrying capacity of distributed rooftop photovoltaic power generation in distribution networks based on high-definition satellite map images" (Wang Haifeng et al., "Guangdong Electric Power", Vol. 36, No. 10, 2023) provides a method for accurately evaluating the developable area of ​​rooftops in the entire county. The developable area of ​​each block obtained by this method through high-definition satellite map images only takes into account the building roof area that can be identified in the image. Therefore, it is difficult to identify the buildable photovoltaic area in courtyards and open spaces in rural areas, and it is difficult to support the partitioned and layered carrying capacity evaluation and operation analysis of low-voltage lines, substations, feeders, transformers and other dimensions. In this method, the number of photovoltaic grid-connected nodes and the location of photovoltaic grid-connected nodes are random, making it difficult to conduct a carrying capacity evaluation that takes into account the actual photovoltaic power generation that has been built, and it is difficult to predict the impact of new photovoltaic installations on the distribution network, so it is difficult to carry out a dynamic evaluation of the open capacity, and it lacks a refined guiding role in the development planning of distributed photovoltaic power generation.

[0004] The existing public 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 the building roof, so as to accurately distinguish the roof area and the non-roof area and obtain the area of ​​the photovoltaic installation area. However, this method does not take into account the correlation between the information of the built or planned new distributed photovoltaics and the power grid topology model, which will lead to large estimation errors. 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 dynamic evaluation of the open capacity of a distribution network, which can realize accurate calculation of the zoned and layered carrying capacity of low-voltage lines, substations, feeders, transformers and other dimensions, and support dynamic measurement of the open capacity in different areas.

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

[0007] The offline calculation step comprises:

[0008] Add the distributed photovoltaic installed capacity and the distributed photovoltaic developable area of ​​the user to which the electric energy meter belongs to the electric energy meter information model to obtain the electric energy meter information extension model;

[0009] Extracting attribute parameters of the electric energy meter within a preset time, and clustering and classifying the electric energy meter according to the attribute parameters;

[0010] Based on the distributed photovoltaic installed capacity and distributed photovoltaic developable area of ​​the user to which the electric energy meter belongs in the electric energy meter information expansion model, the developable area and installed capacity corresponding to each type of electric energy meter are estimated;

[0011] The online calculation step comprises:

[0012] Update photovoltaic access information according to the distributed photovoltaic management and control system information, update the increase and decrease information of user electric energy meters according to the marketing system data, determine the new users, and estimate the distributed photovoltaic installed capacity of the new users;

[0013] Based on the developable area and installed capacity corresponding to the various types of electricity meters, as well as the installed capacity of distributed photovoltaic power generation for new users, the open capacity of each area and each layer is evaluated.

[0014] The developable area of ​​distributed photovoltaic power generation includes: roofs of buildings, courtyards owned by users to whom the electricity meters belong, and open spaces.

[0015] The installation location of the electric energy meter in the electric energy meter information extension model is extended to: the low voltage line, the substation, and the medium voltage line.

[0016] When clustering and classifying the electric energy meters according to the attribute parameters, a K-means clustering method, a hierarchical clustering method, a density clustering method, or a grid-based clustering method is adopted.

[0017] The method of estimating the developable area and installed capacity corresponding to various types of electric energy meters includes the following steps:

[0018] Determine whether the photovoltaic power installed in various types of electric energy meters is less than the threshold value;

[0019] If the proportion of photovoltaic power generation in various types of electric energy meters is less than the threshold, users of electric energy meters with the threshold are randomly sampled for actual measurement, and the actual measurement results are used as the developable area and installed capacity corresponding to the electric energy meters of this type;

[0020] If the proportion of built photovoltaic power in each type of electricity meter is equal to or greater than the threshold, the area and installed capacity of built distributed photovoltaic power in each type of electricity meter will be counted, and the average value will be calculated. The average value will be used as the developable area and installed capacity corresponding to this type of electricity meter.

