A microgrid reconfiguration optimization method, system, and storage medium

Abstract

The present invention discloses a microgrid reconstruction optimization method, system and storage medium, a microgrid reconstruction optimization method, comprising the following steps: S1, establishing the topological structure data of the microgrid; S2, calculating the importance ranking of the connection lines; S3, establishing a maintenance personnel scheduling model, and obtaining the scheduling plan of the maintenance personnel; S4, according to the actual damaged scene after maintenance, constructing a mobile energy storage device rescheduling model. The present invention establishes a maintenance personnel scheduling model according to the importance of the connection lines, and obtains the scheduling plan of the maintenance personnel according to the actual fault scene and the importance ranking of the connection lines, so as to enable the rapid repair of important lines to the maximum extent, identify and prioritize the repair of key damaged lines, and enhance the resilience of the power system after maintenance.

Description

Technical Field

[0001] The present invention relates to the technical field of microgrid fault reconstruction, and in particular, to a microgrid reconstruction optimization method, system and storage medium. Background Art

[0002] With the transformation of the global energy structure and the development of renewable energy technology, microgrids have received extensive attention as a key technology for achieving sustainable energy development and improving the flexibility of power systems. Microgrids can form independently operated small power grids in local areas, integrating various types of power generation resources, energy storage equipment and loads, and have significant environmental and economic benefits. However, the high complexity and dynamic variability of microgrids, especially in the face of extreme events such as natural disasters, how to quickly and effectively perform fault reconstruction to ensure the reliability and safety of power supply has become a popular research direction.

[0003] Mobile energy storage plays a vital role in order to enable microgrid systems to effectively resist environmental damage behaviors and have the ability to restore and support system load in damaged environments such as natural disasters. The flexibility and mobility of mobile energy storage systems make them a core component in microgrid fault management and reconstruction strategies. They can be deployed to areas or key load points most affected by faults in a short period of time to provide emergency power support, thereby minimizing the impact of faults on users and critical services. In addition, by temporarily replacing damaged energy output or compensating for power losses, mobile energy storage helps maintain the balance and stability of the power grid until the fault is completely repaired.

[0004] For severely damaged power systems, it may be difficult to restore the microgrid system to a high degree by only configuring mobile energy storage. Therefore, there are some shortcomings in the existing microgrid reconstruction solutions, which are mainly reflected in the following: in the process of dispatching maintenance resources, few solutions recognize the importance of identifying and prioritizing the repair of key damaged lines. In fact, giving priority to professional maintenance personnel to repair these key damaged lines in a timely manner can significantly enhance the resilience of the power system. Summary of the invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art. To this end, the present invention proposes a microgrid reconstruction optimization method that can identify and prioritize the repair of key damaged lines and enhance the resilience of the power system after repair.

[0006] To achieve the above purpose, the technical solution adopted by the present invention is as follows:

[0007] A microgrid reconstruction optimization method comprises the following steps:

[0008] S1. Establishing topological structure data of a microgrid, wherein the topological structure data includes information of all nodes and connection lines;

[0009] S2. Calculate the importance of the connection line according to the maximum connected sub-circuit network in the circuit network before the connection line is disconnected and the circuit network after the connection line is disconnected, and obtain the importance ranking of the connection line according to the importance of each connection line;

[0010] S3, establishing a maintenance personnel scheduling model, and obtaining a maintenance personnel scheduling plan based on the actual fault scenario and the importance ranking of the connection lines obtained in step S2;

[0011] S4. According to the actual damaged scenario after maintenance, a mobile energy storage device rescheduling model is constructed to obtain a rescheduling plan for each mobile energy storage device and a microgrid topology transformation plan. The mobile energy storage device may be a mobile energy storage vehicle.

[0012] Furthermore, the node information in step S1 includes node load demand and geographic location.

[0013] Further, step S2 specifically includes:

[0014] Calculate and sort the importance of routes according to the route importance evaluation model;

[0015] The line importance evaluation model is specifically as follows:

[0016]

[0017]

[0018]

[0019] For line Line network before disconnection;

[0020] For line The largest connected sub-circuit network in the disconnected circuit network;

[0021] in For line The importance of For line Line network cohesion before disconnection, For line The cohesion of the largest connected sub-circuit network in the disconnected circuit network; , Respectively represent the network and The number of nodes in ; , Respectively represent the network and The average shortest path length between nodes; For Node and The shortest path length. For The number of combinations of selecting 2 nodes from the nodes, For The number of combinations of selecting 2 nodes from the nodes.

