Method for evaluating and analyzing photovoltaic consumption capability of power distribution network

Abstract

The invention relates to the technical field of intelligent power distribution analysis, and discloses a power distribution network photovoltaic consumption capability evaluation and analysis method, which comprises the following steps: obtaining structure information and operation parameters of a target power distribution network; performing fitting processing through a photovoltaic absorption curve fitting mechanism of multi-state parallel disturbance scanning; constructing a distributed absorption capability matrix by adopting a sensitivity weight weighting method; performing local absorbable area identification processing; and executing regional evolution trend analysis. In the prior art, the method depends on static working condition analysis, and especially under the conditions of frequent change of a complex micro-grid topological structure and uncertain dynamic response of a flexible load, rapid estimation of the regional photovoltaic absorption capability is difficult to realize. Due to the fact that the distributed absorption capacity matrix with the state decoupling capacity is constructed and the adjustable threshold value dynamic screening mechanism is fused, rapid evaluation and analysis of the photovoltaic absorption capacity are achieved, and the efficiency of photovoltaic access feasibility analysis is improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent power distribution analysis technology, and in particular to a method for assessing and analyzing the photovoltaic absorption capacity of a power distribution network. Background Technology

[0002] Currently, with the large-scale integration of distributed photovoltaic (PV) power, traditional PV absorption capacity assessment methods based on centralized load forecasting and single-state power flow calculations are gradually revealing a series of problems in practical engineering. Existing technologies typically employ single-point or limited-condition simulations under static operating scenarios, performing node voltage margin or feeder current margin assessments based on constant load and fixed topology conditions to estimate the maximum PV capacity that can be integrated. However, given the increasing prevalence of multi-topology switching, multi-type flexible load responses, and multi-distributed power source coordination in distribution networks, these methods struggle to reflect the evolution of absorption capacity under complex disturbance conditions. Their assessment granularity is coarse, their dynamic adaptability is poor, and they fail to meet the needs of regional regulation and resource coordinated scheduling.

[0003] On the one hand, existing technologies fail to fully consider the nonlinear impact of topology disturbances on the photovoltaic absorption boundary. For example, topology adjustments such as the closing of tie switches, the disconnection of main feeders, or load transfers often cause systematic changes in feeder impedance paths, fault sensitivity, and node voltage drop patterns, resulting in significant fluctuations in the photovoltaic absorption capacity of certain areas, which static models cannot accurately predict or characterize. On the other hand, a large number of flexible load units connected to the distribution network (such as adjustable air conditioning loads, industrial loads, and energy storage response units) have a certain degree of lag, staged response, and counter-regulation potential. Traditional assessment methods often ignore the dynamic characteristics of their actual adjustment process, making it difficult to construct absorption capacity models with feedback capabilities.

[0004] Furthermore, most existing assessment methods focus on single-value assessment indicators at the global or node level, lacking the curve representation and phased trend extraction of absorption capacity during the evolution of disturbance states, thus failing to provide decision support for precise regional regulation or strategy advancement. Sensitivity indicators are only used for voltage safety domain boundary estimation, lacking a linkage and extrapolation mechanism with disturbance state combinations, which is detrimental to achieving spatial distribution modeling and dynamic upper and lower limit estimation of photovoltaic grid connection potential across different regions.

[0005] Therefore, there is an urgent need for a method for assessing and analyzing the photovoltaic absorption capacity of distribution networks, in order to improve the security of photovoltaic access, the accuracy of regional regulation and control and dynamic adaptability, and to achieve rapid assessment and trend analysis of regional, dynamic and trajectory-based photovoltaic access capacity for practical scenarios. Summary of the Invention

[0006] To address the aforementioned technical shortcomings, the purpose of this invention is to propose a method for assessing and analyzing the photovoltaic absorption capacity of distribution networks. This method aims to solve the technical problem that existing technologies rely heavily on static operating condition analysis, especially under conditions of frequent changes in complex microgrid topology and uncertain dynamic responses of flexible loads, making it difficult to quickly estimate regional-level photovoltaic absorption capacity.

[0007] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: The present invention provides a method for assessing and analyzing the photovoltaic absorption capacity of a distribution network.

[0008] The methods for assessing and analyzing the photovoltaic absorption capacity of the distribution network include:

[0009] Step S10: Obtain the structural information and operating parameters of the target distribution network, and construct the current operating state vector based on the structural information and operating parameters through a dual-domain coupled state compression expression mechanism. ;

[0010] Step S20: For the current running state vector The photovoltaic absorption curve fitting mechanism, which employs multi-state parallel perturbation scanning, performs fitting processing and outputs a set of boundary points for the upper limit of absorption. ;

[0011] Step S30: Obtain the actual photovoltaic access location and node sensitivity indicators, combined with the boundary point set of the upper limit of absorption range. A distributed absorption capacity matrix P is constructed using a sensitivity weighting method.

