A distribution network reconstruction method and system based on equilibrium constrained mathematical programming

Abstract

The present invention discloses a distribution network reconstruction method and system based on equilibrium-constrained mathematical programming, which is applied to the field of power system technology. The method includes collecting historical operating data of a target distribution network to obtain a primary distribution network and a secondary distribution network; obtaining a target primary distribution network based on the fluctuation complementarity characteristics between a new energy power station to be connected and each primary distribution network; obtaining a 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; connecting the new energy power station to the target secondary distribution network to obtain a first reconstructed distribution network; processing a switch control optimization model of the first reconstructed distribution network using equilibrium-constrained mathematical programming, executing a distribution network reconstruction strategy that matches the obtained target switch combination, and obtaining a second reconstructed distribution network. The embodiment of the present invention provides a distribution network reconstruction method and system based on equilibrium-constrained mathematical programming, which improves the efficiency of distribution network reconstruction.

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 renewable energy, distributed power sources (DGs) are being integrated into distribution networks in large numbers due to their environmentally friendly, efficient, and flexible nature. However, since the output of renewable energy sources like wind and photovoltaic power is affected by weather and is incompatible with traditional power generation methods, issues such as power flow distribution, voltage stability, and network losses in distribution networks are becoming increasingly complex. Therefore, when renewable energy is integrated into the distribution network, dynamic reconfiguration of the network is necessary to accommodate the integration of renewable energy power stations and ensure the safe, economical, and reliable operation of the 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 technology has low efficiency in reconstructing the distribution network.

[0004] It can be seen that how to improve the efficiency of reconfiguration 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 due to the randomness of renewable energy power generation, the existing distribution network reconstruction technology usually adopts a fixed reconstruction mode and lacks targeted analysis of renewable energy power generation characteristics, resulting in low efficiency of distribution network reconstruction in the existing technology.

[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 operating 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 operating data to obtain each primary distribution network and each secondary distribution network of each primary distribution network;

[0008] Acquire a target primary distribution network based on the fluctuation complementarity characteristics between the new energy power station to be connected and each of the primary distribution networks; acquire a target secondary distribution network based on the power similarity characteristics of all the secondary distribution networks of the new energy power station and the target primary distribution network;

[0009] Connecting 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 be obtained by performing a correlation analysis on a matrixed result of a target network topology structure obtained after connecting to the new energy power station, and adjusting the second node information corresponding to the target network topology structure based on the result of the correlation analysis;

[0010] A switch control optimization model of the first reconstructed distribution network is constructed, and the switch control optimization model is processed using equilibrium constrained mathematical programming to obtain a target switch combination; and functional components of the target distribution network are controlled to execute a distribution network reconstruction strategy that matches the target switch combination to obtain a second reconstructed distribution network.

[0011] As one preferred solution, the step of obtaining a target primary distribution network based on the fluctuation complementarity characteristics between the new energy power station to be connected and each of the primary distribution networks includes:

[0012] constructing a new energy output curve based on the first historical power generation data of the new energy power station, constructing a load curve for each of the first-level distribution networks based on the historical total power load data of all nodes in each of the first-level distribution networks, and constructing a grid output curve for each of the first-level distribution networks based on the second historical power generation data of all nodes in each of the first-level distribution networks;

[0013] Calculating an extreme event co-occurrence factor of the new energy power station and each of the first-level distribution networks based on the complementary characteristics between the new energy output curve and the load curve of each of the first-level distribution networks; wherein the extreme event co-occurrence factor reflects the probability of a sudden drop in new energy output and a sudden increase in load occurring simultaneously;

[0014] Calculating a supply and demand adjustment factor for each of the primary distribution networks based on the difference characteristics between the power grid output curve and the corresponding load curve of each of the primary distribution networks; wherein the supply and demand adjustment factor reflects the urgency with which the supply and demand relationship between the power grid output and the load demand of the primary distribution network needs to be restructured to achieve supply and demand balance;

[0015] All the primary distribution networks are screened using the extreme event co-occurrence factor and the supply and demand adjustment factor to obtain a target primary distribution network.

