Method and system for improving anti-fragility capability of power distribution network in consideration of multi-resource coordination, electronic equipment and medium

Abstract

The invention provides a power distribution network anti-fragility capability improvement method and system considering multi-resource collaboration, electronic equipment and a medium, and belongs to the technical field of power distribution network frame recovery. The method comprises the following steps: S1, under the condition of a power distribution network fault, on the basis of the available resource condition of the power distribution network, determining the power distribution network anti-fragility capability of the power distribution network; constructing an electric vehicle guide decision model in cooperation with multiple types of power generation resources, then solving the model, depicting a destination selection behavior of an electric vehicle, and obtaining a selection strategy of an electric vehicle user for each V2G station; and S2, during the recovery period of the power distribution network frame, the electric vehicles guided to the V2G stations in the step S1 are connected to the power distribution network through the charging piles for discharging, a power distribution network frame recovery model considering multi-resource cooperation is constructed, and then the model is solved to obtain a power distribution network frame recovery strategy. Under the condition of the power distribution network fault, through cooperation of multiple resources, rapid recovery of the power distribution network is realized, and the anti-fragility capability of the power distribution network is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network grid restoration, and in particular to a method, system, electronic equipment and medium for improving the anti-fragility capability of a distribution network taking into account multi-resource collaboration. Background Art

[0002] In recent years, as the complexity and interconnectedness of the power system have increased, the internal and external risks it faces have become increasingly diverse. Natural disasters such as typhoons, earthquakes, floods, as well as uncertain factors such as equipment failure, human error, protection system failure or malfunction, and even cyber attacks may trigger large-scale power outages. These accidents not only lead to power supply interruptions, but may also have serious impacts on economic and social operations and people's lives.

[0003] After a major power outage, the restoration process of the power system is usually long and complicated. First, the power supply of some units needs to be restored through black start technology to form a live island. Then, the power transmission path of the transmission and distribution network is gradually restored, a strong grid structure is established, and finally all loads are fully restored. This process not only tests the technical capabilities and management level of the power system, but is also directly related to the rapid recovery of the social economy and the normal operation of people's lives.

[0004] In this process, the concept of "anti-fragility" has gradually been introduced into the operation and management of power systems. Anti-fragility describes the process by which a system can not only withstand shocks but also learn, adapt and improve its coping capabilities when experiencing uncertain external events. For distribution networks, the improvement of anti-fragility means that in extreme fault conditions, various resources can be quickly mobilized and utilized to achieve rapid recovery after the fault.

[0005] With the rapid development of renewable energy and the popularity of electric vehicles, the types of power generation resources connected to the distribution network are becoming more and more diverse. The access to renewable energy such as wind power and photovoltaic power provides more power options for the distribution network; and electric vehicles, as distributed energy storage units, can not only serve as backup power sources through interaction with the grid (vehicle-to-grid, V2G technology), but also provide emergency support in the event of grid failure. The access to these new resources provides a new opportunity to improve the anti-fragility of the distribution network.

[0006] However, how to effectively coordinate these various types of power generation resources to achieve rapid recovery of the distribution network in the event of a distribution network failure is an important challenge currently faced. Traditional distribution network recovery strategies often focus on the utilization of a single resource and the optimization of the recovery path, while ignoring the synergy between multiple resources and the improvement of the overall recovery efficiency. Therefore, the present invention proposes a method for improving the anti-fragility capacity of a distribution network considering the coordination of multiple resources, aiming to achieve rapid and efficient recovery of the distribution network after a failure by constructing an electric vehicle guided decision model and a distribution network grid recovery model taking into account the coordination of multiple resources. Summary of the invention

[0007] The purpose of the present invention is to provide a method, system, electronic device and medium for improving the anti-fragility capability of a distribution network taking into account the coordination of multiple resources. In the event of a distribution network failure, the distribution network can be quickly restored by coordinating multiple resources, thereby improving the anti-fragility capability of the distribution network.