[0021] The estimation of the distributed photovoltaic installed capacity of the newly added user is specifically as follows: when the newly added user is a user who plans to build a new distributed photovoltaic, the developed area and installed capacity of the distributed photovoltaic of the newly added user are the area and installed capacity registered in the distributed photovoltaic management and control system; when the new user is a new electric energy meter user, the new electric energy meter user is classified based on the clustering classification result, and the developable area and installed capacity corresponding to the electric energy meter to which the classification result belongs are re-estimated, and the re-estimated developable area and installed capacity are used as the developed area and installed capacity of the distributed photovoltaic of the new user.

[0022] When the newly added user is a user who plans to build a new distributed photovoltaic system, it also includes classifying the users who plan to build a new distributed photovoltaic system based on the clustering classification results, and re-estimating the developable area and installed capacity corresponding to the electric energy meter of this category based on the classification results.

[0023] The method of evaluating the open capacity of each area and each layer based on the developable area and installable capacity corresponding to each type of electric energy meter, as well as the newly added distributed photovoltaic installable capacity of users, is specifically as follows: based on the public information model of the distribution network, the PMS system equipment change information, the distributed photovoltaic management and control system data, and the marketing system data are integrated to associate the distributed photovoltaic registration information in the distributed photovoltaic management and control system with the user's electric energy meter data, and update the power grid topology; according to the updated power grid topology, combined with the developable area and installable capacity corresponding to each type of electric energy meter, as well as the newly added distributed photovoltaic installable capacity of users, the partitioned and layered carrying capacity is calculated according to the power supply area and voltage level, and the carrying capacity level and the newly added distributed power supply capacity within the power supply area are determined.

[0024] The online calculation step also includes:

[0025] Determine 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, it will be marked as a weak area under the current penetration rate, and it will be determined whether the proportion of distributed photovoltaic installations has reached 100%;

[0027] If the proportion of distributed photovoltaic installations has not reached 100%, a preset proportion of distributed photovoltaics will be added according to the user's electricity meter data, and new users will be added based on the average distribution construction sequence, and the new users will be classified based on the clustering classification results.

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

[0029] The offline calculation part includes:

[0030] An extension module is used to add the distributed photovoltaic installed capacity and the distributed photovoltaic developable area of ​​the user to which the electric energy meter belongs to the electric energy meter information model to obtain the electric energy meter information extension model;

[0031] A clustering module, used to extract attribute parameters of the electric energy meter within a preset time, and cluster and classify the electric energy meter according to the attribute parameters;

[0032] An estimation module, for estimating the developable area and installed capacity corresponding to various types of electric energy meters based on the distributed photovoltaic installed capacity and distributed photovoltaic developable area of ​​the user to which the electric energy meter belongs in the electric energy meter information extension model;

[0033] The online computing part includes:

[0034] A new user estimation module is used to update photovoltaic access information according to the distributed photovoltaic management and control system information, update the increase and decrease information of the user's electric energy meter according to the marketing system data, determine the new user, and estimate the distributed photovoltaic installed capacity of the new user;

[0035] The evaluation module is used to evaluate the open capacity of each area and each layer based on the developable area and installed capacity corresponding to the various types of electric energy meters, as well as the newly added user's distributed photovoltaic installed capacity.

[0036] The technical solution adopted by the present invention to solve its technical problem is: to provide an electronic device, including a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned method for dynamically evaluating the open capacity of the distribution network when executing the computer program.

[0037] The technical solution adopted by the present invention to solve its technical problem is: providing 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 method for dynamically evaluating the open capacity of the distribution network are implemented.