[0016] As one preferred solution, the calculating of the extreme event co-occurrence factor of the new energy power station and each of the primary distribution networks based on the complementary characteristics between the new energy output curve and the load curve of each of the primary distribution networks includes:

[0017] Extract all first mutation points in the load curve and all second mutation points in the new energy output curve;

[0018] Obtaining a load surge factor for each first mutation point based on neighborhood difference characteristics of each first mutation point; wherein the load surge factor reflects the degree of load surge at the first mutation point;

[0019] Obtaining an output drop factor for each second mutation point in the new energy output curve based on neighborhood difference characteristics of each second mutation point; wherein the output drop factor reflects the degree of the new energy output drop at the second mutation point;

[0020] The second mutation point closest to each first mutation point is used as the coordinated mutation point of each first mutation point; based on the linear relationship between the load surge factors of all the 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 of the primary distribution networks are calculated.

[0021] As one preferred solution, obtaining the load surge factor of each first mutation point based on the neighborhood difference characteristics of each first mutation point includes:

[0022] Taking each first mutation point in each load curve as a center, constructing a first analysis window for each first mutation point;

[0023] Eliminating the first mutation point within each of the first analysis windows to obtain a first mutation elimination window for each of the first mutation points, and taking an average value of all element values within the first mutation elimination window of each of the first mutation points as a neighborhood correction load for each of the first mutation points;

[0024] The load sudden increase factor of each first mutation point is calculated based on the difference between the element value of each first mutation point and the corresponding neighborhood corrected load.

[0025] As one preferred solution, obtaining the output drop factor of each second mutation point in the new energy output curve based on the neighborhood difference characteristics of each second mutation point includes:

[0026] Taking each second mutation point in the new energy output curve as a center, constructing a second analysis window for each second mutation point;

[0027] Eliminating the second mutation point within each second analysis window to obtain a second mutation elimination window for each second mutation point, and taking the average of all element values within the second mutation elimination window for each second mutation point as the neighborhood corrected output value of each second mutation point;

[0028] The output sag factor of each second mutation point is calculated based on the difference between the element value of each second mutation point and the corresponding neighborhood corrected output value.

[0029] As one preferred solution, the calculating of the supply and demand adjustment factor of each primary distribution network based on the difference characteristics between the power grid output curve and the corresponding load curve of each primary distribution network includes:

[0030] Obtaining a grid output margin curve of each of the primary distribution networks based on a difference between the grid output curve of each of the primary distribution networks and the corresponding load curve, wherein the grid output margin curve reflects the remaining amount of electricity after the grid output of the primary distribution network meets the corresponding load demand;

[0031] The average value of all element values of the power grid output margin curve of each primary distribution network is used as the margin margin of each primary distribution network, the maximum value of the margin margins of all primary distribution networks is used as the margin reference value, and the margin shortage index of each primary distribution network is calculated based on the difference characteristics of the margin margin and the margin reference value of each primary distribution network; wherein the margin shortage index reflects the supply and demand relationship between the power grid output and load demand of the primary distribution network;

[0032] Calculating a margin fluctuation index of each of the primary distribution networks according to a degree of fluctuation of the power grid output margin curve of each of the primary distribution networks;

[0033] Based on the linear relationship between the margin deficiency index and the corresponding margin fluctuation index of each primary distribution network, a supply and demand adjustment factor of each primary distribution network is calculated.

[0034] As one of the preferred solutions, the target distribution network is hierarchically processed 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, including:

[0035] Using a community discovery algorithm to process the current network topology structure to obtain each primary distribution network;

[0036] Performing feature extraction on all the historical operating data of each node in each of the primary distribution networks to obtain a target power feature vector for each of the nodes;

[0037] A clustering algorithm is used to perform cluster analysis on the target power characteristic vectors of all the nodes in each of the primary distribution networks to obtain the secondary distribution networks of each of the primary distribution networks.

[0038] As one of the preferred solutions, the acquiring of the target secondary distribution network based on the power similarity characteristics of all the secondary distribution networks of the new energy power station and the target primary distribution network includes:

[0039] Performing feature extraction on all the historical operating data of the new energy power station to obtain a first power feature vector of the new energy power station;

[0040] Acquire a second power characteristic vector of each of the secondary distribution networks of the target primary distribution network based on the target power characteristic vectors of all the nodes of each of the secondary distribution networks of the target primary distribution network;

[0041] The dynamic time warping distance between the first power characteristic vector and the second power characteristic vector of each of the secondary distribution networks of the target primary distribution network is used as a power characteristic difference factor between the first power characteristic vector and each of the secondary distribution networks of the target primary distribution network;

[0042] A ranking analysis is performed on the power characteristic difference factors of all the secondary distribution networks of the target primary distribution network, and a target secondary distribution network is obtained based on a result of the ranking analysis.