[0008] To achieve the above object, the present invention provides a method for improving the anti-fragility capability of a distribution network considering multi-resource collaboration, comprising the following steps:

[0009] Step S1: In the event of a distribution network failure, based on the available resources of the distribution network, such as distributed power sources, mobile energy storage, electric vehicles, etc., an electric vehicle guidance decision model is constructed in coordination with the above types of power generation resources. Then, the electric vehicle guidance decision model is solved to characterize the destination selection behavior of electric vehicles and obtain the selection strategy of electric vehicle users for each V2G station.

[0010] Step S2: During the distribution network framework recovery period, the electric vehicle user's selection strategy for each V2G station in step S1 is used to guide the electric vehicle to each V2G station, and the electric vehicle is connected to the distribution network for discharge through the charging pile, and a distribution network framework recovery model taking into account multi-resource coordination is constructed, and then the distribution network framework recovery model taking into account multi-resource coordination is solved to obtain the distribution network framework recovery strategy.

[0011] Preferably, the objective function of the electric vehicle guidance decision model is as follows:

[0012]

[0013] Among them, Ω EV ,Ω ST are the collections of all electric vehicles and V2G stations respectively; w mn,t is the incentive amount of V2G station n to electric vehicle m at time t; T is the set of all recovery periods.

[0014] Preferably, the constraints of the electric vehicle guidance decision model include V2G station selection constraints, V2G station maximum accommodating electric vehicle number constraints and attraction definition constraints.

[0015] Preferably, the V2G station selection constraints are as follows:

[0016]

[0017] Among them, x mn It is an integer variable, indicating whether electric vehicle m has selected V2G station n.

[0018] Preferably, the maximum number of electric vehicles that can be accommodated by a V2G station is constrained as follows:

[0019]

[0020] Among them, N n is the number of charging piles at V2G station n, indicating the maximum number of electric vehicles that V2G station n can accommodate.

[0021] Preferably, the attraction definition constraint is as follows:

[0022]

[0023] Among them, A mn is the attractiveness of V2G station n to electric vehicle m; a m is the influencing factor coefficient; is the maximum output power of V2G station n; d mn is the distance between electric vehicle m and V2G station n at the time of fault occurrence; Ω T A collection of recovery time periods.

[0024] Preferably, the objective function of the distribution network restoration model taking into account multi-resource coordination is as follows:

[0025]

[0026] Among them, z ij Indicates whether node i is in partition j; represents the active power of unit g belonging to partition j; represents the active power of the load belonging to partition j; represents the active power of unit g belonging to partition k; represents the active power of the load belonging to partition k; n BS represents the number of black start power supplies; N represents the number of nodes.

[0027] Preferably, the constraints of the distribution network framework recovery model taking into account multi-resource collaboration include: four network flow constraints, three partition logic constraints and one partition recovery time constraint. The four network flow constraints are network flow constraint one, network flow constraint two, network flow constraint three and network flow constraint four, and the three partition logic constraints are partition logic constraint one, partition logic constraint two and partition logic constraint three.

[0028] Preferably, the network flow constraint 1 is a network flow constraint representing a node i connected to a non-black start unit or load:

[0029]

[0030] Among them, f ij represents the network flow from node i to partition j; I represents the set of non-black start units; J represents the set of partitions; Ω load represents the set of nodes connected to the load; δ(i) represents the set of lines connected to node i.

[0031] Preferably, the second network flow constraint is a network flow constraint representing a node v that is not connected to a unit or a load:

[0032]

[0033] Among them, Ω bus represents the set of nodes; δ(v) represents the set of links connected to node v.

[0034] Preferably, the network flow constraint three is a network flow constraint representing the node k connected to the black start unit:

[0035]

[0036] Preferably, the network flow constraint 4 is a network flow constraint indicating that the line does not belong to the partition and there is no network flow on the line:

[0037]

[0038] Among them, y lj 0-1 integer variable indicating whether line l is in partition j; Ω line represents a set of nodes; |I| represents the number of nodes.