[0012] Step S40: Based on the distributed absorption capacity matrix P, an adjustable capacity threshold dynamic screening mechanism is used to perform local absorption area identification processing, and output the set of regional absorption upper limit boundaries;

[0013] Step S50: Based on the regional absorption upper limit boundary set, the DBSCAN density clustering method is used to perform regional evolution trend analysis to obtain the regional photovoltaic adjustable absorption capacity evolution map.

[0014] Preferably, in step S10, the structural information of the target distribution network includes a target distribution network topology diagram. Information on node load types and physical parameters; the operating parameters of the target distribution network include real-time load curves, real-time photovoltaic output, a set of response characteristic parameters of flexible load units, and a set of topological influence factors.

[0015] Preferably, in step S10, the structural information and operating parameters of the target distribution network are obtained, and the current operating state vector is constructed based on the structural information and operating parameters through a dual-domain coupled state compression expression mechanism. The steps specifically include:

[0016] Step S101: Obtain the structural information and operating parameters of the target distribution network, and extract the target distribution network topology from the structural information. ;Target distribution network topology diagram A pruning dimensionality reduction method based on voltage sensitivity analysis is used to perform structural dimensionality reduction, resulting in a simplified subgraph set; the current topology state compression code is then constructed based on this simplified subgraph set. , ;in, The current open / closed status of the interconnection switch between the main feeder and the backup feeder; This refers to the current open / closed state of the terminal branch switch containing a large-capacity photovoltaic access point; The current on / off state of the bypass switch of the m-th level transformer area in the target distribution network; ( ) is a binary encoding function used to concatenate the current on / off states of the selected key switches into a set of binary numbers according to their selected number order;

[0017] Step S102: Obtain the set of response characteristic parameters of the i-th flexible load unit from the operating parameters of the target distribution network. , ,in, This represents the maximum adjustable power of the i-th flexible load unit; Let be the response time constant of the i-th flexible load unit; Let be the startup response delay time of the i-th flexible load unit; Let be the start-up and reversal adjustment trend coefficient of the i-th flexible load unit;

[0018] Collect the response power of the i-th flexible load unit at time t. Based on the already responded power The current adjustment status label is obtained by performing a judgment. ;

[0019] Based on the current adjustment status label Response power Construct the set of flexible load response states for the entire network using the maximum adjustable power of the i-th flexible load unit;

[0020] Step S103: Compress code based on the entire network flexible load response state set and the current topology state Construct the current running state vector .

[0021] Preferably, in step S10, the current adjustment status label The formula is expressed as:

[0022]

[0023] in, This represents the initial state before the i-th flexible load unit has triggered a response; The i-th flexible load unit is in the linear growth response stage where the power gradually increases; This is the stage where the responsive power of the i-th flexible load unit has reached its upper limit and remains at a constant saturation value; This refers to the decline stage in which the power of the i-th flexible load unit reverts to its original value due to factors such as reverse regulation or delayed recovery. The theoretical time for the i-th flexible load unit to complete its power saturation response; The control duration for maintaining maximum response power for the i-th flexible load unit.

[0024] Preferably, in step S20, the current running state vector is... The photovoltaic absorption curve fitting mechanism, which employs multi-state parallel perturbation scanning, performs fitting processing and outputs a set of boundary points for the upper limit of absorption. The steps specifically include:

[0025] Step S201: Construct a set of topology disturbance states, including main feeder disconnection, tie switch connection, and section fault isolation; construct a set of flexible load disturbance states in the flexible load unit, including fully releasing adjustable capacity, releasing only part of the adjustable capacity, and exiting regulation or performing reverse regulation; perform a combined Cartesian product on the set of topology disturbance states and the set of flexible load disturbance states to generate a final set of combined states;

[0026] Step S202: Combine the final state set with the current running state vector Input a preset power grid operation assessment model, and the power grid operation assessment model outputs the maximum safe photovoltaic power value. Based on the maximum safe photovoltaic power value, a set of candidate points for assessing the photovoltaic absorption capacity of the target distribution network is obtained.

[0027] Step S203: Based on the candidate point set for photovoltaic absorption capacity assessment, a photovoltaic absorption capacity curve is obtained through piecewise linear fitting. Boundary extraction is performed based on the photovoltaic absorption capacity curve, and the boundary point set of the upper limit interval of absorption capacity is output. .

