A power distribution network dynamic reconstruction method, system, device and storage medium

Abstract

The application discloses a power distribution network dynamic reconstruction method, system and device and a storage medium, wherein the method comprises the following steps: constructing a power distribution network multi-target dynamic reconstruction model; analyzing and processing overall daily load curves according to load power consumption conditions, clustering load variation amounts through a Mean shift clustering algorithm, dividing time periods, and then solving the power distribution network multi-target dynamic reconstruction model; simplifying topology based on a graph theory method, proposing two-stage dynamic reconstruction, obtaining an overall optimal reconstruction result through dynamic reconstruction of a simplified network in the first stage, and confirming local switch states of an original network in the second stage; combining the time period division method and the topology simplification method, simulating and verifying an IEEE33 node system, and realizing dynamic reconstruction of the power distribution network. The Mean shift clustering algorithm is used for clustering and dividing time periods, so that the number of reconstruction time periods is reduced, the calculation complexity is reduced, and 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 field of power system distribution network reconstruction technology, and particularly relates to a distribution network dynamic reconstruction method, system, device and storage medium. BACKGROUND

[0002] The research on dynamic reconstruction of distribution network is of great significance in the development of distributed energy of new energy. With the rapid development of renewable energy, especially the popular application of distributed energy (such as solar energy and wind energy), the traditional distribution network is facing a series of challenges and reform needs. Due to the uncertainty and intermittency of renewable energy, the traditional distribution network may face problems such as overload and voltage fluctuation when facing the access of large-scale renewable energy. Through dynamic reconstruction, the topology structure and operation mode of the distribution network can be flexibly adjusted according to the real-time energy supply and demand situation to effectively cope with the problems caused by energy fluctuation and improve the stability and reliability of the system.

[0003] Because of the large number of switches in the distribution network, and considering the situation in different time periods, the solution of the binary integer variable is complex, which is a typical mixed integer programming problem (MILP), and the complexity of data has constrained the solution of this type of problem. With the rapid development of the national economy, the scale of the power grid and the power consumption also grow rapidly. When heavy overload occurs, the rapid transfer between feeders is of great significance. However, there is limited research on improving the speed of dynamic reconstruction of distribution network as the research focus. Most of the current literature reduces the spatial complexity of the calculation in time and space to improve the calculation speed. The commonly used method in time period division is fuzzy C-means clustering algorithm, which needs prior knowledge of the number of clustering centers and has certain limitations. In space, the calculation speed is improved by partition reconstruction, and there is no method to improve the calculation speed by simplifying the network. SUMMARY

[0004] 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, system, device and storage medium based on Mean shift clustering to divide time periods and simplify topology.

[0005] The first technical solution adopted by the present application is:

[0006] A distribution network dynamic reconstruction method, comprising the following steps:

[0007] Constructing a distribution network multi-objective dynamic reconstruction model considering operation cost and load balancing index comprehensively;

[0008] According to the load power consumption, the overall daily load curve is analyzed and processed, and then the load change amount is clustered through a Mean shift clustering algorithm, so that the time period is divided, and then a multi-objective dynamic reconstruction model of the distribution network is constructed and solved;

[0009] The topology is simplified based on a graph theory method, a two-stage dynamic reconstruction is proposed, the first stage obtains an overall optimal reconstruction result through dynamic reconstruction of the simplified network, and the second stage confirms the local switch state of the original network;

[0010] The dynamic reconstruction of the distribution network is realized through simulation verification of an IEEE33 node system combined with the time period division and topology simplification method, so as to improve the economy and reliability of the operation of the distribution network.

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

[0012]

[0013] In the formula, T is the period of dynamic reconstruction of the distribution network; c 1,t is the network loss amount of the distribution network at time t; c2,t is the number of switch operations of the distribution network at time t; c3,t is the load balancing index of the distribution network at time t; E1, E2 and E3 are weight coefficients of the network loss cost coefficient, the switch operation cost coefficient and the load balancing index respectively.

[0014] Further, the calculation formula of the network loss amount is:

[0015]

[0016] In the formula, K is the total number of branches of the distribution network; r k is the impedance value of the branch k; w k,t is the on-off state of the branch k at time t, 1 for closing and 0 for opening, if the branch is not provided with a switch, it is set to a normally closed state, that is, always 1; P k,t and Q k,t are active and reactive power flowing through the branch k at time t respectively; U k,t is the voltage value of the outgoing node of the branch k at time t; I k,t is the current value flowing through the branch k at time t;

[0017] The calculation formula of the number of switch operations is:

[0018] c 2,t = |w k,t -w k,t-1 |

[0019] The calculation formula of the load balancing index is:

[0020]

[0021] In the formula, I k,max is the maximum current of the branch k, I i,t is the current value flowing through the branch i at time t, I i,max is the maximum current of the branch i.