[0022] Further, in step S3, the maintenance personnel scheduling model takes minimizing the scheduling time and maximizing the importance of the connection line as the optimization goal, and the first optimization objective function of the maintenance personnel scheduling model is:

[0023] ;

[0024] in Gather for the maintenance warehouse, For warehouse point, is the set of power grid nodes to be repaired, is the grid node to be repaired (damaged line), For warehouse point To the grid node to be repaired The distance between is the decision variable, For damaged lines The importance of , They represent the weight of minimizing the scheduling time objective and maximizing the weight of the connection line importance objective respectively.

[0025] Furthermore, the constraints of the first optimization objective function are as follows:

[0026] ;

[0027] ;

[0028] in, Indicates warehouse point Maximum maintenance capability. is a universal quantifier, which means to take any quantity, for example Any warehouse point.

[0029] Further, in step S4, the second optimization objective function of constructing the mobile energy storage device re-dispatch model is:

[0030] ;

[0031] in Represents a collection of nodes, and They represent the collection of standby nodes and energy supply nodes in the power grid respectively, Represents a collection of mobile energy storage vehicles; Represents mobile energy storage vehicle Whether to use the standby node Dynamically dispatch to energy supply nodes Restore power supply to the loads on it; Represents the mobile energy storage vehicle slave node Move to transportation time; Representation Node Load shear on and They represent the weights of the reconstruction recovery time target and the load shedding target, Representation Node The priority of Representation Node Active power demand.

[0032] Furthermore, the second optimization objective function is subject to dynamic scheduling constraints, topology reconstruction constraints, power balance constraints and line status constraints.

[0033] Furthermore, the dynamic scheduling constraint is:

[0034] ;

[0035] ;

[0036] Mobile energy storage vehicle With Node The position matching status.

[0037] The present invention also provides a microgrid reconstruction optimization system, including: a topology data module, used to establish the topology data of the microgrid; a connection line sorting module, used to calculate the importance ranking of the connection lines; a scheduling plan acquisition module, used to establish a maintenance personnel scheduling model, and obtain the scheduling plan for the maintenance personnel according to the actual fault scenario and the importance ranking of the connection lines; a rescheduling module, used to construct a mobile energy storage device rescheduling model according to the actual damage scenario after maintenance, and obtain the rescheduling plan of each mobile energy storage device and the microgrid topology transformation plan.

[0038] The present invention also provides a storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the microgrid reconstruction optimization method is implemented.

[0039] The present invention has the following beneficial effects:

[0040] Firstly, the topological structure data of the microgrid is constructed according to the actual connection situation of the microgrid, the importance of the connecting line is obtained, and the lines are sorted according to the importance of the line. A maintenance personnel scheduling model is established, and the scheduling plan of the maintenance personnel is obtained according to the actual fault scenario and the importance of the connecting line, so that the important lines can be repaired quickly to the maximum extent, the key damaged lines can be identified and repaired preferentially, and the resilience of the power system after maintenance is enhanced; and the importance of the connecting line is calculated according to the line network before the connecting line is disconnected and the maximum connected sub-line network in the line network after the connecting line is disconnected. The importance of the connecting line is reflected by assuming the impact and changes brought to the line network after the connecting line is disconnected. The importance of the line in the power network is evaluated by the change of the line network before and after the line is disconnected, which can well reflect the influence and importance of the connecting line in the line network and provide a reference for subsequent line maintenance scheduling.

[0041] In addition to the above-described purposes, features and advantages, the present invention has other purposes, features and advantages. The present invention will be further described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The drawings constituting a part of this application are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0043] Figure 1 It is a flow chart of a microgrid reconstruction optimization method of the present invention;

[0044] Figure 2 Schematic diagram of the microgrid reconstruction optimization system of the present invention. DETAILED DESCRIPTION

[0045] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0046] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0048] In addition, the descriptions of "first", "second", etc. in the present invention are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0049] Please refer to Figure 1 A microgrid reconstruction optimization method in a preferred embodiment of the present invention includes steps S1, S2, S3 and S4.

[0050] S1. Establishing topological structure data of a microgrid, wherein the topological structure data includes information of all nodes and connection lines, and all connection lines constitute a line network.

[0051] S2. Calculate the importance of the connection lines according to the maximum connected sub-line network in the line network before the connection lines are disconnected and the line network after the connection lines are disconnected, and obtain the importance ranking of the connection lines according to the importance of each connection line.

