Power distribution network reconstruction method and system based on equilibrium constraint mathematical programming

Abstract

The invention discloses a power distribution network reconstruction method and system based on equilibrium constraint mathematical programming, and is applied to the technical field of power systems, and the method comprises the steps: collecting the historical operation data of a target power distribution network, and obtaining a first-stage power distribution network and a second-stage power distribution network; obtaining a target first-level power distribution network according to the fluctuation complementarity characteristics between the new energy power station to be accessed and each first-level power distribution network; obtaining a target secondary power distribution network based on the new energy power station and the power similarity characteristics of all secondary power distribution networks of the target primary power distribution network; connecting the new energy power station to the target secondary power distribution network to obtain a first reconstructed power distribution network; and processing the switch control optimization model of the first reconstructed power distribution network by using equilibrium constraint mathematical programming, and executing a power distribution network reconstruction strategy matched with the obtained target switch combination to obtain a second reconstructed power distribution network. According to the power distribution network reconstruction method and system based on equilibrium constraint mathematical programming provided by the embodiment of the invention, the reconstruction efficiency of the power distribution network is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a distribution network reconstruction method and system based on equilibrium constrained mathematical programming. Background Art

[0002] With the development of new energy, distributed power sources are being connected to the distribution network in large quantities due to their environmental protection, high efficiency and flexibility. However, since the output of new energy such as wind power and photovoltaic power is affected by the weather and does not match the traditional power generation method, the distribution network's power flow distribution, voltage stability and network loss problems are more complicated. Therefore, when new energy is connected to the distribution network, the distribution network needs to be dynamically reconstructed to adapt to the access of new energy power stations and ensure the safe, economical and reliable operation of the power grid.

[0003] Since renewable energy generation is random, and existing distribution network reconstruction technologies usually adopt a fixed reconstruction mode and lack targeted analysis of renewable energy generation characteristics, the existing technologies have low efficiency in reconstructing distribution networks.

[0004] It can be seen that how to improve the efficiency of reconstruction of the distribution network has become a technical problem that needs to be urgently solved by those skilled in the art. Summary of the invention

[0005] The present invention provides a distribution network reconstruction method and system based on equilibrium constrained mathematical programming to solve the problem that the existing distribution network reconstruction technology usually adopts a fixed reconstruction mode and lacks targeted analysis of the characteristics of renewable energy power generation, resulting in low efficiency of distribution network reconstruction in the existing technology due to the randomness of renewable energy power generation.

[0006] In order to solve the above technical problems, an embodiment of the present invention provides a distribution network reconstruction method based on equilibrium constrained mathematical programming.

[0007] Collect all historical operation data of the target distribution network, and perform hierarchical processing on the target distribution network based on the current network topology of the target distribution network and the corresponding historical operation data to obtain each primary distribution network and each secondary distribution network of each primary distribution network; The target primary distribution network is obtained based on the fluctuation complementarity characteristics between the new energy power station to be connected and each of the primary distribution networks; the target secondary distribution network is obtained based on the power similarity characteristics of all the secondary distribution networks of the new energy power station and the target primary distribution network; Connect the new energy power station to the target secondary distribution network to obtain a first reconstructed distribution network. Among them, the first node information of the first reconstructed distribution network is designed to perform a correlation analysis on the matrix result of the target network topology structure obtained after connecting the new energy power station, and adjust the second node information corresponding to the target network topology structure based on the result of the correlation analysis. Construct an optimal model for switch control of the first reconstructed distribution network, process the optimal model for switch control by means of equilibrium constraint mathematical programming to obtain a target switch combination, and control the functional components of the target distribution network to execute a distribution network reconstruction strategy matching the target switch combination to obtain a second reconstructed distribution network.

[0008] As one of the preferred solutions, the obtaining of the target primary distribution network according to the fluctuation complementary characteristics between the new energy power station to be connected and each primary distribution network includes: Construct a new energy output curve based on the first historical power generation data of the new energy power station, construct a load curve for each primary distribution network based on the historical total power load data of all nodes in each primary distribution network, and construct a grid output curve for each primary distribution network based on the second historical power generation data of all nodes in each primary distribution network. Calculate the extreme event co-occurrence factor between the new energy power station and each primary distribution network according to the complementary characteristics between the new energy output curve and the load curve of each primary distribution network. Among them, the extreme event co-occurrence factor reflects the probability of simultaneous occurrence of a sharp drop in new energy output and a sudden increase in load. Calculate the supply-demand adjustment factor for each primary distribution network based on the difference characteristics between the grid output curve and the corresponding load curve of each primary distribution network. Among them, the supply-demand adjustment factor reflects the urgency of reconstructing the supply-demand relationship between the grid output and the load demand of the primary distribution network to achieve supply-demand balance. Screen all the primary distribution networks with the extreme event co-occurrence factor and the supply-demand adjustment factor to obtain the target primary distribution network.