[0043] As one preferred solution, the method further comprises:

[0044] Before the functional component controlling the target distribution network executes the distribution network reconstruction strategy that matches the target switch combination, simulating the distribution network reconstruction strategy;

[0045] The distribution network reconstruction strategy is adjusted based on the simulation results.

[0046] Another embodiment of the present invention provides a distribution network reconstruction system based on equilibrium-constrained mathematical programming, the system comprising:

[0047] A distribution network hierarchical module is configured to collect all historical operating data of a 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 operating data to obtain each primary distribution network and each secondary distribution network of each primary distribution network;

[0048] An access point evaluation module is configured to obtain a target primary distribution network based on the fluctuation complementarity characteristics between the new energy power station to be connected and each of the primary distribution networks; and obtain a target secondary distribution network based on the power similarity characteristics of all the secondary distribution networks of the new energy power station and the target primary distribution network;

[0049] 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 be obtained by performing a correlation analysis on a matrixed result of a target network topology structure obtained after connecting to the new energy power station, and adjusting the second node information corresponding to the target network topology structure based on the result of the correlation analysis;

[0050] A switch reconstruction module is used to construct a switch control optimization model for the first reconstructed distribution network, process the switch control optimization model using equilibrium constrained 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 that matches the target switch combination to obtain a second reconstructed distribution network.

[0051] Compared with the prior art, the embodiments of the present invention have the following advantages:

[0052] Through hierarchical processing, large-scale distribution networks are decomposed into multiple levels and sub-areas, and global complex problems are converted into local optimization problems. When the topology changes, only the affected branches are adjusted instead of the entire network reconstruction, which improves flexibility and response speed, and improves the real-time reconstruction of the distribution network. The target primary distribution network is obtained according to the fluctuation complementarity characteristics between the new energy power station to be connected and each primary distribution network, and the overall power fluctuation is smoothed to achieve peak shaving and valley flattening. The target secondary distribution network is obtained based on the power similarity characteristics of all secondary distribution networks of the new energy power station and the target primary distribution network, ensuring that the new energy power is efficiently consumed and minimizing the impact of the new energy power station on the primary distribution network. Through double screening, a hierarchical decision logic is formed to avoid blind access, comprehensively balance the overall and local optimization, and improve the reconstruction efficiency of the distribution network. The grid structure is converted into a matrix, and the topology is adjusted according to the impact of new energy access to proactively prevent congestion or voltage exceeding the limit; a switch control optimization model for the first reconstructed distribution network is constructed, and the switch control optimization model is processed with equilibrium-constrained mathematical programming to obtain the target switch combination; the functional components of the target distribution network are controlled to execute a distribution network reconstruction strategy that matches the target switch combination to obtain the second reconstructed distribution network. By further adjusting the switch combination, the distribution network is further optimized, and the reconstruction efficiency of the distribution network is further improved; from full network stratification to local access, topology adjustment, and switch optimization, a step-by-step refinement optimization chain is formed, which comprehensively considers fluctuation complementarity, topology adaptation, and switch coordination, effectively alleviating the challenges of new energy randomness to the distribution network, and realizing precise site selection for new energy access and efficient dynamic reconstruction of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 11 is a flow chart of a distribution network reconstruction method based on equilibrium-constrained mathematical programming in one embodiment of the present invention;

[0054] Figure 2 This is an architecture diagram of a distribution network reconfiguration system based on equilibrium-constrained mathematical programming in one embodiment of the present invention;

[0055] Reference numerals:

[0056] 11. Distribution network layering module; 12. Access point evaluation module; 13. Topology reconstruction module; 14. Switch reconstruction module. DETAILED DESCRIPTION

[0057] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. 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 ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0058] In the description of this application, the terms "first," "second," "third," etc. are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Therefore, a feature specified as "first," "second," "third," etc. may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0059] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installed", "connected" and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or an indirect connection through an intermediate medium, or it can be a communication between the two components. The terms "vertical", "horizontal", "left", "right", "up", "down" and similar expressions used herein are for illustrative purposes only, and do not indicate or imply that the device or component referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. The term "and / or" used herein includes any and all combinations of one or more 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.

[0060] In the description of this application, it should be noted that, unless otherwise defined, all technical and scientific terms used in this application have the same meanings as those commonly understood by those skilled in the art. The terms used in this specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. Those skilled in the art will understand the specific meanings of the above terms in this application in specific circumstances.

