Power distribution network dynamic reconfiguration method and device based on decision-oriented time period division, and medium

Abstract

The application discloses a power distribution network dynamic reconstruction method and device based on decision-oriented time period division, and a medium, wherein the method comprises the following steps: constructing a power distribution network dynamic reconstruction model; designing a multi-time decision mutual degree calculation method of the power distribution network dynamic reconstruction problem, completing consistency evaluation of each time decision, calculating a decision optimal solution deviation value between each time under the premise of considering time sequence through single-time reconstruction at each time, and constructing a similarity matrix; introducing a spectral clustering algorithm through the obtained similarity matrix, fusing decision consistency and time continuity, obtaining a corresponding degree matrix and Laplacian matrix, and realizing a decision-oriented time period division method. The application ensures that the segmentation can be reasonably divided according to load balancing requirements and improves the calculation speed, and through the calculation of the optimization result similarity matrix between each time period, the spectral clustering algorithm is used to aggregate the high similarity into a time period, so that the application can be widely applied to the field of power system power distribution network reconstruction technology.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system distribution network reconstruction, and particularly relates to a distribution network dynamic reconstruction method based on decision-oriented time period division, equipment and medium. BACKGROUND

[0002] With the continuous access of distributed photovoltaics, the scale and randomness of distribution networks grow rapidly, the spatiotemporal imbalance of demand-side loads is prominent, light-load and heavy-load feeders coexist, and the safety and economy of distribution network operation are difficult to guarantee. Under this background, dynamic reconstruction of distribution networks has attracted widespread attention. It can determine the states of sectionalizing switches and tie switches, and formulate a dynamic adjustment scheme of the future time period operation mode of the distribution network, so as to solve the negative effects brought by demand-side uncertainty. However, dynamic reconstruction of the distribution network is essentially a typical mixed integer programming (MIP) problem. The solution space of the problem grows exponentially with the number of switches in the distribution network and the time periods to be reconstructed, which means that the time required for solving will increase dramatically as the scale of the distribution network expands and the number of time periods to be reconstructed increases. Therefore, dynamic reconstruction of the distribution network faces huge computational challenges. Therefore, how to effectively reduce the solving time while ensuring the quality of the decision has become a research hotspot in the current dynamic reconstruction of the distribution network.

[0003] In the current research, the solution of dynamic reconstruction of the distribution network can be divided into two categories: artificial intelligence algorithms and mathematical optimization methods. The former is represented by heuristic algorithms such as particle swarm optimization and algorithms such as deep reinforcement learning. These methods can quickly search for a local optimal solution of dynamic reconstruction of the distribution network. However, the solution of artificial intelligence algorithms usually deviates greatly from the optimal solution, and even has an infeasible phenomenon, which is difficult to apply to practical engineering. In contrast, mathematical optimization methods can search for a global optimal solution while strictly meeting the constraints, but this process usually takes a lot of time. In view of this, a large number of studies have discussed model simplification. In particular, these simplification works focus on the time period division method, which combines the reconstruction time periods to reduce the number of variables. This matches the reality that the operation mode of the distribution network does not change frequently. In fact, most dynamic reconstruction models have a switching limit constraint on the operation mode. This means that the reconstruction result will naturally be divided into several time periods, which are defined as optimal time period division in the present application. Such simplification methods essentially determine the optimal time period division result in advance, which is generally acceptable in engineering.

[0004] However, the current time period division research focuses on analyzing the load similarity, that is, clustering and dividing the time period according to the load Euclidean space distance. The application collectively refers to this as a load-oriented time period division method. This method often relies on a hidden assumption that the optimal reconstruction decision at the time of load similarity is applicable to each other. However, this assumption is not strictly applicable to the dynamic reconstruction problem of the distribution network. In fact, according to the multi-parameter programming theory, a slight change in the parameter value is likely to bring about a huge change in the mixed integer programming result. Therefore, under the load-oriented time period division, there is usually a significant gap between the dynamic reconstruction result of the distribution network and the optimal reconstruction. Obviously, if a new time period division method can be found to ensure that the optimal decisions at each time in the time period are applicable to each other, the optimality of the simplified model can be improved. SUMMARY

[0005] To at least partially solve one of the technical problems existing in the prior art, the purpose of the present application is to provide a distribution network dynamic reconstruction method, device and medium based on decision-oriented time period division, which can ensure more reasonable time period division results and improve the dynamic reconstruction model solving speed of the distribution network, and reduce the number of time periods and improve the solving speed through the spectral clustering algorithm.