[0022] Further, the constraint conditions of the power distribution network multi-objective dynamic reconstruction model include power flow constraints, node voltage and branch current constraints, radial network constraints, and switch operation constraints.

[0023] Further, the calculation formula of the power flow constraints is as follows:

[0024]

[0025] In the formula, son(j) and fa(j) represent the child node and the parent node of node j respectively; g j and b j are the conductance and susceptance of node j connected to the ground; P j,t and Q j,t are the active power and reactive power injection of node j at time period t; M is a large number; E is a branch set; and N is a node set.

[0026] Further, the calculation formula of the node voltage and branch current constraints is as follows:

[0027]

[0028] In the formula, V j,t,max and V j,t,min are the upper and lower limits of the voltage of node j at time period t, I ij,t,max and I ij,t,min are the upper and lower limits of the branch current of branch ij at time period t.

[0029] Further, the calculation formula of the radial network constraints is as follows:

[0030]

[0031] In the formula, S is a set of power supply nodes of an upper layer power grid; is an auxiliary variable introduced, which is greater than or equal to 0.

[0032] Further, according to the load power consumption, the overall daily load curve is analyzed and processed, and then the Mean shift clustering algorithm is used to cluster the load change quantity to divide the time period, including:

[0033] Considering the time sequence of the load, time-sequential load periods with similar load amounts are aggregated into a class by a Mean shift clustering algorithm to obtain the time period division result.

[0034] Further, the graph theory method specifically comprises:

[0035] If all switches of the power distribution network are closed at the same time, the topology of the power distribution network is a connected graph; the connected graph is composed of nodes and branches, and the number of branches associated with a node is defined as the node degree; the nodes are divided into power supply nodes, intermediate nodes with a degree of 2, nodes with a degree greater than 2, and terminal nodes with a degree of 1;

[0036] By referring to the construction method of a feasible tree base, combining the different types of switches in the power distribution network, the topology network structure is simplified, and then the dynamic reconstruction of the power distribution network is converted into two-stage dynamic reconstruction; in the first stage, the simplified network is reconstructed to obtain the overall connection result of the power distribution network, and the result is input into the reconstruction of the original network; for the switch state determined in the first stage, the switch state is directly retained, and only the local part with an undetermined switch state is dynamically reconstructed to obtain the final reconstruction result.

[0037] Further, the graph theory method simplifies the topology, and proposes two-stage dynamic reconstruction; in the first stage, the overall optimization reconstruction result is obtained by dynamically reconstructing the simplified network, and in the second stage, the local switch state of the original network is confirmed, comprising:

[0038] All branch switches are closed, the sectional switches are opened, and if there are multiple feeders, the tie switches also need to be opened; the power supply nodes, nodes with a degree of 1, and nodes with a degree greater than 2 at this time are defined as special nodes;

[0039] The special nodes are retained, and the nodes with a degree of 2 between the special nodes are merged into one node; the load amount of the node is the sum of the load amounts of the merged nodes, and the branch impedance values flowing into and out of the node are the maximum impedance values in the merged branches;

[0040] If the terminal node is not connected to the sectional switch or the tie switch, the non-special nodes on the branch are directly merged into the terminal node;

[0041] The number of branches merged in the original network after the topology simplification is represented by the merged degree; n represents the number of merged nodes of the merging point; the branch merged degree flowing into the merging point is n-1, and the branch merged degree flowing out of the merging point is 1; the merged degree flowing into the merging point represents the first n-1 branches associated, and the merged degree flowing out of the merging point represents the nth branch associated;

[0042] After simplifying the topology, the first stage dynamic reconstruction is carried out, the dynamic reconstruction is carried out in the simplified network, the branch result with the merging degree of 1 is reserved to directly input into the dynamic reconstruction of the second stage, the branch with the merging degree greater than 1 is classified and discussed, if closed, the associated branch switch state in the second stage is also set to closed, if disconnected, the associated branch switch state in the second stage is set to an uncertain state, and needs to be determined in the dynamic reconstruction of the second stage.

[0043] The second technical scheme adopted by the application is:

[0044] A power distribution network dynamic reconstruction system comprises:

[0045] A model construction module is configured to construct a power distribution network multi-objective dynamic reconstruction model that comprehensively considers operation cost and load balancing index.

[0046] A time period division module is configured to analyze and process overall daily load curve according to load power consumption, cluster load variation quantity through Meanshift clustering algorithm, divide time periods, and solve through the constructed power distribution network multi-objective dynamic reconstruction model.

[0047] A power distribution network topology simplification module is configured to simplify topology based on graph theory method, propose two-stage dynamic reconstruction, obtain overall optimized reconstruction result through dynamic reconstruction of the simplified network in the first stage, and confirm local switch state of the original network through the second stage.

[0048] A dynamic reconstruction module is configured to combine the time period division and topology simplification methods, simulate and verify IEEE33 node system, and realize dynamic reconstruction of the power distribution network.