[0028] Preferably, in step S30, the actual photovoltaic access location and node sensitivity index are obtained, combined with the set of boundary points of the upper limit of absorption range. The steps for constructing a distributed absorption capacity matrix using the sensitivity weighting method specifically include:

[0029] Step S301: Obtain the actual photovoltaic access location and node sensitivity indicators. The node sensitivity indicators include node voltage change rate, voltage constraint margin, line power flow transfer rate and voltage stability margin. Perform normalization processing on the node sensitivity indicators to generate the sensitivity weight vector corresponding to the actual photovoltaic access location.

[0030] Step S302: Apply sensitivity weight vector to the set of boundary points of the upper limit of absorption interval By assigning weights to each item in the total absorbable capacity matrix, a distributed absorbable capacity matrix P is output.

[0031] Preferably, step S40, which involves performing local absorbable area identification processing based on the distributed absorbability matrix P using an adjustable capacity threshold dynamic filtering mechanism, and outputting a set of regional-level absorbability upper limit boundaries, specifically includes:

[0032] Step S401: For the i-th flexible load unit in the distributed absorption capacity matrix P, obtain its corresponding sensitivity weight W(i), and based on the sensitivity weight W(i) and the current adjustment state label... Construct a dynamically adjustable capacity threshold function T(i);

[0033] Step S402: Apply the dynamic adjustable capacity threshold function T(i) to the distributed absorption capacity matrix P under all disturbance states to obtain the absorption capacity margin matrix of each node under each disturbance state; and perform threshold judgment based on the absorption capacity margin matrix to obtain the absorption capacity mask matrix; the absorption capacity mask matrix is ​​used to indicate whether the node has the ability to absorb photovoltaic power under a specific disturbance state.

[0034] Step S403: Extract the perturbation state index and corresponding node index corresponding to the value 1 in the absorbability mask matrix, and construct a regional absorbability upper limit boundary set based on the perturbation state index and corresponding node index; the regional absorbability upper limit boundary set is used to realize the deduction of flexible scheduling strategy based on partition.

[0035] Preferably, step S40, which involves performing a threshold judgment based on the absorbability margin matrix to obtain the absorbability mask matrix, specifically includes: setting a set of state-sensitive absorbability margin thresholds for the absorbability margin matrix; performing a threshold judgment between the state-sensitive absorbability margin thresholds and the absorbability margin matrix using a conditional discrimination method based on element-level comparison; and constructing an absorbability mask matrix in two-dimensional Boolean matrix format based on the threshold judgment result.

[0036] This invention also provides a system for assessing and analyzing the photovoltaic absorption capacity of a power distribution network, comprising:

[0037] The state vector construction module is used to obtain the structural information and operating parameters of the target distribution network, and construct the current operating state vector based on the structural information and operating parameters through a dual-domain coupled state compression expression mechanism. ;

[0038] The curve fitting module is used to fit the current running state vector. The photovoltaic absorption curve fitting mechanism, which employs multi-state parallel perturbation scanning, performs fitting processing and outputs a set of boundary points for the upper limit of absorption. ;

[0039] The absorption matrix construction module is used to obtain the actual photovoltaic access location and node sensitivity indicators, combined with the boundary point set of the upper limit of absorption range. A distributed absorption capacity matrix P is constructed using a sensitivity weighting method.

[0040] The region identification module is used to perform local absorbable region identification processing based on the distributed absorption capacity matrix P using an adjustable capacity threshold dynamic filtering mechanism, and outputs a set of region-level absorption upper limit boundaries.

[0041] The trend analysis module is used to perform regional evolution trend analysis based on the regional absorption upper limit boundary set using the DBSCAN density clustering method, and obtain the regional photovoltaic adjustable absorption capacity evolution map.

[0042] The present invention also provides a device for assessing and analyzing the photovoltaic absorption capacity of a distribution network, comprising: a memory, a processor, and a distribution network photovoltaic absorption capacity assessment and analysis program stored in the memory and executable on the processor. When the distribution network photovoltaic absorption capacity assessment and analysis program is executed by the processor, a method for assessing and analyzing the photovoltaic absorption capacity of a distribution network is implemented.

[0043] The present invention also provides a computer program product, including a distribution network photovoltaic absorption capacity assessment and analysis program, wherein the distribution network photovoltaic absorption capacity assessment and analysis program implements the distribution network photovoltaic absorption capacity assessment and analysis method when executed by a processor.

[0044] The beneficial effects of this invention are as follows: By constructing a current operating state vector based on dual-domain coupled state compression and introducing a multi-state parallel disturbance scanning mechanism and a sensitivity weighted fitting method, this invention achieves rapid and dynamic evaluation of the photovoltaic absorption capacity of the distribution network under multiple operating conditions, significantly improving evaluation efficiency and adapting to the needs of complex microgrid structures and frequent changes in operating states.

