Distributed photovoltaic network access data fitting system

Abstract

The invention discloses a distributed photovoltaic network access data fitting system. The data system optionally comprises the following data modules: S1, a data arrangement module, S2, an algorithm component module, S3, a distributed photovoltaic network access consumption capability module, S4, a distributed photovoltaic network access adaptability module, S5, a distributed photovoltaic network access simulation module, S6, a distributed photovoltaic network access optimization module and S7, a comprehensive display module. According to the data system, global fitting and optimization can be performed on distributed photovoltaic network access consumption through the hierarchical branch architecture of the multiple data module assemblies and the construction of the internal data processing processes of the data module assemblies.

Description

Technical Field

[0001] The present invention relates to the field of electric power data and information technology, in particular to adaptability assessment and optimization technology for photovoltaic access to distribution networks. Technical Background

[0005] With the adjustment of energy structure and the demand for sustainable development, distributed photovoltaic has developed rapidly, and its scale of access to the grid has continued to expand; the widespread application of distributed photovoltaic has provided support for the diversification and cleanness of energy supply, but it has also brought new challenges to the operation of the distribution network: the access of distributed photovoltaic will have many impacts on the operation of the distribution network, such as voltage quality, which may cause voltage deviation, overvoltage or low voltage and other problems; in terms of power flow, it will change the power flow direction of the traditional distribution network, which may cause line overload, reverse power and other situations; it also has an impact on the line loss rate, and unreasonable access may lead to increased line loss; at the same time, it will also affect safety indicators such as short-circuit capacity and Power supply quality indicators such as three-phase voltage imbalance; the existing power grid system lacks a comprehensive and systematic evaluation method and indicator system; at the same time, the current research on the adaptability of photovoltaic access to the distribution network is not perfect in data collation and cannot effectively integrate multi-source heterogeneous data; in terms of algorithm application, there is a lack of targeted algorithm components to accurately analyze the various operating parameters of the distribution network; in simulation analysis, it is impossible to accurately simulate the operating status of the distribution network after photovoltaic access; in terms of optimization strategy formulation, there is a lack of scientific and reasonable methods to plan the access location and capacity of photovoltaic power sources; in terms of comprehensive display, it is impossible to intuitively and comprehensively display the operation of the distribution network and the adaptability evaluation results of photovoltaic access. Summary of the invention

[0006] The technical problem to be solved by the present invention is to provide a distributed photovoltaic grid-connected data fitting system, which is based on a built-in multi-data module to realize the adaptability of photovoltaic grid access, evaluate the absorption capacity and simulate and optimize the overall grid operation structure.

[0007] In order to solve the above technical problems, the technical solutions adopted by the present invention are as follows:

[0008] A distributed photovoltaic grid-connected data fitting system is disclosed. The data system globally simulates and optimizes the consumption of distributed photovoltaic grid-connected data through the hierarchical branch architecture of multiple data module components and the construction of data processing processes within the data module components.

[0009] The data system can optionally include the following data modules: S1 data sorting module, S2 algorithm component module, S3 distributed photovoltaic grid access absorption capacity module, S4 distributed photovoltaic grid access adaptability module, S5 distributed photovoltaic grid access simulation module, S6 distributed photovoltaic grid access optimization module, and S7 comprehensive display module.

[0010] The S1 data sorting module may optionally include the following submodules: wiring sorting submodule, distribution transformer file sorting submodule, photovoltaic file sorting submodule, photovoltaic curve simulation submodule, wiring outlet voltage submodule, distribution transformer / medium voltage photovoltaic power submodule, user / low voltage photovoltaic power submodule, model impedance sorting submodule, and other data sorting submodules;

[0011] The S2 algorithm component module may optionally include the following submodules: forward-backward substitution submodule, unidirectional power flow submodule, voltage deviation submodule, short-circuit calculation submodule, full-phase power flow submodule, load rate calculation submodule, line loss calculation submodule, and three-phase unbalance submodule;

[0012] The S3 distributed photovoltaic grid-connected absorption capacity module may optionally include: a medium voltage absorption capacity submodule and a low voltage absorption capacity submodule;

[0013] The S4 distributed photovoltaic grid-connection adaptability module optionally includes: a photovoltaic grid-connection adaptability submodule and an adaptability evaluation index submodule;

[0014] The S5 distributed photovoltaic grid-connection simulation module optionally includes: a medium-voltage photovoltaic access submodule, a low-voltage single-phase photovoltaic access submodule, and a low-voltage three-phase photovoltaic access submodule;

[0015] The S6 distributed photovoltaic grid-connection optimization module optionally includes: a photovoltaic power supply access submodule and a photovoltaic power supply capacity submodule;

[0016] The S7 comprehensive display module optionally includes: a medium voltage topology diagram submodule, a low voltage three-phase four-wire mapping submodule, a flow direction diagram submodule, a consumption capacity display submodule, and an adaptability display submodule.

[0017] The S1 data sorting module also includes the following sub-modules: a distribution line model sorting sub-module: building a standardized distribution network flow calculation model based on integrated data; a distribution line operation data sub-module: extracting operation data.

[0018] The S2 algorithm component module further includes the following data algorithm process:

[0019] A1 Distribution Line Flow Calculation: Use flow calculation to analyze the voltage, power, current and other parameters in the power system, determine the voltage and power distribution of each distribution node, and the current and power on the line and equipment;

[0020] A2 Theoretical Line Loss Calculation: For distribution networks with complex network structures, complete metering devices, and complete basic data, a power flow algorithm is constructed to perform theoretical line loss calculations. Specifically, the power flow algorithm is constructed based on the 24 / 48 / 96-point distribution head voltage, distribution transformer load power data, distribution network topology data, conductor / cable calculation parameters, and distribution transformer parameter configuration;

[0021] A3 line overload monitoring and analysis: monitor the distribution network overload situation based on the distribution network flow calculation results and equipment parameters, and improve the refinement of distribution line overload monitoring and analysis;

[0022] A4 line overload monitoring and analysis: line overload monitoring based on the sorted list of heavily overloaded lines;

[0023] A5 branch overload monitoring and analysis: According to the branch line overload situation indicators in the loss reduction plan, the evaluation results that do not meet the threshold are included in the branch line overload weak links;

[0024] A6 branch overload monitoring and analysis: According to the branch line overload indicators in the loss reduction plan, the evaluation results that do not meet the threshold are included in the branch line overload weak links;

[0025] A7 line low voltage monitoring and analysis: monitor the voltage over-limit situation of the distribution network based on the distribution network power flow calculation results and improve the refinement of distribution line voltage quality monitoring and analysis;

[0026] A8 line overvoltage monitoring and analysis: Based on the voltage overlimit line list and assisting the power distribution personnel to perform line overvoltage dual-path monitoring;

[0027] A9 node low voltage monitoring and analysis: According to the node low voltage situation indicators in the loss reduction plan, the evaluation results that do not meet the threshold are included in the node low voltage weak link;

[0028] A10 node overvoltage monitoring and analysis: According to the node overvoltage situation indicators in the loss reduction plan, the evaluation results that do not meet the threshold will be included in the node overvoltage weak link.

[0029] In the S3 distributed photovoltaic grid-connected absorption capacity module, the medium-voltage photovoltaic access and absorption sub-module is specifically set as follows: according to the distributed power supply access plan, distribution network flow calculation is carried out; according to the distribution network flow calculation results, the voltage, load rate, and power quality after the distributed power supply is connected are evaluated; the low-voltage photovoltaic access and absorption sub-module is specifically set as follows: according to the distributed power supply access plan, distribution network flow calculation is carried out; according to the distribution network flow calculation results, the voltage, load rate, power quality, and three-phase imbalance after the distributed power supply is connected are evaluated.