[0006] The first technical solution adopted by the present application is:

[0007] A distribution network dynamic reconstruction method based on decision-oriented time period division, comprising the following steps:

[0008] Constructing a dynamic reconstruction model of the distribution network which comprehensively considers the minimization of network loss and load balancing index;

[0009] Designing a multi-time decision mutual degree calculation method for the dynamic reconstruction problem of the distribution network, completing the consistency evaluation of decisions at each time, calculating the decision optimal solution deviation value between each time under the premise of considering the time sequence through single-time reconstruction at each time, and constructing a similarity matrix;

[0010] By obtaining the similarity matrix, introducing the spectral clustering algorithm, fusing the decision consistency and time continuity, obtaining the corresponding degree matrix and Laplacian matrix, and realizing the decision-oriented time period division method.

[0011] Further, the objective function of the dynamic reconstruction model optimization of the distribution network is:

[0012]

[0013] In the formula, T is the number of hours in the cycle of the dynamic reconstruction of the distribution network; c 1,t is the network loss of the distribution network at time t; c 2,t is the load balancing index of the distribution network at time t; E1 and E2 are respectively the network loss cost coefficient and the load balancing index weight coefficient.

[0014] Further, the network loss amount is calculated as follows:

[0015]

[0016] In the formula, is a branch set of the distribution network; r k is an impedance value of the branch k; w k,t is an on-off state of the branch k at time t, taking 1 when closed and 0 when open, and being set to a normally closed state, i.e., always 1, if the branch is not provided with a switch; I k,t is a current value flowing through the branch k at time t;

[0017] The calculation formula of the load balancing index is:

[0018]

[0019]

[0020] In the formula, I LB,t is a load deviation value at time t, I LB,max is a global load deviation value in the initial state; I k,max is a maximum current of the flowable branch k.

[0021] Further, the constraint conditions of the dynamic reconstruction model of the distribution network include distributed photovoltaic output constraints, mid-section loadable constraints, power flow constraints, node voltage branch current constraints, radial network constraints, and switch operation limit constraints.

[0022] Further, the design of the dynamic reconstruction problem of the distribution network multi-time decision mutual degree calculation method completes the consistency evaluation of each time decision, calculates the decision optimal solution deviation value between each time under the premise of considering the time sequence by single-time reconstruction at each time, and constructs a similarity matrix, including:

[0023] Definition is an optimal value of the dynamic reconstruction of the distribution network at time t, only considering the optimal target of the load balancing index, and the result is represented by the following formula:

[0024]

[0025] In the formula, g(F; t) represents a target value matrix of all feasible decisions of the distribution network at time t;

[0026] The results obtained by reconstruction optimization at different time periods are generally different; if the switch state results at time t m are substituted into the switch state results at time t nIn the network under the time period, the solution result is obtained according to the dynamic reconstruction model of the power distribution network, but the result is obviously worse than the optimal solution of directly performing reconstruction optimization under the time period t n Under the time period t n The deviation value of taking the optimal solution strategy of the time period tmunder the time period t

[0027]

[0028]

[0029]

[0030] In the formula, The value of taking the optimal solution strategy of the time period tmunder the time period t n Under the time period t m The value of taking the optimal solution strategy of the time period tmunder the time period t The value of taking the optimal solution strategy of the time period tmunder the time period t n Under the time period t m The value of taking the optimal solution strategy of the time period tmunder the time period t The importance of the time sequence is represented.

[0031] Since in general cases, δ(t m t n )≠δ(t n t m ), in order to facilitate subsequent analysis and division of the time period by spectral clustering, the symmetric similarity is constructed by summation, and the specific definition is as follows:

[0032] w(t m ,t n )=δ(t m t n )+δ(t n t m )

[0033] The similarity based on decision analysis under the time periods t m and t n is represented by w(t m ,t n ), and the similarity matrix is constructed according to the similarity:

[0034]

[0035] Furthermore, the spectral clustering algorithm is a clustering method based on spectral graph theory. The algorithm consists of two stages: graph construction and graph segmentation. The graph is composed of vertices and edges, where vertices are represented by sample points, which are represented by the number of time periods in the dynamic reconfiguration of the distribution network. Edges represent the similarity between sample points, which are represented by the similarity of decisions under different time periods in the dynamic reconfiguration of the distribution network. Clustering is to cluster vertices with high similarity, that is, to aggregate the number of time periods with high decision similarity.