[0009] As one of the preferred solutions, the calculating of the extreme event co-occurrence factor between the new energy power station and each primary distribution network according to the complementary characteristics between the new energy output curve and the load curve of each primary distribution network includes: Extract all the first mutation points in the load curve and all the second mutation points in the new energy output curve. Obtain the load sudden increase factor for each first mutation point based on the neighborhood difference characteristics of each first mutation point. Among them, the load sudden increase factor reflects the degree of load sudden increase at the first mutation point. Obtain the output sudden drop factor of each of the second mutation points based on the neighborhood difference characteristics of each of the second mutation points in the new energy output curve; wherein, the output sudden drop factor reflects the degree of sudden drop in new energy output at the second mutation point; Take the second mutation point closest to each of the first mutation points as the co-mutation point of each of the first mutation points; based on the linear relationship between the load sudden increase factor of all the first mutation points and the output sudden drop factor of the corresponding co-mutation points, calculate the extreme event co-occurrence factor between the new energy power station and each of the primary distribution networks.

[0010] As one of the preferred solutions, the obtaining of the load sudden increase factor of each of the first mutation points based on the neighborhood difference characteristics of each of the first mutation points includes: With each of the first mutation points in each of the load curves as the center, construct the first analysis window of each of the first mutation points; Exclude the first mutation points within each of the first analysis windows to obtain the first mutation exclusion window of each of the first mutation points, and take the average value of all the element values within the first mutation exclusion window of each of the first mutation points as the neighborhood corrected load of each of the first mutation points; Calculate the load sudden increase factor of each of the first mutation points based on the difference between the element value of each of the first mutation points and the corresponding neighborhood corrected load.

[0011] As one of the preferred solutions, the obtaining of the output sudden drop factor of each of the second mutation points based on the neighborhood difference characteristics of each of the second mutation points in the new energy output curve includes: With each of the second mutation points in the new energy output curve as the center, construct the second analysis window of each of the second mutation points; Exclude the second mutation points within each of the second analysis windows to obtain the second mutation exclusion window of each of the second mutation points, and take the average value of all the element values within the second mutation exclusion window of each of the second mutation points as the neighborhood corrected output value of each of the second mutation points; Calculate the output sudden drop factor of each of the second mutation points based on the difference between the element value of each of the second mutation points and the corresponding neighborhood corrected output value.

[0012] As one of the preferred solutions, the calculating of the supply-demand adjustment factor of each of the primary distribution networks based on the difference characteristics between the grid output curve and the corresponding load curve of each of the primary distribution networks includes: Obtain the grid power output margin curve for each primary distribution network based on the difference between the grid power output curve and the corresponding load curve of each primary distribution network, where the grid power output margin curve reflects the remaining power of the grid power output of the primary distribution network after meeting the corresponding load demand; Take the average value of all element values of the grid power output margin curve of each primary distribution network as the margin adequacy of each primary distribution network, take the maximum value of the margin adequacy of all primary distribution networks as the margin reference value, and calculate the margin shortage index for each primary distribution network based on the difference characteristics between the margin adequacy and the margin reference value of each primary distribution network; where the margin shortage index reflects the supply-demand relationship between the grid power output and the load demand of the primary distribution network; Calculate the margin fluctuation index for each primary distribution network according to the fluctuation degree of the grid power output margin curve of each primary distribution network; Based on the linear relationship between the margin shortage index and the corresponding margin fluctuation index of each primary distribution network, calculate the supply-demand adjustment factor for each primary distribution network.

[0013] As one preferred solution, the hierarchical processing of the target distribution network based on the current network topology structure and the corresponding historical operation data to obtain each primary distribution network and each secondary distribution network of each primary distribution network includes: Process the current network topology structure using a community discovery algorithm to obtain each primary distribution network; Extract features from all the historical operation data of each node in each primary distribution network to obtain the target power feature vector of each node; Use a clustering algorithm to perform clustering analysis on the target power feature vectors of all the nodes in each primary distribution network to obtain each secondary distribution network of each primary distribution network.

[0014] As one preferred solution, the obtaining of the target secondary distribution network based on the power similarity characteristics of the new energy power station and all the secondary distribution networks of the target primary distribution network includes: Extract features from all the historical operation data of the new energy power station to obtain the first power feature vector of the new energy power station; Based on the target power feature vectors of all the nodes of each secondary distribution network of the target primary distribution network, obtain the second power feature vector of each secondary distribution network of the target primary distribution network; Taking the dynamic time warping distance between the first power feature vector and the second power feature vector of each secondary distribution network of the target primary distribution network as the power feature dissimilarity factor between the first power feature vector and each secondary distribution network of the target primary distribution network; Performing sorting analysis on the power feature dissimilarity factors of all the secondary distribution networks of the target primary distribution network, and obtaining a target secondary distribution network based on the result of the sorting analysis.