[0030] The S4 distributed photovoltaic grid-connected adaptability module is specifically configured as follows: based on the distribution network topology node flow calculation results combined with the distribution network basic equipment archives and line-transformer relationship data, substation equipment archives and substation-user relationship data, the distribution transformer power, user photovoltaic power and substation return power data are used to realize the hierarchical and graded photovoltaic penetration rate statistics, i.e. photovoltaic grid-connected adaptability.

[0031] The S6 distributed photovoltaic grid-connection optimization module further has the following built-in data algorithm process: for the selection and optimization of the access location and capacity of photovoltaic power sources, first based on the distribution network topology node flow calculation results and considering the flow path of electricity in the grid structure and the characteristics of load data, then according to the characteristics of photovoltaic power sources, the output power of photovoltaic power sources is affected by external factors such as weather and time. On this basis, the data constraints are relaxed or tightened in accordance with the external factors, and the initial complex data results are converted into a cone-like data form that can use a simple linear programming algorithm to optimize the photovoltaic power access location and capacity calculation; at the same time, the data structure corresponding to the above data processing path is The construction process is as follows: the photovoltaic grid-connected related data initially acquired or constructed (usually initially presented as an unordered flat data structure) is converted into a quasi-conical data structure, an upper-level data node is constructed at the tip of the cone, and the lower-level data nodes are gradually expanded and transitioned to the cone-shaped cone in sequence; the data hierarchy of the quasi-conical data structure is specifically set as needed, and the data relationship between any two adjacent data hierarchies is set as follows: each data node in the data hierarchy closer to the cone tip is linked to multiple sub-data nodes in the data hierarchy closer to the cone-shaped cone; during the execution of the linear programming algorithm, different data hierarchies can be traversed layer by layer or repeatedly to optimize and solve the data for photovoltaic power access location and capacity selection.

[0032] The adaptability evaluation index submodule in the S4 distributed photovoltaic grid-connection adaptability module is specifically constructed as follows:

[0033] B-1. Fusion and collation of multi-source heterogeneous data: According to the data requirements of the adaptability study of photovoltaic access to the distribution network, obtain multi-source heterogeneous data of the distribution network, including optional information such as equipment archive data, topological relationship data, operation collection data, etc., to achieve data fusion, and adopt the following methods to collate abnormal data: Sort the data from the aspects of data integrity, validity, and consistency, and remove abnormal data such as key attributes being empty and redundant; use the power system business knowledge, combined with the relevant power grid design specifications, to sort out all kinds of data that violate the power system business rules and remove abnormal data; use the K-MEANS clustering analysis method to cluster the data, calculate the distance between each analysis object and the cluster center, and judge the data that seriously deviates from the cluster center as abnormal data and remove it;

[0034] B-2. Based on the data collation in B1, further construct the adaptability evaluation system from multiple dimensions, including:

[0035] B-2-1. Dimensionless processing of indicators: The various evaluation indicators in the indicator system are not comparable due to their different dimensions, economic significance, forms of expression and trends of their effects on the overall goal. They must be dimensionless sorted to eliminate the impact of the indicator dimensions before the comprehensive evaluation results can be calculated; the dimensionless sorting methods used include extreme value, standardization, averaging, normalization and standard deviation methods. Considering the need to conduct a comprehensive evaluation of multiple indicators, the normalization sorting method is selected to sort the indicators; normalization is to transform the dimensional expression into a dimensionless expression, restrict the sorted data, improve the efficiency of subsequent data sorting and increase the stability and speed of the program when running;

[0036] B-2-2. Indicator consistency processing: There are "extremely large" indicators, "extremely small" indicators, "medium" indicators, and "interval" indicators. Different quantitative indicators have different values: for the proportion of economically operated lines, it is natural to expect that the larger the value, the better. This type of indicator is called an "extremely large" indicator; for the proportion of super-long lines, it is natural to expect that the smaller the value, the better. This type of indicator is called an "extremely small" indicator; in order to be able to conduct a comprehensive summary, inconsistent indicators are sorted out in the same direction;

[0037] B-2-3. Comprehensive weighting of indicators: Taking into account the expert experience weighting method and the objective weighting method, a combination of subjective and objective weighting methods is adopted to quantitatively analyze the adaptability evaluation indicators of photovoltaic access to the distribution network, and determine the correlation and importance of the indicators with distributed photovoltaic access to the grid.

[0038] The B2 constructs an adaptability index system for distributed photovoltaic access to the grid: considering factors such as the access location of distributed photovoltaics, the grid structure of the distribution network, the operating mode, the load type, the load level, and the time scale, an adaptability index system for distributed photovoltaic access to the grid is constructed from the dimensions of distribution network security, economy, green and low carbon, and power supply quality.

[0039] The comprehensive weighting of the B-2-3 indicators is as follows:

[0040] Subjective weighting: The analytic hierarchy process is based on expert experience and judgment, and transforms the expert experience judgment from directly analyzing and judging many factors at the same time to directly analyzing and judging two factors. The results of the expert experience judgment are scientifically integrated and quantitatively calculated using mathematical methods and combined with qualitative analysis and quantitative judgment to improve the scientificity and accuracy of weight calculation. For the distribution network loss reduction and carbon reduction index evaluation system, the analytic hierarchy process is used, combined with the expert scoring results, to obtain the index weights.

[0041] The specific weighting method is: construct n indicator matrices A_1, A_2, ..., A_k, ..., A_n, and the experts use the 9-level scaling method to assign values ​​to the elements of the judgment matrix according to the importance of row A_i to column A_j in the indicator matrix (for example, if the expert believes that indicator A_i is extremely important than A_j, it will be assigned a value of 9; if it is slightly more important, it may be assigned a value of 3; if the two are equally important, it will be assigned a value of 1, otherwise it will be the corresponding inverse).

[0042] Index weight: According to subjective weighting, the judgment matrix A is obtained, and the maximum eigenvalue of the matrix A is λ_max. The index vector W is set. The product of the subjective judgment matrix A and the vector index W is consistent with the product of the maximum eigenvalue λ_max of the matrix A and the index vector W. Thus, the index vector W (w_1, w_2, ..., w_n) can be obtained, and w_i is the index weight;

[0043] Objective weighting: Principal component analysis, also known as principal component analysis, uses the idea of ​​dimensionality reduction to transform multiple indicators into a few comprehensive indicators (i.e., principal components), where each principal component can reflect most of the information of the original variable, and the information contained is not repeated. Based on the standardized data, the principal component analysis method is used to analyze and obtain the indicator weights;

[0044] The specific empowerment method is:

[0045] Calculate the correlation coefficient r_(x, y) between the indicator data. The calculation method is to multiply each indicator x minus the average value x- by y minus the average value y-, and the sum of each group of such products is the square root of the sum of the squares of each indicator x minus the average value x- multiplied by the sum of the squares of y minus the average value y-;

[0046] Construct the correlation coefficient matrix R, find the characteristic root of R, and the characteristic equation is: |λI_p-R|=0;

[0047] The indicator weight α_i is the sum of the characteristic root λ_i and all the characteristic roots, and the obtained value is the weight of the indicator;

[0048] Comprehensive weighting: Considering the drawbacks of using the analytic hierarchy process and the main cause analysis method alone, in order to make up for the shortcomings of single weighting, the calculation results of the two weighting methods are combined, and the comprehensive weight of the indicators is calculated by combining the two weighting methods:

[0049] The comprehensive weight Z_i is the square root of the product of the subjective weight w_i and the objective weight α_i, which is the sum of all the subjective weights multiplied by the square root of the objective weights.

[0050] Comprehensive evaluation: Based on the weight analysis results of the indicator evaluation system, the comprehensive index method is used to obtain the comprehensive evaluation results of the distribution network, which can effectively assist the development of research on the adaptability of photovoltaic access to the distribution network; the comprehensive score Y is the sum of the indicator weight z_i multiplied by the indicator calculation value x_i divided by the standard value x_0, from i=1 to i=n; n is the number of evaluation indicators.