[0036] Furthermore, the degree matrix and Laplace matrix are obtained in the following way:

[0037] Since the similarity matrix W is a skew-symmetric matrix, the degree matrix D is obtained from the similarity matrix, where D is a diagonal matrix, the values ​​on the diagonal are the sum of the values ​​on the same side of the corresponding similarity matrix, and all other values ​​are 0;

[0038] Calculate the Laplacian matrix L based on the similarity matrix and the degree matrix:

[0039] L=DW

[0040] The Laplacian matrix is ​​standardized, and the eigenvalues ​​and eigenvectors V of the Laplacian matrix L are calculated. e ;

[0041] Sort the eigenvalues ​​and obtain k eigenvalues ​​in order, along with their corresponding eigenvectors, to form a matrix V = {v1, v2, ..., v3}. k K-means clustering is performed on matrix V to obtain the corresponding classification results, that is, the corresponding time period division results.

[0042] Furthermore, the dynamic reconfiguration method for the distribution network also includes a verification step:

[0043] The distribution network system is simulated and verified based on the time period division method. The dynamic reconfiguration model of the distribution network is used to solve the problem, so as to improve the speed of dynamic reconfiguration of the distribution network and enhance the economy and reliability of distribution network operation.

[0044] The second technical solution adopted in this invention is:

[0045] An electronic device includes a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set, or an instruction set, and the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by the processor to realize a dynamic reconfiguration method for distribution networks based on decision-oriented time-segmentation as described above.

[0046] The third technical solution adopted in this invention is:

[0047] A computer-readable storage medium storing at least one instruction, at least one program, code set, or instruction set, wherein the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by a processor to implement a dynamic reconfiguration method for a distribution network based on decision-oriented time-segmentation as described above.

[0048] The fourth technical solution adopted in this invention is:

[0049] A computer program product or computer program includes computer instructions stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium and execute the computer instructions to cause the computer device to perform the method described above.

[0050] The beneficial effects of this invention are as follows: This invention proposes a dynamic reconfiguration method for distribution networks based on decision-oriented time-segmentation. It clusters and divides time periods to ensure that segments are rationally allocated according to load balancing requirements and improves computational speed. By calculating the similarity matrix of optimization results between each time period, a spectral clustering algorithm is used to aggregate periods with high similarity into a single time period. This invention designs a multi-moment decision interoperability calculation method for the dynamic reconfiguration problem of distribution networks, completing the consistency assessment of decisions at each moment. Simultaneously, a spectral clustering algorithm is introduced to integrate decision consistency and temporal continuity, realizing a decision-oriented time-segmentation method. Attached Figure Description

[0051] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following description is provided with accompanying drawings of the relevant technical solutions in the embodiments of the present invention or the prior art. It should be understood that the accompanying drawings described below are only for the purpose of clearly illustrating some embodiments of the technical solutions of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0052] Figure 1 This is a schematic diagram of the dynamic reconfiguration method for distribution networks based on decision-oriented time period division in an embodiment of the present invention;

[0053] Figure 2 This is a schematic diagram of the time period division process based on the decision-oriented spectral clustering algorithm in an embodiment of the present invention;

[0054] Figure 3 This is a typical three-feeder network connection diagram in an embodiment of the present invention;

[0055] Figure 4 This is a typical load variation diagram of a three-feeder network node in an embodiment of the present invention;

[0056] Figure 5This is a schematic diagram illustrating the variance of the load between feeders at different times under different reconstruction methods in a typical three-feeder network embodiment of the present invention. Detailed Implementation

[0057] The embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention. The step numbers in the following embodiments are set only for ease of explanation, and there is no limitation on the order between the steps. The execution order of each step in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0058] In the description of this invention, it should be understood that the orientation descriptions, such as up, down, front, back, left, right, etc., are based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting this invention.

[0059] In the description of this invention, "several" means one or more, "more than" means two or more, "greater than," "less than," and "exceeding" are understood to exclude the stated number, while "above," "below," and "within" are understood to include the stated number. The use of "first" and "second" in the description is merely for distinguishing technical features and should not be construed as indicating or implying relative importance, or implicitly indicating the number of indicated technical features, or implicitly indicating the order of the indicated technical features.