[0052] S3. Establish a maintenance personnel scheduling model, and obtain a maintenance personnel scheduling plan based on the actual fault scenario and the importance ranking of the connection lines obtained in step S2.

[0053] S4. According to the actual damaged scenario after repair, a mobile energy storage device re-dispatch model is constructed to obtain the re-dispatch plan and microgrid topology transformation plan for each mobile energy storage device. In step S3, due to limited maintenance resources and maintenance personnel, when there are many damaged lines, only limited maintenance personnel can be assigned to repair several important damaged lines. After the repair is completed, the topology transformation is updated according to the new topology data of the microgrid; other damaged lines will be subsequently repaired or temporarily not repaired according to needs and importance.

[0054] The present invention provides a microgrid reconstruction optimization method, which first constructs the topological structure data of the microgrid according to the actual microgrid connection situation, obtains the importance of the connection line, and sorts the lines according to the line importance, establishes a maintenance personnel scheduling model, and sorts the connection lines according to the actual fault scenario and the importance of the connection line to obtain the scheduling plan of the maintenance personnel, so as to enable the rapid repair of important lines to the maximum extent, identify and prioritize the repair of key damaged lines, and enhance the resilience of the power system after maintenance. By comprehensively considering and optimizing the scheduling of two reconstruction resources, maintenance resources and mobile energy storage, a set of strategies is formulated that can quickly repair key damaged lines and effectively allocate energy storage resources. The purpose is to maximize the load guarantee capability of the microgrid in the shortest time, thereby ensuring the stability and reliability of the power supply. Through this joint optimization method, the present invention aims to comprehensively improve the response and recovery capabilities of the microgrid in the face of severe challenges.

[0055] In some embodiments of the present invention, the node information in step S1 includes node load demand and geographic location.

[0056] Step S2 specifically includes:

[0057] Calculate and sort the importance of routes according to the route importance evaluation model;

[0058] The line importance evaluation model is specifically as follows:

[0059]

[0060]

[0061]

[0062] For line Line network before disconnection, line For Node and The connection lines formed; For line The largest connected sub-circuit network in the disconnected circuit network. For Line The importance of For line Line network cohesion before disconnection, For line The cohesion of the largest connected sub-circuit network in the disconnected circuit network; , Respectively represent the network and The number of nodes in ; , Respectively represent the network and The average shortest path length between nodes; For Node and The shortest path length. and The node belongs to the corresponding line network. The change in network cohesion before and after the transmission line is disconnected is used as an indicator to evaluate the importance of the line in the power network. The network cohesion mainly depends on the connectivity between the nodes in the network (the average shortest path length between nodes) and the number of nodes. In the network, the shorter the average shortest path between nodes and the smaller the number of nodes, the more "compact" the network is and the higher the cohesion. Due to the majority of radial (tree-like) structures in the power network, according to the definition of network cohesion, disconnecting any line will increase the cohesion of the remaining maximum connected subgraph. Among them, the closer the line is to the center of the network, the fewer nodes are left in the largest connected subgraph, the shorter the average shortest path of the network, the more the cohesion increases, and the higher the importance of the line.

[0063] In step S3, the maintenance personnel scheduling model takes minimizing the scheduling time and maximizing the importance of the connection line as the optimization objectives, and the first optimization objective function of the maintenance personnel scheduling model is:

[0064] ;

[0065] in Gather for the maintenance warehouse, For warehouse point, is the set of grid nodes to be repaired, that is, the set of damaged lines, The grid node to be repaired, i.e. the damaged line , For warehouse point To the grid node to be repaired The distance between is a decision variable, if =1, it means the grid node is to be repaired Attribution warehouse point Repair, if =0, it means Not a warehouse location Carry out repairs; For damaged lines The importance of , They represent the weight of minimizing the scheduling time target and maximizing the weight of the connection line importance target respectively. Thus, by setting the weights, scheduling time and restoration of lines with high importance are taken as two considerations to obtain the optimal maintenance personnel scheduling.

[0066] In some embodiments of the present invention, the constraints of the first optimization objective function are as follows: , indicating that each grid node to be repaired can be repaired by at most one maintenance personnel in the maintenance warehouse area; , indicating that the sum of the maintenance capacity requirements of each grid node to be maintained cannot exceed the total maintenance capacity of the warehouse point; among them, Indicates warehouse point Maximum maintenance capability.