[0051] The beneficial effects of adopting the above technical solution are in the following six aspects. First, through data sorting, which can be optional including wiring sorting, distribution transformer archive sorting, photovoltaic archive sorting, etc., it can realize the effective integration and sorting of multi-source heterogeneous data of the distribution network, and provide an accurate and complete data basis for subsequent analysis and evaluation; second, the algorithm components cover a variety of methods such as forward substitution method, one-way power flow method, voltage deviation calculation, short-circuit calculation, etc., which can accurately analyze the power flow distribution, voltage quality, short-circuit conditions and other operating parameters of the distribution network to ensure the safe operation of the distribution network; third, the photovoltaic grid-connection simulation function includes the simulation of various scenarios such as medium-voltage photovoltaic access, low-voltage single-phase photovoltaic access, and low-voltage three-phase photovoltaic access. It can accurately simulate the operating status of the distribution network under different access methods to provide a reference for the formulation of actual access plans; fourth, the photovoltaic grid-connection optimization function is aimed at the selection and optimization of the access location and capacity of photovoltaic power sources, based on the power flow calculation results of the distribution network topology nodes and considering the flow path and load data characteristics of the power in the grid structure, and then according to the characteristics of the photovoltaic power source The output power of the photovoltaic power source Affected by external factors such as weather and time, on this basis, the data constraints are relaxed or tightened in accordance with the external factors, and the initial complex data results are converted into a cone-shaped data form that can use a simple linear programming algorithm to optimize the access location and capacity of photovoltaic power sources; during the execution of the linear programming algorithm, different data levels can be traversed layer by layer or repeatedly to optimize and solve the data for the selection of photovoltaic power access location and capacity; fifthly, in terms of adaptability assessment, a combination of subjective and objective weighting methods is used to quantitatively analyze the indicators and construct a comprehensive indicator system, which can comprehensively and accurately evaluate the adaptability of photovoltaic access to the distribution network, and provide a scientific basis for the reasonable access of distributed photovoltaics and the optimized operation of the distribution network; finally, the comprehensive display function includes medium-voltage topology diagrams, low-voltage three-phase four-wire mapping, flow direction diagrams, absorption capacity assessment display, adaptability assessment display, etc., which can intuitively display the operation of the distribution network and the adaptability assessment results of photovoltaic access, and can intuitively understand the effect after photovoltaic access to the grid, which is convenient for power staff to analyze and make decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 : The overall architecture diagram of the distributed photovoltaic grid-connected data simulation system;

[0053] Figure 2: The specific flow chart of the comprehensive evaluation of distributed photovoltaic grid access adaptability of the adaptability evaluation index submodule in the distributed photovoltaic grid access adaptability module;

[0054] Figure 3 : A schematic diagram of the structure of the judgment matrix A constructed when subjective weighting is performed in the comprehensive weighting method of indicators;

[0055] Figure 4 : Theoretical model management interface diagram;

[0056] Figure 5 : Wiring operation data interface diagram

[0057] Figure 6 :Interface diagram of medium voltage photovoltaic access to distribution network absorption capacity assessment

[0058] Figure 7 : Power flow calculation interface diagram;

[0059] Figure 8 : Line heavy / overload monitoring and analysis interface diagram;

[0060] Fig. 9 : Branch heavy / overload monitoring and analysis interface diagram;

[0061] Fig.10 : Line voltage over-limit monitoring and analysis interface diagram;

[0062] Fig.11 : Node voltage over-limit monitoring and analysis interface diagram;

[0063] Fig.12 : Photovoltaic access to distribution network adaptability assessment interface diagram;

[0064] Fig.13 : Software deployment diagram. Specific embodiments

[0065] See attached Figure 1 The present invention is a distributed photovoltaic grid-connected data fitting system, which has a data sorting module, an algorithm component module, a distributed photovoltaic grid-connected absorption capacity module, a distributed photovoltaic grid-connected adaptability module, a distributed photovoltaic grid-connected simulation module, a distributed photovoltaic grid-connected optimization module, and a comprehensive display module. The following table shows its functional architecture.

[0066] Table 1 Application architecture

[0067]

[0068]

[0069] 1. About the data sorting module: The data sorting module mainly includes the following sub-modules: wiring sorting sub-module, distribution transformer archive sorting sub-module, photovoltaic archive sorting sub-module, photovoltaic curve simulation sub-module, distribution outlet voltage sub-module, distribution transformer / medium voltage photovoltaic power sub-module, user / low voltage photovoltaic power sub-module, model impedance sorting sub-module, and other data sorting sub-modules; The data sorting module also includes the following sub-modules: Distribution line model sorting sub-module: Build a standardized distribution network flow calculation model based on integrated data; Wiring operation data sub-module: Extract operation data, see the table below for details.

[0070] Table 2 Wiring operation data table

[0071]

[0072]

[0073] Table 3 Wiring operation data definition table

[0074]

[0075]

[0076] 1. Photovoltaic file arrangement: In the distribution network of Yangquan area, for a specific distributed photovoltaic power station, record its detailed information, including: the name of the photovoltaic power station is "Yangquan Sunshine Photovoltaic Power Station", the installation location is a certain town in a certain district of Yangquan City, the voltage level of the connected distribution network is 10kV, the installed capacity is 5MW, the photovoltaic panel model used is "XX High-efficiency Photovoltaic Panel", the manufacturer is "XX Photovoltaic Technology Co., Ltd.", the production date is May 2023, the expected service life is 25 years, and the maintenance record of the photovoltaic power station and other information;

[0077] 2. Distribution outlet voltage collation: Taking a 10kV distribution line in Yangquan as an example, the data collation module collects voltage data on the distribution outlet side in real time: on October 15, 2023, the voltage value is recorded every 15 minutes to obtain 96 points of voltage data for the whole day. For example, at 0:00, the distribution outlet voltage is 10.1kV; at 0:15, the voltage is 10.08kV; and so on, the voltage data for the whole day forms a time series;

[0078] 3. Model impedance sorting: For a distribution transformer of a certain model in the distribution network in Yangquan area, its model is "S11-1000kVA", and its impedance parameters are recorded through the model impedance sorting function. The short-circuit impedance of the transformer is 4.5%, the no-load loss is 1.15kW, and the load loss is 10.3kW.

[0079] 2. Algorithm component module: The algorithm component module further has the following data algorithm process:

[0080] A1 Distribution Line Flow Calculation: Use flow calculation to analyze the voltage, power, current and other parameters in the power system, determine the voltage and power distribution of each distribution node, and the current and power on the line and equipment;

[0081] A2 Theoretical line loss calculation: Single-phase power flow method For distribution networks with complex network structures, complete metering devices, and complete basic data, the power flow method should be used to calculate theoretical line losses. This method requires distribution network topology data, conductor / cable calculation parameters, and distribution transformer parameters; the operating data requires 24 / 48 / 96 points of distribution line head voltage, distribution transformer load power data, etc.