[0049] The third technical scheme adopted by the application is:

[0050] A power distribution network dynamic reconstruction device comprises:

[0051] At least one processor;

[0052] At least one memory is configured to store at least one program.

[0053] When the at least one program is executed by the at least one processor, the at least one processor implements the above method.

[0054] The fourth technical scheme adopted by the application is:

[0055] A computer readable storage medium stores a processor executable program, and the processor executable program is used to execute the above method when executed by a processor.

[0056] The beneficial effects of the present application are: the present application reduces the number of reconstructed time periods by clustering and dividing the time periods through the Mean shift clustering algorithm, thereby reducing the complexity of the calculation. On this basis, the network topology is simplified, the calculation speed is improved through two-stage dynamic reconstruction, which can provide quick power supply decision-making opinions for power grid operation personnel, avoid the waste of resources and improve the power quality of users. BRIEF DESCRIPTION OF DRAWINGS

[0057] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following introduces the drawings of the related technical solutions in the embodiments of the present application or the prior art. It should be understood that the drawings in the following introduction are only for the convenience of clearly describing part of the embodiments of the technical solutions of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the premise.

[0058] Figure 1 is a step flow chart of a power distribution network dynamic reconstruction method in the embodiments of the present application;

[0059] Figure 2 is a time period reconstruction flowchart based on the Mean shift clustering algorithm in the embodiments of the present application;

[0060] Figure 3 is a segmented graph of the IEEE33 node system daily load curve in the embodiments of the present application;

[0061] Figure 4 is a connection diagram of the IEEE33 node system in the embodiments of the present application;

[0062] Figure 5 is a simplified link diagram of the IEEE33 node system in the embodiments of the present application. DETAILED DESCRIPTION

[0063] The embodiments of the present application are described in detail below, and the examples of the embodiments are shown in the drawings, wherein the same or similar reference signs represent the same or similar elements or elements with the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary and are only used to explain the present application, and cannot be understood as limiting the present application. For the step numbers in the following embodiments, they are only set for the convenience of explanation and description, and the order between the steps is not limited in any way, and the execution order of each step in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0064] In the description of the present application, it should be understood that the orientation description, such as the orientation or position relationship indicated by the upper, lower, front, rear, left, right and the like, is based on the orientation or position relationship shown in the drawings, and is only for the convenience of describing the present application and simplifying the description, and does not indicate or imply that the device or element indicated must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the present application.

[0065] In the description of the present application, one or more is understood as one or more, two or more is understood as two or more, greater than, less than, more than, etc. are understood as not including the number, above, below, etc. are understood as including the number. If the first, second is described, it is only for the purpose of distinguishing technical features, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features or the sequence of indicated technical features. In addition, the association relationship of the associated objects described by "and / or" indicates that there can be three kinds of relationships, for example, A and / or B can represent: A exists alone, A and B exist together, and B exists alone. The character " / " generally represents an "or" relationship between the front and rear associated objects.

[0066] In the description of the present application, unless otherwise explicitly limited, the words such as setting, installing, connecting and the like should be broadly understood, and the person skilled in the art can reasonably determine the specific meaning of the above words in the present application in combination with the specific content of the technical solution.

[0067] In view of the existing technical problems, the present application provides a power distribution network dynamic reconstruction method based on Mean shift clustering period division and topology simplification, which is used to solve the technical problem that the dynamic reconstruction of the current power distribution network is difficult to solve due to the large number of switch variables. The Mean shift clustering algorithm is used to divide the time period, thereby reducing the number of time periods and simplifying the network, and the two-stage reconstruction is used to improve the solving speed.

[0068] As shown in Figure 1 The present embodiment provides a power distribution network dynamic reconstruction method, which comprises the following steps:

[0069] S1, a power distribution network multi-objective dynamic reconstruction model considering operation cost and load balancing index is constructed.

[0070] As an optional implementation, the objective function of the power distribution network multi-objective dynamic reconstruction model is:

[0071]

[0072] In the formula, T is the number of hours in the period of power distribution network dynamic reconstruction; c 1,t is the network loss of the power distribution network at time t; c2,t is the number of switch operations of the power distribution network at time t;c3,t is the load balancing index of the power distribution network at time t; E1, E2 and E3 are weight coefficients of the network loss cost coefficient, the switch operation cost coefficient and the load balancing index respectively.

[0073] The calculation formula of the network loss amount is:

[0074]

[0075] In the formula, K is the total number of branches of the power distribution network; r k is the impedance value of the branch k; w k,t is the on-off state of the branch k at time t, 1 when closed and 0 when disconnected, and is set to a normally closed state, i.e., always 1, if the branch is not provided with a switch; P k,t and Q k,t are the active and reactive power flowing through the branch k at time t respectively; U k,t is the voltage value of the node flowing out of the branch k at time t; I k,t is the current value flowing through the branch k at time t.