[0039] Preferably, the partition logic constraint 1 is a partition logic constraint indicating that each black start unit, non-black start unit, and load belongs to a certain partition:

[0040]

[0041] Preferably, the second partition logic constraint is a partition logic constraint indicating that if a line belongs to the partition, then the nodes connected to the line also belong to the partition:

[0042]

[0043] Here, |δ(v)| represents the number of links connected to node v.

[0044] Preferably, the partition logic constraint three is a partition logic constraint indicating that each node can only belong to one partition:

[0045]

[0046] Preferably, the partition recovery time constraints are as follows:

[0047]

[0048] Among them, t r Indicates the power system recovery time; T line Indicates the line charging time; Indicates the time required for the unit to recover; Ω G Represents a collection of units.

[0049] The present invention also provides a distribution network anti-fragility capability enhancement system considering multi-resource collaboration, comprising:

[0050] The electric vehicle guidance decision module is used to build an electric vehicle guidance decision model based on the available resources of the distribution network and coordinate various types of power generation resources in the event of a distribution network failure. The electric vehicle guidance decision model is then solved to characterize the destination selection behavior of electric vehicles and obtain the selection strategy of electric vehicle users for each V2G station.

[0051] The distribution network framework recovery module is used to guide electric vehicles to each V2G station by utilizing the electric vehicle users' selection strategy for each V2G station during the distribution network framework recovery period, connect the electric vehicles to the distribution network through charging piles for discharge, build a distribution network framework recovery model taking into account multi-resource coordination, and then solve the distribution network framework recovery model taking into account multi-resource coordination to obtain the distribution network framework recovery strategy.

[0052] The present invention also provides a computer device, comprising: a memory and a processor; the memory stores a computer program, and when the processor executes the computer program, the steps of the above-mentioned method for improving the anti-fragility capability of the distribution network considering multi-resource collaboration are implemented.

[0053] The present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned method for improving the anti-fragility capability of the distribution network considering multi-resource collaboration are implemented.

[0054] Therefore, the present invention adopts the above-mentioned method, system, electronic device and medium for improving the anti-fragility capacity of the distribution network taking into account the coordination of multiple resources, and the beneficial technical effects are as follows: comprehensively considering various types of power generation resources in extreme scenarios, rationally planning and allocating the output of each resource through mathematical optimization, and realizing the coordination of multiple resources; it can effectively decide the distribution network grid recovery strategy, accelerate the distribution network grid recovery process, and improve the overall recovery speed of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 is the distribution network topology;

[0056] Figure 2 This is the distribution network restoration process. DETAILED DESCRIPTION

[0057] The technical solution of the present invention is further described below through the accompanying drawings and embodiments.

[0058] Unless otherwise defined, technical or scientific terms used in the present invention shall have the common meanings understood by one having ordinary skills in the field to which the present invention belongs.

[0059] Embodiment 1

[0060] The present invention provides a method for improving the anti-fragility capability of a distribution network considering multi-resource collaboration, comprising the following steps:

[0061] Step S1. The present invention takes the various types of power generation resources connected to the distribution network as the research object. First, the available resources of the distribution network are explained in the case of a distribution network failure. An electric vehicle guidance decision model is constructed in coordination with various types of power generation resources to characterize the destination selection behavior of electric vehicles.

[0062] After a distribution network failure occurs, dispatchers will issue invitations to electric vehicle users based on the fault conditions of the distribution network and the available power generation resources in the network, and set fault recovery incentive prices for each V2G station in real time. Therefore, how electric vehicle users choose V2G stations is affected by factors such as the driving time of electric vehicles, the power of V2G stations, the number of charging piles, and incentive prices.