[0060] In the description of this invention, unless otherwise explicitly defined, terms such as "set up," "install," and "connect" should be interpreted broadly, and those skilled in the art can reasonably determine the specific meaning of the above terms in this invention in conjunction with the specific content of the technical solution.

[0061] Example 1

[0062] like Figure 1 As shown, this embodiment provides a dynamic reconfiguration method for distribution networks based on decision-oriented time period division, including the following steps:

[0063] S1: Construct a dynamic reconfiguration model for the distribution network that comprehensively considers minimizing network loss and load balance index.

[0064] The objective function for optimizing the dynamic reconfiguration model of the distribution network is:

[0065]

[0066] In the formula, T represents the number of hours within the period of dynamic reconfiguration of the distribution network; c 1,t c represents the network loss of the distribution network at time t. 2,t Let E1 be the load balance index of the distribution network at time t; E2 and E1 are the network loss cost coefficient and the load balance index weighting coefficient, respectively.

[0067] Specifically, the formula for calculating network loss is as follows:

[0068]

[0069] In the formula, For the set of distribution network branches; r k Here is the impedance value of branch k; w k,t Let I represent the switch-closed state of branch k at time t. It is set to 1 when closed and 0 when open. If the branch has no switch, it is set to normally closed, i.e., always 1. k,t Let t be the current flowing through branch k at time t.

[0070] The formula for calculating the load balancing index is:

[0071]

[0072]

[0073] In the formula, I LB,t Let I be the load deviation value at time t. LB,max I represents the global load deviation value under the initial state. k,max Let k be the maximum current in the flowable branch.

[0074] In some embodiments, the constraints of the distribution network dynamic reconfiguration model include distributed photovoltaic power output constraints, mid-range load constraints, power flow constraints, node voltage and branch current constraints, radial network constraints, and switch operation restriction constraints.

[0075] 1) The calculation formula for the distributed photovoltaic power output constraint is as follows:

[0076]

[0077] In the formula, and Let represent the actual power and available power of distributed photovoltaic node i at time t, respectively; ε represents the set of distributed photovoltaic nodes.

[0078] 2) The formula for the intermediate load constraint is as follows:

[0079]

[0080]

[0081] In the formula, These represent the reduction in active and reactive loads at node i at time t, respectively. Indicates the power reduction rate; Represents a set of nodes; This represents the set of power supply nodes in the upper-level power grid.

[0082] 3) The formula for calculating the power flow constraint is:

[0083]

[0084]

[0085]

[0086]

[0087]

[0088]

[0089]

[0090] In the formula: P i,t and Q i,t P represents the active power and reactive power injection at node i at time t, respectively; ij,t and Q ij,t Let them represent the active power flow and reactive power flow from node i to node j at time t, respectively. and G represents the square of the current in branch ij and the square of the voltage at node i at time t; i and b i Indicates the conductance and susceptance from node i to ground; w ij,t This indicates the on / off state of nodes i through j at time t, where 0 indicates disconnection and 1 indicates merging. and Let represent the active power demand and reactive power demand of load node i at time t, respectively; M represents a relatively large number. This represents the set of branches containing switches.

[0091] 4) The calculation formulas for the node voltage and branch current constraints are as follows:

[0092]

[0093]

[0094]

[0095] In the formula: Vi,t I represents the voltage value at node i at time t; ij,t I represents the branch current value of branch ij at time t; ij,t,max and I ij,t,min V represents the upper and lower limits of the branch current in branch ij at time t; i,t,max and V i,t,min This represents the upper and lower limits of the voltage at node i at time t.

[0096] 5) The calculation formula for the radial network constraint is as follows:

[0097]

[0098]

[0099]

[0100]

[0101] In the formula: wz ij,t wf ij,t This is an auxiliary variable that is introduced, and its value is greater than or equal to 0.

[0102] 6) The calculation formula for the switch operation constraint is as follows:

[0103]

[0104] In the formula, Let A be a segmented variable, representing the set of times covered by time period a; A represents the limit on the number of actions per day.