[0076] The calculation formula of the switch operation number is:

[0077] c 2,t = |w k,t -w k,t-1 |

[0078] The calculation formula of the load balancing index is:

[0079]

[0080] In the formula, I k,max is the maximum current of the branch k that can flow.

[0081] Further, as an optional implementation, in order to better implement the method, the constraint conditions of the power distribution network multi-objective dynamic reconstruction model include power flow constraints, node voltage branch current constraints, radial network constraints and switch operation constraints.

[0082] The calculation formula of the power flow constraints is:

[0083]

[0084]

[0085] In the formula, son(j) and fa(j) represent the child node and the parent node of node j respectively; g j and b j are the conductance and susceptance of node j connected to the ground; P j,t and Q j,tPij(t) is the active power and reactive power injection of node j at time period t; M is a large number; E is a branch set; and N is a node set.

[0086] The calculation formula of the node voltage and branch current constraint is as follows:

[0087]

[0088] In the formula, Vj(t) is the voltage upper limit of node j at time period t, Iij(t) is the branch current upper limit of branch ij at time period t, Vj(t) is the voltage lower limit of node j at time period t, and Iij(t) is the branch current lower limit of branch ij at time period t. j,t,max and V j,t,min In the formula, Vj(t) is the voltage upper limit of node j at time period t, Iij(t) is the branch current upper limit of branch ij at time period t, Vj(t) is the voltage lower limit of node j at time period t, and Iij(t) is the branch current lower limit of branch ij at time period t. ij,t,max and I ij,t,min In the formula, Vj(t) is the voltage upper limit of node j at time period t, Iij(t) is the branch current upper limit of branch ij at time period t, Vj(t) is the voltage lower limit of node j at time period t, and Iij(t) is the branch current lower limit of branch ij at time period t.

[0089] The calculation formula of the radial network constraint is as follows:

[0090]

[0091] In the formula, S is the power supply node set of the upper-layer power grid, and S is the set of nodes that are not in S. is an auxiliary variable introduced, which is greater than or equal to 0.

[0092] S2, according to the load power consumption, the overall daily load curve is analyzed and processed, and the time period is divided through the Mean shift clustering algorithm for load change amount clustering, and then the model constructed through step S1 is solved, and a specific flow chart is shown in Figure 2 .

[0093] In some embodiments, the time period division method described above is specifically: considering the time sequence of the load, the time sequence is used to aggregate the time periods with similar load quantities into a category through the Mean shift clustering algorithm. Compared with other clustering algorithms, the advantage of the embodiment algorithm lies in that it does not rely on prior knowledge of the aggregation center number, and can quickly determine the aggregation center under different load data to obtain the result of time period division by considering the density around each load data point. Through this method, the number of time periods is reduced for dynamic reconstruction, the binary integer variable to be considered is effectively reduced, and the calculation speed of dynamic reconstruction can be effectively improved.

[0094] The basic principle of the Mean shift clustering algorithm is described as follows: the Mean shift clustering algorithm is derived from the non-parametric estimation of the gradient of the density function, and the non-parametric estimation is to estimate the density function from the sample set. Generally, kernel density is used for estimation, and the most commonly used kernel function is the Gaussian kernel. The principle is to set a kernel function for each sample point, then add them together to obtain the kernel density estimation.

[0095] Suppose that there is a d-dimensional data set with a size of n, and the bandwidth of the kernel function K is a parameter h. The kernel density estimation of the data set can be expressed by the following formula:

[0096]

[0097] Where: K(x) is a radially symmetric function.

[0098] By performing gradient analysis on the above formula, we can get the mean shift vector m of the sample. h (x), whose expression is:

[0099]

[0100] Where: g(s) = -k'(s).

[0101] The results of dividing the daily load curve of the IEEE33-node system into time periods according to the above description are as follows: Figure 3 shown.

[0102] S3. Simplify the topology based on graph theory methods. Based on step S2, a two-stage dynamic reconstruction is proposed. In the first stage, the simplified network is dynamically reconstructed to obtain the overall optimized reconstruction result, and then the local switch status of the original network is confirmed in the second stage.

[0103] If all switches in a distribution network are closed simultaneously, the network's topology is a connected graph G. Graph G consists of nodes and branches, and the number of branches associated with a node is called the node degree. Nodes can be categorized as power nodes, intermediate nodes with degree = 2, nodes with degree > 2, and terminal nodes with degree = 1. When constructing the feasible tree base, terminal nodes not relevant to the circuit and intermediate nodes with degree = 2 are directly deleted, retaining only power nodes and nodes with degree > 2. This simplified graph significantly reduces the number of spanning trees, making it easier to determine the feasibility of the topology.