[0038] Beneficial Effects

[0039] Due to the adoption of the above-mentioned technical scheme, the present invention has the following advantages and positive effects compared with the prior art: the present invention establishes an electric meter expansion model, increases the photovoltaic construction status and the developable area, realizes the association between distributed photovoltaic management data and network topology data, and obtains the developable area information corresponding to each type of electric meter through a classification and clustering method. On this basis, the dynamic change accurate information of distributed photovoltaic installed capacity and location is obtained, thereby realizing the accurate calculation of the partitioned and layered carrying capacity of low-voltage lines, substations, feeders, transformers and other dimensions, and supporting the dynamic measurement of the open capacity in different regions. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1is a flow chart of a method for dynamically evaluating the open capacity of a distribution network according to a first embodiment of the present invention;

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

[0042] Figure 3 is a flow chart of the evaluation of the openable capacity of each zone and each layer in the first embodiment of the present invention;

[0043] Figure 4 It is a flow chart of the weak link assessment of the power grid in the first embodiment of the present invention. DETAILED DESCRIPTION

[0044] The present invention will be further described below in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not intended 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 fall within the scope limited by the appended claims of the application equally.

[0045] The first embodiment of the present invention relates to a method for dynamically evaluating the open capacity of a distribution network. Figure 1 As shown, it includes offline calculation steps and online calculation steps.

[0046] Wherein, the offline calculation step includes:

[0047] Step 1: Add the distributed photovoltaic installed capacity and the distributed photovoltaic developable area of ​​the user to which the electric energy meter belongs to the electric energy meter information model to obtain the electric energy meter information extension model.

[0048] The traditional electricity meter information model includes multiple contents, mainly including basic information of the electricity meter, meter measurement data, meter event records, meter operation status, meter environmental parameters, meter communication status, etc.

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

[0050] Electricity meter measurement data: including electric energy, voltage, current, power factor, frequency and other parameters, which can reflect the working status and electricity consumption of the meter.

[0051] Electricity meter event records: including power outage events, overload events, short circuit events, etc., can record abnormal conditions and fault conditions of the electricity meter.

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

[0053] Electricity meter environmental parameters: including temperature, humidity, air pressure and other environmental parameters, which can reflect the working environment and safety status of the electricity meter.

[0054] The communication status of the electric energy meter: includes the signal strength of the communication module, the connection status of the communication module, etc., which can reflect the communication status and data transmission status of the electric energy meter.

[0055] This step is expanded on the basis of the traditional electricity meter information model, mainly expanding the content of the distributed photovoltaic installed capacity of the user to which the electricity meter belongs and the content of the distributed photovoltaic developable area, which can reflect the photovoltaic conditions of the users of the power grid and the installed capacity. Among them, the distributed photovoltaic developable area includes the roof of the building, and also includes the photovoltaic areas such as courtyards and open spaces owned by the users to whom the electricity meter belongs. In addition, the electricity meter installation location of the basic information of the electricity meter in the electricity meter information expansion model of this step is expanded to the low-voltage line, the substation, and the medium-voltage line.

[0056] It is worth mentioning that the electric energy meters mentioned in this implementation only include the electric energy meters that directly trade with the power grid company, and do not include the lower-level electric energy meters in industrial and commercial buildings.

[0057] Step 2: extracting attribute parameters of the electric energy meter within a preset time, and clustering and classifying the electric energy meter according to the attribute parameters.

[0058] The electric energy meter can be divided into three categories according to the basic user information attribute values: household users, industrial users and commercial users. This step extracts the statistical characteristic values ​​of the electric energy, voltage, current, power factor, frequency, maximum load utilization hours, annual maximum power and annual minimum power and their occurrence time (including hour, date, month, quarter), maximum single-day maximum and minimum power difference and their occurrence time (including date, month, quarter) of the electric energy meter within a year as the attribute parameters of each electric energy meter for clustering.