[0105] The equivalent formula using the big M method for relaxation is as follows:

[0106]

[0107]

[0108] 1-M(1-z t )≤W t ≤Mz t ,t∈(T-1);

[0109]

[0110] In the formula: To assist with the binary variable, 0 indicates that the switch of branch k did not activate from time t to time t+1, and 1 indicates that it activated; W t As an auxiliary continuous variable, z represents the number of switches that operate from time t to time t+1; tThis indicates whether all switches have been activated from time t to time t+1, with 1 indicating that a switch has been activated and 0 indicating that no switch has been activated.

[0111] S2: Design a method for calculating the interoperability of multi-time decision-making in the dynamic reconfiguration problem of distribution network, complete the consistency assessment of decision-making at each time, and calculate the deviation of the optimal solution of decision-making at each time by performing single-time reconfiguration at each time, taking into account the temporal sequence, and construct a similarity matrix based on this.

[0112] Define F t * The optimal value for dynamic reconfiguration of the distribution network during time period t, considering only the optimal target of the load balance index, is expressed by the following formula:

[0113] F t * =argming(F;t)

[0114] In the formula, g(F;t) represents the target value matrix of all feasible decisions for the distribution network at time t.

[0115] Reconstruction optimization performed at different time periods generally yields different results. If t... m Substitute the on / off state results of the time period into t n In the network at time intervals, the solution can be obtained based on the model in the previous section, but this result will obviously be inferior to that at time intervals. n The optimal solution for direct reconstruction optimization within a given time period can be defined as the normalized difference between the two solutions, considering temporal sequence, as the deviation value for deciding to adopt the optimal solution for time period tm under time period tn, denoted by δ(t1t2):

[0116]

[0117]

[0118]

[0119] In the formula, This indicates that after considering the temporal sequence, in t n t under time period m The value of the optimal solution decision for the time period; Indicates at t n t under time period m The value of the optimal solution decision for the time period; Indicates the importance of the time sequence.

[0120] Since δ(t) is generally used m t n )≠δ(t n t mTo facilitate subsequent analysis and the segmentation of time periods using spectral clustering, a symmetrical similarity is constructed through summation, specifically defined as follows:

[0121] w(t m ,t n )=δ(t m t n )+δ(t n t m )

[0122] With w(t) m ,t n ) indicates that at t m and t n Similarity based on decision analysis over a given time period, obviously, when w(t) m ,t n The smaller the value of ), the higher the similarity between the two time periods, and the greater the probability of obtaining the optimal solution when making the same decision; conversely, the smaller the similarity, the lower the similarity. Furthermore, the similarity matrix is ​​constructed based on the similarity:

[0123]

[0124] From the above equation, we can see that when n∈T, w(t) n ,t n ) = 0, W is a skew-symmetric matrix.

[0125] S3: Obtain the similarity matrix through step S2, introduce the spectral clustering algorithm, integrate decision consistency and time continuity to obtain the corresponding degree matrix and Laplace matrix, and realize the decision-oriented time period segmentation method.

[0126] Spectral clustering is a clustering method based on spectral graph theory. The algorithm consists of two stages: graph construction and graph partitioning. The graph is composed of vertices and edges. The samples are represented by the graph, and clustering is achieved by partitioning the vertices. Vertices are generally represented by sample points, which in distribution network dynamic reconfiguration are represented by the number of time periods. Edges represent the similarity between sample points, which in distribution network dynamic reconfiguration are represented by the similarity of decisions at different time periods. Clustering involves grouping vertices with high similarity, that is, aggregating the time periods with high decision similarity.

[0127] Spectral clustering algorithms consist of two stages: graph construction and graph partitioning. In a network, if its node set is defined as V and its edge set as E, then the graph formed by this network is G = (V, E). Spectral clustering is an algorithm based on graph theory, capable of finding the most suitable clustering solution for various spatial sample shapes. Unlike previous clustering methods, it is more adaptable to partitioning high-dimensional data and achieves better results in handling multi-class community partitioning. When using spectral clustering, the first step is to obtain the adjacency matrix of the network graph, i.e., the skew-symmetric similarity matrix W. The algorithm aims to calculate and partition this similarity matrix into different classes, where the similarity between points within each class is high, while the similarity between points in different classes is low. Spectral clustering algorithms require cutting the graph itself to achieve partitioning, and it inherently employs several different cutting criteria, with standardized cutting being the most commonly used.