[0104] This embodiment draws on the construction method of a feasible tree base and simplifies the topological grid structure based on the different switch types in the distribution network. It then converts the dynamic reconstruction of the distribution network into a two-stage dynamic reconstruction. In the first stage, the simplified network is reconstructed to obtain the overall connection result of the distribution network. This result is input into the reconstruction of the original network in the second stage. The switch states that have been determined in the first stage are directly retained, and only the local switch states with uncertain switch states are dynamically reconstructed to obtain the final reconstruction result. The specific steps are summarized as follows:

[0105] 1) Close all branch switches and open the section switches. If there are multiple feeders, the tie switch also needs to be opened. Define the power nodes, nodes with degree = 1, and nodes with degree > 2 as special nodes.

[0106] 2) Keep special nodes and merge nodes with degree = 2 on branches between special nodes into one node, the load of which is the sum of the loads of the merged nodes, and the branch impedance value flowing into and out of the node is the maximum impedance value in the merged branches;

[0107] 3) If the terminal node is not connected to sectionalizing switches or tie switches, then merge the non-special nodes on the branch directly to the terminal node;

[0108] 4) The number of branches merged in the original network after topology simplification is represented by "merging degree", the number of merged nodes at the merging point is represented by n, the merging degree of the branch flowing into the merging point is n-1, and the merging degree of the branch flowing out of the merging point is 1, the merging degree of the branch flowing into the merging point represents the first n-1 branches associated, and the merging degree of the branch flowing out of the merging point represents the nth branch associated;

[0109] 5) After simplifying the topology, the first stage of dynamic reconstruction is performed, and the dynamic reconstruction is performed in the simplified network, the branch with merging degree 1 is retained for direct input to the second stage of dynamic reconstruction, and the branch with merging degree greater than 1 is classified and discussed, if closed, the associated branch switch state is also set to closed in the second stage, if open, the associated branch switch state is set to an uncertain state in the second stage, which needs to be determined in the dynamic reconstruction of the second stage.

[0110] S4, based on step S2 and step S3, the method of dividing the period and simplifying the topology is simulated and verified for IEEE33 node system, to realize dynamic reconstruction of distribution network, and to improve the economy and reliability of distribution network operation.

[0111] Take IEEE33 node as an example for illustration, as shown in Figure 4 , the special nodes are marked in red, and the other black nodes are load nodes with degree = 2.

[0112] The non-special nodes and the associated branches are merged to obtain the simplified network topology connection diagram, as shown in Figure 5

[0113] By comparing Figure 4 and Figure 5 , it can be seen that the number of node branches in the original network topology is 37, if the dynamic reconstruction is directly performed on the original network, the complexity of the calculation is 2 37 , while the number of branches in the simplified network is only 24, and considering the radial constraint of distribution network operation, the results of the simplified reconstruction are mapped to the original network, at most only 15 (4+3+3+3+2) branches, at this time, through two-stage dynamic reconstruction, the calculation complexity is 2 24 +2 15 , compared with directly performing dynamic reconstruction, the complexity is significantly reduced.​

[0114] To verify the dynamic reconfiguration method of distribution network based on the Mean shift clustering algorithm period division and the topological simplification method based on graph theory can effectively solve the problem of difficulty in solving caused by too many binary variables in dynamic reconfiguration of distribution network while ensuring that the obtained result is acceptable and better than the initial state result. Based on this, the dynamic reconfiguration of the IEEE33 node system under different reconfiguration schemes is carried out according to the above steps, and the obtained result is shown in Table 1:

[0115] Table 1 Reconfiguration result of 33 node system

[0116]

[0117] As can be seen from Table 1, in the initial state, the objective function value is 14620.45, and the result of model optimization is 10582.89, and the running time is 114.51s. Obviously, when solving by the model, the optimal solution result can be significantly reduced, proving the effectiveness of model optimization, but there is still a problem, which is that the running time is long. In this paper, GAP is set to 0.2 for solving, which shows that there is still a large solution space, and certain means can be taken to improve the calculation speed. After dividing the time period, although the solving speed is improved, the solving time is still very long. In order to compare with the result without dividing the time period, the solving time TimeLimit of the time period reconfiguration optimization is set to 114s. When the network is reconfigured and optimized by dividing the time period, better results can be obtained in the same time, which also shows that after dividing the time period by the clustering algorithm, the calculation speed can be effectively improved, and the calculation time can be reduced. After simplifying the network for reconfiguration, the optimal solution result is slightly improved, but the running time is greatly reduced, thereby verifying the effectiveness of the method of reconfiguring the simplified network, which can effectively reduce the running time and improve the calculation speed. It can be concluded that by the model of the application, the optimization and reconfiguration of the distribution network can effectively improve the economy and load balancing, and by the mean shift clustering algorithm, the time period can be divided and the network can be simplified, which can improve the calculation speed without seriously affecting the optimal solution result, which has great significance.