[0061] With the large-scale integration of renewable energy sources like wind and photovoltaic power, distribution network operations are facing unprecedented challenges. The intermittent and fluctuating nature of renewable energy generation significantly conflicts with the rigid regulation of traditional power generation methods, leading to new complexities in distribution network operations.

[0062] However, existing distribution network reconstruction technologies usually adopt a fixed reconstruction mode and lack targeted analysis of the characteristics of renewable energy power generation, resulting in low distribution network reconstruction efficiency.

[0063] An embodiment of the present invention provides a distribution network reconstruction method based on equilibrium constraint mathematical programming. For details, see Figure 1 , Figure 1 The figure shows a flow chart of a distribution network reconstruction method based on equilibrium-constrained mathematical programming in one embodiment of the present invention.

[0064] Step S1: Collect all historical operation data of the target distribution network, and perform layered 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.

[0065] Step S2: Obtain a target primary distribution network based on the fluctuation complementarity characteristics between the new energy power station to be connected and each primary distribution network; obtain a 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.

[0066] Step S3: 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 a correlation analysis on the matrix result of the target network topology structure obtained after connecting to 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.

[0067] Step S4: Construct a switch control optimization model for the first reconstructed distribution network, process the switch control optimization model using equilibrium constrained mathematical programming to obtain a target switch combination; control the functional components of the target distribution network to execute a distribution network reconstruction strategy that matches the target switch combination to obtain a second reconstructed distribution network.

[0068] This embodiment provides a distribution network reconstruction method based on equilibrium constrained mathematical programming, which decomposes a large-scale distribution network into multiple levels and sub-areas through hierarchical processing, converts global complex problems into local optimization problems, and allows only the affected branches to be adjusted when the topology changes, rather than the entire network to be reconstructed, thereby improving flexibility and response speed, and improving the real-time reconstruction of the distribution network; obtains the target primary distribution network based on the fluctuation complementarity characteristics between the new energy power station to be connected and each primary distribution network, smoothes the overall power fluctuation, and realizes peak shaving and valley flattening; 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, ensures that the new energy power is efficiently consumed, minimizes the impact of the new energy power station on the primary distribution network, forms a hierarchical decision logic through double screening, avoids blind connection, comprehensively balances the overall and local optimization, and improves The reconstruction efficiency of the distribution network is improved; by converting the topology structure into a matrix, the topology structure is adjusted according to the impact after the access of new energy, and congestion or voltage exceeding the limit is proactively prevented; a switch control optimization model of the first reconstructed distribution network is constructed, and the switch control optimization model is processed with equilibrium constrained mathematical programming to obtain the target switch combination; the functional components of the target distribution network are controlled to execute a distribution network reconstruction strategy that matches the target switch combination to obtain the second reconstructed distribution network. By further adjusting the switch combination, the distribution network is further optimized, and the reconstruction efficiency of the distribution network is further improved; from full network stratification to local access, topology adjustment, and switch optimization, a step-by-step refinement optimization chain is formed, which comprehensively considers fluctuation complementarity, topology adaptation, and switch coordination, effectively alleviating the challenges of new energy randomness to the distribution network, and realizing accurate site selection for new energy access and efficient dynamic reconstruction of the distribution network.

[0069] In one embodiment, in step S3, the new energy power station is connected to the target secondary distribution network to obtain a first reconstructed distribution network, including:

[0070] The new energy power station is connected to the target secondary distribution network. The corresponding node-branch adjacency matrix, node admittance matrix and branch impedance matrix are constructed according to the obtained target network topology. Matrix operations are performed through the node admittance matrix and the branch impedance matrix. Based on the results of the matrix operations, the branch connection relationship of the target network topology is adjusted through the node-branch adjacency matrix, the node admittance and branch impedance are optimized, and the first reconstructed distribution network is obtained.

[0071] For example, after a 3MW wind farm is connected to node 12 of a 15-node distribution network containing a ring network, a circulating current is induced, causing branch 6-9 to be overloaded. The loop impedance matrix of the target network topology after the 3MW wind farm is connected is constructed, the circulating current path (branches 6-9, 9-12, and 12-6) is identified, and the current contribution of the wind farm injection to branch 6-9 is calculated to be 65%. By making the elements in the node-branch adjacency matrix A ，Disconnect the branch 9-12 with the highest impedance to force power flow redistribution.