[0082] A3 line overload monitoring and analysis: Based on the distribution network flow calculation results and equipment parameters, the distribution network overload situation is monitored to improve the refinement of distribution line overload monitoring and analysis;

[0083] A4 line overload monitoring and analysis: sort out the list of heavily overloaded lines and assist distribution personnel in the project approval and post-evaluation of distribution network technical transformation projects;

[0084] A5 branch overload monitoring and analysis: Based on the branch line overload situation indicators in the loss reduction planning evaluation system, the evaluation results that do not meet the threshold are included in the branch line overload weak links to provide a reference for further optimization strategies;

[0085] A6 branch overload monitoring and analysis: Based on the branch line overload indicators in the loss reduction planning evaluation system, the evaluation results that do not meet the threshold are included in the branch line overload weak links to provide a reference for further optimization strategies;

[0086] A7 line low voltage monitoring and analysis: Based on the distribution network power flow calculation results, monitor the voltage limit of the distribution network and improve the refinement of distribution line voltage quality monitoring and analysis;

[0087] A8 line overvoltage monitoring and analysis: sort out the list of voltage-exceeding lines and assist distribution personnel in optimizing distribution network operations;

[0088] A9 node low voltage monitoring and analysis: Based on the node low voltage indicators in the loss reduction planning evaluation system, the evaluation results that do not meet the threshold are included in the node low voltage weak link to provide a reference for further optimization strategy;

[0089] A10 node overvoltage monitoring and analysis: According to the node overvoltage situation indicators in the loss reduction planning evaluation system, the evaluation results that do not meet the threshold are included in the node overvoltage weak links to provide a reference for further optimization strategies.

[0090] 1. Application of forward-backward substitution method: In a distribution network area in Yangquan area that includes distributed photovoltaics, the forward-backward substitution method is used to calculate the power flow distribution: there are 5 10kV distribution lines in this area, and each line is connected to a different number of distribution transformers and distributed photovoltaic power sources. First, according to the topological structure and component parameters of the distribution network, a node admittance matrix is ​​established; then, starting from the power node (optionally including distributed photovoltaic power sources and substations), the power flow of each branch is calculated according to the forward process, that is, the current and power are calculated based on the known voltage and impedance; finally, in the back-substitution process, the node voltage is updated according to the branch power flow; through multiple iterations, until the change in node voltage meets the convergence condition. For example, after 5 iterations, the change in node voltage is less than 0.001kV. At this time, the power flow distribution results obtained can be used to analyze the voltage level, power distribution, and current load rate of each node of the distribution network after photovoltaic access to the grid, so as to evaluate the impact of photovoltaic access on the operation of the distribution network;

[0091] 2. Short-circuit calculation: A distribution network under a 110kV substation in Yangquan area, when photovoltaic power is connected to the grid, performs short-circuit calculation: a short-circuit fault occurs at a node, which is connected to a 10kV distribution line, and a distributed photovoltaic power source with a capacity of 2MW is connected to the line; the short-circuit current is calculated according to the short-circuit calculation algorithm combined with the topological structure of the distribution network, line parameters (such as resistance, reactance) and the equivalent model of the distributed photovoltaic power source (determined as a PQ node according to its grid connection method). In the calculation process, the output characteristics of the distributed photovoltaic power source at the moment of short circuit are fully considered, such as whether to provide short-circuit current and the size of the short-circuit current provided; through short-circuit calculation simulation, the short-circuit current at the short-circuit point is 5kA (simulated value).

[0092] 3. Three-phase unbalance calculation: In a low-voltage substation in Yangquan area, multiple distributed photovoltaic power sources are connected. The three-phase current data of the substation at a certain moment is obtained through data collection. The current of phase A is 100A, the current of phase B is 120A, and the current of phase C is 80A. The three-phase current imbalance algorithm is used to calculate the three-phase current imbalance. First, the average value of the three-phase current I is calculated. avg =(100+120+80) / 3=100A; Then, calculate the three-phase current imbalance according to the formula =I max -I min ) / I avg ×100%=(120-80) / 100×100%=40%. According to the calculation results, the impact of photovoltaic access to the grid on the three-phase imbalance of the substation can be evaluated. If the three-phase imbalance exceeds the allowable limit (for example, 15%), it may cause problems such as increased transformer losses and unstable motor operation. Appropriate measures need to be taken to adjust, such as optimizing the access location of distributed photovoltaics and adjusting load distribution. See the table below for the substation capacity.

[0093] Table 4 Main transformer capacity of 220 kV and above substations in a certain city in 2022

[0094] Serial number Substation Name Main transformer capacity (MVA) Year of commissioning 1 500kV Yangquan Substation 2×1000 2008-11-08 2 220kV Hailuowan Substation 2×180 1998-06-25 3 220kV Yuxian Substation 2×180 2013-06-30 4 220kV Lianzhuang Substation 2×150 2003-08-30 5 220kV Suyu Substation 2×150 2010-04-30 6 220kV Changling Substation 2×180 1983-08-13 7 220kV Hongwei Substation 2×150 1995-11-11 8 220kV Wenchi Substation 2×150 2004-07-02 9 220kV Taodong Substation 2×180 2011-12-23

[0095] Table 5 Main transformer capacity of 110 kV substation in a certain city in 2022

[0096] Serial number Device Name Voltage level Capacity(MVA) Commissioning date 1 110kV Chengbei Substation AC 110 kV 80 2009-09-17 2 110kV Yangjiazhuang Substation AC 110 kV 100 2010-12-15 3 110kV Canshi Substation AC 110 kV 100 1985-10-20 4 110kV Zhongshe Substation AC 110 kV 80 2008-12-31 5 110kV Xiyu Substation AC 110 kV 63 1980-07-24 6 110kV Wudu Substation AC 110 kV 80 1998-01-28 7 110kV Boli Substation AC 110 kV 100 1978-12-20 8 110kV Tuolingtou Substation AC 110 kV 90 2006-12-29 9 110kV Yinying Substation AC 110 kV 71.5 2001-01-24 10 110kV Xinxing Substation AC 110 kV 80 2007-06-19 11 110kV Weibo Substation AC 110 kV 126 1989-11-29 12 110kV City Substation AC 110 kV 80 2001-12-03 13 110kV Xichengwu Substation AC 110 kV 100 2015-02-12 14 110kV Niangziguan Substation AC 110 kV 32 1980-01-01 15 110kV Liujiacun Substation AC 110 kV 100 2012-12-05 16 110kV Development East Substation AC 110 kV 213 2014-01-24 17 110kV Maipo Substation AC 110 kV 100 2016-12-14 18 110kV Chenjiazhuang Substation AC 110 kV 71.5 2000-08-04 19 110kV Baiyangshu Substation AC 110 kV 81.5 1990-04-23 20 110kV Majiaping Substation AC 110 kV 163 1955-10-01 21 110kV Qingyatou Substation AC 110 kV 100 2013-08-01 22 110kV Chengxi Substation AC 110 kV 63 1966-08-06 23 110kV Xiushui Substation AC 110 kV 100 2000-12-01 24 110kV Xiling Substation AC 110 kV 63 1979-05-01 25 110kV Nanlou Substation AC 110 kV 80 2005-12-01 26 110kV Xincun Substation AC 110 kV 100 2013-06-22 27 110kV Nanshangzhuang Substation AC 110 kV 100 2013-08-20 28 110kV Huangshayan Substation AC 110 kV 100 2019-04-30

[0097] Table 6 Main transformer capacity of a 35 kV substation in a certain city in 2022

[0098]

[0099]

[0100] 3. Distributed photovoltaic grid access and absorption capacity module: The distributed photovoltaic grid access and absorption capacity module is further divided into: medium-voltage photovoltaic access and absorption module: according to the distributed power access plan, the distribution network flow calculation is carried out; according to the distribution network flow calculation results, the voltage, load rate, and power quality after the distributed power access are evaluated; low-voltage photovoltaic access and absorption module: according to the distributed power access plan, the distribution network flow calculation is carried out; according to the distribution network flow calculation results, the voltage, load rate, power quality, and three-phase imbalance after the distributed power access are evaluated; the prediction and results of power, load, and power demand are shown in the following table:

[0101] Table 7 Electricity forecast table for a city

[0102]

[0103] Table 8 Power forecast results

[0104]

[0105]

[0106] Table 9 Electricity demand forecast

[0107]