[0118] In summary, compared with the prior art, the present application has the following advantages and beneficial effects: with the development of national economy, the power consumption increases rapidly, and the scale of power grid is also expanding, and the action space of distribution network switch is also increasing, which makes the dynamic reconstruction of distribution network more difficult to solve, based on this, the present application proposes a dynamic reconstruction method of distribution network based on Mean shift clustering division period and topology simplification, the Mean shift clustering algorithm has unique advantages compared with other clustering algorithms without prior knowledge of the number of aggregation centers, the period is divided by clustering to reduce the number of reconstruction periods and the complexity of calculation. On this basis, the network topology is simplified, and the calculation speed is improved by two-stage dynamic reconstruction, which can provide quick power supply decision for power grid operation personnel, avoid waste of resources and improve power quality of users.

[0119] The embodiment also provides a dynamic reconstruction system of distribution network, comprising:

[0120] A model construction module is configured to construct a multi-objective dynamic reconstruction model of distribution network which comprehensively considers operation cost and load balancing index.

[0121] A period division module is configured to analyze and process overall daily load curve according to load power consumption, and then cluster load variation by Meanshift clustering algorithm, divide the period, and solve by the constructed multi-objective dynamic reconstruction model of distribution network.

[0122] A distribution network topology simplification module is configured to simplify topology based on graph theory method, and propose two-stage dynamic reconstruction, the first stage obtains overall optimal reconstruction result by dynamic reconstruction of simplified network, and the second stage confirms local switch state of original network.

[0123] A dynamic reconstruction module is configured to combine the period division and topology simplification method, simulate and verify IEEE33 node system, and realize dynamic reconstruction of distribution network.

[0124] The dynamic reconstruction system of distribution network can execute the dynamic reconstruction method of distribution network provided by the method embodiment, and can execute any combination of the method embodiment to implement the steps, and has the corresponding functions and beneficial effects of the method.

[0125] The embodiment also provides a dynamic reconstruction device of distribution network, comprising:

[0126] At least one processor;

[0127] At least one memory is configured to store at least one program;

[0128] When the at least one program is executed by the at least one processor, the at least one processor realizesFigure 1 the method shown.

[0129] The power distribution network dynamic reconfiguration device of the embodiment can execute the power distribution network dynamic reconfiguration method provided by the method embodiment of the application, can execute the implementation steps of any combination of the method embodiment, and has the corresponding functions and beneficial effects of the method.

[0130] The application also discloses a computer program product or a computer program, which comprises 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 the processor executes the computer instructions to enable the computer device to execute the method. Figure 1 the method shown.

[0131] The embodiment also provides a storage medium storing instructions or programs that can execute the power distribution network dynamic reconfiguration method provided by the method embodiment of the application. When the instructions or programs are executed, the implementation steps of any combination of the method embodiment can be executed, and the corresponding functions and beneficial effects of the method are possessed.

[0132] In some alternative embodiments, the functions / operations mentioned in the block diagram can not occur in the order mentioned in the operation diagram. For example, depending on the functions / operations involved, two blocks shown in succession can actually be executed substantially simultaneously or the blocks can sometimes be executed in reverse order. In addition, the embodiments presented and described in the flowcharts of the application are provided by way of example, with the purpose of providing a more comprehensive understanding of the technology. The disclosed method is not limited to the operations and logical flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed and in which sub-operations described as part of larger operations are independently executed.

[0133] Furthermore, although the present application is described in the context of functional modules, it is to be understood that one or more of the described functions and / or features can be integrated in a single physical device and / or software module, or one or more functions and / or features can be implemented in separate physical devices or software modules. It will also be appreciated that detailed discussion of the actual implementation of each module is not necessary to an understanding of the application. Rather, the actual implementation of the modules, in combination with their attributes, functions, and internal relationships, are to be understood within the context of the devices disclosed herein. Thus, those skilled in the art with access to patents, scientific journals, and other public sources known by those skilled in the art will be able to practice the application as set forth in the claims without undue experimentation, using ordinary skill in the art along with the present disclosure. It is also to be understood that the specific concepts disclosed are merely illustrative and that the scope of the present application is to be determined by the entire scope of the claims, along with all equivalents of the claims and their equivalents.

[0134] If the functions are implemented in software, the functions can be stored in or implemented as one or more computer program products, which can be incorporated into a computer-readable medium for use by or in connection with an apparatus, method or system as described herein. The computer-readable medium can be a machine-readable storage device, a machine-readable storage substrate, a memory device, or a computer-readable signal. The computer-readable medium can be, for example, but is not limited to, volatile or non-volatile memory, a floppy diskette, a compact disc read-only memory (CD-ROM), a magnetic tape, a flash drive, a hard disk drive, a programmable read-only memory (PROM), a random access memory (RAM), a reprogrammable read-only memory (REPROM) or a similar medium. The computer-readable medium can be paper, on which the program is printed. The computer-readable medium can be a medium, such as RAM, ROM, PROM, a tape, a hard disk or a compact disc, which can store data for use by or in connection with an application program. The computer-readable medium can also be a medium that carries such data also, or a medium that merges, for use by or in connection with an application program. The computer-readable medium can also be paper or any other medium, on which the program is printed.