[0072] In one embodiment, step S2 obtains a target primary distribution network based on the fluctuation complementarity characteristics between the new energy power station to be connected and each primary distribution network, including:

[0073] Step S201: constructing a new energy output curve based on first historical power generation data of the new energy power station, constructing a load curve for each primary distribution network based on historical total power load data of all nodes in each primary distribution network, and constructing a grid output curve for each primary distribution network based on second historical power generation data of all nodes in each primary distribution network;

[0074] Step S202: Calculating the extreme event co-occurrence factor of the new energy power station and each primary distribution network based on 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 a sudden drop in new energy output and a sudden increase in load occurring simultaneously;

[0075] Step S203: Calculating a supply and demand adjustment factor for each primary distribution network based on the difference characteristics between the power output curve and the corresponding load curve of each primary distribution network; wherein the supply and demand adjustment factor reflects the urgency of reconstructing the supply and demand relationship between the power output and load demand of the primary distribution network to achieve supply and demand balance;

[0076] Step S204: Screen all primary distribution networks using the extreme event co-occurrence factor and the supply and demand adjustment factor to obtain a target primary distribution network.

[0077] This embodiment provides a distribution network reconstruction method based on equilibrium-constrained mathematical programming. It quantifies the co-occurrence probability of sudden drops in renewable energy output through extreme event co-occurrence factors, such as the co-occurrence probability of wind power quiet periods and sudden load increases, such as the peak air-conditioning load in summer, to identify extreme risk scenarios of the power grid-renewable energy combination and prioritize the avoidance of high-risk access solutions. It quantifies the insufficient regulation capacity of the distribution network itself and the urgency of relying on external renewable energy supplementation through supply and demand adjustment factors. The improvement effect is more significant after connecting distribution networks with high supply and demand adjustment factors, that is, those with prominent internal supply and demand contradictions, to renewable energy, thereby optimizing resource allocation efficiency. Through dual quantitative screening of extreme events and supply and demand adjustment, it ensures that the integration of renewable energy minimizes system risks and maximizes the potential for improving the supply and demand balance, laying a solid foundation for subsequent topology reconstruction and switch optimization, and improving the efficiency of distribution network reconstruction.

[0078] In one embodiment, in step S202, the extreme event co-occurrence factor of the new energy power station and each primary distribution network is calculated based on the complementarity characteristics between the new energy output curve and the load curve of each primary distribution network, including:

[0079] Extract all first mutation points in the load curve and all second mutation points in the renewable 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;

[0080] Obtaining an output drop factor for each second mutation point based on neighborhood difference characteristics of each second mutation point in the new energy output curve; wherein the output drop factor reflects the degree of output drop of the new energy source at the second mutation point;

[0081] 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.

[0082] 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 cooperative 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.

[0083] In this step, the load surge factor of each first mutation point is obtained based on the neighborhood difference characteristics of each first mutation point, including:

[0084] With each first mutation point in each load curve as the center, a first analysis window for each first mutation point is constructed; the first mutation points within each first analysis window are eliminated to obtain the first mutation elimination window for each first mutation point, and the average value of all element values within the first mutation elimination window of each first mutation point is used as the neighborhood correction load of each first mutation point; the load surge factor of each first mutation point is calculated based on the difference between the element value of each first mutation point and the corresponding neighborhood correction load.

[0085] 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:

[0086] With each second mutation point in the new energy output curve as the center, a second analysis window for each second mutation point is constructed; the second mutation points within each second analysis window are eliminated to obtain a second mutation elimination window for each second mutation point, and the average value of all element values within the second mutation elimination window for each second mutation point is used as the neighborhood corrected output value of each second mutation point; the output sag factor of each second mutation point is calculated based on the difference between the element value of each second mutation point and the corresponding neighborhood corrected output value.

[0087] In one embodiment, the step S203 calculates the supply and demand adjustment factor of each primary distribution network based on the difference characteristics between the power output curve and the corresponding load curve of each primary distribution network, including:

[0088] Obtaining a grid output margin curve for each primary distribution network based on a difference between the grid output curve and the corresponding load curve of each primary distribution network, 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;

[0089] The average value of all element values of the power grid output margin curve of each primary distribution network is used as the margin margin of each primary distribution network, and the maximum value of the margin margins of all primary distribution networks is used as the margin reference value. Based on the difference characteristics of the margin margins and margin reference values of each primary distribution network, the margin shortage index of each primary distribution network is calculated; wherein, the margin shortage index reflects the supply and demand relationship between the power grid output and load demand of the primary distribution network;

[0090] The margin fluctuation index of each primary distribution network is calculated according to the fluctuation degree of the grid output margin curve of each primary distribution network; based on the linear relationship between the margin shortage index of each primary distribution network and the corresponding margin fluctuation index, the supply and demand adjustment factor of each primary distribution network is calculated.