[0108] 1. Evaluation of medium-voltage absorption capacity: Take a 10kV distribution line in Yangquan as an example. There are 10 medium-voltage users on the line, with a total load of 3MW. The capacity of distributed photovoltaic power sources connected to the line is 1.5MW. The medium-voltage absorption capacity of the line is evaluated through the photovoltaic grid absorption capacity evaluation system: First, obtain the relevant parameters of the line, such as the conductor model, length, resistance, reactance, etc., as well as the capacity and parameters of the transformer; then, according to the output characteristic curve of distributed photovoltaics and the load curve of the area, perform flow calculation: Assuming that on a typical day, the light intensity is strong and the output of distributed photovoltaics reaches 2MW, the power distribution, voltage drop and load rate of the line are calculated at this time; the evaluation results show that the line has power reverse transmission in certain periods, and the load rate of the transformer is also close to 80%. According to the evaluation results, it can be judged that the medium-voltage absorption capacity of the line is close to saturation, and measures need to be taken to improve the absorption capacity: optimize the reactive compensation device of the line, adjust the power generation strategy of distributed photovoltaics, or upgrade the line;

[0109] 2. Evaluation of low-voltage absorption capacity: There are 50 residential users in a rural low-voltage area in Yangquan, with a distribution capacity of 200kVA and a connected low-voltage distributed photovoltaic power capacity of 50kW. The low-voltage absorption capacity of the area is evaluated: the power consumption data of the area and the power generation data of the photovoltaic power source are collected, and the power balance under different seasons and weather conditions is analyzed: in the busy farming season in summer, the residential power load is low during the day, but the power generation power of the photovoltaic power source is high, and there may be a power surplus at this time. Through evaluation and calculation, the low-voltage absorption capacity indicators of the area in summer are obtained: residual capacity, voltage deviation rate, three-phase imbalance, etc.; if the voltage deviation rate is found to exceed the allowable range (±7%), or the three-phase imbalance is high (more than 15%), it means that there are problems with the low-voltage absorption capacity of the area, and corresponding measures need to be taken to improve it: install low-voltage reactive compensation devices, adjust the access location of photovoltaic power sources, or sort out the power load of residential users, etc.

[0110] 4. Distributed photovoltaic grid-connected adaptability module: Based on the distribution network topology node flow calculation results, combined with the distribution network basic equipment archives and line-transformer relationship data, substation equipment archives and substation-user relationship data, the distribution transformer power, user photovoltaic power and substation return power data are used to realize the hierarchical and graded photovoltaic penetration rate statistics, that is, photovoltaic grid-connected adaptability.

[0111] 1. Display of adaptability evaluation indicators: Display of adaptability evaluation indicators for a distribution network area actually connected to distributed photovoltaics in Yangquan area. The evaluation levels are shown in the following table:

[0112] Table 10 Evaluation level classification table

[0113]

[0114]

[0115] See attached Figure 2 , which is a specific flow chart of the comprehensive evaluation of distributed photovoltaic grid-connection adaptability of the adaptability evaluation index submodule in the distributed photovoltaic grid-connection adaptability module.

[0116] The comprehensive index weighting method for adaptability evaluation combines objective weighting with subjective weighting. The specific operation is as follows:

[0117] Taking into account the drawbacks of using the analytic hierarchy process and the main cause analysis method alone, in order to make up for the shortcomings of single weighting, the calculation results of the two weighting methods are integrated, and the two weighting methods are combined to calculate the comprehensive weight of the indicators:

[0118]

[0119] Among them, w i is the weight calculated by the hierarchical analysis method, α i is the weight calculated by correlation analysis, z i is the comprehensive weight, n is the number of indicators;

[0120] Comprehensive evaluation: Based on the weight analysis results of the index evaluation system, the comprehensive index method is used to obtain the comprehensive evaluation results of the distribution network.

[0121] It can effectively assist the development of research on the adaptability of distributed photovoltaic access to distribution networks;

[0122]

[0123] Among them, Y is the comprehensive score; x i is the calculated value of the indicator; x0 is the standard value, which needs to be flexibly set according to actual needs; n is the number of evaluation indicators; z i is the indicator weight;

[0124] For example: Reverse load rate: At a certain moment, the output of the distributed power source is 2MW, the equivalent power load is 1.5MW, and the actual operating limit of the transformer is 5MW, then the reverse load rate = (2-1.5) / 5×100% = 10%;

[0125] New distributed power capacity: Assuming that the equipment operation margin factor in the area is 0.8 and the current maximum reverse load rate is 30%, the new distributed power capacity can be increased by (1-0.3)×5×0.8=2.8MW;

[0126] Main transformer load rate: The rated capacity of a main transformer is 10MVA, and the current load is 8MVA, then the main transformer load rate = 8 / 10 = 80%;

[0127] Wiring loss rate: If a certain wiring supplies 100,000 kWh of electricity in a period of time and sells 95,000 kWh of electricity, then the wiring loss rate = (100,000-95,000) / 100,000 × 100% = 5%;

[0128] 10kV voltage deviation rate: The rated voltage of a 10kV line is 10kV, and the actual measured voltage is 10.3kV, then the voltage deviation rate = (10.3-10) / 10×100% = 3%;

[0129] Three-phase voltage unbalance: The three-phase voltages in a certain area are 220V, 215V, and 218V respectively, then the average phase voltage = (220+215+218) / 3 = 217.67V, and the three-phase voltage unbalance = (220-215) / 217.67×100%≈2.3%;

[0130] Distributed power generation penetration rate: The total installed capacity of distributed photovoltaic power generation in the region is 10MW, and the total load peak is 50MW, then the distributed power generation penetration rate = 10 / 50 = 20%;

[0131] Through the display of these indicators, we can intuitively understand the adaptability of the distribution network in different aspects after photovoltaic grid connection, providing a basis for further analysis and evaluation.

[0132] 2. Hierarchical and graded photovoltaic penetration statistics: In the distribution network in Yangquan area, photovoltaic penetration statistics are conducted at different levels such as units, substations, distribution lines, and substations;

[0133] Taking the State Grid Yangquan Power Supply Company as an example, the company has 100 distributed photovoltaic power stations with a total installed capacity of 50MW. The total capacity of public transformers within the company is 500MVA, and the total capacity of private transformers is 300MVA. Then the photovoltaic penetration rate = 50 / (500+300)×100%≈6.25%;

[0134] Substation dimension: Taking a 220kV substation in Yangquan as an example, the total installed capacity of distributed photovoltaic power generation in the power supply area of ​​the substation is 10MW, and the total capacity of the main transformer of the substation is 100MVA. The photovoltaic penetration rate of the substation = 10 / 100×100% = 10%; the margin level is 1-10% = 90%. Assuming that there is no return power on the 220kV side of the main transformer, the "220kV return" is displayed as "No". The substation has 20 photovoltaic distribution lines, 30 photovoltaic stations, 5 photovoltaic special transformer users, and a daily power generation of 5000kWh;

[0135] Wiring dimension: Taking a 10kV wiring as an example, the total installed capacity of distributed photovoltaics under this wiring is 2MW, the total capacity of public transformers is 20MVA, and the total capacity of dedicated transformers is 10MVA. The photovoltaic penetration rate = 2 / (20+10)×100%≈6.67%; the margin level is 1-6.6%≈93.33%. Assuming that the negative power at the wiring gateway is 0, "Whether to return" is displayed as "No". The line loss rate is 4% on a typical day. According to the flow results, no heavy overload occurs. The voltage compliance rate is 99%, and the short-circuit capacity is estimated to be (20+2)×1.5=33kA. There are 10 photovoltaic stations and 2 photovoltaic dedicated transformer users under this wiring, with a daily power generation of 2000kWh;

[0136] Substation dimension: Take a substation as an example. The substation's distribution transformer capacity is 500kVA, and the total installed capacity of distributed photovoltaic is 30kW. The photovoltaic penetration rate = 30 / 500×100% = 6%; the margin level is 1-6% = 94%. Assuming that the negative power of the substation's distribution transformer is 0, "Whether to return" is displayed as "No". The short-circuit capacity is estimated to be (500+30)×1.5=795kA. The three-phase imbalance is calculated by collecting data to be 3%. There are 5 photovoltaic users in this substation, with a total installed capacity of 30kW and a daily power generation of 150kWh.