[0015] As one preferred solution, the method further includes: Before controlling the functional components of the target distribution network to execute the distribution network reconstruction strategy matching the target switch combination, performing simulation on the distribution network reconstruction strategy; Adjusting the distribution network reconstruction strategy based on the result of the simulation.

[0016] Another embodiment of the present invention provides a distribution network reconstruction system based on equilibrium constraint mathematical programming, and the system includes: A distribution network layering module, configured to collect all historical operation data of the target distribution network, perform layering processing on the target distribution network based on the current network topology structure of the target distribution network and the corresponding historical operation data, and obtain each primary distribution network and each secondary distribution network of each primary distribution network; An access point evaluation module, configured to obtain a target primary distribution network according to the fluctuation complementary feature between the new energy power station to be connected and each primary distribution network; obtaining a target secondary distribution network based on the power similarity feature between the new energy power station and all the secondary distribution networks of the target primary distribution network; A topology reconstruction module, configured to connect the new energy power station to the target secondary distribution network to obtain a first reconstructed distribution network; wherein, the first node information of the first reconstructed distribution network is designed to perform correlation analysis on the matrixed result of the target network topology structure after connecting the new energy power station, and adjust the second node information corresponding to the target network topology structure based on the result of the correlation analysis; A switch reconstruction module, configured to construct a switch control optimization model of the first reconstructed distribution network, process the switch control optimization model with equilibrium constraint mathematical programming to obtain a target switch combination; controlling the functional components of the target distribution network to execute the distribution network reconstruction strategy matching the target switch combination to obtain a second reconstructed distribution network.

[0017] Compared with the prior art, the beneficial effects of the embodiments of the present invention are at least one of the following: The large-scale distribution network is decomposed into multiple levels and sub-regions through hierarchical processing, transforming the global complex problem into a local optimization problem. It allows only the affected branches to be adjusted when the topology changes, rather than reconstructing the entire network, enhancing flexibility and response speed, and improving the real-time performance of distribution network reconstruction. The target primary distribution network is obtained based on the fluctuation complementarity characteristics between the new energy power stations to be connected and each primary distribution network, smoothing the overall power fluctuation, achieving peak shaving and valley filling. The target secondary distribution network is obtained based on the power similarity characteristics of all secondary distribution networks of the new energy power stations and the target primary distribution network, ensuring the efficient consumption of new energy power and minimizing the impact of new energy power stations on the primary distribution network. Through double screening, a hierarchical decision-making logic is formed to avoid blind access, comprehensively balance the optimization of the whole and the part, and improve the reconstruction efficiency of the distribution network. By transforming the topological structure into a matrix and adjusting the topological structure according to the impact after the access of new energy, congestion or voltage over-limit is actively prevented. A switching control optimization model of the first reconstructed distribution network is constructed, and the switching control optimization model is processed by equilibrium constraint mathematical programming to obtain the target switching combination. The functional components of the target distribution network are controlled to execute the distribution network reconstruction strategy matching the target switching combination to obtain the second reconstructed distribution network. By further adjusting the switching combination, the distribution network is further optimized, and the reconstruction efficiency of the distribution network is further improved. From the network-wide hierarchical to local access, topological adjustment, and switch optimization, a gradually refined optimization chain is formed, comprehensively considering fluctuation complementarity, topological adaptability, and switch coordination, effectively alleviating the challenges of new energy randomness to the distribution network, and realizing the precise siting of new energy access and the efficient dynamic reconstruction of the distribution network. Description of the Drawings

[0018] Figure 1 It is a schematic flowchart of a distribution network reconstruction method based on equilibrium constraint mathematical programming in one embodiment of the present invention; Figure 2 It is an architecture diagram of a distribution network reconstruction system based on equilibrium constraint mathematical programming in one embodiment of the present invention; Reference Signs: 11, Distribution network layering module; 12, Access point evaluation module; 13, Topological reconstruction module; 14, Switch reconstruction module. Detailed Embodiments

[0019] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. The purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0020] In the description of this application, the terms "first", "second", "third", etc. are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first", "second", "third", etc. may explicitly or implicitly include one or more of such features. In the description of this application, unless otherwise stated, the meaning of "a plurality" is two or more.

[0021] In the description of this application, it should be noted that unless otherwise clearly specified and defined, the terms "installed", "connected", "coupled" should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral connection; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the communication inside two components. The terms "vertical", "horizontal", "left", "right", "up", "down" and similar expressions used herein are only for illustrative purposes and do not indicate or imply that the indicated device or element must have a specific orientation, be constructed and operated in a specific orientation, and thus cannot be construed as a limitation of the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0022] In the description of this application, it should be noted that unless otherwise defined, all technical and scientific terms used in this invention have the same meaning as commonly understood by those of ordinary skill in the technical field to which this invention belongs. The terms used in the specification of this invention are only for the purpose of describing specific embodiments and are not intended to limit this invention. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0023] With the large-scale grid connection of renewable energy sources such as wind power and photovoltaic power, the operation of the distribution network faces unprecedented challenges. The intermittent and volatile characteristics of new energy power generation form a significant contradiction with the rigid regulation characteristics of traditional power generation methods, resulting in new complex characteristics in the operation of the distribution network.