[0045] This invention improves the accuracy of identifying regional absorption boundaries by constructing a sensitivity-driven distributed absorption capacity matrix, an adjustable capacity threshold screening mechanism, and a regional evolution map clustering analysis. It also enables the analysis of the trend evolution of regional absorption capacity, providing data support and prediction capabilities for grid-side regulation strategies and photovoltaic grid connection planning. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 This is a flowchart illustrating the first embodiment of a method for assessing and analyzing the photovoltaic absorption capacity of a power distribution network according to the present invention.

[0048] Figure 2 This is a schematic diagram of the compressed operating state distribution in the principal component space of the first embodiment of the photovoltaic absorption capacity assessment and analysis method for distribution networks according to the present invention.

[0049] Figure 3 This is a schematic diagram comparing the fitting curves of the absorption boundary before and after the compression mechanism in the first embodiment of the photovoltaic absorption capacity assessment and analysis method for distribution networks of the present invention.

[0050] Figure 4 This is a schematic diagram of the equipment used in the method for assessing and analyzing the photovoltaic absorption capacity of a power distribution network according to the present invention. Detailed Implementation

[0051] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0052] Example 1: As Figure 1 The diagram shown is a flowchart of the first embodiment of the photovoltaic absorption capacity assessment and analysis method for distribution networks of the present invention, which presents the first embodiment of the photovoltaic absorption capacity assessment and analysis method for distribution networks of the present invention.

[0053] In the first embodiment, the method for assessing and analyzing the photovoltaic absorption capacity of the distribution network includes:

[0054] Step S10: Obtain the structural information and operating parameters of the target distribution network, and construct the current operating state vector based on the structural information and operating parameters through a dual-domain coupled state compression expression mechanism. ;

[0055] It should be noted that the dual-domain coupled state compression expression mechanism refers to simultaneously considering the structural information and operational parameter information of the target distribution network. Based on the fusion of multi-source heterogeneous data, it constructs a compact state vector that can be used to characterize the operational state features. Structural information includes, but is not limited to, static structural parameters such as the node topology, feeder distribution, line impedance parameters, switch status, and equipment connection methods of the distribution network. Operational parameters include, but are not limited to, dynamic operational indicators such as voltage amplitude, power flow, load level, photovoltaic output, and flexible load regulation status of each node. The state compression expression mechanism encodes and normalizes the structural and operational domains separately, and combines principal component analysis, low-rank embedding, or autoencoder techniques to compress and express high-dimensional state data, outputting a unified format current operational state vector as the input basis for subsequent absorption capacity assessment.

[0056] Understandably, this mechanism, by integrating the static structure and dynamic state of the distribution network, can comprehensively express the current system operating characteristics, providing basic data support for subsequent processing such as absorption capacity boundary extraction, sensitivity analysis, and area identification, thereby improving the accuracy and practicality of the evaluation results.

[0057] It should be understood that, compared with traditional schemes that only construct operational status descriptions based on node voltage or power flow distribution, this mechanism can more accurately capture the combined impact of topology changes, flexible load responses, and operational fluctuations on absorption capacity in the distribution network, effectively reducing information redundancy and improving evaluation efficiency. It is particularly suitable for rapid evolution analysis under multiple disturbance conditions.

[0058] For example, such as Figure 2 As shown, the structural domain and runtime domain information are extracted using principal component analysis to obtain the first three principal components, which are used to characterize the state differences between different nodes. These components are then fused to form a 12-dimensional state compression vector. Figure 3 As shown, the upper limit of absorption capacity under five typical perturbation states was fitted using both the traditional method and the method of this invention. The results show that the fitting curve under this method is closer to the simulation reference value, and the curve fluctuation between different perturbation states is significantly reduced, indicating that the state vector expression has better generalization ability.

[0059] Step S20: For the current running state vector The photovoltaic absorption curve fitting mechanism, which employs multi-state parallel perturbation scanning, performs fitting processing and outputs a set of boundary points for the upper limit of absorption. ;

[0060] It should be noted that the photovoltaic absorption curve fitting mechanism of multi-state parallel perturbation scanning refers to the following: based on the current operating state vector constructed in step S10, photovoltaic access scenarios under different perturbation amplitudes and dimensions (such as different nodes, different access power levels, different load response states, etc.) are introduced to construct multiple sets of parallel perturbation sample sets; for each set of perturbation samples, under the premise of keeping the system constraints (such as node voltage, current limits, and feeder power flow limits) from being violated, a piecewise approximation algorithm is used to search for the upper limit of photovoltaic injection power, and finally obtain the maximum absorbable power point under each perturbation path. By aggregating and organizing the maximum absorbable power points of all perturbation paths, a set of boundary points of photovoltaic absorption capacity in the perturbation state space is formed, which is used to characterize the absorption capacity limit range under the current operating state.