[0059] For each type of user's meter data points, clustering is performed according to the following sub-steps:

[0060] Sub-step (1): First, randomly select K objects {x 1 ,x 2 ,…,x k} as the initial cluster center to form the initial cluster center set S (0) ={x 1 ,x 2 ,…,x k}={s 1 (0) ,s 2 (0) ,…,s k (0)};

[0061] For each data point x, calculate its Euclidean distance d(x,x) to each cluster center. i (0) ), if d(x,s i (0) )≤d(x,s j (0) ), , then assign the data point x to the Cth i (0) Class, forming the initial classification C i (0) ={C 1 (0) ,C 2 (0) ,…,C k (0)};

[0062] Sub-step (2): Recalculate each cluster center s using formula (1) i (1) , get the new cluster center S (1) ={s 1 (1) ,s 2 (1) ,…,s k (1)}, calculate each data point x and each new cluster center s i (1) The Euclidean distance d(x,s i (1) ), if d(x,s i (1) )≤d(x,s j (1) ), , then the data point x is classified as the Cth i (1) Class, forming a new classification C i (1) ={C 1 (1) ,C 2 (1) ,…,C k (1)};

[0063] (1)

[0064] Where n i It is a class C i (0) The number of samples in .

[0065] Sub-step (3): Repeat step (2) m times to obtain a new cluster center S(m) ={s 1 (m) ,s 2 (m) ,…,s k (m)} and category C i (m) ={C 1 (m) ,C 2 (m) ,…,C k (m)}, if the cluster center does not change or reaches the maximum number of iterations M, that is, S (m) ≈S (m+1) or C (m) ≈C (m+1) or , go to sub-step (4), otherwise return to sub-step (2);

[0066] Sub-step (4): Use formula (2) to calculate the clustering effectiveness evaluation function f(x), determine the optimal number of classifications k, and output the classification results.

[0067] (2)

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

[0069] (3)

[0070] D w (k) is the sum of the intra-class distances of all n data points, and the calculation formula is as follows:

[0071] (4)

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

[0073] (5)

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

[0075] (6)

[0076] When f(x) takes the minimum value, the corresponding number of classifications k is the optimal number of clusters, and the corresponding classification result C={C 1 ,C 2 ,…,C k} is the optimal classification.

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

[0078] Step 3: based on the distributed photovoltaic installed capacity and distributed photovoltaic developable area of ​​the user to which the electric energy meter belongs in the electric energy meter information expansion model, estimate the developable area and installed capacity corresponding to each type of electric energy meter.

[0079] This step is as follows Figure 2 As shown, it is determined whether the proportion of built photovoltaics in various types of electric energy meters is less than 10%; if the proportion of built photovoltaics in various types of electric energy meters is less than 10%, 10% of the electric energy meter users are randomly sampled for actual measurement, and the actual measurement results are used as the developable area and installed capacity corresponding to this type of electric energy meter. Among them, if the proportion of built photovoltaics is zero, it means that there are no built photovoltaic projects in various types of electric energy meters; if the proportion of built photovoltaics in various types of electric energy meters is equal to or greater than 10%, the area and installed capacity of built distributed photovoltaics in various types of electric energy meters are counted, and the average value is calculated, and the average value is used as the developable area and installed capacity corresponding to this type of electric energy meter. It is worth mentioning that during the construction process, this implementation method will also continuously dynamically update the estimation results with newly built data.

[0080] The online calculation steps include:

[0081] Step 4: Update the photovoltaic access information according to the distributed photovoltaic management and control system information, update the user's electric energy meter increase and decrease information according to the marketing system data, determine the new user, and estimate the distributed photovoltaic installed capacity of the new user.

[0082] In this step, the photovoltaic access information is first updated according to the information of the distributed photovoltaic control system, and the increase and decrease information of the user's electric energy meter is updated according to the marketing system data, so as to determine the newly added users. The newly added users in this embodiment include two categories: users who plan to build new distributed photovoltaics and users who have added electric energy meters. For users who plan to build new distributed photovoltaics, the developed area and installed capacity of the distributed photovoltaics of the newly added users are the area and installed capacity registered in the distributed photovoltaic control system, and the grid topology is updated according to the relevant information for subsequent analysis and calculation. At the same time, based on the classification and clustering method and results of step 2, the users who plan to build new distributed photovoltaics are classified, and as new samples, the developed area and installed capacity corresponding to the electric energy meters of their respective categories are regularly updated through step 3. For users with new electric energy meters, based on the classification and clustering method and results of step 2, the users of the newly added electric energy meters are classified, and the developed area and installed capacity corresponding to the electric energy meters of this category are re-estimated in the manner of step 3, and the re-estimated results are used as the developed area and installed capacity of the distributed photovoltaics of the newly added users.