[0024] However, the existing distribution network reconfiguration technologies usually adopt fixed reconfiguration modes and lack targeted analysis of the characteristics of new energy power generation, resulting in low reconfiguration efficiency of the distribution network.

[0025] An embodiment of the present invention provides a distribution network reconfiguration method based on equilibrium constraint mathematical programming. Specifically, please refer to Figure 1 , Figure 1 which is shown as a schematic flow chart of a distribution network reconfiguration method based on equilibrium constraint mathematical programming in one of the embodiments of the present invention.

[0026] Step S1: Collect all the historical operation data of the target distribution network, and perform hierarchical processing on the target distribution network based on the current network topology structure of the target distribution network and the corresponding historical operation data to obtain each primary distribution network and each secondary distribution network of each primary distribution network.

[0027] Step S2: Obtain the target primary distribution network according to the fluctuation complementary characteristics between the new energy power station to be connected and each primary distribution network; obtain the target secondary distribution network based on the power similarity characteristics between the new energy power station and all the secondary distribution networks of the target primary distribution network.

[0028] Step S3: Connect the new energy power station to the target secondary distribution network to obtain the first reconstructed distribution network; among them, the first node information of the first reconstructed distribution network is designed to perform correlation analysis on the matrix result of the target network topology structure obtained after connecting the new energy power station, and adjust the second node information corresponding to the target network topology structure based on the result of the correlation analysis.

[0029] Step S4: Construct an optimization model for switch control of the first reconstructed distribution network, process the switch control optimization model with equilibrium constraint mathematical programming to obtain the target switch combination; control the functional components of the target distribution network to execute the distribution network reconstruction strategy matching the target switch combination to obtain the second reconstructed distribution network.

[0030] A distribution network reconstruction method based on equilibrium constraint mathematical programming provided in this embodiment decomposes a large-scale distribution network into multiple levels and sub-regions through hierarchical processing, transforms the global complex problem into a local optimization problem, allows only the affected branches to be adjusted when the topology changes instead of reconstructing the entire network, improves flexibility and response speed, and enhances the real-time performance of the distribution network reconstruction; obtains the target primary distribution network according to the fluctuation complementarity characteristics between the new energy power station to be connected and each primary distribution network, smooths the overall power fluctuation, realizes peak shaving and valley filling, and obtains the target secondary distribution network based on the power similarity characteristics of all secondary distribution networks of the new energy power station and the target primary distribution network to ensure the efficient consumption of new energy power and minimize the impact of the new energy power station on the primary distribution network. Through double screening, a hierarchical decision-making logic is formed to avoid blind access, comprehensively balance the optimization of the whole and the part, and improve the efficiency of the distribution network reconstruction; transforms the topological structure into a matrix, adjusts the topological structure according to the impact after the access of new energy, and actively prevents congestion or voltage over-limit; constructs an optimization model for switch control of the first reconstructed distribution network, processes the switch control optimization model with equilibrium constraint mathematical programming to obtain the target switch combination; controls the functional components of the target distribution network to execute the distribution network reconstruction strategy matching the target switch combination to obtain the second reconstructed distribution network, and further optimizes the distribution network by further adjusting the switch combination, further improving the efficiency of the distribution network reconstruction; forms a gradually refined optimization chain from the whole network layer to local access, topological adjustment, and switch optimization, comprehensively considers fluctuation complementarity, topological adaptability, and switch coordination, effectively alleviates the challenges of new energy randomness to the distribution network, and realizes the precise location of new energy access and the efficient dynamic reconstruction of the distribution network.

[0031] In one embodiment, in step S3, connecting the new energy power station to the target secondary distribution network to obtain the first reconstructed distribution network includes: Connect the new energy power station to the target secondary distribution network, construct the corresponding node-branch adjacency matrix, node admittance matrix, and branch impedance matrix according to the obtained target network topology, perform matrix operations through the node admittance matrix and the branch impedance matrix, and adjust the branch connection relationship of the target network topology based on the results of the matrix operations to optimize the node admittance and branch impedance to obtain the first reconstructed distribution network.

[0032] For example, after connecting a 3MW wind farm to node 12 in a 15-node distribution network with a loop network, it causes a circulating current resulting in overload of branch 6-9. Construct the loop impedance matrix of the target network topology after connecting the 3MW wind farm, identify the circulating current path (branches 6-9, 9-12, 12-6), calculate that the current contribution ratio of the wind farm injection to branch 6-9 reaches 65%, and by making the elements in the node-branch adjacency matrix A ，Disconnect the branch with the highest disconnection impedance 9-12 to force the power flow to redistribute.