[0137] 5. Distributed photovoltaic grid access simulation module:

[0138] 1. Medium-voltage photovoltaic access simulation: In a 10kV medium-voltage distribution network in Yangquan area, simulate the access of a 5MW distributed photovoltaic power station: First, set the parameters of the distributed photovoltaic power station in the software, including the efficiency of the photovoltaic panels, the installation angle, the efficiency of the inverter, etc.; then, according to the topological structure and operation data of the distribution network, simulate the operation of the photovoltaic power station under different light intensities and loads: at 10 am, the light intensity is strong, and the output of the distributed photovoltaic power station reaches 4MW. At this time, observe the voltage changes of each node in the distribution network, the power flow of the line, and the load of the transformer; through the simulation results, it can be analyzed that the impact of photovoltaic access on the voltage adjustment, power balance and equipment load of the medium-voltage distribution network can provide a reference for the actual photovoltaic access plan. If the simulation finds that the voltage of some nodes exceeds the allowable upper limit (for example, 10.7kV), you can consider taking reactive compensation measures or adjusting the output of the photovoltaic power station to solve it;

[0139] 2. Low-voltage three-phase photovoltaic access simulation: In a rural low-voltage substation in Yangquan area, a 10kW three-phase distributed photovoltaic power access is simulated: there are 50 households in the substation, and the original load is mainly for residential electricity and a small amount of agricultural production electricity; through the photovoltaic grid access simulation function, the access location of the photovoltaic power supply is set to the low-voltage side bus of a distribution transformer in the substation, simulating the power generation of the photovoltaic power supply and the impact on the power supply quality of the substation under different seasons and weather conditions: on sunny days in summer, the power generation of the photovoltaic power supply is relatively high, reaching 8kW at 12 noon. At this time, the three-phase voltage balance of the substation, the current distribution of the line and the voltage quality of the residential users are observed. Through simulation, it is found that due to the access of photovoltaic power, the voltage of some users in the substation increases during the noon period, which may affect the user's electrical equipment. In view of this situation, in order to ensure that the user's voltage quality is within a reasonable range, a voltage regulator can be installed on the user side or the access method of the photovoltaic power supply can be optimized.

[0140] 6. Distributed photovoltaic grid-connection optimization module: For the selection and optimization of the access location and capacity of photovoltaic power sources, first based on the distribution network topology node flow calculation results and considering the flow path of electricity in the grid structure and the characteristics of load data, then according to the characteristics of photovoltaic power sources, the output power of photovoltaic power sources is affected by external factors such as weather and time. On this basis, the data constraints are relaxed or tightened in accordance with the external factors, and the initial complex data results are converted into a cone-shaped data form that can be used for the optimization calculation of the photovoltaic power access location and capacity using a simple linear programming algorithm; at the same time, the data structure process corresponding to the above data processing path is: the initial The acquired or constructed photovoltaic grid-connected related data (usually initially presented as an unordered flat data structure) is converted into a quasi-conical data structure, and an upper-level data node is constructed at the tip of the cone, and then sequentially expanded to gradually transition to the lower-level data nodes at the cone frustum; the data level data of the quasi-conical data structure is specifically set as needed, and the data relationship between any two adjacent data levels is set as follows: each data node in the data level closer to the cone tip is linked to multiple sub-data nodes in the data level closer to the cone frustum; during the execution of the linear programming algorithm, different data levels can be traversed layer by layer or repeatedly to optimize and solve the data of photovoltaic power access location and capacity selection;

[0141] 1. Recommendation of medium-voltage photovoltaic location: In a planned photovoltaic grid-connected project in Yangquan area, the software's medium-voltage photovoltaic location recommendation function was used for optimization: there is an industrial park in the area, with multiple 10kV distribution rooms and a large number of industrial loads. By analyzing the distribution network structure, load distribution and existing distributed power supply conditions of the park, combined with the algorithm in the software, the system recommends building a new 10MW distributed photovoltaic power station at a certain location in the park; the recommendation is based on the fact that the location is close to the load center, which can effectively reduce line losses, and the distribution network capacity and voltage stability in the area can better accommodate the output of the photovoltaic power station; at the same time, considering factors such as the construction cost and land use of the photovoltaic power station, the recommended location has more convenient construction conditions and lower land costs;

[0142] 2. Recommendation of low-voltage photovoltaic position and phase difference: In a low-voltage distribution network transformation project in a town in Yangquan area, a batch of distributed photovoltaic power sources need to be connected: the software's low-voltage photovoltaic position and related recommendation functions are used for planning; taking a low-voltage substation in a block as an example, the substation has 300 households, the original distribution capacity is 500kVA, and it adopts a three-phase four-wire power supply. Through the analysis of the load distribution, line impedance and user power consumption rules of the substation, the software recommends connecting distributed photovoltaic power sources at several locations in the substation, and gives phase difference recommendations for each access point: in the middle of the substation, it is recommended to connect a 3kW single-phase photovoltaic power source to phase A; at the edge of the substation, it is recommended to connect a 5kW three-phase photovoltaic power source to phases A, B, and C respectively. Such a recommendation scheme takes into account the three-phase balance problem of the substation, avoids the problem of overload or voltage imbalance of a phase caused by photovoltaic access, and also makes full use of the space and power resources of the substation, improving the access efficiency and power supply quality of distributed photovoltaics.

[0143] 7. Comprehensive display module:

[0144] 1. Display of medium-voltage topology diagram: Taking a medium-voltage distribution network area in Yangquan as an example, the distribution network structure of the area is displayed through the medium-voltage topology diagram in the comprehensive display function. In the topology diagram, the connection between the 220kV substation, the 110kV substation and each 10kV distribution room is clearly displayed: the 220kV Yangquan substation is connected to the 110kV A substation through a double-circuit line, and the 110kV A substation supplies power to the surrounding industrial and residential areas through multiple 10kV lines. The name, length, conductor model, and number and capacity of the connected distribution transformers are marked on each 10kV line; at the same time, the access location and capacity of the distributed photovoltaic power station are also marked on the topology diagram: a 5MW distributed photovoltaic power station is connected at the end of a 10kV line, and conventional power sources and distributed power sources are distinguished by different icons and colors;

[0145] 2. Flow chart display: In a distribution network system including distributed photovoltaic in Yangquan area, the flow chart is used to display the distribution of power flow. Take the operation data at a certain moment as an example: In the flow chart, the direction of the arrow indicates the flow direction of power, and the size of the arrow indicates the size of power. The arrow starting from the substation indicates that the power source transmits power to the distribution network, and the arrow pointing to the substation indicates that the distributed photovoltaic power source sends power back to the substation (if any) or the remaining power flows back after the load absorbs power from the distribution network;

[0146] 3. Absorption capacity evaluation and display: For multiple photovoltaic grid-connected areas in Yangquan, the absorption capacity evaluation and display are carried out. Take one of the areas as an example: this area contains several 10kV distribution lines and low-voltage substations. Through software analysis and calculation, the following absorption capacity evaluation results are obtained:

[0147] Medium-voltage absorption capacity: On a certain 10kV distribution line, the maximum distributed photovoltaic capacity that can be connected is 8MW, and the current connected capacity is 5MW. During peak hours, the load rate of this line reached 70%, the voltage deviation was between -3% and +5%, and the line loss rate was 4%. According to the results, the medium-voltage absorption capacity of this line is at a medium level, and there is still a certain amount of room for connection, but during peak hours, it is necessary to pay close attention to the voltage and load conditions to prevent overload and voltage limit problems;