[0091] It should be noted that the grid output margin curve reflects the remaining power of the primary distribution network after the grid output meets the corresponding load demand. For example, the corresponding element value of the margin curve of a photovoltaic-dominated distribution network is positive when photovoltaic power generation is high at noon, and the corresponding element value in the grid output margin curve is negative when the load is peak at night, accurately locating the period of supply and demand contradiction; 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 and demand regulation factor, the absorption benefit can be maximized, the reliability and economy of grid operation can be improved, and the reconstruction efficiency of the distribution network can be improved.

[0092] In one embodiment, in step S1, the target distribution network is hierarchically processed 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, including:

[0093] The community discovery algorithm is used to process the current network topology to obtain each primary distribution network. Feature extraction is performed on all historical operating data of each node in each primary distribution network to obtain the target power feature vector of each node. A clustering algorithm is used to perform cluster analysis on the target power feature vectors of all nodes in each primary distribution network to obtain each secondary distribution network of each primary distribution network.

[0094] 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 will not be described in detail in this embodiment.

[0095] In one embodiment, in step S2, 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 includes:

[0096] Perform feature extraction on all historical operating data of the new energy power station to obtain the first power feature vector of the new energy power station;

[0097] Obtaining a second power characteristic vector of each secondary distribution network of the target primary distribution network based on the target power characteristic vectors of all nodes of each secondary distribution network of the target primary distribution network;

[0098] 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 is used as a power feature difference factor between the first power feature vector and each secondary distribution network of the target primary distribution network;

[0099] A ranking analysis is performed on the power characteristic difference factors of all secondary distribution networks of the target primary distribution network, and the target secondary distribution network is obtained based on the results of the ranking analysis.

[0100] In one embodiment, before controlling the functional components of the target distribution network to execute the distribution network reconstruction strategy that matches the target switch combination, the distribution network reconstruction strategy is simulated; and the distribution network reconstruction strategy is adjusted based on the simulation results.

[0101] Another embodiment of the present invention provides a distribution network reconstruction system based on equilibrium-constrained mathematical programming, the system comprising:

[0102] The distribution network hierarchical module 11 is used to collect all historical operating data of the target distribution network, and hierarchically process the target distribution network based on the current network topology of the target distribution network and the corresponding historical operating data to obtain each primary distribution network and each secondary distribution network of each primary distribution network;

[0103] The access point evaluation module 12 is configured to obtain a target primary distribution network based on the fluctuation complementarity characteristics between the new energy power station to be connected and each primary distribution network; and obtain a 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;

[0104] A topology reconstruction module 13 is 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 be obtained by performing a correlation analysis on the matrixed result of the target network topology structure obtained after connecting the new energy power station, and adjusting the second node information corresponding to the target network topology structure based on the result of the correlation analysis;

[0105] The switch reconstruction module 14 is used to construct a switch control optimization model for the first reconstructed distribution network, process the switch control optimization model using equilibrium constrained mathematical programming to obtain a target switch combination; control the functional components of the target distribution network to execute a distribution network reconstruction strategy that matches the target switch combination to obtain a second reconstructed distribution network.

[0106] For details, see Figure 2 , Figure 2 The figure shows an architecture diagram of a distribution network reconstruction system based on equilibrium-constrained mathematical programming in one embodiment of the present invention.