[0033] In one embodiment, in step S2, obtaining the target primary distribution network according to the fluctuation complementarity characteristics between the new energy power generation station to be connected and each primary distribution network includes: Step S201: Construct a new energy output curve based on the first historical power generation data of the new energy power generation station, construct a load curve for each primary distribution network based on the historical total power load data of all nodes in each primary distribution network, and construct a grid output curve for each primary distribution network based on the second historical power generation data of all nodes in each primary distribution network; Step S202: Calculate the extreme event co-occurrence factor between the new energy power generation station and each primary distribution network according to the complementarity characteristics between the new energy output curve and the load curve of each primary distribution network; wherein, the extreme event co-occurrence factor reflects the probability of the simultaneous occurrence of a sudden drop in new energy output and a sudden increase in load; Step S203: Calculate the supply-demand adjustment factor for each primary distribution network based on the difference characteristics between the grid output curve and the corresponding load curve of each primary distribution network; wherein, the supply-demand adjustment factor reflects the urgency of reconstructing the supply-demand relationship between the grid output and the load demand of the primary distribution network to achieve supply-demand balance; Step S204: Screen all primary distribution networks with the extreme event co-occurrence factor and the supply-demand adjustment factor to obtain the target primary distribution network.

[0034] A distribution network reconstruction method based on equilibrium constraint mathematical programming provided in this embodiment quantifies the sudden drop in new energy output through the extreme event co-occurrence factor, such as the co-occurrence probability of a windless period of wind power and a sudden increase in load, such as the peak load of air conditioners in summer, identifies the extreme risk scenarios of the grid-new energy combination, and preferentially avoids high-risk access schemes; quantifies the shortage of the self-regulation ability of the distribution network through the supply-demand adjustment factor, and the urgency of relying on external new energy supplementation. Connecting a distribution network with a high supply-demand adjustment factor, that is, a prominent internal supply-demand contradiction, will have a more significant improvement effect after connecting new energy, optimizing the resource allocation efficiency; through the double quantification screening of extreme events and supply-demand adjustment, it ensures that the access of new energy minimizes the system risk and maximizes the potential for improving supply-demand balance, laying a reliable foundation for subsequent topology reconstruction and switch optimization, and improving the efficiency of distribution network reconstruction.

[0035] In one embodiment, in step S202, calculating the extreme event co-occurrence factor between the new energy power generation station and each primary distribution network according to the complementarity characteristics between the new energy output curve and the load curve of each primary distribution network includes: Extract all first mutation points in the load curve and all second mutation points in the new energy output curve; obtain the load surge factor of each first mutation point based on the neighborhood difference characteristics of each first mutation point; wherein the load surge factor reflects the degree of load surge at the first mutation point; Based on the neighborhood difference characteristics of each second mutation point in the new energy output curve, the output sudden drop factor of each second mutation point is obtained; wherein the output sudden drop factor reflects the degree of sudden drop of the new energy output at the second mutation point; The second mutation point closest to each first mutation point is taken as the coordinated mutation point of each first mutation point; based on the linear relationship between the load surge factors of all first mutation points and the output drop factors of the corresponding coordinated mutation points, the extreme event co-occurrence factors of the new energy power station and each primary distribution network are calculated.

[0036] As an embodiment of the present application, the absolute value of the difference between the load surge factor of each first mutation point and the output drop factor of the corresponding coordinated mutation point is used as the extreme difference index of each first mutation point, and the average value of the extreme difference indexes of all first mutation points in the load curve of each primary distribution network is used as the extreme event co-occurrence factor of each primary distribution network.

[0037] In this step, the load sudden increase factor of each first mutation point is obtained based on the neighborhood difference characteristics of each first mutation point, including: Taking each first mutation point in each load curve as the center, construct the first analysis window of each first mutation point; eliminate the first mutation points in each first analysis window to obtain the first mutation elimination window of each first mutation point, and take the average value of all element values in the first mutation elimination window of each first mutation point as the neighborhood correction load of each first mutation point; calculate the load surge factor of each first mutation point based on the difference between the element value of each first mutation point and the corresponding neighborhood correction load.

[0038] In this step, the output drop factor of each second mutation point in the new energy output curve is obtained based on the neighborhood difference characteristics of each second mutation point, including: Taking each second mutation point in the new energy output curve as the center, construct a second analysis window for each second mutation point; eliminate the second mutation points in each second analysis window to obtain the second mutation elimination window for each second mutation point, and take the average value of all element values in the second mutation elimination window of each second mutation point as the neighborhood corrected output value of each second mutation point; calculate the output drop factor of each second mutation point based on the difference between the element value of each second mutation point and the corresponding neighborhood corrected output value.