[0148] Low-voltage absorption capacity: In a low-voltage area, the maximum capacity of the distribution transformer is 400kVA, and the connected distributed photovoltaic capacity is 100kW. When there is sufficient sunlight during the day, the power factor of the area can reach above 0.95, the voltage deviation is between -5% and +7%, and the three-phase imbalance is less than 10%. Through evaluation, it can be seen that the absorption capacity of the low-voltage area is good, and the access of distributed photovoltaics has little impact on the operation of the area. However, in extreme weather or sudden load changes, it is still necessary to monitor the changes in voltage and power factor;

[0149] 4. Adaptability assessment display: The adaptability assessment display of photovoltaic access to the grid is carried out for different distribution network areas in Yangquan area. In area A, the following adaptability assessment results are displayed:

[0150] Safety: After the photovoltaic power generation in this area was connected to the grid, the short-circuit calculation showed that under the maximum short-circuit current, the circuit breaker could open normally and the short-circuit current did not exceed the tolerance of the equipment; the load rate of the main transformer was within a reasonable range, and no heavy overload occurred, ensuring the safe operation of the power equipment; at the same time, the voltage deviation rate was within the standard requirement of ±7%, and the three-phase voltage imbalance was also controlled below 3%, meeting the requirements of power supply quality for voltage stability, ensuring the safe and reliable operation of the user's electrical equipment;

[0151] Economical aspect: By comparing and analyzing the line loss rate before and after the photovoltaic grid connection in this area, it is found that the line loss rate is reduced by 2 percentage points after the connection, which effectively saves energy costs. In addition, according to the power generation income and investment cost estimation of the photovoltaic power station, it is expected that good economic benefits can be achieved during the project life cycle, and the investment payback period is 8 years (assumed value);

[0152] Green and low-carbon aspects: Statistics on the total installed capacity and power generation of distributed photovoltaics in the region show that the carbon dioxide emissions reduction has reached X tons per year (calculated based on actual power generation and related conversion factors), making a positive contribution to local energy conservation, emission reduction and environmental protection. In addition, the penetration rate of distributed photovoltaic power sources has gradually increased, and the energy structure has been optimized to a certain extent, moving towards a green and low-carbon direction;

[0153] Power supply quality: The voltage qualification rate in this area has reached more than 98%, meeting the user's requirements for power supply quality. In addition to the good voltage deviation and three-phase imbalance indicators mentioned above, through the analysis of user complaint rates and power quality monitoring data, it is found that the access of photovoltaic power to the grid has not had a significant negative impact on the power supply quality, but has improved the reliability and stability of power supply to a certain extent.

[0154] 8. System Implementation:

[0155] Based on the above embodiments and repeated testing and research on the system, the practical application of the system has been achieved:

[0156] In the system implementation part of the present invention, we show in detail how to transform the theoretical model into a practical software interface, and provide rich charts and data views so that users can intuitively understand and use the system;

[0157] 1. Theoretical model management interface

[0158] Interface display: see attached Figure 4 ,The theoretical model management interface provides users with an intuitive ,operation environment on which users can configure and manage data models;

[0159] Function description: Users can add, modify or delete theoretical models of distribution networks, including power flow calculation model, short circuit calculation model, etc. The system provides an input box for model parameters, and users can adjust the parameters according to actual needs;

[0160] 2. Wiring operation data interface

[0161] Interface display: see attached Figure 5 ,The distribution operation data interface displays the real-time operation data of the ,distribution network, including voltage, current, power, etc.;

[0162] Function description: Users can monitor the operating status of the distribution network through this interface. The system provides real-time data update and historical data query functions;

[0163] 3. Medium voltage photovoltaic access to distribution network absorption capacity assessment interface

[0164] Interface display: see attached Figure 6 The medium-voltage photovoltaic access distribution network absorption capacity assessment interface provides an assessment of the grid absorption capacity after photovoltaic access;

[0165] Function description: See attached Figure 8 -Attached Fig.11 , users can input the capacity and location of the PV power station, and the system will automatically calculate and display the grid absorption capacity after access, including key indicators such as voltage change and load rate;

[0166] 4. Power flow calculation interface

[0167] Interface display: see attached Figure 7 ,The flow calculation interface is one of the core functions of the system, which ,provides the function of flow calculation of the distribution network;

[0168] Function description: See attached Figure 8 -Attached Fig. 9 , users can set calculation parameters, such as load changes, photovoltaic output changes, etc. The system will automatically perform power flow calculations and display the calculation results and power flow distribution diagrams of the power grid;

[0169] 5. Photovoltaic access to distribution network adaptability assessment interface

[0170] Interface display: see attached Fig.12 ,The PV access to distribution network adaptability assessment interface provides an adaptability assessment for PV access to the distribution network;

[0171] Function description: Users can view the evaluation results of multiple dimensions such as safety, economy, green and low carbon, and power supply quality after photovoltaic grid connection;

[0172] 6. System deployment

[0173] Deployment description: See attached Fig.13 ,The system can be deployed on a variety of hardware platforms, including servers, workstations, and cloud platforms, and supports a variety of operating systems, such as Windows, Linux, etc.;

[0174] User access: Users can access the system through a browser or dedicated client software to perform operations such as data input, model configuration, and calculation result viewing.

[0175] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A distributed photovoltaic grid-connected data fitting system, characterized by: The data system globally fits and optimizes the absorption of distributed photovoltaic grid access through the hierarchical branching architecture of multiple data module components and the construction of internal data processing processes of data module components.

2. A distributed photovoltaic grid-connected data fitting system according to claim 1, characterized in that: The data system can optionally include the following data modules: S1 data sorting module, S2 algorithm component module, S3 distributed photovoltaic grid access absorption capacity module, S4 distributed photovoltaic grid access adaptability module, S5 distributed photovoltaic grid access simulation module, S6 distributed photovoltaic grid access optimization module, and S7 comprehensive display module.

3. A distributed photovoltaic grid-connected data fitting system according to claim 2, characterized in that: The S1 data sorting module may optionally include the following submodules: wiring sorting submodule, distribution transformer file sorting submodule, photovoltaic file sorting submodule, photovoltaic curve simulation submodule, wiring outlet voltage submodule, distribution transformer / medium voltage photovoltaic power submodule, user / low voltage photovoltaic power submodule, model impedance sorting submodule, and other data sorting submodules; The S2 algorithm component module may optionally include the following submodules: forward-backward substitution submodule, unidirectional power flow submodule, voltage deviation submodule, short-circuit calculation submodule, full-phase power flow submodule, load rate calculation submodule, line loss calculation submodule, and three-phase unbalance submodule; The S3 distributed photovoltaic grid-connected absorption capacity module may optionally include: a medium voltage absorption capacity submodule and a low voltage absorption capacity submodule; The S4 distributed photovoltaic grid-connection adaptability module optionally includes: a photovoltaic grid-connection adaptability submodule and an adaptability evaluation index submodule; The S5 distributed photovoltaic grid-connection simulation module optionally includes: a medium-voltage photovoltaic access submodule, a low-voltage single-phase photovoltaic access submodule, and a low-voltage three-phase photovoltaic access submodule; The S6 distributed photovoltaic grid-connection optimization module optionally includes: a photovoltaic power supply access submodule and a photovoltaic power supply capacity submodule; The S7 comprehensive display module optionally includes: a medium voltage topology diagram submodule, a low voltage three-phase four-wire mapping submodule, a flow direction diagram submodule, a consumption capacity display submodule, and an adaptability display submodule.