[0083] It can be seen that the distributed photovoltaic developable area estimation method based on classification and clustering technology in this implementation method has similar buildings in the same area. The classification and clustering distributed photovoltaic developable area estimation method can greatly reduce the workload of measuring one by one while ensuring accuracy.

[0084] Step 5: Based on the developable area and installed capacity corresponding to each type of electric energy meter, and the newly added user's distributed photovoltaic installed capacity, the available capacity of each area and each layer is evaluated.

[0085] like Figure 3 As shown, this step is based on the public information model of the distribution network, integrates the PMS system equipment change information, distributed photovoltaic management and control system data, and marketing system data, associates the distributed photovoltaic registration information in the distributed photovoltaic management and control system with the user's electric energy meter data, and updates the power grid topology; then, according to the updated power grid topology, combined with the developable area and installed capacity corresponding to the various types of electric energy meters, and the newly added distributed photovoltaic installed capacity of users, in accordance with the "Guidelines for Assessment of Carrying Capacity of Distributed Power Generation Access to the Grid" (DL / T 2041-2019), from overall to local, from high voltage to low voltage, according to the power supply area and voltage level, calculate the partition and layered carrying capacity, determine the carrying capacity level and the newly added distributed power capacity in the power supply area, and publish the open capacity of each area and layer in the current state.

[0086] It is not difficult to find that the calculation of open capacity using a combination of offline and online methods in this embodiment can ensure the accuracy of the model and results and the speed of analysis. At the same time, it can be dynamically updated with changes in network topology to achieve 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 location and capacity, thereby accurately calculating the impact of photovoltaic access on the power grid. This embodiment expands the electric energy meter model to achieve real-time association between the grid-connected location and installed capacity information of newly added distributed photovoltaic approval projects and the grid topology information. Through the data fusion of distributed photovoltaic management and control systems, PMS systems, marketing systems, electricity consumption information collection systems, etc., the newly added photovoltaic installed capacity and the total amount that can be developed for each voltage level line and each power supply zone can be estimated respectively, thereby achieving fast and accurate calculation of the open capacity of hierarchical zones.

[0087] In this embodiment, the online calculation step also includes the evaluation of weak links in the power grid, and its process is as follows: Figure 4 As shown in the figure, the specific process is: determine whether there is an area where the newly added capacity is less than or equal to 0. If so, mark it as a weak area under the current penetration rate, and determine whether the distributed photovoltaic installation ratio reaches 100%. If the distributed photovoltaic installation ratio reaches 100%, the process ends. If the distributed photovoltaic installation ratio does not reach 100%, a certain proportion of distributed photovoltaic systems are added according to the user's electric energy meter data. At the same time, based on the principle of fairness and justice, new distributed photovoltaic system nodes (i.e., new users) are added according to the average distribution construction sequence. According to the classification result of step 2, the new users are classified and matched, and combined with the estimation result of the distributed photovoltaic access capacity in step 3, the newly added distributed photovoltaic installed capacity of each node is estimated, the power load data in the power consumption information collection system and the distributed photovoltaic installed capacity information data in the distributed photovoltaic control system are integrated, and the power meter is associated with the location information to update the grid topology and the distributed photovoltaic penetration rate. Then, the grid partition and layered carrying capacity and the newly added capacity are calculated after adding a certain proportion of photovoltaic systems. Then, it is determined whether there is an area where the newly added capacity is less than or equal to 0. If so, it is marked as a weak area under the current penetration rate until the distributed photovoltaic installation ratio reaches 100%.