[0063] The attractiveness of each V2G station to electric vehicle users is quantified, and the factors that affect the choice of electric vehicle users are comprehensively considered. The optimization model is solved to obtain the user's selection strategy for each V2G station. The distribution network guides users to discharge to the expected V2G station by paying incentives to users. Therefore, the objective function of the model is to minimize the incentive amount paid by the distribution network:

[0064]

[0065] Among them, Ω EV ,Ω ST are the collections of all electric vehicles and V2G stations respectively; w mn,t is the incentive amount of V2G station n to electric vehicle m at time t; T is the set of all recovery periods.

[0066] The constraints of the electric vehicle guidance decision model include V2G station selection constraints, the maximum number of electric vehicles that can be accommodated by the V2G station constraints, and the attraction definition constraints.

[0067] The V2G station selection constraints are as follows:

[0068]

[0069] Among them, x mn is an integer variable, indicating whether electric vehicle m has selected V2G station n. The maximum number of electric vehicles that a V2G station can accommodate is constrained as follows:

[0070]

[0071] Among them, N n is the number of charging piles at V2G station n, indicating the maximum number of electric vehicles that V2G station n can accommodate.

[0072] The attraction definition constraints are as follows:

[0073]

[0074] Among them, A mn is the attractiveness of V2G station n to electric vehicle m; a m is the influencing factor coefficient; is the maximum output power of V2G station n; d mn is the distance between electric vehicle m and V2G station n at the time of fault occurrence; Ω T A collection of recovery time periods.

[0075] Step S2: During the grid recovery period, the main factor limiting the grid recovery speed is the shortage of black start power sources, the lack of available power points in the system, and the inability to restore the power-lost lines. At this time, a large number of electric vehicles distributed throughout the city can be used to connect to the grid through charging piles to discharge, assist the distribution network in realizing grid recovery, and speed up the recovery of the distribution network.

[0076] During the distribution network framework recovery period, the electric vehicle users' selection strategy for each V2G station in step S1 is used to guide the electric vehicle to each V2G station, and the electric vehicle is connected to the distribution network for discharge through the charging pile, and a distribution network framework recovery model considering multi-resource coordination is constructed. Then, the distribution network framework recovery model considering multi-resource coordination is solved to obtain the distribution network framework recovery strategy.

[0077] The objective function of the distribution network restoration model considering multi-resource coordination is as follows:

[0078]

[0079] Among them, z ij Indicates whether node i is in partition j; represents the active power of unit g belonging to partition j; represents the active power of the load belonging to partition j; represents the active power of unit g belonging to partition k; represents the active power of the load belonging to partition k; n BS represents the number of black start power supplies; N represents the number of nodes.

[0080] The constraints of the distribution network restoration model taking into account multi-resource collaboration include: four network flow constraints, three partition logic constraints and one partition restoration time constraint. The four network flow constraints are network flow constraint one, network flow constraint two, network flow constraint three and network flow constraint four, and the three partition logic constraints are partition logic constraint one, partition logic constraint two and partition logic constraint three.

[0081] The network flow constraint 1 represents the network flow constraint of node i connected to non-black start units or loads:

[0082]

[0083] Among them, f ij represents the network flow from node i to partition j; I represents the set of non-black start units; J represents the set of partitions; Ω load represents the set of nodes connected to the load; δ(i) represents the set of lines connected to node i.

[0084] The network flow here is different from the power flow. The power flow is defined according to the power of the line. The network flow here is used to characterize the path from the black start unit to the non-black start unit and load. The network flow is emitted from the black start unit, and the total number is the sum of the number of non-black start units and loads in the partition. When passing through a load or non-black start unit, the network flow consumes 1. If it passes through an ordinary node, the network flow flowing into the node is equal to the network flow flowing out of the node. Using the network flow, the path from the black start unit to the non-black start unit and load in the partitioned network can be characterized.

[0085] The second network flow constraint represents the network flow constraint of the node v that is not connected to the unit or load. The nodes that are not connected to any components may not be included in the partition, so the network flow flowing through these nodes is set to 0, that is, the incoming network flow is equal to the outgoing network flow.

[0086]

[0087] Among them, Ω bus represents the set of nodes; δ(v) represents the set of links connected to node v.