[0039] In one embodiment, calculating the supply-demand adjustment factor for each primary distribution network based on the difference characteristics between the grid output curve and the corresponding load curve in step S203 includes: Obtaining the grid output margin curve for each primary distribution network based on the difference between the grid output curve and the corresponding load curve of each primary distribution network, where the grid output margin curve reflects the remaining power of the grid output of the primary distribution network after meeting the corresponding load demand; Taking the average value of all element values of the grid output margin curve of each primary distribution network as the margin abundance of each primary distribution network, taking the maximum value of the margin abundances of all primary distribution networks as the margin reference value, and calculating the margin shortage index for each primary distribution network based on the difference characteristics between the margin abundance and the margin reference value of each primary distribution network; where the margin shortage index reflects the supply-demand relationship between the grid output and the load demand of the primary distribution network; Calculating the margin fluctuation index for each primary distribution network according to the fluctuation degree of the grid output margin curve of each primary distribution network; calculating the supply-demand adjustment factor for each primary distribution network based on the linear relationship between the margin shortage index and the corresponding margin fluctuation index of each primary distribution network.

[0040] It should be noted that the grid output margin curve reflects the remaining power of the grid output of the primary distribution network after meeting the corresponding load demand. For example, in a PV-dominated distribution network, the element value corresponding to the margin curve at noon when PV generation is large is positive, and the element value corresponding to the margin curve during the peak load at night is negative, accurately positioning the supply-demand contradiction period; the margin fluctuation index reflects the difficulty of grid regulation. By connecting new energy power stations to the primary distribution network with a high supply-demand adjustment factor, the maximum consumption benefit can be achieved, the reliability and economy of grid operation can be improved, and thus the reconstruction efficiency of the distribution network can be provided.

[0041] In one embodiment, in step S1, hierarchical processing is performed on the target distribution network based on the current network topology structure and the corresponding historical operation data of the target distribution network to obtain each primary distribution network and each secondary distribution network of each primary distribution network, including: Processing the current network topology structure using a community discovery algorithm to obtain each primary distribution network; extracting the target power feature vectors of each node for all historical operation data of each node in each primary distribution network; performing clustering analysis on the target power feature vectors of all nodes in each primary distribution network using a clustering algorithm to obtain each secondary distribution network of each primary distribution network.

[0042] It should be noted that the community discovery algorithm is used to identify densely connected subgraphs in a network, and its goal is to maximize the modularity of the network. The community discovery algorithm is a well-known technology, and it will not be elaborated in this embodiment.

[0043] In one embodiment, in step S2, obtaining the target secondary distribution network based on the power similarity characteristics of all secondary distribution networks of the new energy power station and the target primary distribution network includes: Performing feature extraction on all historical operation data of the new energy power station to obtain the first power feature vector of the new energy power station; Based on the target power feature vectors of all nodes of each secondary distribution network of the target primary distribution network, obtaining the second power feature vector of each secondary distribution network of the target primary distribution network; Taking the dynamic time warping (DTW) distance between the first power feature vector and the second power feature vector of each secondary distribution network of the target primary distribution network as the power feature dissimilarity factor between the first power feature vector and each secondary distribution network of the target primary distribution network; Performing sorting analysis on the power feature dissimilarity factors of all secondary distribution networks of the target primary distribution network, and obtaining the target secondary distribution network based on the result of the sorting analysis.

[0044] In one embodiment, before controlling the functional components of the target distribution network to execute the distribution network reconstruction strategy matching the target switch combination, simulating the distribution network reconstruction strategy; adjusting the distribution network reconstruction strategy based on the result of the simulation.

[0045] Another embodiment of the present invention provides a distribution network reconstruction system based on equilibrium constraint mathematical programming, and the system includes: The distribution network layering module 11 is used to collect all historical operation data of the target distribution network, perform layering processing on the target distribution network based on the current network topology structure of the target distribution network and the corresponding historical operation data, and obtain each primary distribution network and each secondary distribution network of each primary distribution network; The access point evaluation module 12 is used to obtain the target primary distribution network according to the fluctuation complementarity characteristics between the new energy power station to be connected and each primary distribution network; obtaining the target secondary distribution network based on the power similarity characteristics of the new energy power station and all secondary distribution networks of the target primary distribution network; The topology reconstruction module 13 is used to connect the new energy power station to the target secondary distribution network to obtain the first reconstructed distribution network; wherein, the first node information of the first reconstructed distribution network is designed to perform correlation analysis on the matrixed result of the target network topology structure after connecting the new energy power station, and adjust the second node information corresponding to the target network topology structure based on the result of the correlation analysis; The switch reconstruction module 14 is used to construct an optimization model for switch control of the first reconstructed distribution network, process the switch control optimization model by equilibrium constraint mathematical programming to obtain the target switch combination, and control the functional components of the target distribution network to execute the distribution network reconstruction strategy matching the target switch combination to obtain the second reconstructed distribution network.