[0088] This implementation method adds a certain proportion of distributed photovoltaic systems according to the number of household meters. Based on the principle of fairness and justice, the dynamic evaluation method of distributed photovoltaic carrying capacity of the distribution network that adds distributed photovoltaic system nodes according to the evenly distributed construction sequence takes into account the actual developable scale and the fairness of the construction and development process.

[0089] The second embodiment of the present invention relates to a device for dynamically evaluating the open capacity of a distribution network, comprising an offline calculation part and an online calculation part;

[0090] The offline calculation part includes:

[0091] An extension module is used to add the distributed photovoltaic installed capacity and the distributed photovoltaic developable area of ​​the user to which the electric energy meter belongs to the electric energy meter information model to obtain the electric energy meter information extension model;

[0092] A clustering module, used to extract attribute parameters of the electric energy meter within a preset time, and cluster and classify the electric energy meter according to the attribute parameters;

[0093] An estimation module, for estimating the developable area and installed capacity corresponding to various types of electric energy meters based on the distributed photovoltaic installed capacity and distributed photovoltaic developable area of ​​the user to which the electric energy meter belongs in the electric energy meter information extension model;

[0094] The online computing part includes:

[0095] A new user estimation module is used to update photovoltaic access information according to the distributed photovoltaic management and control system information, update the increase and decrease information of the user's electric energy meter according to the marketing system data, determine the new user, and estimate the distributed photovoltaic installed capacity of the new user;

[0096] The evaluation module is used to evaluate the open capacity of each area and each layer based on the developable area and installed capacity corresponding to the various types of electric energy meters, as well as the newly added user's distributed photovoltaic installed capacity.

[0097] The developable area of ​​distributed photovoltaic power generation includes: roofs of buildings, courtyards owned by users to whom the electricity meters belong, and open spaces.

[0098] The installation location of the electric energy meter in the electric energy meter information extension model is extended to: the low voltage line, the substation, and the medium voltage line.

[0099] When the clustering module clusters and classifies the electric energy meters according to the attribute parameters, a K-means clustering method, a hierarchical clustering method, a density clustering method, or a grid-based clustering method is used.

[0100] The estimation module comprises:

[0101] A judgment unit, used to judge whether the proportion of built photovoltaic power in various types of electric energy meters is less than a threshold;

[0102] The first estimation unit is used to perform actual measurement by randomly sampling users of electric energy meters with the threshold value when the proportion of built photovoltaic power generation in various types of electric energy meters is less than a threshold value, and use the actual measurement results as the developable area and installed capacity corresponding to the electric energy meters of this type;

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

[0104] When the newly added user estimation module estimates the distributed photovoltaic installed capacity of the newly added user, when the newly added user is a user who plans to build a new distributed photovoltaic, the distributed photovoltaic developable area and installed capacity of the newly added user are the area and installed capacity registered in the distributed photovoltaic management and control system; when the newly added user is a new electric energy meter user, the newly added electric energy meter user is classified based on the clustering classification result, and the developable area and installed capacity corresponding to the electric energy meter to which the classification result belongs are re-estimated, and the re-estimated developable area and installed capacity are used as the distributed photovoltaic developable area and installed capacity of the new user.

[0105] When the newly added user is a user who plans to build a new distributed photovoltaic system, it also includes classifying the users who plan to build a new distributed photovoltaic system based on the clustering classification results, and re-estimating the developable area and installed capacity corresponding to the electric energy meter of this category based on the classification results.

[0106] The evaluation module is based on the public information model of the distribution network, integrates the PMS system equipment change information, distributed photovoltaic management and control system data, and marketing system data, associates the distributed photovoltaic registration information in the distributed photovoltaic management and control system with the user's electric energy meter data, and updates the power grid topology; according to the updated power grid topology, combined with the developable area and installed capacity corresponding to each type of electric energy meter, and the newly added user distributed photovoltaic installed capacity, the partitioned and layered carrying capacity is calculated according to the power supply area and voltage level, and the carrying capacity level and the newly added distributed power supply capacity in the power supply area are determined.