[0088] The third network flow constraint represents the network flow constraint of the node k connected to the black-start unit. In a partition, the number of network flows emitted by the black-start unit is equal to the number of all target nodes in the partition. The target node refers to the non-black-start unit or load.

[0089]

[0090] Network flow constraint 4 indicates that the line does not belong to the partition, and there is no network flow on the line:

[0091]

[0092] Among them, y lj 0-1 integer variable indicating whether line l is in partition j; Ω line represents a set of nodes; |I| represents the number of nodes.

[0093] When lj = 0, that is, the line does not belong to partition j, and no network flow flows through it; when y lj =1, that is, the line belongs to partition j. At this time, network flow flows through the line, and its range is limited to within I.

[0094] Partition logic constraint 1 is a partition logic constraint indicating that each black start unit, non-black start unit, and load belongs to a certain partition. In the network, the loads of all nodes belong to at most one partition. However, for black start units, non-black start units, and loads, their nodes must be divided into partitions to ensure that each non-black start unit and load has a corresponding black start unit to supply power to it.

[0095]

[0096] The second partition logic constraint is a partition logic constraint indicating that if a line belongs to the partition, then the nodes connected to the line also belong to the partition. If the nodes connected to the line do not belong to the partition, since the boundary of the partition is the line, the corresponding line does not belong to the partition either.

[0097]

[0098] Here, |δ(v)| represents the number of links connected to node v.

[0099] Partition logic constraint three is a partition logic constraint indicating that each node can only belong to one partition:

[0100]

[0101] The partition recovery time constraint indicates that the partition recovery time is set to the charging time of all lines in the partition plus the charging time of all non-black start units:

[0102]

[0103] Among them, t r Indicates the power system recovery time; T line Indicates the line charging time; Indicates the time required for the unit to recover; Ω G Represents a collection of units.

[0104] In order to verify the method of the present invention, the method of the present invention is applied to the following distribution network example. Figure 1 The figure shows a 33-node distribution network, where node 9 is connected to the upper transmission network. The distribution network contains multiple types of power generation resources, including 5 non-black start units that do not have black start capabilities, such as thermal power units, nuclear power units, etc., and 3 black start units, such as hydropower, wind turbines, and photovoltaic power stations. At the same time, nodes 5, 20, 22, 30, and 31 are connected to V2G sites, and electric vehicles widely distributed in the distribution network can move to the V2G sites to reversely transmit power to the grid. After an extreme event occurs, the connection between the distribution network and the transmission network is disconnected, and the distribution network loses its main power supply. At this time, the distribution network can only rely on local power generation resources to restore power supply until the transmission channel from the transmission network to the distribution network is restored.

[0105] The method for improving the anti-fragility capability of distribution networks considering multi-resource collaboration proposed in this invention is applied to solve the distribution network grid recovery method after extreme events. The results are as follows: Figure 2 As shown in the figure, the green line represents the power recovery path. After the fault occurs, all units are in a shutdown state. At this time, the BSG with black start capability starts to self-start, and then supplies power to the distribution network from nodes 17, 8 and 26 respectively, gradually restoring the power-off grid and expanding the energized area. At the same time, as electric vehicles gradually arrive at the V2G site, nodes 5, 20, 22, 30 and 31 are connected to large-scale electric vehicles respectively, so that the distribution network has more power generation resources and can restore the grid more quickly. As the energized area inside the distribution network gradually expands, a backbone grid that supplies power to non-black start units has been successfully constructed. At this time, the distribution network can gradually restore local large-scale generators, and then restore local important loads.

[0106] Embodiment 2

[0107] A distribution network anti-fragility capability enhancement system considering multi-resource coordination, comprising:

[0108] The electric vehicle guidance decision module is used to build an electric vehicle guidance decision model based on the available resources of the distribution network and coordinate various types of power generation resources in the event of a distribution network failure. The electric vehicle guidance decision model is then solved to characterize the destination selection behavior of electric vehicles and obtain the selection strategy of electric vehicle users for each V2G station.