[0135] The logic and / or steps represented in the flowcharts and / or otherwise described herein, for example, can be embodied in non-transitory computer- readable media, which can be executed by an instruction execution system, apparatus, or device, such as a computer-based system, processor, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this specification, a "computer-readable medium" can be any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. The computer-readable medium can be a product of "tangible" storage. The computer-readable medium can include an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device. The computer-readable medium can include a computer program product. The computer-readable medium can include a computer readable storage medium.

[0136] More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection (electronic) having one or more wires, a portable computer diskette (magnetic), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can also be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, for example, via optical scanning of the paper or other medium, then compiled, interpreted, or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.

[0137] It should be understood that aspects of the application can be implemented in hardware, software, firmware or combinations thereof. In the above described embodiments, various steps or methods can be implemented in software or firmware that is stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any of the following technologies, or combinations thereof, can be used with the necessary modifications: a discrete logic circuit(s) having logic gates for implementing logic functions upon an application of data signals, an application specific integrated circuit having appropriate combinational logic gates, a programmable gate array(s) (PGA), a field programmable gate array (FPGA), and / or the like.

[0138] In the above description of the present specification, reference to the description of the terms "one embodiment", "another embodiment", or "certain embodiments" or the like means that a particular feature, structure, material or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. The illustrative appearances of the above described terms in the specification do not necessarily refer to the same embodiment or example. Moreover, the particular features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0139] Although embodiments of the present application have been shown and described, it would be recognized by those of ordinary skill in the art that various changes, modifications, substitutions, and alterations can be made hereto without departing from the spirit and scope of the application, which is defined by the following claims and their equivalents.

[0140] The above is a specific description of the preferred embodiments of the present application, but the present application is not limited to the above-described embodiments, and those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present application, and these equivalent modifications or substitutions are included in the scope defined by the claims of the present application.

Claims

1. A method for dynamic reconstruction of a distribution network, characterized in that: The following steps are involved: Construct a multi-objective dynamic reconstruction model for distribution networks that comprehensively considers operating costs and load balancing index; The overall daily load curve is analyzed and processed based on the load power consumption. The load changes are then clustered using the Mean shift clustering algorithm to divide the time periods. This is then solved using the constructed multi-objective dynamic reconstruction model of the distribution network. Based on graph theory, the topology is simplified and a two-stage dynamic reconstruction is proposed. In the first stage, the simplified network is dynamically reconstructed to obtain the overall optimized reconstruction result. In the second stage, the local switch status of the original network is confirmed. Combining the time period division and topology simplification methods, the IEEE33-node system is simulated and verified to achieve dynamic reconstruction of the distribution network; The graph-theoretic approach is used to simplify the topology and propose a two-stage dynamic reconstruction. In the first stage, the simplified network is dynamically reconstructed to obtain an overall optimized reconstruction result. The second stage then confirms the local switch status of the original network, including: Close all branch switches and open section switches, and define the power nodes, nodes with degree = 1, and nodes with degree > 2 as special nodes. Special nodes are retained, and nodes with degree = 2 on branches between special nodes are merged into one node. The load of this node is the sum of the loads of all merged nodes, and the impedance values ​​of the branches flowing into and out of this node are the maximum impedance values ​​among the merged branches. If the terminal node is not connected to a section switch or tie switch, the non-special nodes on the branch are directly merged into the terminal node; The merging degree represents the number of branches in the original network merged after the topology is simplified. n represents the number of merged nodes at the merging point. The merging degree of the branch flowing into the merging point is n-1, and the merging degree of the branch flowing out of the merging point is 1. The merging degree of the flowing into the merging point represents the first n-1 associated branches, and the merging degree of the flowing out of the merging point represents the associated nth branch. After simplifying the topology, the first phase of dynamic reconstruction is performed. Dynamic reconstruction is performed in the simplified network. The results of branches with a merging degree of 1 are retained and directly input into the second phase of dynamic reconstruction. Branches with a merging degree greater than 1 are classified and discussed. If closed, the switch state of the associated branch in the second phase is also set to closed. If disconnected, the switch state of the associated branch in the second phase is set to an uncertain state and needs to be determined in the dynamic reconstruction of the second phase.

2. A method for dynamic reconstruction of a distribution network according to claim 1, characterized in that: The objective function of the distribution network multi-objective dynamic reconstruction model is: Where, T The period for dynamic reconfiguration of the distribution network; for Network loss of the distribution network at any moment; for Number of distribution network switch operations at any moment; for Load balancing index of distribution network at every moment; are the weight coefficients of network loss cost coefficient, switch operation cost coefficient and load balancing index respectively.