4. A distributed photovoltaic grid-connected data fitting system according to claim 2, characterized in that: The S1 data sorting module also includes the following sub-modules: a distribution line model sorting sub-module: building a standardized distribution network flow calculation model based on integrated data; a distribution line operation data sub-module: extracting operation data.

5. A distributed photovoltaic grid-connected data fitting system according to claim 2, characterized in that: The S2 algorithm component module further includes the following data algorithm process: A1 Distribution Line Flow Calculation: Use flow calculation to analyze the voltage, power, current and other parameters in the power system, determine the voltage and power distribution of each distribution node, and the current and power on the line and equipment; A2 Theoretical Line Loss Calculation: For distribution networks with complex network structures, complete metering devices, and complete basic data, a power flow algorithm is constructed to perform theoretical line loss calculations. Specifically, the power flow algorithm is constructed based on the 24 / 48 / 96-point distribution head voltage, distribution transformer load power data, distribution network topology data, conductor / cable calculation parameters, and distribution transformer parameter configuration; A3 line overload monitoring and analysis: monitor the distribution network overload situation based on the distribution network flow calculation results and equipment parameters, and improve the refinement of distribution line overload monitoring and analysis; A4 line overload monitoring and analysis: line overload monitoring based on the sorted list of heavily overloaded lines; A5 branch overload monitoring and analysis: According to the branch line overload situation indicators in the loss reduction plan, the evaluation results that do not meet the threshold are included in the branch line overload weak links; A6 branch overload monitoring and analysis: According to the branch line overload indicators in the loss reduction plan, the evaluation results that do not meet the threshold are included in the branch line overload weak links; A7 line low voltage monitoring and analysis: monitor the voltage over-limit situation of the distribution network based on the distribution network power flow calculation results and improve the refinement of distribution line voltage quality monitoring and analysis; A8 line overvoltage monitoring and analysis: Based on the voltage overlimit line list and assisting the power distribution personnel to perform line overvoltage dual-path monitoring; A9 node low voltage monitoring and analysis: According to the node low voltage situation indicators in the loss reduction plan, the evaluation results that do not meet the threshold are included in the node low voltage weak link; A10 node overvoltage monitoring and analysis: According to the node overvoltage situation indicators in the loss reduction plan, the evaluation results that do not meet the threshold will be included in the node overvoltage weak link.

6. A distributed photovoltaic grid-connected data fitting system according to claim 2, characterized in that: In the S3 distributed photovoltaic grid-connected absorption capacity module, the medium-voltage photovoltaic access and absorption sub-module is specifically set as follows: according to the distributed power supply access plan, distribution network flow calculation is carried out; according to the distribution network flow calculation results, the voltage, load rate, and power quality after the distributed power supply is connected are evaluated; the low-voltage photovoltaic access and absorption sub-module is specifically set as follows: according to the distributed power supply access plan, distribution network flow calculation is carried out; according to the distribution network flow calculation results, the voltage, load rate, power quality, and three-phase imbalance after the distributed power supply is connected are evaluated.

7. A distributed photovoltaic grid-connected data fitting system according to claim 2, characterized in that: The S4 distributed photovoltaic grid-connected adaptability module is specifically configured as follows: based on the distribution network topology node flow calculation results combined with the distribution network basic equipment archives and line-transformer relationship data, substation equipment archives and substation-user relationship data, the distribution transformer power, user photovoltaic power and substation return power data are used to realize the hierarchical and graded photovoltaic penetration rate statistics, i.e. photovoltaic grid-connected adaptability.

8. A distributed photovoltaic grid-connected data fitting system according to claim 2, characterized in that: The S6 distributed photovoltaic grid-connection optimization module further has the following built-in data algorithm process: for the selection and optimization of the access location and capacity of photovoltaic power sources, first based on the distribution network topology node flow calculation results and considering the flow path of electricity in the grid structure and load data characteristics, then according to the characteristics of photovoltaic power sources, the output power of photovoltaic power sources is affected by external factors such as weather and time. On this basis, the data constraints are relaxed or tightened to adapt to the external factors, and the initial complex data results are converted into a cone-like structure that can use a simple linear programming algorithm to perform photovoltaic power access location and capacity optimization calculations. At the same time, the data structure process corresponding to the above data processing path is: converting the photovoltaic grid-connected related data initially acquired or constructed (usually initially presented as an unordered flat data structure) into a quasi-conical data structure, constructing an upper-level data node at the tip of the cone, and sequentially expanding and gradually transitioning to the lower-level data nodes at the cone frustum; the data level data of the quasi-conical data structure is specifically set as needed, and the data relationship between any two adjacent data levels is set as follows: each data node in the data level closer to the cone tip is linked to multiple sub-data nodes in the data level closer to the cone frustum; During the execution of the linear programming algorithm, different data levels can be traversed layer by layer or repeatedly to optimize and solve the data for photovoltaic power access location and capacity selection.

9. A distributed photovoltaic grid-connected data fitting system according to claim 2, characterized in that: The adaptability evaluation index submodule in the S4 distributed photovoltaic grid-connection adaptability module is specifically constructed as follows: B-1. Fusion and collation of multi-source heterogeneous data: According to the data requirements of the adaptability study of photovoltaic access to the distribution network, obtain multi-source heterogeneous data of the distribution network, including optional information such as equipment archive data, topological relationship data, operation collection data, etc., to achieve data fusion, and adopt the following methods to collate abnormal data: Sort the data from the aspects of data integrity, validity, and consistency, and remove abnormal data such as key attributes being empty and redundant; use the power system business knowledge, combined with the relevant power grid design specifications, to sort out all kinds of data that violate the power system business rules and remove abnormal data; use the K-MEANS clustering analysis method to cluster the data, calculate the distance between each analysis object and the cluster center, and judge the data that seriously deviates from the cluster center as abnormal data and remove it; B-2. Based on the data collation in B1, further construct the adaptability evaluation system from multiple dimensions, including: B-2-1. Dimensionless processing of indicators: The various evaluation indicators in the indicator system are not comparable due to their different dimensions, economic significance, forms of expression and trends of their effects on the overall goal. They must be dimensionless sorted to eliminate the impact of the indicator dimensions before the comprehensive evaluation results can be calculated; the dimensionless sorting methods used include extreme value, standardization, averaging, normalization and standard deviation methods. Considering the need to conduct a comprehensive evaluation of multiple indicators, the normalization sorting method is selected to sort the indicators; normalization is to transform the dimensional expression into a dimensionless expression, restrict the sorted data, improve the efficiency of subsequent data sorting and increase the stability and speed of the program when running; B-2-2. Indicator consistency processing: There are "extremely large" indicators, "extremely small" indicators, "medium" indicators, and "interval" indicators. Different quantitative indicators have different values: for the proportion of economically operated lines, it is natural to expect that the larger the value, the better. This type of indicator is called an "extremely large" indicator; for the proportion of super-long lines, it is natural to expect that the smaller the value, the better. This type of indicator is called an "extremely small" indicator; in order to be able to conduct a comprehensive summary, inconsistent indicators are sorted out in the same direction; B-2-3. Comprehensive weighting of indicators: Taking into account the expert experience weighting method and the objective weighting method, a combination of subjective and objective weighting methods is adopted to quantitatively analyze the adaptability evaluation indicators of photovoltaic access to the distribution network, and determine the correlation and importance of the indicators with distributed photovoltaic access to the grid.

10. A distributed photovoltaic grid-connected data fitting system according to claim 9, characterized in that: The B2 constructs an adaptability index system for distributed photovoltaic access to the grid: considering factors such as the access location of distributed photovoltaics, the grid structure of the distribution network, the operating mode, the load type, the load level, and the time scale, an adaptability index system for distributed photovoltaic access to the grid is constructed from the dimensions of distribution network security, economy, green and low carbon, and power supply quality.

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