[0107] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A distribution network reconstruction method based on equilibrium constrained mathematical programming, characterized in that: include: Collect all historical operating 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 operating data to obtain each primary distribution network and each secondary distribution network of each primary distribution network; Acquire a target primary distribution network based on the fluctuation complementarity characteristics between the new energy power station to be connected and each of the primary distribution networks; acquire a target secondary distribution network based on the power similarity characteristics of all the secondary distribution networks of the new energy power station and the target primary distribution network; Connecting 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 be obtained by performing a correlation analysis on a matrixed result of a target network topology structure obtained after connecting to the new energy power station, and adjusting the second node information corresponding to the target network topology structure based on the result of the correlation analysis; Constructing a switch control optimization model for the first reconfigured distribution network, processing the switch control optimization model using equilibrium-constrained mathematical programming to obtain a target switch combination; controlling functional components of the target distribution network to execute a distribution network reconstruction strategy that matches the target switch combination to obtain a second reconfigured distribution network; The step of obtaining a target primary distribution network according to the fluctuation complementarity characteristics between the new energy power station to be connected and each of the primary distribution networks includes: constructing a new energy output curve based on the first historical power generation data of the new energy power station, constructing a load curve for each of the first-level distribution networks based on the historical total power load data of all nodes in each of the first-level distribution networks, and constructing a grid output curve for each of the first-level distribution networks based on the second historical power generation data of all nodes in each of the first-level distribution networks; Calculating an extreme event co-occurrence factor of the new energy power station and each of the first-level distribution networks based on the complementary characteristics between the new energy output curve and the load curve of each of the first-level distribution networks; wherein the extreme event co-occurrence factor reflects the probability of a sudden drop in new energy output and a sudden increase in load occurring simultaneously; Calculating a supply and demand adjustment factor for each of the primary distribution networks based on the difference characteristics between the power grid output curve and the corresponding load curve of each of the primary distribution networks; wherein the supply and demand adjustment factor reflects the urgency with which the supply and demand relationship between the power grid output and the load demand of the primary distribution network needs to be restructured to achieve supply and demand balance; All the primary distribution networks are screened using the extreme event co-occurrence factor and the supply and demand adjustment factor to obtain a target primary distribution network.

2. A distribution network reconstruction method based on equilibrium constrained mathematical programming according to claim 1, characterized in that: The calculating, based on the complementary characteristics between the new energy output curve and the load curve of each of the primary distribution networks, of the extreme event co-occurrence factors of the new energy power station and each of the primary distribution networks comprises: Extract all first mutation points in the load curve and all second mutation points in the new energy output curve; Obtaining a load surge factor for each first mutation point based on neighborhood difference characteristics of each first mutation point; wherein the load surge factor reflects the degree of load surge at the first mutation point; Obtaining an output drop factor for each second mutation point in the new energy output curve based on neighborhood difference characteristics of each second mutation point; wherein the output drop factor reflects the degree of the new energy output drop at the second mutation point; The second mutation point closest to each first mutation point is used as the coordinated mutation point of each first mutation point; based on the linear relationship between the load surge factors of all the 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 of the primary distribution networks are calculated.

3. A distribution network reconstruction method based on equilibrium constrained mathematical programming according to claim 2, characterized in that: The obtaining of the load surge factor of each first mutation point based on the neighborhood difference feature of each first mutation point includes: Taking each first mutation point in each load curve as a center, constructing a first analysis window for each first mutation point; Eliminating the first mutation point within each of the first analysis windows to obtain a first mutation elimination window for each of the first mutation points, and taking an average value of all element values within the first mutation elimination window of each of the first mutation points as a neighborhood correction load for each of the first mutation points; The load sudden increase factor of each first mutation point is calculated based on the difference between the element value of each first mutation point and the corresponding neighborhood corrected load.

4. The method for reconfiguring a distribution network based on equilibrium-constrained mathematical programming according to claim 2, wherein: The obtaining of the output drop factor of each second mutation point based on the neighborhood difference characteristics of each second mutation point in the new energy output curve includes: Taking each second mutation point in the new energy output curve as a center, constructing a second analysis window for each second mutation point; Eliminating the second mutation point within each second analysis window to obtain a second mutation elimination window for each second mutation point, and taking the average of all element values within the second mutation elimination window for each second mutation point as the neighborhood corrected output value of each second mutation point; The output sag factor of each second mutation point is calculated based on the difference between the element value of each second mutation point and the corresponding neighborhood corrected output value.

5. The method for reconfiguring a distribution network based on equilibrium-constrained mathematical programming according to claim 1, wherein: The calculating of the supply and demand adjustment factor of each primary distribution network based on the difference characteristics between the power grid output curve and the corresponding load curve of each primary distribution network includes: Obtaining a grid output margin curve of each of the primary distribution networks based on a difference between the grid output curve of each of the primary distribution networks and the corresponding load curve, wherein the grid output margin curve reflects the remaining amount of electricity after the grid output of the primary distribution network meets the corresponding load demand; The average value of all element values of the power grid output margin curve of each primary distribution network is used as the margin margin of each primary distribution network, the maximum value of the margin margins of all primary distribution networks is used as the margin reference value, and the margin shortage index of each primary distribution network is calculated based on the difference characteristics of the margin margin and the margin reference value of each primary distribution network; wherein the margin shortage index reflects the supply and demand relationship between the power grid output and load demand of the primary distribution network; Calculating a margin fluctuation index of each of the primary distribution networks according to a degree of fluctuation of the power grid output margin curve of each of the primary distribution networks; Based on the linear relationship between the margin deficiency index and the corresponding margin fluctuation index of each primary distribution network, a supply and demand adjustment factor of each primary distribution network is calculated.