3. A method for dynamic reconstruction of a distribution network according to claim 2, characterized in that: The calculation formula of the network loss is: Where, K is the total number of distribution network branches; For branch k Impedance value; For branch k At the moment t The switch is closed, it takes 1 when closed and 0 when open. If there is no switch in the branch, it is set to the normally closed state, that is, it is always 1; and Branch k At the moment t Active and reactive power flowing through; For the moment t Shizhi Road k The outflow node voltage value; for t Flowing through the branch at all times k The current value; The calculation formula for the number of switch operations is: Where, For branch k At the moment t -1 is the switch closed state; The calculation formula of the load balancing index is: Where, For flowable branches k The maximum current, for t Flowing through the branch at all times The current value, For flowable branches The maximum current.

4. A method for dynamic reconstruction of a distribution network according to claim 1, characterized in that: The constraints of the distribution network multi-objective dynamic reconstruction model include power flow constraints, node voltage and branch current constraints, radial network constraints, and switch operation constraints.

5. A method for dynamic reconstruction of a distribution network according to claim 1, characterized in that: The overall daily load curve is analyzed and processed based on the load power consumption. The load changes are then clustered using the Mean Shift clustering algorithm to divide the time periods, including: Considering the temporal nature of the load, the time periods with similar load amounts are clustered into one category in chronological order using the Mean shift clustering algorithm to obtain the result of time period division.

6. A method for dynamic reconstruction of a distribution network according to claim 1, characterized in that: The graph theory method is specifically as follows: If all switches in the distribution network are closed simultaneously, the topology of the distribution network is a connected graph. This connected graph consists of nodes and branches. The number of branches associated with a node is defined as the node degree. Nodes are divided into power nodes, intermediate nodes with degree = 2, nodes with degree > 2, and terminal nodes with degree = 1. Drawing on the construction method of feasible tree base, the topological grid structure is simplified in combination with the different types of switches in the distribution network, and then the dynamic reconstruction of the distribution network is converted into a two-stage dynamic reconstruction. In the first stage, the simplified network is reconstructed to obtain the overall connection result of the distribution network, which is then input into the reconstruction of the original network in the second stage. The switch status that has been determined in the first stage is directly retained, and only the local switch status with uncertain status is dynamically reconstructed to obtain the final reconstruction result.

7. A distribution network dynamic reconstruction system, characterized in that: include: Model building module, used to build a multi-objective dynamic reconstruction model of the distribution network that comprehensively considers operating costs and load balancing index; The time division module is used to analyze and process the overall daily load curve according to the load power consumption, and then cluster the load changes using the Meanshift clustering algorithm to realize the division of time periods, and then solve the problem through the constructed multi-objective dynamic reconstruction model of the distribution network; The distribution network topology simplification module is used to simplify the topology based on graph theory methods and proposes a two-stage dynamic reconstruction. In the first stage, the simplified network is dynamically reconstructed to obtain the overall optimized reconstruction result, and the second stage confirms the local switch status of the original network. Dynamic Reconfiguration Module, which combines time segmentation and topology simplification to simulate and verify the IEEE 33-node system and achieve dynamic reconfiguration of the distribution network; The graph-theoretic approach is used to simplify the topology and propose a two-stage dynamic reconstruction. In the first stage, the simplified network is dynamically reconstructed to obtain an overall optimized reconstruction result. The second stage then confirms the local switch status of the original network, including: Close all branch switches and open section switches, and define the power nodes, nodes with degree = 1, and nodes with degree > 2 as special nodes. Special nodes are retained, and nodes with degree = 2 on branches between special nodes are merged into one node. The load of this node is the sum of the loads of all merged nodes, and the impedance values ​​of the branches flowing into and out of this node are the maximum impedance values ​​among the merged branches. If the terminal node is not connected to a section switch or tie switch, the non-special nodes on the branch are directly merged into the terminal node; The merging degree represents the number of branches in the original network merged after the topology is simplified. n represents the number of merged nodes at the merging point. The merging degree of the branch flowing into the merging point is n-1, and the merging degree of the branch flowing out of the merging point is 1. The merging degree of the flowing into the merging point represents the first n-1 associated branches, and the merging degree of the flowing out of the merging point represents the associated nth branch. After simplifying the topology, the first phase of dynamic reconstruction is performed. Dynamic reconstruction is performed in the simplified network. The results of branches with a merging degree of 1 are retained and directly input into the second phase of dynamic reconstruction. Branches with a merging degree greater than 1 are classified and discussed. If closed, the switch state of the associated branch in the second phase is also set to closed. If disconnected, the switch state of the associated branch in the second phase is set to an uncertain state and needs to be determined in the dynamic reconstruction of the second phase.

8. A distribution network dynamic reconstruction device, characterized in that: include: at least one processor; at least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the method according to any one of claims 1 to 6.

9. A computer-readable storage medium storing a program executable by a processor, characterized in that: The processor-executable program is used to perform the method according to any one of claims 1 to 6 when executed by the processor.

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