[0061] Understandably, this mechanism, by introducing multi-path disturbance modeling, can cover the adaptability of photovoltaic grid integration under various scenarios such as operational fluctuations, load response, and topology switching in the distribution network, significantly improving the comprehensiveness and accuracy of the absorption capacity fitting. Simultaneously, the adoption of a parallel computing architecture can fully utilize multi-core computing resources to accelerate the boundary point extraction process, demonstrating good real-time performance and scalability in practical deployments.

[0062] It should be understood that traditional methods mostly use static single-state scanning or preset node test points to assess the photovoltaic absorption limit, failing to fully consider the dynamic impact of system disturbance evolution on absorption capacity, and are difficult to adapt to the rapid response requirements under multi-state disturbance scenarios. In contrast, this step adopts a joint processing method of disturbance parallel modeling and boundary fitting, which can dynamically capture the "critical region" in the state space and quickly output a multi-dimensional boundary point set, enhancing the assessment mechanism's adaptability to the complex operating characteristics of distribution networks.

[0063] For example, in a simulation experiment using a typical IEEE-33 node power distribution network as the test network, five types of disturbance paths were set (node ​​load increase, enhanced photovoltaic injection, flexible load hysteresis, line switch change, and voltage deviation disturbance), with 10 levels of disturbance amplitude set for each path. The absorption limit search was performed in parallel. The fitting calculation for all 50 disturbance states was completed in approximately 150 seconds, outputting a set of 50 multi-dimensional absorption capacity boundary points. Compared to the traditional serial node-by-node evaluation method (which takes approximately 470 seconds), the mechanism of this invention reduces the time consumption by approximately 68%, and the boundary distribution is more refined, effectively supporting subsequent sensitivity weighting and region identification operations.

[0064] Step S30: Obtain the actual photovoltaic access location and node sensitivity indicators, combined with the boundary point set of the upper limit of absorption range. A distributed absorption capacity matrix P is constructed using a sensitivity weighting method.

[0065] It should be noted that the sensitivity weighting method refers to using the voltage-power sensitivity index of each node in the distribution network as a weighting factor to perform a weighted mapping on the boundary point set of the absorption upper limit interval obtained in step S20, thereby obtaining the distributed photovoltaic absorption capacity matrix P for all nodes. The node sensitivity index reflects the sensitivity of the photovoltaic injected power at each node to operational constraints such as voltage deviation and line power flow exceeding limits, and is usually obtained through analysis of the node injected disturbance and the power flow equation.

[0066] It should be understood that, compared with the existing technology of constructing photovoltaic access capacity distribution through static node capacity lookup tables or average allocation, this step introduces a sensitivity weighting mechanism, which can dynamically reflect the unbalanced impact of photovoltaic disturbances on system operation constraints. It is particularly suitable for distribution network environments with multi-source fluctuations, tight voltage constraints, or flexible load responses.

[0067] For example, in a 33-node distribution network case, nodes 1-5 are selected as candidate photovoltaic (PV) access nodes. Ten sets of upper limit boundary points for absorption under different disturbance states are constructed, and the voltage sensitivity weight of each node is obtained using the linear least squares method. The sensitivity weights and boundary point data are fused to construct an absorption capacity matrix P (10×5 dimensional). A significantly lower absorption capacity is observed at nodes 2 and 4, reflecting these as local bottlenecks. Further simulations show that the control strategy based on the P matrix reduces the risk of voltage exceedance by approximately 31.4% compared to the average allocation scheme, and improves the overall PV access capacity by approximately 12.7%.

[0068] Step S40: Based on the distributed absorption capacity matrix P, an adjustable capacity threshold dynamic screening mechanism is used to perform local absorption area identification processing, and output the set of regional absorption upper limit boundaries;

[0069] It should be noted that the adjustable capacity threshold dynamic screening mechanism refers to introducing a set of state-dependent dynamic capacity threshold functions T(i) based on the remaining margin of absorption capacity of each node in the distributed absorption capacity matrix P, which are used to perform node-level screening. This dynamic capacity threshold function T(i) not only considers the state characteristics of node i, such as historical voltage fluctuation amplitude, sensitivity level, and flexible load response delay, but can also be dynamically adjusted according to external disturbances such as the overall load factor and photovoltaic output change trend, thereby avoiding the problems of over-screening or under-screening caused by static thresholds.