[0067] In some embodiments of the present invention, in step S4, the second optimization objective function of constructing the mobile energy storage device re-dispatch model is:

[0068] ;

[0069] in Represents the set of nodes, that is, the set of all nodes in the network, and They represent the collection of standby nodes and energy supply nodes in the power grid respectively, Represents a collection of mobile energy storage vehicles; Indicates the ability of the mobile energy storage vehicle to restore power supply. Specifically, Represents mobile energy storage vehicle Whether to use the standby node Dynamically dispatch to energy supply nodes Restore power to the loads on it, =1, yes, =0 means no; Represents the mobile energy storage vehicle slave node Move to transportation time; Representation Node Load shear on and They represent the weights of the reconstruction recovery time target and the load shedding target, Representation Node The priority of Representation Node Active power demand.

[0070] In a further embodiment of the present invention, the second optimization objective function is subject to dynamic scheduling constraints, topology reconstruction constraints, power balance constraints and line status constraints, so that the optimal solution obtained conforms to various conditional constraints and meets the requirements.

[0071] Furthermore, the dynamic scheduling constraint is:

[0072] ;

[0073] ;

[0074] In the first phase, each mobile energy storage vehicle is preconfigured at a specific standby node. This constraint indicates that only when the standby node There is any mobile energy storage vehicle When Only in the second stage can it be dynamically dispatched to the energy supply node. Mobile energy storage vehicle With Node The position matching state means that the mobile energy storage vehicle Is it configured on the node? When configured on a node, the value is 1; when not configured on a node, the value is 0.

[0075] It means ensuring that each mobile energy storage vehicle can only be powered from the standby node Re-dispatch to a power supply node .

[0076] In a further embodiment of the present invention, the topology reconstruction constraint to which the second optimization objective function is subjected is:

[0077] ;

[0078] ;

[0079]

[0080] ;

[0081] ;

[0082] ;

[0083] ;

[0084] ;

[0085] C is the set of feeder root nodes; Representation Node and nodes The connectivity status, when connected, the value is 1, when not connected, the value is 0; Indicates the original network (when not damaged) and A node is a collection of nodes that are connected. Representation Node Connection status with the mobile energy storage vehicle, When , it indicates that the node There is a connection with the mobile energy storage vehicle, 0 represents the node There is no connection to the mobile energy storage vehicle. , Respectively represent nodes Whether it is connected to a mobile energy storage vehicle and whether it is a feeder root node. Indicates line Whether it is damaged. If it is damaged, the value is 1; if it is not damaged, the value is 0. Indicates the preset upper limit of the damage line. Representation Node Is it The parent node of yes When the parent node of The value is 1, otherwise the value is 0. Representation Node Is it The parent node of yes When the parent node of The value is 1, otherwise the value is 0.

[0086] constraint and Indicates that if the node Connected to a mobile energy storage vehicle or it is a feeder root node, then the node is the power node; Indicates that the number of actually damaged lines cannot exceed the preset upper limit; Indicates line To be repaired and restored to normal condition; restraint and Indicates line Only damaged maintenance personnel will be dispatched to the line for maintenance; , and It ensures that the reconstructed power system always maintains a radial topology.

[0087] The power balance constraint is:

[0088]

[0089] The starting node is represented by The line; Indicates the destination node is The line; Indicates line The phase angle of the starting node; Indicates line The phase angle of the destination node; , Represents nodes The actual power generation of the energy storage vehicle and the feeder root node at , Represent the maximum power generation of the mobile energy storage vehicle and the feeder root node respectively; Indicates line The flow power on Indicates load Active power demand on. Indicates line The reactance, Representation Node The phase angle on . Constraint Ensure the node The inflow and outflow on the MOSFET are equal to achieve power balance. Determine the power flow based on the node phase angle and line status; if the line Not damaged or damaged and repaired, then The value is 1, otherwise it is 0. Guaranteed Node The load shear on the node is at 0 and The power requirements of and They respectively indicate that the actual power generation of the mobile energy storage vehicle and the feeder root node cannot exceed their maximum power generation.

[0090] It should be noted that this expression is nonlinear and the introduction of intermediate variables To replace this term, the formula It can be equivalently converted into the following formula through the big M method:

[0091]

[0092] The line state constraints are:

[0093] ;

[0094] ;

[0095] Indicates a collection of damaged lines. Indicates that the switch on the damaged line can only be closed after repair. is the line damage state constraint; "M" is a very large positive number, which is used as a coefficient to express the limits of some variables or constraints.