[0046] Specifically, please refer to Figure 2 , Figure 2 which shows the architecture diagram of a distribution network reconstruction system based on equilibrium constraint mathematical programming in one of the embodiments of the present invention.

[0047] The above embodiments merely represent several implementation manners of the present invention. The description thereof is relatively specific and detailed, but it should not be construed as a limitation to the scope of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the appended claims.

Claims

1. A distribution network reconstruction method based on equilibrium constraint mathematical programming, characterized in that, Including: Collect all historical operation data of the target distribution network, perform hierarchical processing on the target distribution network based on the current network topology of the target distribution network and the corresponding historical operation data to obtain each primary distribution network and each secondary distribution network of each primary distribution network; Obtain the target primary distribution network according to the fluctuation complementary characteristics between the new energy power station to be connected and each primary distribution network; obtain the target secondary distribution network based on the power similarity characteristics between the new energy power station and all secondary distribution networks of the target primary distribution network; Connect the new energy power station to the target secondary distribution network to obtain the first reconstructed distribution network; wherein, the first node information of the first reconstructed distribution network is designed to perform correlation analysis on the matrix result of the target network topology obtained after connecting the new energy power station, and adjust the second node information corresponding to the target network topology based on the result of the correlation analysis; Construct the switch control optimization model of the first reconstructed distribution network, process the switch control optimization model with equilibrium constraint mathematical programming to obtain the target switch combination; control the functional components of the target distribution network to execute the distribution network reconstruction strategy matching the target switch combination to obtain the second reconstructed distribution network.

2. The method for reconstructing a distribution network based on equilibrium constraint mathematical programming according to claim 1, wherein The obtaining the target primary distribution network according to the fluctuation complementary characteristics between the new energy power station to be connected and each primary distribution network includes: Construct a new energy output curve based on the first historical power generation data of the new energy power station, construct a load curve for each primary distribution network based on the historical total power load data of all nodes in each primary distribution network, and construct a grid output curve for each primary distribution network based on the second historical power generation data of all nodes in each primary distribution network; Calculate the extreme event co-occurrence factor between the new energy power station and each primary distribution network according to the complementary characteristics between the new energy output curve and the load curve of each primary distribution network; wherein, the extreme event co-occurrence factor reflects the probability of simultaneous occurrence of a sudden drop in new energy output and a sudden increase in load; Calculate the supply-demand adjustment factor for each primary distribution network based on the difference characteristics between the grid output curve and the corresponding load curve of each primary distribution network; wherein, the supply-demand adjustment factor reflects the urgency of reconstructing the supply-demand relationship between the grid output and the load demand of the primary distribution network to achieve supply-demand balance; Screen all primary distribution networks with the extreme event co-occurrence factor and the supply-demand adjustment factor to obtain the target primary distribution network.

3. A distribution network reconfiguration method based on equilibrium constraint mathematical programming according to claim 2, characterized in that The calculating the extreme event co-occurrence factor between the new energy power station and each primary distribution network according to the complementary characteristics between the new energy output curve and the load curve of each primary distribution network includes: Extract all first mutation points in the load curve and all second mutation points in the new energy output curve; Obtain the load surge factor for each of the first mutation points based on the neighborhood difference features of each of the first mutation points; wherein, the load surge factor reflects the degree of load surge at the first mutation point; Obtain the output sudden drop factor for each of the second mutation points based on the neighborhood difference features of each of the second mutation points in the new energy output curve; wherein, the output sudden drop factor reflects the degree of new energy output sudden drop at the second mutation point; Take the second mutation point closest to each of the first mutation points as the co-mutation point for each of the first mutation points; based on the linear relationship between the load surge factors of all the first mutation points and the output sudden drop factors of the corresponding co-mutation points, calculate the extreme event co-occurrence factor between the new energy power station and each of the primary distribution networks.

4. The method for reconstructing a distribution network based on equilibrium-constrained mathematical programming according to claim 3, wherein The obtaining the load surge factor for each of the first mutation points based on the neighborhood difference features of each of the first mutation points includes: With each of the first mutation points in each of the load curves as the center, construct the first analysis window for each of the first mutation points; Exclude the first mutation points within each of the first analysis windows to obtain the first mutation exclusion window for each of the first mutation points, and take the average value of all the element values within the first mutation exclusion window of each of the first mutation points as the neighborhood corrected load for each of the first mutation points; Calculate the load surge factor for each of the first mutation points based on the difference between the element value of each of the first mutation points and the corresponding neighborhood corrected load.

5. The method for reconfiguring a distribution network based on equilibrium constraint mathematical programming according to claim 3, characterized in that The obtaining the output sudden drop factor for each of the second mutation points based on the neighborhood difference features of each of the second mutation points in the new energy output curve includes: With each of the second mutation points in the new energy output curve as the center, construct the second analysis window for each of the second mutation points; Exclude the second mutation points within each of the second analysis windows to obtain the second mutation exclusion window for each of the second mutation points, and take the average value of all the element values within the second mutation exclusion window of each of the second mutation points as the neighborhood corrected output value for each of the second mutation points; Calculate the output sudden drop factor for each of the second mutation points based on the difference between the element value of each of the second mutation points and the corresponding neighborhood corrected output value.