6. The method for reconfiguring a distribution network based on equilibrium-constrained mathematical programming according to claim 1, wherein: The target distribution network is subjected to hierarchical processing 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, including: Using a community discovery algorithm to process the current network topology structure to obtain each primary distribution network; Performing feature extraction on all the historical operating data of each node in each of the primary distribution networks to obtain a target power feature vector for each of the nodes; A clustering algorithm is used to perform cluster analysis on the target power characteristic vectors of all the nodes in each of the primary distribution networks to obtain the secondary distribution networks of each of the primary distribution networks.

7. A distribution network reconstruction method based on equilibrium constrained mathematical programming according to claim 6, characterized in that: The acquiring of the target secondary distribution network based on the power similarity characteristics of all the secondary distribution networks of the new energy power station and the target primary distribution network includes: Performing feature extraction on all the historical operating data of the new energy power station to obtain a first power feature vector of the new energy power station; Acquire a second power characteristic vector of each of the secondary distribution networks of the target primary distribution network based on the target power characteristic vectors of all the nodes of each of the secondary distribution networks of the target primary distribution network; The dynamic time warping distance between the first power characteristic vector and the second power characteristic vector of each of the secondary distribution networks of the target primary distribution network is used as a power characteristic difference factor between the first power characteristic vector and each of the secondary distribution networks of the target primary distribution network; A ranking analysis is performed on the power characteristic difference factors of all the secondary distribution networks of the target primary distribution network, and a target secondary distribution network is obtained based on a result of the ranking analysis.

8. The method for reconfiguring a distribution network based on equilibrium-constrained mathematical programming according to claim 1, wherein: The method further comprises: Before the functional component controlling the target distribution network executes the distribution network reconstruction strategy that matches the target switch combination, simulating the distribution network reconstruction strategy; The distribution network reconstruction strategy is adjusted based on the simulation results.

9. A distribution network reconstruction system based on equilibrium constraint mathematical programming, characterized in that: The system comprises: A distribution network hierarchical module is configured to collect all historical operating data of a 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 operating data to obtain each primary distribution network and each secondary distribution network of each primary distribution network; An access point evaluation module is configured to obtain a target primary distribution network based on the fluctuation complementarity characteristics between the new energy power station to be connected and each of the primary distribution networks; and obtain a target secondary distribution network based on the power similarity characteristics of all the secondary distribution networks of the new energy power station and 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 be obtained by performing a correlation analysis on a matrixed result of a target network topology structure obtained after connecting to the new energy power station, and adjusting 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 for the first reconstructed distribution network, process the switch control optimization model using equilibrium-constrained mathematical programming to obtain a target switch combination, and control functional components of the target distribution network to execute a distribution network reconstruction strategy that matches the target switch combination to obtain a second reconstructed distribution network; The step of obtaining a target primary distribution network according to the fluctuation complementarity characteristics between the new energy power station to be connected and each of the primary distribution networks includes: constructing a new energy output curve based on the first historical power generation data of the new energy power station, constructing a load curve for each of the first-level distribution networks based on the historical total power load data of all nodes in each of the first-level distribution networks, and constructing a grid output curve for each of the first-level distribution networks based on the second historical power generation data of all nodes in each of the first-level distribution networks; Calculating an extreme event co-occurrence factor of the new energy power station and each of the first-level distribution networks based on the complementary characteristics between the new energy output curve and the load curve of each of the first-level distribution networks; wherein the extreme event co-occurrence factor reflects the probability of a sudden drop in new energy output and a sudden increase in load occurring simultaneously; Calculating a supply and demand adjustment factor for each of the primary distribution networks based on the difference characteristics between the power grid output curve and the corresponding load curve of each of the primary distribution networks; wherein the supply and demand adjustment factor reflects the urgency with which the supply and demand relationship between the power grid output and the load demand of the primary distribution network needs to be restructured to achieve supply and demand balance; All the primary distribution networks are screened using the extreme event co-occurrence factor and the supply and demand adjustment factor to obtain a target primary distribution network.

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