[0109] The distribution network framework recovery module is used to guide electric vehicles to each V2G station by utilizing the electric vehicle users' selection strategy for each V2G station during the distribution network framework recovery period, connect the electric vehicles to the distribution network through charging piles for discharge, build a distribution network framework recovery model taking into account multi-resource coordination, and then solve the distribution network framework recovery model taking into account multi-resource coordination to obtain the distribution network framework recovery strategy.

[0110] If the above functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program code.

[0111] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0112] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0113] It is worth noting that the contents not elaborated in detail in the present invention are all prior art and are well known to those skilled in the art.

[0114] Therefore, the present invention adopts the above-mentioned method, system, electronic device and medium for improving the anti-fragility capability of the distribution network taking into account the coordination of multiple resources. In the event of a distribution network failure, the distribution network can be quickly recovered by coordinating multiple resources, thereby improving the anti-fragility capability of the distribution network.

[0115] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for improving the anti-fragility capability of a distribution network considering multi-resource collaboration, characterized in that: The following steps are involved: Step S1: In the event of a distribution network failure, based on the available resources of the distribution network, various types of power generation resources are coordinated to build an electric vehicle guidance decision model, and then the electric vehicle guidance decision model is solved to characterize the destination selection behavior of electric vehicles and obtain the selection strategy of electric vehicle users for each V2G station; Step S2: During the distribution network framework recovery period, the electric vehicle user's selection strategy for each V2G station in step S1 is used to guide the electric vehicle to each V2G station, and the electric vehicle is connected to the distribution network for discharge through the charging pile, and a distribution network framework recovery model taking into account multi-resource coordination is constructed, and then the distribution network framework recovery model taking into account multi-resource coordination is solved to obtain the distribution network framework recovery strategy.

2. According to claim 1, a method for improving the anti-fragility capability of a distribution network considering multi-resource collaboration is characterized in that: The objective function of the electric vehicle guidance decision model is as follows: Among them, Ω EV ,Ω ST are the collections of all electric vehicles and V2G stations respectively; w mn,t is the incentive amount of V2G station n to electric vehicle m at time t; T is the set of all recovery periods.

3. A method for improving the anti-fragility capability of a distribution network considering multi-resource collaboration according to claim 2, characterized in that: The constraints of the electric vehicle guidance decision model include V2G station selection constraints, the maximum number of electric vehicles that can be accommodated by the V2G station constraints, and the attraction definition constraints.

4. A method for improving the anti-fragility capability of a distribution network considering multi-resource collaboration according to claim 3, characterized in that: The V2G station selection constraints are as follows: Among them, x mn is an integer variable, indicating whether electric vehicle m has selected V2G station n.

5. A method for improving the anti-fragility capability of a distribution network considering multi-resource collaboration according to claim 4, characterized in that: The maximum number of electric vehicles that a V2G station can accommodate is constrained as follows: Among them, N n is the number of charging piles at V2G station n, indicating the maximum number of electric vehicles that V2G station n can accommodate.

6. A method for improving the anti-fragility capability of a distribution network considering multi-resource collaboration according to claim 5, characterized in that: The attraction definition constraints are as follows: Among them, A mn is the attractiveness of V2G station n to electric vehicle m; a m is the influencing factor coefficient; is the maximum output power of V2G station n; d mn is the distance between electric vehicle m and V2G station n at the time of fault occurrence; Ω T A collection of recovery time periods.

7. A method for improving the anti-fragility capability of a distribution network considering multi-resource collaboration according to claim 6, characterized in that: The objective function of the distribution network restoration model considering multi-resource coordination is as follows: Among them, z ij Indicates whether node i is in partition j; represents the active power of unit g belonging to partition j; represents the active power of the load belonging to partition j; represents the active power of unit g belonging to partition k; represents the active power of the load belonging to partition k; n BS represents the number of black start power supplies; N represents the number of nodes.