[0128] See Figure 2 After calculating the similarity matrix W at different times during the dynamic reconfiguration of the distribution network, the graph can be segmented using the following steps:

[0129] Step 1: Calculate the degree matrix D, where D is a diagonal matrix, the values ​​on the diagonal are the sum of the values ​​on the same side of the corresponding similarity matrix, and all other values ​​are 0;

[0130] Step 2: Calculate the corresponding Laplacian matrix. The formula is as follows:

[0131] L=DW

[0132] Step 3: Standardize the Laplace matrix. The calculation formula is as follows:

[0133]

[0134] Step 4: Calculate the eigenvalues ​​and eigenvectors V of the Laplacian matrix L. e ;

[0135] Step 5: Extract the eigenvectors corresponding to the k smallest eigenvalues ​​to form a matrix V = {v1, v2, ..., v...} k Then, K-means clustering is performed on matrix V to obtain the corresponding classification results, which in turn yields the corresponding time period segmentation results.

[0136] S4: The distribution network system is simulated and verified based on the time period division method in steps S2 and S3. The model in step S1 is used for solution to improve the speed of dynamic reconfiguration of the distribution network and enhance the economy and reliability of distribution network operation.

[0137] The following detailed explanation is provided in conjunction with the accompanying drawings and specific methods. To verify the effectiveness of the method proposed in the embodiments of the present invention, a simulation verification is performed using a typical three-feeder network as an example, wherein the network wiring diagram is as follows.Figure 3 As shown.

[0138] Table 1 shows the typical three-feeder network connection relationships and line impedance information:

[0139] Table 1. Typical Three-Feder Network Connections and Line Impedance Information

[0140]

[0141] Before reconstructing the network, the load at each load point in the three-feeder network at 24 time points was generated using random numbers. The results are as follows: Figure 4 As shown.

[0142] Considering that the changes in the daily operating status of the power grid are limited in actual operation, generally controlled to within six times, this paper sets the number of time periods to 5 and the value of the number of actions A per day to 4 to ensure that it does not exceed this value. The results of dividing the time periods using the FCM algorithm and decision spectrum-based clustering are shown in Table 2:

[0143] Table 2. Time Period Division Results of Different Algorithms

[0144]

[0145] By dynamically reconfiguring a typical three-feeder network system, the result of dynamic reconfiguration without time period division is defined as the optimal reconfiguration (OPR) result. The superiority of decision-based reconfiguration (DBR) is determined by comparing no reconfiguration (NR), OPR, and load-based time period division reconfiguration (LBR). The results are shown in Table 3.

[0146] Table 3. Reconfiguration results of a typical 3-feeder network

[0147]

[0148]

[0149] The results show that distribution network reconfiguration can effectively reduce distribution network losses and load balancing index. In terms of computational efficiency, LBR and DBR under time-segmentation are more efficient than OPR, reducing computation time by more than 4000 seconds. Regarding reconfiguration objectives, both methods are weaker than the optimal decision method in terms of improvement effect. Specifically, DBR achieves 92.5% and 93.6% of the improvement effect of the optimal decision method in terms of network losses and load deviation, respectively, significantly higher than LBR's 65.8% and 42.1%. To more intuitively reflect the advantages of DBR in load balancing, the load variance of each feeder under different reconfiguration methods is displayed as follows: Figure 5 As shown, DBR and ORP have similar balancing effects, with a significant reduction in load variance. However, due to limitations in time period division, LBR exacerbates load variance in some time periods. Taking the interval from time 10 to time 13 as an example, both ORP and DBR divide this interval into a single time period for optimization, resulting in a significant decrease in load. LBR, however, divides this interval into two separate time periods, 4-11 and 12-17, causing the load deviation within this interval to increase rather than decrease. Furthermore, based on the segmentation of the reconstruction scheme, DBR and ORP have three completely identical segments, and the other two segments are also quite similar, while LBR's segmentation is completely different. This further illustrates the superiority of decision-oriented approaches.

[0150] Example 2

[0151] This invention also provides an electronic device, which includes a processor and a memory. The memory stores at least one instruction, at least one program, a code set, or an instruction set. The at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by the processor to achieve the following: Figure 1 and Figure 2 This paper presents a dynamic reconfiguration method for distribution networks based on decision-oriented time period division.

[0152] It is understood that the memory may include random access memory (RAM) or read-only memory. Optionally, the memory may include non-transitory computer-readable storage medium. The memory can be used to store instructions, programs, code, code sets, or instruction sets. The memory may include a stored program area and a stored data area, wherein the stored program area may store instructions for implementing an operating system, instructions for at least one function, instructions for implementing the various method embodiments described above, etc.; the stored data area may store data created according to the use of the server, etc.