[0096] Reference Figure 2 The present invention also provides a microgrid reconstruction optimization system, including: a topology data module, used to establish the topology data of the microgrid; a connection line sorting module, used to calculate the importance ranking of the connection lines; a scheduling plan acquisition module, used to establish a maintenance personnel scheduling model, and obtain the scheduling plan of the maintenance personnel according to the actual fault scenario and the importance ranking of the connection lines; a rescheduling module, used to construct a mobile energy storage device rescheduling model according to the actual damage scenario after maintenance, and obtain the rescheduling plan of each mobile energy storage device and the microgrid topology transformation plan.

[0097] The present invention also provides a storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned microgrid reconstruction optimization method is implemented.

[0098] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A microgrid reconstruction optimization method, characterized in that: The steps include: S1. Establishing topological structure data of a microgrid, wherein the topological structure data includes information of all nodes and connection lines; S2. Calculate the importance of the connection line according to the maximum connected sub-circuit network in the circuit network before the connection line is disconnected and the circuit network after the connection line is disconnected, and obtain the importance ranking of the connection line according to the importance of each connection line; S3, establishing a maintenance personnel scheduling model, and obtaining a maintenance personnel scheduling plan based on the actual fault scenario and the importance ranking of the connection lines obtained in step S2; S4. According to the actual damage scenario after maintenance, a mobile energy storage device re-dispatch model is constructed to obtain the re-dispatch plan of each mobile energy storage device and the microgrid topology transformation plan; In step S3, the maintenance personnel scheduling model takes minimizing the scheduling time and maximizing the importance of the connection line as the optimization objectives, and the first optimization objective function of the maintenance personnel scheduling model is: ； in Gather for the maintenance warehouse, For warehouse point, is the set of power grid nodes to be repaired, is the grid node to be repaired. For warehouse point To the grid node to be repaired The distance between is the decision variable, For damaged lines The importance of , They represent the weight of minimizing the scheduling time objective and maximizing the importance of the connection line objective respectively; The constraints of the first optimization objective function are as follows: ； ； in, Indicates warehouse point Maximum maintenance capability; In step S4, the second optimization objective function of constructing the mobile energy storage device re-dispatch model is: ； in Represents a collection of nodes, and They represent the collection of standby nodes and energy supply nodes in the power grid respectively, Represents a collection of mobile energy storage vehicles; Indicates the ability of the mobile energy storage vehicle to restore power supply; Represents the mobile energy storage vehicle slave node Move to transportation time; Representation Node Load shear on and They represent the weights of the reconstruction recovery time target and the load shedding target, Representation Node The priority of Representation Node Active power demand; The second optimization objective function is subject to dynamic scheduling constraints, topology reconstruction constraints, power balance constraints and line status constraints; The dynamic scheduling constraints are: ； ； Mobile energy storage vehicle With Node The position matching status.

2. The microgrid reconstruction optimization method according to claim 1, characterized in that: The node information in step S1 includes the node load demand and geographical location.

3. The microgrid reconstruction optimization method according to claim 1, characterized in that: Step S2 specifically includes: Calculate and sort the importance of routes according to the route importance evaluation model; The line importance evaluation model is specifically: For line Line network before disconnection; For Line The largest connected sub-circuit network in the disconnected circuit network; in For line The importance of For line Line network cohesion before disconnection, For line The cohesion of the largest connected sub-circuit network in the disconnected circuit network; , Respectively represent the network and The number of nodes in ; , Respectively represent the network and The average shortest path length between nodes; For Node and The shortest path length.

4. A microgrid reconstruction optimization system, used to execute the microgrid reconstruction optimization method according to any one of claims 1 to 3, characterized in that: include: A topology data module is used to establish the topology data of the microgrid; A connection line ranking module is used to calculate the importance ranking of the connection lines; The scheduling scheme acquisition module is used to establish a maintenance personnel scheduling model and obtain the scheduling scheme for maintenance personnel according to the actual fault scenario and the importance of the connection line; The re-dispatch module is used to construct a re-dispatch model for mobile energy storage equipment according to the actual damage scenario after maintenance, and obtain the re-dispatch plan for each mobile energy storage equipment and the microgrid topology transformation plan.

5. A storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the microgrid reconstruction optimization method according to any one of claims 1 to 3 is implemented.

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