8. A method for improving the anti-fragility capability of a distribution network considering multi-resource collaboration according to claim 7, characterized in that: The constraints of the distribution network restoration model taking into account multi-resource collaboration include: four network flow constraints, three partition logic constraints and one partition restoration time constraint. The four network flow constraints are network flow constraint one, network flow constraint two, network flow constraint three and network flow constraint four, and the three partition logic constraints are partition logic constraint one, partition logic constraint two and partition logic constraint three.

9. A method for improving the anti-fragility capability of a distribution network considering multi-resource collaboration according to claim 8, characterized in that: The network flow constraint 1 represents the network flow constraint of node i connected to non-black start units or loads: Among them, f ij represents the network flow from node i to partition j; I represents the set of non-black start units; J represents the set of partitions; Ω load represents the set of nodes connected to the load; δ(i) represents the set of lines connected to node i.

10. A method for improving the anti-fragility capability of a distribution network considering multi-resource collaboration according to claim 9, characterized in that: The second network flow constraint represents the network flow constraint of the node v that is not connected to the unit or load: Among them, Ω bus represents the set of nodes; δ(v) represents the set of links connected to node v.

11. A method for improving the anti-fragility capability of a distribution network considering multi-resource collaboration according to claim 10, characterized in that: The third network flow constraint represents the network flow constraint of the node k connected to the black start unit:

12. A method for improving the anti-fragility capability of a distribution network considering multi-resource collaboration according to claim 11, characterized in that: Network flow constraint 4 indicates that the line does not belong to the partition, and there is no network flow on the line: Among them, y lj 0-1 integer variable indicating whether line l is in partition j; Ω line represents a set of nodes; |I| represents the number of nodes.

13. A method for improving the anti-fragility capability of a distribution network considering multi-resource collaboration according to claim 12, characterized in that: Partition logic constraint 1 is a partition logic constraint indicating that each black start unit, non-black start unit, and load belongs to a certain partition:

14. A method for improving the anti-fragility capability of a distribution network considering multi-resource collaboration according to claim 13, characterized in that: Partition logic constraint 2 is a partition logic constraint indicating that if a line belongs to the partition, the nodes connected to the line also belong to the partition: Here, |δ(v)| represents the number of links connected to node v.

15. A method for improving the anti-fragility capability of a distribution network considering multi-resource collaboration according to claim 14, characterized in that: Partition logic constraint three is a partition logic constraint indicating that each node can only belong to one partition:

16. A method for improving the anti-fragility capability of a distribution network considering multi-resource collaboration according to claim 15, characterized in that: The partition recovery time constraints are as follows: Among them, t r Indicates the power system recovery time; T line Indicates the line charging time; Indicates the time required for the unit to recover; Ω G Represents a collection of units.

17. A distribution network anti-fragility capability enhancement system considering multi-resource collaboration, characterized in that: include: The electric vehicle guidance decision module is used to build an electric vehicle guidance decision model based on the available resources of the distribution network and coordinate various types of power generation resources in the event of a distribution network failure. The electric vehicle guidance decision model is then solved to characterize the destination selection behavior of electric vehicles and obtain the selection strategy of electric vehicle users for each V2G station. The distribution network framework recovery module is used to guide electric vehicles to each V2G station by utilizing the electric vehicle users' selection strategy for each V2G station during the distribution network framework recovery period, connect the electric vehicles to the distribution network through charging piles for discharge, build a distribution network framework recovery model taking into account multi-resource coordination, and then solve the distribution network framework recovery model taking into account multi-resource coordination to obtain the distribution network framework recovery strategy.

18. A computer device comprising: Memory and processor; The memory stores a computer program, characterized in that when the processor executes the computer program, the steps of the method for improving the anti-fragility capability of a distribution network considering multi-resource collaboration described in any one of claims 1-16 are implemented.

19. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for improving the anti-fragility capability of a distribution network considering multi-resource collaboration as described in any one of claims 1-16 are implemented.

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