6. The method for reconstructing a distribution network based on equilibrium constraint mathematical programming according to claim 2, wherein, The calculating the supply-demand regulation factor for each of the primary distribution networks based on the difference features between the grid output curve and the corresponding load curve of each of the primary distribution networks includes: Obtain the grid output margin curve for each of the primary distribution networks based on the difference between the grid output curve and the corresponding load curve of each of the primary distribution networks, wherein the grid output margin curve reflects the remaining power of the grid output of the primary distribution network after meeting the corresponding load demand; The average value of all element values of the grid output margin curve of each of the primary distribution networks is used as the margin adequacy of each of the primary distribution networks, and the maximum value of the margin adequacy of all the primary distribution networks is used as the margin reference value. Based on the difference characteristics between the margin adequacy of each of the primary distribution networks and the margin reference value, the margin shortage index of each of the primary distribution networks is calculated; wherein, the margin shortage index reflects the supply-demand relationship between the grid output and the load demand of the primary distribution network. The margin fluctuation index of each of the primary distribution networks is calculated according to the fluctuation degree of the grid output margin curve of each of the primary distribution networks. Based on the linear relationship between the margin shortage index and the corresponding margin fluctuation index of each of the primary distribution networks, the supply-demand adjustment factor of each of the primary distribution networks is calculated.

7. The method for reconstructing a distribution network based on equilibrium-constrained mathematical programming according to claim 1, wherein, The hierarchical processing of the target distribution network based on the current network topology structure and the corresponding historical operation data of the target distribution network to obtain each primary distribution network and each secondary distribution network of each of the primary distribution networks includes: The community discovery algorithm is used to process the current network topology structure to obtain each primary distribution network. Feature extraction is performed on all the historical operation data of each node in each of the primary distribution networks to obtain the target power feature vector of each node. The clustering algorithm is used to perform clustering analysis on the target power feature vectors of all the nodes in each of the primary distribution networks to obtain each secondary distribution network of each of the primary distribution networks.

8. The method for reconstructing a distribution network based on equilibrium-constrained mathematical programming according to claim 7, wherein The obtaining of the target secondary distribution network based on the power similarity characteristics of the new energy power station and all the secondary distribution networks of the target primary distribution network includes: Feature extraction is performed on all the historical operation data of the new energy power station to obtain the first power feature vector of the new energy power station. Based on the target power feature vectors of all the nodes of each secondary distribution network of the target primary distribution network, the second power feature vector of each secondary distribution network of the target primary distribution network is obtained. The dynamic time warping distance between the first power feature vector and the second power feature vector of each secondary distribution network of the target primary distribution network is used as the power feature dissimilarity factor between the first power feature vector and each secondary distribution network of the target primary distribution network. Sorting analysis is performed on the power feature dissimilarity factors of all the secondary distribution networks of the target primary distribution network, and based on the result of the sorting analysis, the target secondary distribution network is obtained.

9. The method for reconstructing a distribution network based on equilibrium constraint mathematical programming according to claim 1, characterized in that, The method further includes: Before the functional components controlling the target distribution network execute the distribution network reconstruction strategy matching the target switch combination, the distribution network reconstruction strategy is simulated. Based on the result of the simulation, the distribution network reconstruction strategy is adjusted.

10. A distribution network reconstruction system based on equilibrium constraint mathematical programming, characterized in that, The system includes: The distribution network layering module is used to collect all historical operation data of the target distribution network, and perform layering processing on the target distribution network based on the current network topology of the target distribution network and the corresponding historical operation data, so as to obtain each primary distribution network and each secondary distribution network of each primary distribution network; The access point evaluation module is used to obtain the target primary distribution network according to the fluctuation complementary characteristics between the new energy power station to be connected and each primary distribution network; obtain the target secondary distribution network based on the power similarity characteristics between the new energy power station and all secondary distribution networks of the target primary distribution network; The topology reconstruction module is used to connect the new energy power station to the target secondary distribution network to obtain the first reconstructed distribution network; wherein, the first node information of the first reconstructed distribution network is designed to perform correlation analysis on the matrix result of the target network topology obtained after connecting the new energy power station, and adjust the second node information corresponding to the target network topology based on the result of the correlation analysis; The switch reconstruction module is used to construct an optimization model for switch control of the first reconstructed distribution network, process the optimization model for switch control by means of equilibrium constraint mathematical programming to obtain the target switch combination; control the functional components of the target distribution network to execute the distribution network reconstruction strategy matching the target switch combination to obtain the second reconstructed distribution network.

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