[0070] Understandably, this mechanism filters the absorption capacity matrix P row by row and column by column to form a binary mask with spatial sparsity and state selectivity. It then identifies connected regions that simultaneously satisfy absorption capacity sufficiency under multiple perturbation states by combining physical adjacency relationships. The final output "region-level absorption capacity upper limit boundary set" not only includes the set of numbers for all core nodes in the region but also identifies key evaluation indicators such as the maximum absorption capacity upper limit for each region, the voltage stability margin of the covered nodes, and the average local sensitivity, providing fundamental information for subsequent regional evolution trend extraction and resource scheduling adjustment.

[0071] It should be understood that, compared with the existing technologies that generally use fixed capacity thresholds (such as 0.2MW) or identification methods based on a single voltage limit indicator, this mechanism achieves flexible discrimination of node response capabilities under different disturbance scenarios through a dynamic threshold function. This effectively overcomes the problems of traditional methods where the regional identification results rely on a single state snapshot and lack adaptability. It has stronger stability and accuracy in handling complex situations such as multi-state disturbance analysis, flexible load collaborative control, and high-proportion photovoltaic penetration.

[0072] For example, in the simulation evaluation of a typical 10kV distribution network, the constructed distributed absorption capacity matrix P covers five typical load scenarios and three combinations of photovoltaic fluctuation disturbances. Using the traditional static threshold method (such as uniformly setting a lower absorption limit of 0.2MW), only two absorbable areas were identified, with a maximum absorption limit of 1.85MW. However, by adopting the dynamic screening mechanism proposed in this invention, three areas with stronger boundary continuity and higher adjustment margins were successfully identified, increasing the total absorption capacity limit to 2.64MW. The boundary contours of these areas better match the actual locations of sensitive load clusters, significantly reducing the misjudgment rate.

[0073] Step S50: Based on the regional absorption upper limit boundary set, the DBSCAN density clustering method is used to perform regional evolution trend analysis to obtain the regional photovoltaic adjustable absorption capacity evolution map.

[0074] It should be noted that DBSCAN density clustering is a density-based unsupervised clustering algorithm. Its core idea is to cluster based on the neighborhood density between sample points, without relying on a preset number of clusters. It can effectively discover high-density regions of arbitrary shapes and automatically remove isolated points. In this step, for the set of regional absorption capacity upper bounds under multiple perturbation scenarios obtained in step S40, the features such as the center position, absorption capacity upper bound, node density, and mean sensitivity of each region are encoded into multi-dimensional vectors to form the clustering input dataset. By setting reasonable minimum sample number and neighborhood radius parameters, the DBSCAN clustering operation is performed to aggregate regions with similar boundary change trends, thereby identifying region clusters with the same absorption capacity evolution trajectory.

[0075] Understandably, this clustering process can extract stable evolution patterns from multi-state, multi-scenario boundary sets, eliminate isolated and anomalous regions, and establish a cluster map of regional photovoltaic absorption capacity evolution under disturbances. This facilitates grid dispatchers in quickly identifying different regional categories such as "stable," "mutational," and "fluctuating" regions, and formulating targeted resource coordination strategies. The final output "Regional-level Adjustable Photovoltaic Absorption Capacity Evolution Map" uses cluster numbers as indexes to describe the evolutionary characteristics of various regional clusters under different disturbance states, including the range of absorption capacity variation, adjustment sensitivity, and boundary continuity.

[0076] It should be understood that, compared with traditional evaluation methods based on manual rules or fixed regional divisions, this step can adaptively identify the inherent correlation of regional absorption feature evolution through density clustering algorithms, improve the objectivity and interpretability of regional classification, and avoid the problem that regional identification results depend on manual division scales. It is particularly suitable for dimensionality reduction abstraction and trend modeling of high-dimensional absorption boundary data in large-scale distribution networks.

[0077] Example 2: Furthermore, the present invention provides a distribution network photovoltaic absorption capacity assessment and analysis system, which employs a distribution network photovoltaic absorption capacity assessment and analysis method from the above embodiments, and can solve a technical problem in the assessment and analysis of distribution network photovoltaic absorption capacity. Compared with the prior art, the beneficial effects of the distribution network photovoltaic absorption capacity assessment and analysis system provided by the present invention are the same as those of the distribution network photovoltaic absorption capacity assessment and analysis method provided in the above embodiments, and other technical features of the distribution network photovoltaic absorption capacity assessment and analysis system are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0078] Example 3: This invention provides a device for assessing and analyzing the photovoltaic absorption capacity of a power distribution network. Please refer to... Figure 4A distribution network photovoltaic (PV) absorption capacity assessment and analysis device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform a distribution network PV absorption capacity assessment and analysis method as described in Embodiment 1 above. The distribution network PV absorption capacity assessment and analysis device in this embodiment may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), and vehicle terminals (e.g., vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. This distribution network PV absorption capacity assessment and analysis device is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this invention. A distribution network photovoltaic absorption capacity assessment and analysis device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory 1002 or a program loaded from a storage device 1003 into a random access memory 1004. The random access memory 1004 also stores various programs and data required for the operation of the distribution network photovoltaic absorption capacity assessment and analysis device. The processing unit 1001, the read-only memory 1002, and the random access memory 1004 are interconnected via a bus 1005. An I / O interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. Communication device 1009 allows a distribution network photovoltaic absorption capacity assessment and analysis device to communicate wirelessly or wiredly with other devices to exchange data. Although the figure shows a distribution network photovoltaic absorption capacity assessment and analysis device with various systems, it should be understood that implementation or possession of all the systems shown is not required. More or fewer systems may be implemented alternatively.