[0107] The online computing part also includes:

[0108] A newly added capacity determination unit, used to determine whether there is a power supply area where the newly added capacity is less than or equal to zero;

[0109] An identification and judgment unit is used to identify a power supply area where the newly added capacity is less than or equal to zero as a weak area under the current penetration rate, and to judge whether the proportion of distributed photovoltaic installation has reached 100%;

[0110] A new unit is used to add a preset proportion of distributed photovoltaics according to the user's electricity meter data when the proportion of distributed photovoltaic installation has not reached 100%, and to add new users based on the average distribution construction sequence, and to classify the new users based on the clustering classification results.

[0111] A third embodiment of the present invention relates to an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned method for dynamically evaluating the open capacity of a distribution network when executing the computer program.

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

[0113] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take 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 codes.

[0114] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0115] These computer program instructions may 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, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction method, which is implemented in the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0116] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1The steps for the functions specified in one or more boxes.

[0117] 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 who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for dynamically evaluating the open capacity of a distribution network, characterized in that: Includes offline calculation steps and online calculation steps; The offline calculation step comprises: Add the distributed photovoltaic installed capacity and the distributed photovoltaic developable area of ​​the user to which the electric energy meter belongs to the electric energy meter information model to obtain the electric energy meter information extension model; Extracting attribute parameters of the electric energy meter within a preset time, and clustering and classifying the electric energy meter according to the attribute parameters; Based on the distributed photovoltaic installed capacity and distributed photovoltaic developable area of ​​the user to which the electric energy meter belongs in the electric energy meter information expansion model, the developable area and installed capacity corresponding to each type of electric energy meter are estimated; The online calculation step comprises: Update the photovoltaic access information according to the distributed photovoltaic management and control system information, update the user's electric energy meter increase and decrease information according to the marketing system data, and determine the new users based on the photovoltaic access information and the user's electric energy meter increase and decrease information, and estimate the distributed photovoltaic installed capacity of the new users; Based on the developable area and installed capacity corresponding to the various types of electricity meters, as well as the installed capacity of distributed photovoltaic power generation for new users, the open capacity of each area and each layer is evaluated.

2. The method for dynamically evaluating the open capacity of a distribution network according to claim 1, characterized in that: The developable area of ​​distributed photovoltaic power generation includes: roofs of buildings, courtyards owned by users to whom the electricity meters belong, and open spaces.

3. The method for dynamically evaluating the open capacity of a distribution network according to claim 1, characterized in that: The installation location of the electric energy meter in the electric energy meter information extension model is extended to: the low voltage line, the substation, and the medium voltage line.

4. The method for dynamically evaluating the open capacity of a distribution network according to claim 1, characterized in that: When clustering and classifying the electric energy meters according to the attribute parameters, a K-means clustering method, a hierarchical clustering method, a density clustering method, or a grid-based clustering method is adopted.

5. The method for dynamically evaluating the open capacity of a distribution network according to claim 1, characterized in that: The estimation of the developable area and installed capacity corresponding to various types of electric energy meters includes the following steps: Determine whether the proportion of photovoltaic power installed in various types of electric energy meters is less than the threshold; If the proportion of photovoltaic power generation in various types of electric energy meters is less than the threshold, users of electric energy meters with the threshold are randomly sampled for actual measurement, and the actual measurement results are used as the developable area and installed capacity corresponding to the electric energy meters of this type; If the proportion of built photovoltaic power in each type of electricity meter is equal to or greater than the threshold, the area and installed capacity of built distributed photovoltaic power in each type of electricity meter will be counted, and the average value will be calculated. The average value will be used as the developable area and installed capacity corresponding to this type of electricity meter.