[0153] A processor may include one or more processing cores. The processor connects to various parts of the server via various interfaces and lines, executing instructions, programs, code sets, or instruction sets stored in memory, and accessing data stored in memory to perform various server functions and process data. Optionally, the processor may be implemented using at least one of the following hardware forms: Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), and Programmable Logic Array (PLA). The processor may integrate one or more of the following: Central Processing Unit (CPU) and Modem. The CPU primarily handles the operating system and applications; the modem handles wireless communication. It is understood that the modem may also be implemented as a separate chip without being integrated into the processor.

[0154] Since this electronic device is the electronic device corresponding to the dynamic reconfiguration method of distribution network based on decision-oriented time period division in the embodiments of the present invention, and the principle of solving the problem by this electronic device is similar to that of the method, the implementation of this electronic device can refer to the implementation process of the above method embodiments, and the repeated parts will not be described again.

[0155] Example 3

[0156] This invention also provides a computer-readable storage medium storing at least one instruction, at least one program, a code set, or an instruction set, wherein the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by a processor to achieve the following: Figure 1 and Figure 2 This paper presents a dynamic reconfiguration method for distribution networks based on decision-oriented time period division.

[0157] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-Erasable Programmable Read-Only Memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium capable of carrying or storing data.

[0158] Since the storage medium is the storage medium corresponding to the dynamic reconfiguration method of distribution network based on decision-oriented time period division in the embodiments of the present invention, and the principle of the storage medium in solving the problem is similar to that of the method, the implementation of the storage medium can refer to the implementation process of the above method embodiments, and the repeated parts will not be described again.

[0159] Example 4

[0160] In some possible implementations, various aspects of the methods of the embodiments of the present invention can also be implemented as a program product comprising program code that, when run on a computer device, causes the computer device to perform the steps of a dynamic reconfiguration method for a distribution network based on decision-oriented time-segmentation according to various exemplary embodiments of this application as described above. The executable computer program code or "code" used to perform the various embodiments can be written in high-level programming languages ​​such as C, C++, C#, Smalltalk, Java, JavaScript, Visual Basic, Structured Query Language (e.g., Transact-SQL), Perl, or in various other programming languages.

[0161] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0162] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0163] The above embodiments are merely illustrative of the technical concept and features of the present invention, and are intended to enable those skilled in the art to understand the content of the present invention and implement it accordingly. They should not be construed as limiting the scope of protection of the present invention. All equivalent changes or modifications made based on the essence of the content of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A dynamic reconfiguration method for distribution networks based on decision-oriented time period division, characterized in that, Includes the following steps: Construct a dynamic reconfiguration model for the distribution network that comprehensively considers minimizing network losses and load balancing index; A method for calculating the interoperability of multi-moment decision-making in the dynamic reconfiguration problem of distribution networks is designed to complete the consistency assessment of decisions at each moment. By performing single-moment reconfiguration at each moment, the deviation of the optimal decision solution between each moment is calculated under the premise of considering the temporal sequence, and a similarity matrix is ​​constructed. By obtaining the similarity matrix, a spectral clustering algorithm is introduced to integrate decision consistency and temporal continuity, resulting in the corresponding degree matrix and Laplace matrix, thus realizing a decision-oriented time period segmentation method. The proposed method for calculating the interoperability of multi-time decision-making in the dynamic reconfiguration problem of the distribution network completes the consistency assessment of decisions at each time moment. By performing single-time reconfiguration at each time moment, and considering the temporal sequence, the method calculates the deviation of the optimal decision solution between each time moment, and constructs a similarity matrix, including: definition for t The dynamic reconfiguration of the distribution network during a given time period only considers the optimal value when the load balance index is optimal, and the result is expressed by the following formula: In the formula, Indicates the distribution network in t The objective value matrix of all feasible decisions at time t; Restructuring and optimization are performed at different time periods; if... t m Substitute the on / off state results of the time period into t n In a network under a specific time period, the solution obtained is based on the dynamic reconfiguration model of the distribution network, but this result is obviously inferior to that obtained in a network under a specific time period. t n The optimal solution for direct reconstruction optimization within a given time period; considering temporal sequence, the normalized difference between the two is defined as... t n Take measures during the time period t m The deviation value of the optimal solution decision for the time period is used as... express: In the formula, This indicates that after considering the timing, t n Take measures during the time period t m The value of the optimal solution decision for the time period; Indicates in t n t under time period m The value of the optimal solution decision for the time period; Indicates the importance of the time sequence; Due to the general situation To facilitate subsequent analysis and the segmentation of time periods using spectral clustering, a symmetrical similarity is constructed through summation, specifically defined as follows: by Indicates at t m and t n Similarity is calculated based on decision analysis over a given time period, and a similarity matrix is ​​constructed accordingly. 。 2. The method for dynamic reconfiguration of distribution networks based on decision-oriented time period division according to claim 1, characterized in that, The objective function for optimizing the dynamic reconfiguration model of the distribution network is: In the formula, T represents the number of hours within the period of dynamic reconfiguration of the distribution network; for Network losses in the distribution network at any given time; for The load balance index of the power distribution network at any given time; These are the network loss cost coefficient and the load balancing index weight coefficient, respectively.