[0079] Example 4: This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described method for assessing and analyzing the photovoltaic absorption capacity of a distribution network. The computer program product provided by this invention can solve a technical problem related to assessing and analyzing the photovoltaic absorption capacity of a distribution network. Compared with the prior art, the beneficial effects of the computer program product provided by this invention are the same as those of the photovoltaic absorption capacity assessment and analysis method for a distribution network provided in the above embodiments, and will not be repeated here.

[0080] In particular, according to the embodiments disclosed in this invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from read-only memory 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this invention.

[0081] It should be understood that the various parts disclosed in this invention can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics may be combined in any suitable manner in one or more embodiments or examples.

[0082] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for assessing and analyzing the photovoltaic absorption capacity of a distribution network, characterized in that, The methods include: Step S10: Obtain the structural information and operating parameters of the target distribution network, and construct the current operating state vector based on the structural information and operating parameters through a dual-domain coupled state compression expression mechanism. ; Step S20: For the current running state vector The photovoltaic absorption curve fitting mechanism, which employs multi-state parallel perturbation scanning, performs fitting processing and outputs a set of boundary points for the upper limit of absorption. ; Step S30: Obtain the actual photovoltaic access location and node sensitivity indicators, combined with the boundary point set of the upper limit of absorption range. A distributed absorption capacity matrix P is constructed using a sensitivity weighting method. Step S40: Based on the distributed absorption capacity matrix P, an adjustable capacity threshold dynamic screening mechanism is used to perform local absorption area identification processing, and output the set of regional absorption upper limit boundaries; Step S50: Based on the regional absorption upper limit boundary set, the DBSCAN density clustering method is used to perform regional evolution trend analysis to obtain the regional photovoltaic adjustable absorption capacity evolution map.

2. The method for assessing and analyzing the photovoltaic absorption capacity of a distribution network as described in claim 1, characterized in that, In step S10, the structural information of the target distribution network includes the target distribution network topology diagram. The information includes node load types and physical parameters; the operating parameters of the target distribution network include real-time load curves, real-time photovoltaic output, the set of response characteristic parameters of flexible load units, and the set of topological influence factors.

3. The method for assessing and analyzing the photovoltaic absorption capacity of a distribution network as described in claim 2, characterized in that, In step S10, the structural information and operating parameters of the target distribution network are obtained, and the current operating state vector is constructed based on the structural information and operating parameters through a dual-domain coupled state compression expression mechanism. The steps specifically include: Step S101: Obtain the structural information and operating parameters of the target distribution network, and extract the target distribution network topology from the structural information. ;Target distribution network topology diagram A pruning dimensionality reduction method based on voltage sensitivity analysis is used to perform structural dimensionality reduction, resulting in a simplified subgraph set; the current topology state compression code is then constructed based on this simplified subgraph set. , ;in, The current open / closed status of the interconnection switch between the main feeder and the backup feeder; This refers to the current open / closed state of the terminal branch switch containing a large-capacity photovoltaic access point; The current on / off state of the bypass switch of the m-th level transformer area in the target distribution network; ( ) is a binary encoding function used to concatenate the current on / off states of the selected key switches into a set of binary numbers according to their selected number order; Step S102: Obtain the set of response characteristic parameters of the i-th flexible load unit from the operating parameters of the target distribution network. , ,in, This represents the maximum adjustable power of the i-th flexible load unit; Let be the response time constant of the i-th flexible load unit; Let be the startup response delay time of the i-th flexible load unit; Let be the start-up and reversal adjustment trend coefficient of the i-th flexible load unit; Collect the response power of the i-th flexible load unit at time t. Based on the already responded power The current adjustment status label is obtained by performing a judgment. ; Based on the current adjustment status label Response power Construct the set of flexible load response states for the entire network using the maximum adjustable power of the i-th flexible load unit; Step S103: Compress code based on the entire network flexible load response state set and the current topology state Construct the current running state vector .