6. The method for dynamically evaluating the open capacity of a distribution network according to claim 1, characterized in that: The estimation of the distributed photovoltaic installed capacity of the newly added user is specifically as follows: when the newly added user is a user who plans to build a new distributed photovoltaic, the developed area and installed capacity of the distributed photovoltaic of the newly added user are the area and installed capacity registered in the distributed photovoltaic management and control system; when the new user is a new electric energy meter user, the new electric energy meter user is classified based on the clustering classification result, and the developable area and installed capacity corresponding to the electric energy meter to which the classification result belongs are re-estimated, and the re-estimated developable area and installed capacity are used as the developed area and installed capacity of the distributed photovoltaic of the new user.

7. The method for dynamically evaluating the open capacity of a distribution network according to claim 6, characterized in that: When the newly added user is a user who plans to build a new distributed photovoltaic system, it also includes classifying the users who plan to build a new distributed photovoltaic system based on the clustering classification results, and re-estimating the developable area and installed capacity corresponding to the electric energy meter of this category based on the classification results.

8. The method for dynamically evaluating the open capacity of a distribution network according to claim 1, characterized in that: The method of evaluating the open capacity of each area and each layer based on the developable area and installable capacity corresponding to each type of electric energy meter, as well as the newly added distributed photovoltaic installable capacity of users, is specifically as follows: based on the public information model of the distribution network, the PMS system equipment change information, the distributed photovoltaic management and control system data, and the marketing system data are integrated to associate the distributed photovoltaic registration information in the distributed photovoltaic management and control system with the user's electric energy meter data, and update the power grid topology; according to the updated power grid topology, combined with the developable area and installable capacity corresponding to each type of electric energy meter, as well as the newly added distributed photovoltaic installable capacity of users, the partitioned and layered carrying capacity is calculated according to the power supply area and voltage level, and the carrying capacity level and the newly added distributed power supply capacity within the power supply area are determined.

9. The method for dynamically evaluating the open capacity of a distribution network according to claim 1, characterized in that: The online calculation step also includes: Determine whether there is a power supply area where the newly added capacity is less than or equal to zero; If there is a power supply area where the newly added capacity is less than or equal to zero, it will be marked as a weak area under the current penetration rate, and it will be determined whether the proportion of distributed photovoltaic installations has reached 100%; If the proportion of distributed photovoltaic installation has not reached 100%, a preset proportion of distributed photovoltaic will be added according to the user's electricity meter data. At the same time, new users will be added based on the average distribution construction sequence, and the new users will be classified based on the clustering classification results.

10. A device for dynamically evaluating the open capacity of a distribution network, characterized in that: Includes offline calculation part and online calculation part; The offline calculation part includes: An extension module is used to add the distributed photovoltaic installed capacity and the distributed photovoltaic developable area of ​​the user to which the electric energy meter belongs to the electric energy meter information model to obtain the electric energy meter information extension model; A clustering module, used to extract attribute parameters of the electric energy meter within a preset time, and cluster and classify the electric energy meter according to the attribute parameters; An estimation module, for estimating the developable area and installed capacity corresponding to various types of electric energy meters based on the distributed photovoltaic installed capacity and distributed photovoltaic developable area of ​​the user to which the electric energy meter belongs in the electric energy meter information extension model; The online computing part includes: A new user estimation module is used to update photovoltaic access information according to the distributed photovoltaic management and control system information, update the increase and decrease information of the user's electric energy meter according to the marketing system data, determine the new user, and estimate the distributed photovoltaic installed capacity of the new user; The evaluation module is used to evaluate the open capacity of each area and each layer based on the developable area and installed capacity corresponding to the various types of electric energy meters, as well as the newly added user's distributed photovoltaic installed capacity.

11. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method for dynamically evaluating the open capacity of a distribution network as claimed in any one of claims 1 to 9 are implemented.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for dynamically evaluating the available capacity of a distribution network as claimed in any one of claims 1 to 9 are implemented.

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