3. The method for dynamic reconfiguration of distribution networks based on decision-oriented time period division according to claim 1, characterized in that, The formula for calculating network loss is as follows: In the formula, For distribution network branch collection; branch road k The impedance value; branch road k At any moment t The switch is set to the closed state, with a value of 1 when closed and 0 when open. If there is no switch in the branch, it is set to the normally closed state, i.e., always 1. for t Flowing through the side road k The current value; The formula for calculating the load balancing index is as follows: In the formula, for t Load deviation value at time, This represents the global load deviation value under the initial state. For traversable branch roads k The maximum current.

4. The method for dynamic reconfiguration of distribution networks based on decision-oriented time period division according to claim 1, characterized in that, The constraints of the distribution network dynamic reconfiguration model include distributed photovoltaic power output constraints, mid-range load constraints, power flow constraints, node voltage and branch current constraints, radial network constraints, and switch operation restriction constraints.

5. The method for dynamic reconfiguration of distribution networks based on decision-oriented time period division according to claim 1, characterized in that, The spectral clustering algorithm is a clustering method based on spectral graph theory. The algorithm consists of two stages: graph construction and graph segmentation. The graph is composed of vertices and edges. Vertices are represented by sample points, which are represented by the number of time periods in the dynamic reconfiguration of the distribution network. Edges represent the similarity between sample points, which are represented by the similarity of decisions under different time periods in the dynamic reconfiguration of the distribution network. Clustering is to cluster vertices with high similarity, that is, to aggregate the number of time periods with high decision similarity.

6. The method for dynamic reconfiguration of distribution networks based on decision-oriented time period division according to claim 1, characterized in that, The degree matrix and Laplace matrix are obtained in the following way: Due to the similarity matrix W Given a skew-symmetric matrix, the degree matrix is ​​obtained from the similarity matrix. D ,in D It is a diagonal matrix, where the values ​​on the diagonal are the sums of the values ​​on the same side of the corresponding similarity matrix, and all other values ​​are 0; Calculate the Laplacian matrix based on the similarity matrix and the degree matrix. L : Standardize the Laplacian matrix and calculate the Laplacian matrix. L eigenvalues ​​and eigenvectors V e ; Sort the eigenvalues ​​and obtain k eigenvalues ​​in order, along with their corresponding eigenvectors, to form a matrix. For the matrix V Perform K-means clustering to obtain the corresponding classification results, which in turn yields the corresponding time period segmentation results.

7. The method for dynamic reconfiguration of distribution networks based on decision-oriented time period division according to claim 1, characterized in that, The dynamic reconfiguration method for distribution networks also includes a verification step: The distribution network system is simulated and verified based on the time period division method. The dynamic reconfiguration model of the distribution network is used to solve the problem, so as to improve the speed of dynamic reconfiguration of the distribution network and enhance the economy and reliability of distribution network operation.

8. An electronic device, characterized in that, The electronic device includes a processor and a memory, the memory storing at least one instruction, at least one program, a code set, or an instruction set, the at least one instruction, the at least one program, the code set, or the instruction set being loaded and executed by the processor to implement the method as described in any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that, The storage medium stores at least one instruction, at least one program, code set, or instruction set, wherein the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by a processor to implement the method as described in any one of claims 1 to 7.

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