4. The method for assessing and analyzing the photovoltaic absorption capacity of a distribution network as described in claim 3, characterized in that, In step S10, the current adjustment status label The formula is expressed as: ； in, This represents the initial state before the i-th flexible load unit has triggered a response; The i-th flexible load unit is in the linear growth response stage where the power gradually increases; This is the stage where the responsive power of the i-th flexible load unit has reached its upper limit and remains at a constant saturation value; This refers to the decline stage in which the power of the i-th flexible load unit reverts to its original value due to factors such as reverse regulation or delayed recovery. The theoretical time for the i-th flexible load unit to complete its power saturation response; The control duration for maintaining maximum response power for the i-th flexible load unit.

5. The method for assessing and analyzing the photovoltaic absorption capacity of a distribution network as described in claim 1, characterized in that, In step S20, for the current running state vector The photovoltaic absorption curve fitting mechanism, which employs multi-state parallel perturbation scanning, performs fitting processing and outputs a set of boundary points for the upper limit of absorption. The steps specifically include: Step S201: Construct a set of topology disturbance states, including main feeder disconnection, tie switch connection, and section fault isolation; construct a set of flexible load disturbance states in the flexible load unit, including fully releasing adjustable capacity, releasing only part of the adjustable capacity, and exiting regulation or performing reverse regulation; perform a combined Cartesian product on the set of topology disturbance states and the set of flexible load disturbance states to generate a final set of combined states; Step S202: Combine the final state set with the current running state vector Input a preset power grid operation assessment model, and the power grid operation assessment model outputs the maximum safe photovoltaic power value. Based on the maximum safe photovoltaic power value, a set of candidate points for assessing the photovoltaic absorption capacity of the target distribution network is obtained. Step S203: Based on the candidate point set for photovoltaic absorption capacity assessment, a photovoltaic absorption capacity curve is obtained through piecewise linear fitting. Boundary extraction is performed based on the photovoltaic absorption capacity curve, and the boundary point set of the upper limit interval of absorption capacity is output. .

6. The method for assessing and analyzing the photovoltaic absorption capacity of a distribution network as described in claim 1, characterized in that, In step S30, the actual photovoltaic access location and node sensitivity indicators are obtained, combined with the boundary point set of the upper limit of absorption range. The steps for constructing a distributed absorption capacity matrix using the sensitivity weighting method specifically include: Step S301: Obtain the actual photovoltaic access location and node sensitivity indicators. The node sensitivity indicators include node voltage change rate, voltage constraint margin, line power flow transfer rate and voltage stability margin. Perform normalization processing on the node sensitivity indicators to generate the sensitivity weight vector corresponding to the actual photovoltaic access location. Step S302: Apply sensitivity weight vector to the set of boundary points of the upper limit of absorption interval By assigning weights to each item in the total absorbable capacity matrix, a distributed absorbable capacity matrix P is output.

7. The method for assessing and analyzing the photovoltaic absorption capacity of a distribution network as described in claim 3, characterized in that, Step S40, which involves using an adjustable capacity threshold dynamic filtering mechanism based on the distributed absorption capacity matrix P to perform local absorption area identification processing and output the set of regional absorption upper limit boundaries, specifically includes: Step S401: For the i-th flexible load unit in the distributed absorption capacity matrix P, obtain its corresponding sensitivity weight W(i), and based on the sensitivity weight W(i) and the current adjustment state label... Construct a dynamically adjustable capacity threshold function T(i); Step S402: Apply the dynamic adjustable capacity threshold function T(i) to the distributed absorption capacity matrix P under all disturbance states to obtain the absorption capacity margin matrix of each node under each disturbance state; and perform threshold judgment based on the absorption capacity margin matrix to obtain the absorption capacity mask matrix; the absorption capacity mask matrix is ​​used to indicate whether the node has the ability to absorb photovoltaic power under a specific disturbance state. Step S403: Extract the perturbation state index and corresponding node index corresponding to the value 1 in the absorbability mask matrix, and construct a regional absorbability upper limit boundary set based on the perturbation state index and corresponding node index; the regional absorbability upper limit boundary set is used to realize the deduction of flexible scheduling strategy based on partition.

8. The method for assessing and analyzing the photovoltaic absorption capacity of a distribution network as described in claim 7, characterized in that, In step S40, the step of performing threshold judgment based on the absorbability margin matrix to obtain the absorbability mask matrix specifically includes: setting a set of state-sensitive absorbability margin thresholds for the absorbability margin matrix; performing threshold judgment between the state-sensitive absorbability margin thresholds and the absorbability margin matrix using a conditional discrimination method based on element-level comparison; and constructing an absorbability mask matrix in two-dimensional Boolean matrix format based on the result of the threshold judgment.

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